Target discrimination method for SAR images based on semisupervised co-training

NASA Astrophysics Data System (ADS)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

Unresolved Galaxy Classifier for ESA/Gaia mission: Support Vector Machines approach

NASA Astrophysics Data System (ADS)

Bellas-Velidis, Ioannis; Kontizas, Mary; Dapergolas, Anastasios; Livanou, Evdokia; Kontizas, Evangelos; Karampelas, Antonios

A software package Unresolved Galaxy Classifier (UGC) is being developed for the ground-based pipeline of ESA's Gaia mission. It aims to provide an automated taxonomic classification and specific parameters estimation analyzing Gaia BP/RP instrument low-dispersion spectra of unresolved galaxies. The UGC algorithm is based on a supervised learning technique, the Support Vector Machines (SVM). The software is implemented in Java as two separate modules. An offline learning module provides functions for SVM-models training. Once trained, the set of models can be repeatedly applied to unknown galaxy spectra by the pipeline's application module. A library of galaxy models synthetic spectra, simulated for the BP/RP instrument, is used to train and test the modules. Science tests show a very good classification performance of UGC and relatively good regression performance, except for some of the parameters. Possible approaches to improve the performance are discussed.

Supervised learning with decision margins in pools of spiking neurons.

PubMed

Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

2014-10-01

Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

PubMed

Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

2015-01-01

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

PubMed

Morabito, Francesco Carlo; Campolo, Maurizio; Mammone, Nadia; Versaci, Mario; Franceschetti, Silvana; Tagliavini, Fabrizio; Sofia, Vito; Fatuzzo, Daniela; Gambardella, Antonio; Labate, Angelo; Mumoli, Laura; Tripodi, Giovanbattista Gaspare; Gasparini, Sara; Cianci, Vittoria; Sueri, Chiara; Ferlazzo, Edoardo; Aguglia, Umberto

2017-03-01

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

DTIC Science & Technology

2007-04-04

machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection.

PubMed

Noto, Keith; Brodley, Carla; Slonim, Donna

2012-01-01

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach.

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

PubMed Central

Brodley, Carla; Slonim, Donna

2011-01-01

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal” instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach. PMID:22639542

Learning With Mixed Hard/Soft Pointwise Constraints.

PubMed

Gnecco, Giorgio; Gori, Marco; Melacci, Stefano; Sanguineti, Marcello

2015-09-01

A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples of supervised learning, which can be violated at the cost of some penalization (quantified by the choice of a suitable loss function) play the role of soft pointwise constraints. Constrained variational calculus is exploited to derive a representer theorem that provides a description of the functional structure of the optimal solution to the proposed learning paradigm. It is shown that such an optimal solution can be represented in terms of a set of support constraints, which generalize the concept of support vectors and open the doors to a novel learning paradigm, called support constraint machines. The general theory is applied to derive the representation of the optimal solution to the problem of learning from hard linear pointwise constraints combined with soft pointwise constraints induced by supervised examples. In some cases, closed-form optimal solutions are obtained.

Application of support vector machine for the separation of mineralised zones in the Takht-e-Gonbad porphyry deposit, SE Iran

NASA Astrophysics Data System (ADS)

Mahvash Mohammadi, Neda; Hezarkhani, Ardeshir

2018-07-01

Classification of mineralised zones is an important factor for the analysis of economic deposits. In this paper, the support vector machine (SVM), a supervised learning algorithm, based on subsurface data is proposed for classification of mineralised zones in the Takht-e-Gonbad porphyry Cu-deposit (SE Iran). The effects of the input features are evaluated via calculating the accuracy rates on the SVM performance. Ultimately, the SVM model, is developed based on input features namely lithology, alteration, mineralisation, the level and, radial basis function (RBF) as a kernel function. Moreover, the optimal amount of parameters λ and C, using n-fold cross-validation method, are calculated at level 0.001 and 0.01 respectively. The accuracy of this model is 0.931 for classification of mineralised zones in the Takht-e-Gonbad porphyry deposit. The results of the study confirm the efficiency of SVM method for classification the mineralised zones.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

PubMed Central

Hepworth, Philip J.; Nefedov, Alexey V.; Muchnik, Ilya B.; Morgan, Kenton L.

2012-01-01

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide. PMID:22319115

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

PubMed

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Supervised Classification Processes for the Characterization of Heritage Elements, Case Study: Cuenca-Ecuador

NASA Astrophysics Data System (ADS)

Briones, J. C.; Heras, V.; Abril, C.; Sinchi, E.

2017-08-01

The proper control of built heritage entails many challenges related to the complexity of heritage elements and the extent of the area to be managed, for which the available resources must be efficiently used. In this scenario, the preventive conservation approach, based on the concept that prevent is better than cure, emerges as a strategy to avoid the progressive and imminent loss of monuments and heritage sites. Regular monitoring appears as a key tool to identify timely changes in heritage assets. This research demonstrates that the supervised learning model (Support Vector Machines - SVM) is an ideal tool that supports the monitoring process detecting visible elements in aerial images such as roofs structures, vegetation and pavements. The linear, gaussian and polynomial kernel functions were tested; the lineal function provided better results over the other functions. It is important to mention that due to the high level of segmentation generated by the classification procedure, it was necessary to apply a generalization process through opening a mathematical morphological operation, which simplified the over classification for the monitored elements.

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

PubMed

Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

2016-01-01

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

NASA Astrophysics Data System (ADS)

Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

2011-03-01

RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

Prototype Vector Machine for Large Scale Semi-Supervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Kai; Kwok, James T.; Parvin, Bahram

2009-04-29

Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of themore » kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.« less

Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma.

PubMed

Shahriyari, Leili

2017-11-03

One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set. Additionally, for each of these normalization methods, we use two different normalization strategies: normalizing samples (files) or normalizing features (genes). Regardless of normalization methods, a support vector machine (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in predicting the vital status of the patients. However, the fitting time of SVM depended on the normalization methods, and it reached its minimum fitting time when files were normalized to the unit length. Furthermore, among all 12 learning algorithms and 6 different normalization techniques, the Bernoulli naive Bayes model after standardizing files had the best performance in terms of maximizing the accuracy as well as minimizing the fitting time. We also investigated the effect of dimensionality reduction methods on the performance of the supervised ML algorithms. Reducing the dimension of the data set did not increase the maximum accuracy of 78%. However, it leaded to discovery of the 7SK RNA gene expression as a predictor of survival in patients with colon adenocarcinoma with accuracy of 78%. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Unsupervised active learning based on hierarchical graph-theoretic clustering.

PubMed

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

PubMed

Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

2016-10-01

Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Supervised machine learning and active learning in classification of radiology reports.

PubMed

Nguyen, Dung H M; Patrick, Jon D

2014-01-01

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Defeating abusive supervision: Training supervisors to support subordinates.

PubMed

Gonzalez-Morales, M Gloria; Kernan, Mary C; Becker, Thomas E; Eisenberger, Robert

2018-04-01

Although much is known about the antecedents and consequences of abusive supervision, scant attention has been paid to investigating procedures to reduce its frequency. We conducted a quasiexperiment to examine the effects of supervisor support training on subordinate perceptions of abusive supervision and supervisor support. Supervisors (n = 23) in 4 restaurants were trained in 4 supportive supervision strategies (benevolence, sincerity, fairness, and experiential processing) during 4 2-hr sessions over a period of 2 months. We compared perceived supervisor support and abusive supervision before and 9 months after training for 208 employees whose supervisors received support training and 241 employees in 4 similar control restaurants. Compared to employees in the control restaurants, employees whose supervisors received the support training reported higher levels of perceived supervisor support and less abusive supervision. These findings suggest that a relatively brief training program can help managers become more supportive and less abusive. Theoretical and practical implications for effectively managing abusive supervision are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A new feature constituting approach to detection of vocal fold pathology

NASA Astrophysics Data System (ADS)

Hariharan, M.; Polat, Kemal; Yaacob, Sazali

2014-08-01

In the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.

Using Support Vector Machine Ensembles for Target Audience Classification on Twitter

PubMed Central

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. PMID:25874768

Using support vector machine ensembles for target audience classification on Twitter.

PubMed

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

PubMed Central

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.

PubMed

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-06-13

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

NASA Astrophysics Data System (ADS)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

Predicting protein amidation sites by orchestrating amino acid sequence features

NASA Astrophysics Data System (ADS)

Zhao, Shuqiu; Yu, Hua; Gong, Xiujun

2017-08-01

Amidation is the fourth major category of post-translational modifications, which plays an important role in physiological and pathological processes. Identifying amidation sites can help us understanding the amidation and recognizing the original reason of many kinds of diseases. But the traditional experimental methods for predicting amidation sites are often time-consuming and expensive. In this study, we propose a computational method for predicting amidation sites by orchestrating amino acid sequence features. Three kinds of feature extraction methods are used to build a feature vector enabling to capture not only the physicochemical properties but also position related information of the amino acids. An extremely randomized trees algorithm is applied to choose the optimal features to remove redundancy and dependence among components of the feature vector by a supervised fashion. Finally the support vector machine classifier is used to label the amidation sites. When tested on an independent data set, it shows that the proposed method performs better than all the previous ones with the prediction accuracy of 0.962 at the Matthew's correlation coefficient of 0.89 and area under curve of 0.964.

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning.

PubMed

Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

2013-04-01

Quasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results. Copyright © 2013 Elsevier Ltd. All rights reserved.

Haptic exploration of fingertip-sized geometric features using a multimodal tactile sensor

NASA Astrophysics Data System (ADS)

Ponce Wong, Ruben D.; Hellman, Randall B.; Santos, Veronica J.

2014-06-01

Haptic perception remains a grand challenge for artificial hands. Dexterous manipulators could be enhanced by "haptic intelligence" that enables identification of objects and their features via touch alone. Haptic perception of local shape would be useful when vision is obstructed or when proprioceptive feedback is inadequate, as observed in this study. In this work, a robot hand outfitted with a deformable, bladder-type, multimodal tactile sensor was used to replay four human-inspired haptic "exploratory procedures" on fingertip-sized geometric features. The geometric features varied by type (bump, pit), curvature (planar, conical, spherical), and footprint dimension (1.25 - 20 mm). Tactile signals generated by active fingertip motions were used to extract key parameters for use as inputs to supervised learning models. A support vector classifier estimated order of curvature while support vector regression models estimated footprint dimension once curvature had been estimated. A distal-proximal stroke (along the long axis of the finger) enabled estimation of order of curvature with an accuracy of 97%. Best-performing, curvature-specific, support vector regression models yielded R2 values of at least 0.95. While a radial-ulnar stroke (along the short axis of the finger) was most helpful for estimating feature type and size for planar features, a rolling motion was most helpful for conical and spherical features. The ability to haptically perceive local shape could be used to advance robot autonomy and provide haptic feedback to human teleoperators of devices ranging from bomb defusal robots to neuroprostheses.

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.

PubMed

El-Sayed, Hesham; Sankar, Sharmi; Daraghmi, Yousef-Awwad; Tiwari, Prayag; Rattagan, Ekarat; Mohanty, Manoranjan; Puthal, Deepak; Prasad, Mukesh

2018-05-24

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique

NASA Astrophysics Data System (ADS)

Ismail, Shuhaida; Mohamad Pandiahi, Siraj; Shabri, Ani; Mustapha, Aida

2018-04-01

The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The forecasting models predict the monthly river flow obtained from the three models individually for river flow data and the accuracy of the all models were then compared against each other. The monthly river flow of the said river has been forecasted using these three models. The obtained results were compared and statistically analysed. Then, the results of this analytical comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It was found that LSSVM has he higher r with the value of 0.934 compared to other models. This indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Active learning-based information structure analysis of full scientific articles and two applications for biomedical literature review.

PubMed

Guo, Yufan; Silins, Ilona; Stenius, Ulla; Korhonen, Anna

2013-06-01

Techniques that are capable of automatically analyzing the information structure of scientific articles could be highly useful for improving information access to biomedical literature. However, most existing approaches rely on supervised machine learning (ML) and substantial labeled data that are expensive to develop and apply to different sub-fields of biomedicine. Recent research shows that minimal supervision is sufficient for fairly accurate information structure analysis of biomedical abstracts. However, is it realistic for full articles given their high linguistic and informational complexity? We introduce and release a novel corpus of 50 biomedical articles annotated according to the Argumentative Zoning (AZ) scheme, and investigate active learning with one of the most widely used ML models-Support Vector Machines (SVM)-on this corpus. Additionally, we introduce two novel applications that use AZ to support real-life literature review in biomedicine via question answering and summarization. We show that active learning with SVM trained on 500 labeled sentences (6% of the corpus) performs surprisingly well with the accuracy of 82%, just 2% lower than fully supervised learning. In our question answering task, biomedical researchers find relevant information significantly faster from AZ-annotated than unannotated articles. In the summarization task, sentences extracted from particular zones are significantly more similar to gold standard summaries than those extracted from particular sections of full articles. These results demonstrate that active learning of full articles' information structure is indeed realistic and the accuracy is high enough to support real-life literature review in biomedicine. The annotated corpus, our AZ classifier and the two novel applications are available at http://www.cl.cam.ac.uk/yg244/12bioinfo.html

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

PubMed

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

An online semi-supervised brain-computer interface.

PubMed

Gu, Zhenghui; Yu, Zhuliang; Shen, Zhifang; Li, Yuanqing

2013-09-01

Practical brain-computer interface (BCI) systems should require only low training effort for the user, and the algorithms used to classify the intent of the user should be computationally efficient. However, due to inter- and intra-subject variations in EEG signal, intermittent training/calibration is often unavoidable. In this paper, we present an online semi-supervised P300 BCI speller system. After a short initial training (around or less than 1 min in our experiments), the system is switched to a mode where the user can input characters through selective attention. In this mode, a self-training least squares support vector machine (LS-SVM) classifier is gradually enhanced in back end with the unlabeled EEG data collected online after every character input. In this way, the classifier is gradually enhanced. Even though the user may experience some errors in input at the beginning due to the small initial training dataset, the accuracy approaches that of fully supervised method in a few minutes. The algorithm based on LS-SVM and its sequential update has low computational complexity; thus, it is suitable for online applications. The effectiveness of the algorithm has been validated through data analysis on BCI Competition III dataset II (P300 speller BCI data). The performance of the online system was evaluated through experimental results on eight healthy subjects, where all of them achieved the spelling accuracy of 85 % or above within an average online semi-supervised learning time of around 3 min.

Detecting Parkinsons' symptoms in uncontrolled home environments: a multiple instance learning approach.

PubMed

Das, Samarjit; Amoedo, Breogan; De la Torre, Fernando; Hodgins, Jessica

2012-01-01

In this paper, we propose to use a weakly supervised machine learning framework for automatic detection of Parkinson's Disease motor symptoms in daily living environments. Our primary goal is to develop a monitoring system capable of being used outside of controlled laboratory settings. Such a system would enable us to track medication cycles at home and provide valuable clinical feedback. Most of the relevant prior works involve supervised learning frameworks (e.g., Support Vector Machines). However, in-home monitoring provides only coarse ground truth information about symptom occurrences, making it very hard to adapt and train supervised learning classifiers for symptom detection. We address this challenge by formulating symptom detection under incomplete ground truth information as a multiple instance learning (MIL) problem. MIL is a weakly supervised learning framework that does not require exact instances of symptom occurrences for training; rather, it learns from approximate time intervals within which a symptom might or might not have occurred on a given day. Once trained, the MIL detector was able to spot symptom-prone time windows on other days and approximately localize the symptom instances. We monitored two Parkinson's disease (PD) patients, each for four days with a set of five triaxial accelerometers and utilized a MIL algorithm based on axis parallel rectangle (APR) fitting in the feature space. We were able to detect subject specific symptoms (e.g. dyskinesia) that conformed with a daily log maintained by the patients.

How to create more supportive supervision for primary healthcare: lessons from Ngamiland district of Botswana: co-operative inquiry group.

PubMed

Nkomazana, Oathokwa; Mash, Robert; Wojczewski, Silvia; Kutalek, Ruth; Phaladze, Nthabiseng

2016-01-01

Supportive supervision is a way to foster performance, productivity, motivation, and retention of health workforce. Nevertheless there is a dearth of evidence of the impact and acceptability of supportive supervision in low- and middle-income countries. This article describes a participatory process of transforming the supervisory practice of district health managers to create a supportive environment for primary healthcare workers. The objective of the study was to explore how district health managers can change their practice to create a more supportive environment for primary healthcare providers. A facilitated co-operative inquiry group (CIG) was formed with Ngamiland health district managers. CIG belongs to the participatory action research paradigm and is characterised by a cyclic process of observation, reflection, planning, and action. The CIG went through three cycles between March 2013 and March 2014. Twelve district health managers participated in the inquiry group. The major insights and learning that emerged from the inquiry process included inadequate supervisory practice, perceptions of healthcare workers' experiences, change in the managers' supervision paradigm, recognition of the supervisors' inadequate supervisory skills, and barriers to supportive supervision. Finally, the group developed a 10-point consensus on what they had learnt regarding supportive supervision. Ngamiland health district managers have come to appreciate the value of supportive supervision and changed their management style to be more supportive of their subordinates. They also developed a consensus on supportive supervision that could be adapted for use nationally. Supportive supervision should be prioritised at all levels of the health system, and it should be adequately resourced.

Improved Anomaly Detection using Integrated Supervised and Unsupervised Processing

NASA Astrophysics Data System (ADS)

Hunt, B.; Sheppard, D. G.; Wetterer, C. J.

There are two broad technologies of signal processing applicable to space object feature identification using nonresolved imagery: supervised processing analyzes a large set of data for common characteristics that can be then used to identify, transform, and extract information from new data taken of the same given class (e.g. support vector machine); unsupervised processing utilizes detailed physics-based models that generate comparison data that can then be used to estimate parameters presumed to be governed by the same models (e.g. estimation filters). Both processes have been used in non-resolved space object identification and yield similar results yet arrived at using vastly different processes. The goal of integrating the results of the two is to seek to achieve an even greater performance by building on the process diversity. Specifically, both supervised processing and unsupervised processing will jointly operate on the analysis of brightness (radiometric flux intensity) measurements reflected by space objects and observed by a ground station to determine whether a particular day conforms to a nominal operating mode (as determined from a training set) or exhibits anomalous behavior where a particular parameter (e.g. attitude, solar panel articulation angle) has changed in some way. It is demonstrated in a variety of different scenarios that the integrated process achieves a greater performance than each of the separate processes alone.

Benchmarking protein classification algorithms via supervised cross-validation.

PubMed

Kertész-Farkas, Attila; Dhir, Somdutta; Sonego, Paolo; Pacurar, Mircea; Netoteia, Sergiu; Nijveen, Harm; Kuzniar, Arnold; Leunissen, Jack A M; Kocsor, András; Pongor, Sándor

2008-04-24

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

VizieR Online Data Catalog: Gamma-ray AGN type determination (Hassan+, 2013)

NASA Astrophysics Data System (ADS)

Hassan, T.; Mirabal, N.; Contreras, J. L.; Oya, I.

2013-11-01

In this paper, we employ Support Vector Machines (SVMs) and Random Forest (RF) that embody two of the most robust supervised learning algorithms available today. We are interested in building classifiers that can distinguish between two AGN classes: BL Lacs and FSRQs. In the 2FGL, there is a total set of 1074 identified/associated AGN objects with the following labels: 'bzb' (BL Lacs), 'bzq' (FSRQs), 'agn' (other non-blazar AGN) and 'agu' (active galaxies of uncertain type). From this global set, we group the identified/associated blazars ('bzb' and 'bzq' labels) as the training/testing set of our algorithms. (2 data files).

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

2018-02-01

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Machine learning for the assessment of Alzheimer's disease through DTI

NASA Astrophysics Data System (ADS)

Lella, Eufemia; Amoroso, Nicola; Bellotti, Roberto; Diacono, Domenico; La Rocca, Marianna; Maggipinto, Tommaso; Monaco, Alfonso; Tangaro, Sabina

2017-09-01

Digital imaging techniques have found several medical applications in the development of computer aided detection systems, especially in neuroimaging. Recent advances in Diffusion Tensor Imaging (DTI) aim to discover biological markers for the early diagnosis of Alzheimer's disease (AD), one of the most widespread neurodegenerative disorders. We explore here how different supervised classification models provide a robust support to the diagnosis of AD patients. We use DTI measures, assessing the structural integrity of white matter (WM) fiber tracts, to reveal patterns of disrupted brain connectivity. In particular, we provide a voxel-wise measure of fractional anisotropy (FA) and mean diffusivity (MD), thus identifying the regions of the brain mostly affected by neurodegeneration, and then computing intensity features to feed supervised classification algorithms. In particular, we evaluate the accuracy of discrimination of AD patients from healthy controls (HC) with a dataset of 80 subjects (40 HC, 40 AD), from the Alzheimer's Disease Neurodegenerative Initiative (ADNI). In this study, we compare three state-of-the-art classification models: Random Forests, Naive Bayes and Support Vector Machines (SVMs). We use a repeated five-fold cross validation framework with nested feature selection to perform a fair comparison between these algorithms and evaluate the information content they provide. Results show that AD patterns are well localized within the brain, thus DTI features can support the AD diagnosis.

How to create more supportive supervision for primary healthcare: lessons from Ngamiland district of Botswana: co-operative inquiry group

PubMed Central

Nkomazana, Oathokwa; Mash, Robert; Wojczewski, Silvia; Kutalek, Ruth; Phaladze, Nthabiseng

2016-01-01

Background Supportive supervision is a way to foster performance, productivity, motivation, and retention of health workforce. Nevertheless there is a dearth of evidence of the impact and acceptability of supportive supervision in low- and middle-income countries. This article describes a participatory process of transforming the supervisory practice of district health managers to create a supportive environment for primary healthcare workers. Objective The objective of the study was to explore how district health managers can change their practice to create a more supportive environment for primary healthcare providers. Design A facilitated co-operative inquiry group (CIG) was formed with Ngamiland health district managers. CIG belongs to the participatory action research paradigm and is characterised by a cyclic process of observation, reflection, planning, and action. The CIG went through three cycles between March 2013 and March 2014. Results Twelve district health managers participated in the inquiry group. The major insights and learning that emerged from the inquiry process included inadequate supervisory practice, perceptions of healthcare workers’ experiences, change in the managers’ supervision paradigm, recognition of the supervisors’ inadequate supervisory skills, and barriers to supportive supervision. Finally, the group developed a 10-point consensus on what they had learnt regarding supportive supervision. Conclusion Ngamiland health district managers have come to appreciate the value of supportive supervision and changed their management style to be more supportive of their subordinates. They also developed a consensus on supportive supervision that could be adapted for use nationally. Supportive supervision should be prioritised at all levels of the health system, and it should be adequately resourced. PMID:27345024

Unpacking Clinical Supervision in Transitional and Permanent Supportive Housing: Scrutiny or Support?

PubMed

Choy-Brown, Mimi; Stanhope, Victoria; Tiderington, Emmy; Padgett, Deborah K

2016-07-01

Behavioral health organizations use clinical supervision to ensure professional development and practice quality. This qualitative study examined 35 service coordinators' perspectives on supervision in two distinct supportive housing program types (permanent and transitional). Thematic analysis of in-depth interviews yielded three contrast themes: support versus scrutiny, planned versus impromptu time, and housing first versus treatment first. Supervisory content and format resulted in differential perceptions of supervision, thereby influencing opportunities for learning. These findings suggest that unpacking discrete elements of supervision enactment in usual care settings can inform implementation of recovery-oriented practice.

Unpacking clinical supervision in transitional and permanent supportive housing: Scrutiny or support?

PubMed Central

Choy-Brown, Mimi; Stanhope, Victoria; Tiderington, Emmy; Padgett, Deborah K.

2015-01-01

Behavioral health organizations use clinical supervision to ensure professional development and practice quality. This qualitative study examined 35 service coordinators' perspectives on supervision in two distinct supportive housing program types (permanent and transitional). Thematic analysis of in-depth interviews yielded three contrast themes: support versus scrutiny, planned versus impromptu time, and Housing First versus Treatment First. Supervisory content and format resulted in differential perceptions of supervision, thereby influencing opportunities for learning. These findings suggest that unpacking discrete elements of supervision enactment in usual care settings can inform implementation of recovery-oriented practice. PMID:26066866

Supervised DNA Barcodes species classification: analysis, comparisons and results

PubMed Central

2014-01-01

Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode classification methods. On empirical data their classification performances are at a comparable level to the other methods. Conclusions The classification analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community. PMID:24721333

Assessing the druggability of protein-protein interactions by a supervised machine-learning method.

PubMed

Sugaya, Nobuyoshi; Ikeda, Kazuyoshi

2009-08-25

Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM). To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways. Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.

Supervising undergraduate research: a collective approach utilising groupwork and peer support.

PubMed

Baker, Mary-Jane; Cluett, Elizabeth; Ireland, Lorraine; Reading, Sheila; Rourke, Susan

2014-04-01

Nursing education now requires graduate entry for professional registration. The challenge is to ensure that students develop independence and team working in a resource effective manner. The dissertation is one opportunity for this. To evaluate changing from individual dissertation supervision to group peer supervision. Group supervision was implemented for one cohort. Dissertation outcomes were compared with two previous cohorts. Student evaluative data was assessed. Group supervision did not adversely affect dissertation outcomes (p=0.85). 88% of students reported peer supervision to be helpful, with themes being 'support and sharing', and 'progress and moving forward'. Peer group support provided consistent supervision harnessing the energy and resources of the students and Faculty, without adversely affecting outcomes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Broad Absorption Line Quasar catalogues with Supervised Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scaringi, Simone; Knigge, Christian; Cottis, Christopher E.

2008-12-05

We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5 quasar spectra in order to create a large catalogue of broad absorption line quasars (BALQSOs). We first discuss the problems with BALQSO catalogues constructed using the conventional balnicity and/or absorption indices (BI and AI), and then describe the supervised LVQ network we have trained to recognise BALQSOs. The resulting BALQSO catalogue should be substantially more robust and complete than BI-or AI-based ones.

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Supervising community health workers in low-income countries – a review of impact and implementation issues

PubMed Central

Hill, Zelee; Dumbaugh, Mari; Benton, Lorna; Källander, Karin; Strachan, Daniel; Asbroek, Augustinus ten; Tibenderana, James; Kirkwood, Betty; Meek, Sylvia

2014-01-01

Background Community health workers (CHWs) are an increasingly important component of health systems and programs. Despite the recognized role of supervision in ensuring CHWs are effective, supervision is often weak and under-supported. Little is known about what constitutes adequate supervision and how different supervision strategies influence performance, motivation, and retention. Objective To determine the impact of supervision strategies used in low- and middle-income countries and discuss implementation and feasibility issues with a focus on CHWs. Design A search of peer-reviewed, English language articles evaluating health provider supervision strategies was conducted through November 2013. Included articles evaluated the impact of supervision in low- or middle-income countries using a controlled, pre-/post- or observational design. Implementation and feasibility literature included both peer-reviewed and gray literature. Results A total of 22 impact papers were identified. Papers were from a range of low- and middle-income countries addressing the supervision of a variety of health care providers. We classified interventions as testing supervision frequency, the supportive/facilitative supervision package, supervision mode (peer, group, and community), tools (self-assessment and checklists), focus (quality assurance/problem solving), and training. Outcomes included coverage, performance, and perception of quality but were not uniform across studies. Evidence suggests that improving supervision quality has a greater impact than increasing frequency of supervision alone. Supportive supervision packages, community monitoring, and quality improvement/problem-solving approaches show the most promise; however, evaluation of all strategies was weak. Conclusion Few supervision strategies have been rigorously tested and data on CHW supervision is particularly sparse. This review highlights the diversity of supervision approaches that policy makers have to choose from and, while choices should be context specific, our findings suggest that high-quality supervision that focuses on supportive approaches, community monitoring, and/or quality assurance/problem solving may be most effective. PMID:24815075

Linear time relational prototype based learning.

PubMed

Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

2012-10-01

Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

A new local-global approach for classification.

PubMed

Peres, R T; Pedreira, C E

2010-09-01

In this paper, we propose a new local-global pattern classification scheme that combines supervised and unsupervised approaches, taking advantage of both, local and global environments. We understand as global methods the ones concerned with the aim of constructing a model for the whole problem space using the totality of the available observations. Local methods focus into sub regions of the space, possibly using an appropriately selected subset of the sample. In the proposed method, the sample is first divided in local cells by using a Vector Quantization unsupervised algorithm, the LBG (Linde-Buzo-Gray). In a second stage, the generated assemblage of much easier problems is locally solved with a scheme inspired by Bayes' rule. Four classification methods were implemented for comparison purposes with the proposed scheme: Learning Vector Quantization (LVQ); Feedforward Neural Networks; Support Vector Machine (SVM) and k-Nearest Neighbors. These four methods and the proposed scheme were implemented in eleven datasets, two controlled experiments, plus nine public available datasets from the UCI repository. The proposed method has shown a quite competitive performance when compared to these classical and largely used classifiers. Our method is simple concerning understanding and implementation and is based on very intuitive concepts. Copyright 2010 Elsevier Ltd. All rights reserved.

Supportive supervision for medicines management in government health facilities in Kiambu County, Kenya: a health workers' perspective.

PubMed

Agoro, Oscar Otieno; Osuga, Ben Onyango; Adoyo, Maureen

2015-01-01

Effective supportive supervision is widely recognized as essential for optimal management of medicines in government health facilities and also in contributing towards improved access and utilization of health services. This study sought to examine the extent supportive supervision for medicines management in government health facilities from a health worker perspective. A cross-sectional study was done targeting health workers managing medicines in government health facilities in Kiambu County. One hundred and thirty eight respondents took part in the study which explored the quality of supportive supervision from a health worker's perspective, and also examined the factors influencing their contentment with the level of supervision received. A statistical analysis was done using SPSS 21 and Excel 2013. Supervisory visits from all levels of health management were not regularly done, standard checklists were not routinely used, and action plans irregularly developed and followed up. Only 54 (38.6%) respondents were satisfied with the levels of supportive supervision that they received, with satisfaction significantly differing across the professional cadres, χ (2) (12, n = 138) = 29.762, p = .003; across the different tiers of health facilities, r s (138) = 0.341, p < .001; and with the education levels of the respondents, r s (138) = 0.381, p < .001. The study concluded that supportive supervision for medicines management that government health facilities received was still inadequate, and health workers were dissatisfied with the level of supervision that they received. The study recommends a review of the support supervision policy at the county level to address the unearthed inefficiencies and improve supervision for medicines management in government health facilities.

Clinical supervision in a community setting.

PubMed

Evans, Carol; Marcroft, Emma

Clinical supervision is a formal process of professional support, reflection and learning that contributes to individual development. First Community Health and Care is committed to providing clinical supervision to nurses and allied healthcare professionals to support the provision and maintenance of high-quality care. In 2012, we developed new guidelines for nurses and AHPs on supervision, incorporating a clinical supervision framework. This offers a range of options to staff so supervision accommodates variations in work settings and individual learning needs and styles.

The Juggling Act of Supervision in Community Mental Health: Implications for Supporting Evidence-Based Treatment.

PubMed

Dorsey, Shannon; Pullmann, Michael D; Kerns, Suzanne E U; Jungbluth, Nathaniel; Meza, Rosemary; Thompson, Kelly; Berliner, Lucy

2017-11-01

Supervisors are an underutilized resource for supporting evidence-based treatments (EBTs) in community mental health. Little is known about how EBT-trained supervisors use supervision time. Primary aims were to describe supervision (e.g., modality, frequency), examine functions of individual supervision, and examine factors associated with time allocation to supervision functions. Results from 56 supervisors and 207 clinicians from 25 organizations indicate high prevalence of individual supervision, often alongside group and informal supervision. Individual supervision serves a wide range of functions, with substantial variation at the supervisor-level. Implementation climate was the strongest predictor of time allocation to clinical and EBT-relevant functions.

Genetic Classification of Populations Using Supervised Learning

PubMed Central

Bridges, Michael; Heron, Elizabeth A.; O'Dushlaine, Colm; Segurado, Ricardo; Morris, Derek; Corvin, Aiden; Gill, Michael; Pinto, Carlos

2011-01-01

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case–control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies. PMID:21589856

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

PubMed

Salvatore, C; Cerasa, A; Castiglioni, I; Gallivanone, F; Augimeri, A; Lopez, M; Arabia, G; Morelli, M; Gilardi, M C; Quattrone, A

2014-01-30

Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP). Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice. Copyright © 2013 Elsevier B.V. All rights reserved.

When Family-Supportive Supervision Matters: Relations between Multiple Sources of Support and Work-Family Balance

ERIC Educational Resources Information Center

Greenhaus, Jeffrey H.; Ziegert, Jonathan C.; Allen, Tammy D.

2012-01-01

This study examines the mechanisms by which family-supportive supervision is related to employee work-family balance. Based on a sample of 170 business professionals, we found that the positive relation between family-supportive supervision and balance was fully mediated by work interference with family (WIF) and partially mediated by family…

The effects of clinical supervision on supervisees and patients in cognitive behavioral therapy: a systematic review.

PubMed

Alfonsson, Sven; Parling, Thomas; Spännargård, Åsa; Andersson, Gerhard; Lundgren, Tobias

2018-05-01

Clinical supervision is a central part of psychotherapist training but the empirical support for specific supervision theories or features is unclear. The aims of this study were to systematically review the empirical research literature regarding the effects of clinical supervision on therapists' competences and clinical outcomes within Cognitive Behavior Therapy (CBT). A comprehensive database search resulted in 4103 identified publications. Of these, 133 were scrutinized and in the end 5 studies were included in the review for data synthesis. The five studies were heterogeneous in scope and quality and only one provided firm empirical support for the positive effects of clinical supervision on therapists' competence. The remaining four studies suffered from methodological weaknesses, but provided some preliminary support that clinical supervision may be beneficiary for novice therapists. No study could show benefits from supervision for patients. The research literature suggests that clinical supervision may have some potential effects on novice therapists' competence compared to no supervision but the effects on clinical outcomes are still unclear. While bug-in-the-eye live supervision may be more effective than standard delayed supervision, the effects of specific supervision models or features are also unclear. There is a continued need for high-quality empirical studies on the effects of clinical supervision in psychotherapy.

Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

2017-05-01

The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

NASA Astrophysics Data System (ADS)

Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

2009-07-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

Spatial Mutual Information Based Hyperspectral Band Selection for Classification

PubMed Central

2015-01-01

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results. PMID:25918742

Cancer classification through filtering progressive transductive support vector machine based on gene expression data

NASA Astrophysics Data System (ADS)

Lu, Xinguo; Chen, Dan

2017-08-01

Traditional supervised classifiers neglect a large amount of data which not have sufficient follow-up information, only work with labeled data. Consequently, the small sample size limits the advancement of design appropriate classifier. In this paper, a transductive learning method which combined with the filtering strategy in transductive framework and progressive labeling strategy is addressed. The progressive labeling strategy does not need to consider the distribution of labeled samples to evaluate the distribution of unlabeled samples, can effective solve the problem of evaluate the proportion of positive and negative samples in work set. Our experiment result demonstrate that the proposed technique have great potential in cancer prediction based on gene expression.

Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

PubMed

Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

2017-05-01

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

Function approximation using combined unsupervised and supervised learning.

PubMed

Andras, Peter

2014-03-01

Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

Fast and robust segmentation of white blood cell images by self-supervised learning.

PubMed

Zheng, Xin; Wang, Yong; Wang, Guoyou; Liu, Jianguo

2018-04-01

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

Emotional Literacy Support Assistants' Views on Supervision Provided by Educational Psychologists: What EPs Can Learn from Group Supervision

ERIC Educational Resources Information Center

Osborne, Cara; Burton, Sheila

2014-01-01

The Educational Psychology Service in this study has responsibility for providing group supervision to Emotional Literacy Support Assistants (ELSAs) working in schools. To date, little research has examined this type of inter-professional supervision arrangement. The current study used a questionnaire to examine ELSAs' views on the supervision…

Vector Magnetograph Design

NASA Technical Reports Server (NTRS)

Chipman, Russell A.

1996-01-01

This report covers work performed during the period of November 1994 through March 1996 on the design of a Space-borne Solar Vector Magnetograph. This work has been performed as part of a design team under the supervision of Dr. Mona Hagyard and Dr. Alan Gary of the Space Science Laboratory. Many tasks were performed and this report documents the results from some of those tasks, each contained in the corresponding appendix. Appendices are organized in chronological order.

PhD Student Emotional Exhaustion: The Role of Supportive Supervision and Self-Presentation Behaviours

ERIC Educational Resources Information Center

Devine, Kay; Hunter, Karen H.

2017-01-01

This research examines doctoral student perceptions of emotional exhaustion relative to supportive supervision and the use of impression management (IM) and facades of conformity (FOC). Results indicated that supportive supervision significantly reduced emotional exhaustion and the use of self-presentation behaviours, while the use of FOC…

The Use of E-supervision to Support Speech-Language Pathology Graduate Students during Student Teaching Practica

PubMed Central

Carlin, Charles H.; Boarman, Katie; Carlin, Emily; Inselmann, Karissa

2013-01-01

In the present feasibility study, e-supervision was used to provide university liaison supervision to speech-language pathology (SLP) graduate students enrolled in student teaching practica. Utilizing a mixed methodology approach, interview and survey data were compared in order to identify similarities and differences between in-person and e-supervision, and guide future practice. Results showed e-supervised graduate students perceived that they received adequate supervision, feedback, support, and communication. Further, e-supervision provided additional benefits to supervisors, children on the caseload, and universities. Despite the benefits, disadvantages emerged. Implications for future practice and limitations of the study were identified. PMID:25945215

Clinical supervision for nurses in administrative and leadership positions: a systematic literature review of the studies focusing on administrative clinical supervision.

PubMed

Sirola-Karvinen, Pirjo; Hyrkäs, Kristiina

2006-11-01

The aim of this systematic literature review was to describe administrative clinical supervision from the nursing leaders', directors' and administrators' perspective. Administrative clinical supervision is a timely and important topic as organizational structures in health care and nursing leadership are changing in addition to the increasing number of complex challenges present in health care. The material in this review was drawn from national and international databases including doctoral dissertations, distinguished thesis and peer-reviewed articles. The material was analysed by means of content analysis. The theoretical framework for the analysis was based on the three main functions of clinical supervision: administrative, educational and supportive. The findings demonstrated that the experiences of the administrative clinical supervision and its supportiveness were varying. The intervention was seen to provide versatility of learning experiences and support in challenging work experiences. Administrative clinical supervision effects and assures the quality of care. The effects as a means of development were explained through its resemblance to a leading specialist community. The findings support earlier perceptions concerning the importance and significance of administrative clinical supervision for nursing managers and administrators. However, more research is needed to develop administrative clinical supervision and to increase understanding of theoretical assumptions and relationships of the concepts on the background.

Mandated Supervision: An Intervention for Disciplined Professionals

ERIC Educational Resources Information Center

Cobia, Debra C.; Pipes, Randolph B.

2002-01-01

Mandated supervision with disciplined mental health professionals is defined and discussed. In the absence of empirical support for supervision in this context, sources of theoretical support are provided. Risks, benefits, and implications for practice for supervisors are also discussed.

Implementing a sustainable clinical supervision model for Isles nurses in Orkney.

PubMed

Hall, Ian

2018-03-02

The Isles Network of Care (INOC) community nurses work at the extreme of the remote and rural continuum, working mostly as lone practitioners. Following the development of sustainable clinical supervision model for Isles nurses in Orkney, clinical supervision was found to improve both peer support and governance for this group of isolated staff. A literature overview identified the transition of clinical supervision in general nursing over 24 years from 'carrot' to 'stick'. The study included a questionnaire survey that was sent to the 2017 Queen's Nursing Institute Scotland cohort to elicit information about the nurses' experience of clinical supervision. The survey found that 55% provide supervision and 40% receive it. Health board encouragement of its use was found to be disappointingly low at 40%. The INOC nurses were surveyed about the new peer-support (restorative) model, which relies on video-conference contact to allow face to face interaction between isolated isles nurses. Feedback prompted a review of clinical supervision pairings, and the frequency and methods of meeting. The need for supervisor training led to agreement with the Remote and Rural Health Education Alliance to provide relevant support. The perceived benefits of supervision included increased support and reflection, and improved relationships with isolated colleagues.

Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques.

PubMed

Yin, Zhong; Zhang, Jianhua

2014-07-01

Identifying the abnormal changes of mental workload (MWL) over time is quite crucial for preventing the accidents due to cognitive overload and inattention of human operators in safety-critical human-machine systems. It is known that various neuroimaging technologies can be used to identify the MWL variations. In order to classify MWL into a few discrete levels using representative MWL indicators and small-sized training samples, a novel EEG-based approach by combining locally linear embedding (LLE), support vector clustering (SVC) and support vector data description (SVDD) techniques is proposed and evaluated by using the experimentally measured data. The MWL indicators from different cortical regions are first elicited by using the LLE technique. Then, the SVC approach is used to find the clusters of these MWL indicators and thereby to detect MWL variations. It is shown that the clusters can be interpreted as the binary class MWL. Furthermore, a trained binary SVDD classifier is shown to be capable of detecting slight variations of those indicators. By combining the two schemes, a SVC-SVDD framework is proposed, where the clear-cut (smaller) cluster is detected by SVC first and then a subsequent SVDD model is utilized to divide the overlapped (larger) cluster into two classes. Finally, three-class MWL levels (low, normal and high) can be identified automatically. The experimental data analysis results are compared with those of several existing methods. It has been demonstrated that the proposed framework can lead to acceptable computational accuracy and has the advantages of both unsupervised and supervised training strategies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy.

PubMed

Asadi, Hamed; Dowling, Richard; Yan, Bernard; Mitchell, Peter

2014-01-01

Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke. We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data. We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408). We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.

Automated novelty detection in the WISE survey with one-class support vector machines

NASA Astrophysics Data System (ADS)

Solarz, A.; Bilicki, M.; Gromadzki, M.; Pollo, A.; Durkalec, A.; Wypych, M.

2017-10-01

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources - novelties or even anomalies - whose existence and properties cannot be easily predicted from earlier observations. Such objects can be efficiently located with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. The OCSVM method detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but more importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample, but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues. The catalogues of outlier data are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/606/A39

Clinical supervision and nursing students' professional competence: support-seeking behaviour and the attachment styles of students and mentors.

PubMed

Moked, Zahava; Drach-Zahavy, Anat

2016-02-01

To examine whether the interdependent attachment style of students is positively related to their support-seeking behaviour during supervision and whether their over-dependent and counter-dependent attachment styles are negatively related to it. Second, to determine whether the mentors' attachment styles moderate the relationship between the students' support-seeking behaviours and their professional competence, such that this relationship is stronger when supervisors are characterized by higher independent attachment style. The mentor-student encounter during nursing clinical supervision is expected to create a supportive environment aimed at promoting support-seeking behaviours and subsequent positive supervision outcomes. Bowlby's attachment theory suggests that the three attachment styles - independent, counter-dependent and over-dependent - may have implications for clinical supervision. A correlative-prospective study. One hundred and seventy-eight students and 66 clinical mentors completed questionnaires at the beginning and end of a clinical supervision session during 2012-2013. Results demonstrated that high compared with low independent nursing students tended to seek less support. Second, students who seek less support evaluated their professional competence as higher than students who seek more support. Third, mentor's counter-dependent attachment style moderated the relationship between students' support-seeking behaviour and their professional competencies. The results allude to the detrimental meaning of support-seeking in the eyes of nursing students. Results can guide administrators in promoting supervision processes that are compatible with the students' independent learning style, while also preventing the negative implications of autonomic learning. Furthermore, as mentors' counter-dependent attachment style can hinder students' support-seeking, attachment styles should be considered in the selection of mentors. © 2015 John Wiley & Sons Ltd.

The impact of a supportive supervision intervention on health workers in Niassa, Mozambique: a cluster-controlled trial.

PubMed

Madede, Tavares; Sidat, Mohsin; McAuliffe, Eilish; Patricio, Sergio Rogues; Uduma, Ogenna; Galligan, Marie; Bradley, Susan; Cambe, Isabel

2017-09-02

Regular supportive supervision is critical to retaining and motivating staff in resource-constrained settings. Previous studies have shown the particular contribution that supportive supervision can make to improving job satisfaction amongst over-stretched health workers in such settings. The Support, Train and Empower Managers (STEM) study designed and implemented a supportive supervision intervention and measured its' impact on health workers using a controlled trial design with a three-arm pre- and post-study in Niassa Province in Mozambique. Post-intervention interviews with a small sample of health workers were also conducted. The quantitative measurements of job satisfaction, emotional exhaustion and work engagement showed no statistically significant differences between end-line and baseline. The qualitative data collected from health workers post the intervention showed many positive impacts on health workers not captured by this quantitative survey. Health workers perceived an improvement in their performance and attributed this to the supportive supervision they had received from their supervisors following the intervention. Reports of increased motivation were also common. An unexpected, yet important consequence of the intervention, which participants directly attributed to the supervision intervention, was the increase in participation and voice amongst health workers in intervention facilities.

Supervision Experiences of Professional Counselors Providing Crisis Counseling

ERIC Educational Resources Information Center

Dupre, Madeleine; Echterling, Lennis G.; Meixner, Cara; Anderson, Robin; Kielty, Michele

2014-01-01

In this phenomenological study, the authors explored supervision experiences of 13 licensed professional counselors in situations requiring crisis counseling. Five themes concerning crisis and supervision were identified from individual interviews. Findings support intensive, immediate crisis supervision and postlicensure clinical supervision.

The role of imaging based prostate biopsy morphology in a data fusion paradigm for transducing prognostic predictions

NASA Astrophysics Data System (ADS)

Khan, Faisal M.; Kulikowski, Casimir A.

2016-03-01

A major focus area for precision medicine is in managing the treatment of newly diagnosed prostate cancer patients. For patients with a positive biopsy, clinicians aim to develop an individualized treatment plan based on a mechanistic understanding of the disease factors unique to each patient. Recently, there has been a movement towards a multi-modal view of the cancer through the fusion of quantitative information from multiple sources, imaging and otherwise. Simultaneously, there have been significant advances in machine learning methods for medical prognostics which integrate a multitude of predictive factors to develop an individualized risk assessment and prognosis for patients. An emerging area of research is in semi-supervised approaches which transduce the appropriate survival time for censored patients. In this work, we apply a novel semi-supervised approach for support vector regression to predict the prognosis for newly diagnosed prostate cancer patients. We integrate clinical characteristics of a patient's disease with imaging derived metrics for biomarker expression as well as glandular and nuclear morphology. In particular, our goal was to explore the performance of nuclear and glandular architecture within the transduction algorithm and assess their predictive power when compared with the Gleason score manually assigned by a pathologist. Our analysis in a multi-institutional cohort of 1027 patients indicates that not only do glandular and morphometric characteristics improve the predictive power of the semi-supervised transduction algorithm; they perform better when the pathological Gleason is absent. This work represents one of the first assessments of quantitative prostate biopsy architecture versus the Gleason grade in the context of a data fusion paradigm which leverages a semi-supervised approach for risk prognosis.

A systematic review of supportive supervision as a strategy to improve primary healthcare services in Sub-Saharan Africa.

PubMed

Bailey, Claire; Blake, Carolyn; Schriver, Michael; Cubaka, Vincent Kalumire; Thomas, Tisa; Martin Hilber, Adriane

2016-01-01

It may be assumed that supportive supervision effectively builds capacity, improves the quality of care provided by frontline health workers, and positively impacts clinical outcomes. Evidence on the role of supervision in Sub-Saharan Africa has been inconclusive, despite the critical need to maximize the workforce in low-resource settings. To review the published literature from Sub-Saharan Africa on the effects of supportive supervision on quality of care, and health worker motivation and performance. A systematic review of seven databases of both qualitative and quantitative studies published in peer-reviewed journals. Selected studies were based in primary healthcare settings in Sub-Saharan Africa and present primary data concerning supportive supervision. Thematic synthesis where data from the identified studies were grouped and interpreted according to prominent themes. Supportive supervision can increase job satisfaction and health worker motivation. Evidence is mixed on whether this translates to increased clinical competence and there is little evidence of the effect on clinical outcomes. Results highlight the lack of sound evidence on the effects of supportive supervision owing to limitations in research design and the complexity of evaluating such interventions. The approaches required a high level of external inputs, which challenge the sustainability of such models. Copyright © 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Initial experiences and innovations in supervising community health workers for maternal, newborn, and child health in Morogoro region, Tanzania.

PubMed

Roberton, Timothy; Applegate, Jennifer; Lefevre, Amnesty E; Mosha, Idda; Cooper, Chelsea M; Silverman, Marissa; Feldhaus, Isabelle; Chebet, Joy J; Mpembeni, Rose; Semu, Helen; Killewo, Japhet; Winch, Peter; Baqui, Abdullah H; George, Asha S

2015-04-09

Supervision is meant to improve the performance and motivation of community health workers (CHWs). However, most evidence on supervision relates to facility health workers. The Integrated Maternal, Newborn, and Child Health (MNCH) Program in Morogoro region, Tanzania, implemented a CHW pilot with a cascade supervision model where facility health workers were trained in supportive supervision for volunteer CHWs, supported by regional and district staff, and with village leaders to further support CHWs. We examine the initial experiences of CHWs, their supervisors, and village leaders to understand the strengths and challenges of such a supervision model for CHWs. Quantitative and qualitative data were collected concurrently from CHWs, supervisors, and village leaders. A survey was administered to 228 (96%) of the CHWs in the Integrated MNCH Program and semi-structured interviews were conducted with 15 CHWs, 8 supervisors, and 15 village leaders purposefully sampled to represent different actor perspectives from health centre catchment villages in Morogoro region. Descriptive statistics analysed the frequency and content of CHW supervision, while thematic content analysis explored CHW, supervisor, and village leader experiences with CHW supervision. CHWs meet with their facility-based supervisors an average of 1.2 times per month. CHWs value supervision and appreciate the sense of legitimacy that arises when supervisors visit them in their village. Village leaders and district staff are engaged and committed to supporting CHWs. Despite these successes, facility-based supervisors visit CHWs in their village an average of only once every 2.8 months, CHWs and supervisors still see supervision primarily as an opportunity to check reports, and meetings with district staff are infrequent and not well scheduled. Supervision of CHWs could be strengthened by streamlining supervision protocols to focus less on report checking and more on problem solving and skills development. Facility health workers, while important for technical oversight, may not be the best mentors for certain tasks such as community relationship-building. We suggest further exploring CHW supervision innovations, such as an enhanced role for community actors, who may be more suitable to support CHWs engaged primarily in health promotion than scarce and over-worked facility health workers.

A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder

PubMed Central

Yu, J S; Xue, A Y; Redei, E E; Bagheri, N

2016-01-01

Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A notable barrier in addressing this public health threat involves reliable identification of the disorder, as many affected individuals remain undiagnosed or misdiagnosed. An objective blood-based diagnostic test using transcript levels of a panel of markers would provide an invaluable tool for MDD as the infrastructure—including equipment, trained personnel, billing, and governmental approval—for similar tests is well established in clinics worldwide. Here we present a supervised classification model utilizing support vector machines (SVMs) for the analysis of transcriptomic data readily obtained from a peripheral blood specimen. The model was trained on data from subjects with MDD (n=32) and age- and gender-matched controls (n=32). This SVM model provides a cross-validated sensitivity and specificity of 90.6% for the diagnosis of MDD using a panel of 10 transcripts. We applied a logistic equation on the SVM model and quantified a likelihood of depression score. This score gives the probability of a MDD diagnosis and allows the tuning of specificity and sensitivity for individual patients to bring personalized medicine closer in psychiatry. PMID:27779627

The Effect of Personalization on Smartphone-Based Fall Detectors

PubMed Central

Medrano, Carlos; Plaza, Inmaculada; Igual, Raúl; Sánchez, Ángel; Castro, Manuel

2016-01-01

The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training. PMID:26797614

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

NASA Astrophysics Data System (ADS)

Ranaie, Mehrdad; Soffianian, Alireza; Pourmanafi, Saeid; Mirghaffari, Noorollah; Tarkesh, Mostafa

2018-03-01

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

PubMed Central

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T.; Alcázar-Ramírez, José D.; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A.

2015-01-01

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI. PMID:26664493

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment.

PubMed

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T; Alcázar-Ramírez, José D; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A

2015-01-01

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI.

Impact of community-based vector control on house infestation and Trypanosoma cruzi infection in Triatoma infestans, dogs and cats in the Argentine Chaco.

PubMed

Cardinal, M V; Lauricella, M A; Marcet, P L; Orozco, M M; Kitron, U; Gürtler, R E

2007-09-01

The relative impact of two community-based vector control strategies on house infestation by Triatoma infestans and Trypanosoma cruzi infection in bugs, domestic dogs and cats was assessed in two neighboring rural areas comprising 40 small villages and 323 houses in one of the regions most endemic for Chagas disease in northern Argentina. The prevalence and abundance of domestic infestation were 1.5- and 6.5-fold higher, respectively, in the area under pulsed, non-supervised control actions operating under the guidelines of the National Vector Control Program (NCVP) than in the area under sustained, supervised surveillance carried out jointly by the UBA research team and NCVP. The prevalence of infestation and infection varied widely among village groups within each area. In the pulsed control area, the prevalence of infection in bugs, dogs and cats was two- to three-fold higher than in the area under sustained surveillance, most of the infected animals qualified as autochthonous cases, and evidence of recent transmission was observed. Infection was highly aggregated at the household level and fell close to the 80/20 rule. Using multiple logistic regression analysis clustered by household, infection in dogs was associated positively and significantly with variables reflecting local exposure to infected T. infestans, thus demonstrating weak performance of the vector surveillance system. For high-risk areas in the Gran Chaco region, interruption of vector-mediated domestic transmission of T. cruzi requires residual insecticide spraying that is more intense, of a higher quality and sustained in time, combined with community participation and environmental management measures.

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Assess the feasibility of the high-speed railway construction in China by measuring the traffic demand elastic

NASA Astrophysics Data System (ADS)

Yu, Nan; Cao, Yu

2017-05-01

The traffic demand elastic is proposed as a new indicator in this study to measure the feasibility of the high-speed railway construction in a more intuitive way. The Matrix Completion (MC) and Semi-Supervised Support Vector Machine (S3VM) are used to realize the measurement and prediction of this index on the basis of the satisfaction investigation on the 326 inter-city railways in china. It is demonstrated that instead of calculating the economic benefits brought by the construction of high-speed railway, this indicator can find the most urgent railways to be improved by directly evaluate the existing railway facilities from the perspective of transportation service improvement requirements.

Space Object Classification Using Fused Features of Time Series Data

NASA Astrophysics Data System (ADS)

Jia, B.; Pham, K. D.; Blasch, E.; Shen, D.; Wang, Z.; Chen, G.

In this paper, a fused feature vector consisting of raw time series and texture feature information is proposed for space object classification. The time series data includes historical orbit trajectories and asteroid light curves. The texture feature is derived from recurrence plots using Gabor filters for both unsupervised learning and supervised learning algorithms. The simulation results show that the classification algorithms using the fused feature vector achieve better performance than those using raw time series or texture features only.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

Physical Human Activity Recognition Using Wearable Sensors.

PubMed

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-12-11

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Physical Human Activity Recognition Using Wearable Sensors

PubMed Central

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-01-01

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject. PMID:26690450

Applying active learning to supervised word sense disambiguation in MEDLINE.

PubMed

Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

2013-01-01

This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models.

Applying active learning to supervised word sense disambiguation in MEDLINE

PubMed Central

Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

2013-01-01

Objectives This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. Methods We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Results Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. Conclusions This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models. PMID:23364851

Enhancing clinical concept extraction with distributional semantics

PubMed Central

Cohen, Trevor; Wu, Stephen; Gonzalez, Graciela

2011-01-01

Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged f-measure for exact match increased from 80.3% to 82.3% and the micro-averaged f-measure based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data. PMID:22085698

Nurse managers' conceptions of quality management as promoted by peer supervision.

PubMed

Hyrkäs, Kristiina; Koivula, Meeri; Lehti, Kristiina; Paunonen-Ilmonen, Marita

2003-01-01

The aim of the study was to describe nurse managers' conceptions of quality management in their work as promoted by peer supervision. Quality management is one of the topical issues in a nurse manager's demanding and changing work. As first-line managers, they have a key role in quality management which is seen to create the system and environment for high quality services and quality improvement. Despite the official recommendations and definitions of quality management, several published reports have shown that there is no single solution for quality management. Peer supervision or the support provided by it to nursing managers have rarely been a subject of study. This study was carried out at Tampere University Hospital between 1996 and 1998. The peer supervision intervention was organized once a month, 2 hours at a time and in closed supervisor-led groups of nine nurse managers. Data were collected by themed interviews. Fifteen nurse managers participated in the study. The data were analysed using the phenomenographic method. Two main categories were formed of nurse managers' conceptions. The first described supportive and reflective characteristics of peer supervision. This main category was described by horizontal, hierarchical categories of support from peer group and reflection. The second main category described nurse managers' conceptions of individual development of leadership during peer supervision. This main category was also described by three horizontal categories: personal growth, finding psychological resources and internalization of leadership. The finding of this study show that peer supervision benefited nurse managers in quality management through reflection and support. The reflective and supportive characteristics of peer supervision promoted the nurse managers' individual development, but also that of leadership. It can be concluded that peer supervision promotes quality management in nurse managers' work.

Clinical supervision: an important part of every nurse's practice.

PubMed

Bifarin, Oladayo; Stonehouse, David

2017-03-23

Clinical supervision involves a supportive relationship between supervisor and supervisee that facilitates reflective learning and is part of professional socialisation. Clinical supervision can take many different forms and may be adapted to suit local circumstances. A working agreement is required between the parties to the supervision and issues surrounding confidentiality must be understood. High-quality clinical supervision leads to greater job satisfaction and less stress. When it is absent or inadequate, however, the results can be serious and it is particularly important that student nurses are well supported in this way. Further research in this area is necessary.

Supporting Early Childhood Practitioners through Relationship-Based, Reflective Supervision

ERIC Educational Resources Information Center

Bernstein, Victor J.; Edwards, Renee C.

2012-01-01

Reflective supervision is a relationship-based practice that supports the professional development of early childhood practitioners. Reflective supervision helps practitioners cope with the intense feelings and stress that are generated when working with at-risk children and families. It allows them to focus on the purpose and goals of the program…

Measuring Process Elements in Reflective Supervision: An Instrument in the Making

ERIC Educational Resources Information Center

Finello, Karen Moran; Heffron, Mary Claire; Stroud, Barbara

2016-01-01

Reflective supervision is increasingly mandated in evidence-based infant and early childhood programs and is, therefore, experiencing rapid expansion across the United States. The growing interest in reflective supervision has led to new questions about how to train, support, and gauge the competency of supervisors who are supporting and educating…

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

Encoding Dissimilarity Data for Statistical Model Building.

PubMed

Wahba, Grace

2010-12-01

We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A "newbie" algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a Smoothing Spline ANOVA penalized likelihood model, a Support Vector Machine, or any model that will admit Reproducing Kernel Hilbert Space components, for nonparametric regression, supervised learning, or semi-supervised learning. Future work and open questions are discussed. The papers are: F. Lu, S. Keles, S. Wright and G. Wahba 2005. A framework for kernel regularization with application to protein clustering. Proceedings of the National Academy of Sciences 102, 12332-1233.G. Corrada Bravo, G. Wahba, K. Lee, B. Klein, R. Klein and S. Iyengar 2009. Examining the relative influence of familial, genetic and environmental covariate information in flexible risk models. Proceedings of the National Academy of Sciences 106, 8128-8133F. Lu, Y. Lin and G. Wahba. Robust manifold unfolding with kernel regularization. TR 1008, Department of Statistics, University of Wisconsin-Madison.

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

Classification of ROTSE Variable Stars using Machine Learning

NASA Astrophysics Data System (ADS)

Wozniak, P. R.; Akerlof, C.; Amrose, S.; Brumby, S.; Casperson, D.; Gisler, G.; Kehoe, R.; Lee, B.; Marshall, S.; McGowan, K. E.; McKay, T.; Perkins, S.; Priedhorsky, W.; Rykoff, E.; Smith, D. A.; Theiler, J.; Vestrand, W. T.; Wren, J.; ROTSE Collaboration

2001-12-01

We evaluate several Machine Learning algorithms as potential tools for automated classification of variable stars. Using the ROTSE sample of ~1800 variables from a pilot study of 5% of the whole sky, we compare the effectiveness of a supervised technique (Support Vector Machines, SVM) versus unsupervised methods (K-means and Autoclass). There are 8 types of variables in the sample: RR Lyr AB, RR Lyr C, Delta Scuti, Cepheids, detached eclipsing binaries, contact binaries, Miras and LPVs. Preliminary results suggest a very high ( ~95%) efficiency of SVM in isolating a few best defined classes against the rest of the sample, and good accuracy ( ~70-75%) for all classes considered simultaneously. This includes some degeneracies, irreducible with the information at hand. Supervised methods naturally outperform unsupervised methods, in terms of final error rate, but unsupervised methods offer many advantages for large sets of unlabeled data. Therefore, both types of methods should be considered as promising tools for mining vast variability surveys. We project that there are more than 30,000 periodic variables in the ROTSE-I data base covering the entire local sky between V=10 and 15.5 mag. This sample size is already stretching the time capabilities of human analysts.

Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

PubMed

Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

2017-01-01

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.

Inspiring health worker motivation with supportive supervision: a survey of lady health supervisor motivating factors in rural Pakistan.

PubMed

Rabbani, Fauziah; Shipton, Leah; Aftab, Wafa; Sangrasi, Kashif; Perveen, Shagufta; Zahidie, Aysha

2016-08-17

Community health worker motivation is an important consideration for improving performance and addressing maternal, newborn, and child health in low and middle-income countries. Therefore, identifying health system interventions that address motivating factors in resource-strained settings is essential. This study is part of a larger implementation research project called Nigraan, which is intervening on supportive supervision in the Lady Health Worker Programme to improve community case management of pneumonia and diarrhea in rural Pakistan. This study explored the motivation of Lady Health Supervisors, a cadre of community health workers, with particular attention to their views on supportive supervision. Twenty-nine lady health supervisors enrolled in Nigraan completed open-ended structured surveys with questions exploring factors that affect their motivation. Thematic analysis was conducted using a conceptual framework categorizing motivating factors at individual, community, and health system levels. Supportive supervision, recognition, training, logistics, and salaries are community and health system motivating factors for lady health supervisors. Lady health supervisors are motivated by both their role in providing supportive supervision to lady health workers and by the supervisory support received from their coordinators and managers. Family support, autonomy, and altruism are individual level motivating factors. Health system factors, including supportive supervision, are crucial to improving lady health supervisor motivation. As health worker motivation influences their performance, evaluating the impact of health system interventions on community health worker motivation is important to improving the effectiveness of community health worker programs.

Perceptions on evaluative and formative functions of external supervision of Rwandan primary healthcare facilities: A qualitative study.

PubMed

Schriver, Michael; Cubaka, Vincent Kalumire; Itangishaka, Sylvere; Nyirazinyoye, Laetitia; Kallestrup, Per

2018-01-01

External supervision of primary healthcare facilities in low- and middle-income countries often has a managerial main purpose in which the role of support for professional development is unclear. To explore how Rwandan primary healthcare supervisors and providers (supervisees) perceive evaluative and formative functions of external supervision. Qualitative, exploratory study. Focus group discussions: three with supervisors, three with providers, and one mixed (n = 31). Findings were discussed with individual and groups of supervisors and providers. Evaluative activities occupied providers' understanding of supervision, including checking, correcting, marking and performance-based financing. These were presented as sources of motivation, that in self-determination theory indicate introjected regulation. Supervisors preferred to highlight their role in formative supervision, which may mask their own and providers' uncontested accounts that systematic performance evaluations predominated supervisors' work. Providers strongly requested larger focus on formative and supportive functions, voiced as well by most supervisors. Impact of performance evaluation on motivation and professional development is discussed. While external supervisors intended to support providers' professional development, our findings indicate serious problems with this in a context of frequent evaluations and performance marking. Separating the role of supporter and evaluator does not appear as the simple solution. If external supervision is to improve health care services, it is essential that supervisors and health centre managers are competent to support providers in a way that transparently accounts for various performance pressures. This includes delivery of proper formative supervision with useful feedback, maintaining an effective supervisory relationship, as well as ensuring providers are aware of the purpose and content of evaluative and formative supervision functions.

Exploring Organizational Barriers to Strengthening Clinical Supervision of Psychiatric Nursing Staff: A Longitudinal Controlled Intervention Study.

PubMed

Gonge, Henrik; Buus, Niels

2016-05-01

This article reports findings from a longitudinal controlled intervention study of 115 psychiatric nursing staff. The twofold objective of the study was: (a) To test whether the intervention could increase clinical supervision participation and effectiveness of existing supervision practices, and (b) To explore organizational constraints to implementation of these strengthened practices. Questionnaire responses and registration of participation in clinical supervision were registered prior and subsequent to the intervention consisting of an action learning oriented reflection on staff's existing clinical supervision practices. Major organizational changes in the intervention group during the study period obstructed the implementation of strengthened clinical supervision practices, but offered an opportunity for studying the influences of organizational constraints. The main findings were that a) diminishing experience of social support from colleagues was associated with reduced participation in clinical supervision, while b) additional quantitative demands were associated with staff reporting difficulties finding time for supervision. This probably explained a negative development in the experienced effectiveness of supervision. It is concluded that organizational support is an imperative for implementation of clinical supervision.

Psoriasis image representation using patch-based dictionary learning for erythema severity scoring.

PubMed

George, Yasmeen; Aldeen, Mohammad; Garnavi, Rahil

2018-06-01

Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Supporting and Supervising Teachers Working With Adults Learning English. CAELA Network Brief

ERIC Educational Resources Information Center

Young, Sarah

2009-01-01

This brief provides an overview of the knowledge and skills that administrators need in order to support and supervise teachers of adult English language learners. It begins with a review of resources and literature related to teacher supervision in general and to adult ESL education. It continues with information on the background and…

Supervisors Watching Supervisors: The Deconstructive Possibilities and Tensions of Team Supervision

ERIC Educational Resources Information Center

Manathunga, Catherine

2012-01-01

Many universities have introduced team supervision as a means of intervening in the intensity of the traditional supervisor-student dyad. This policy is intended to provide students with a great support during their candidature and to share the burden of sole supervision. It is also a pedagogy that seeks to support students' engagement with new…

Supervision of community health workers in Mozambique: a qualitative study of factors influencing motivation and programme implementation.

PubMed

Ndima, Sozinho Daniel; Sidat, Mohsin; Give, Celso; Ormel, Hermen; Kok, Maryse Catelijne; Taegtmeyer, Miriam

2015-09-01

Community health workers (CHWs) in Mozambique (known as Agentes Polivalentes Elementares (APEs)) are key actors in providing health services in rural communities. Supervision of CHWs has been shown to improve their work, although details of how it is implemented are scarce. In Mozambique, APE supervision structures and scope of work are clearly outlined in policy and rely on supervisors at the health facility of reference. The aim of this study was to understand how and which aspects of supervision impact on APE motivation and programme implementation. Qualitative research methodologies were used. Twenty-nine in-depth interviews were conducted to capture experiences and perceptions of purposefully selected participants. These included APEs, health facility supervisors, district APE supervisors and community leaders. Interviews were recorded, translated and transcribed, prior to the development of a thematic framework. Supervision was structured as dictated by policy but in practice was irregular and infrequent, which participants identified as affecting APE's motivation. When it did occur, supervision was felt to focus more on fault-finding than being supportive in nature and did not address all areas of APE's work - factors that APEs identified as demotivating. Supervisors, in turn, felt unsupported and felt this negatively impacted performance. They had a high workload in health facilities, where they had multiple roles, including provision of health services, taking care of administrative issues and supervising APEs in communities. A lack of resources for supervision activities was identified, and supervisors felt caught up in administrative issues around APE allowances that they were unable to solve. Many supervisors were not trained in providing supportive supervision. Community governance and accountability mechanisms were only partially able to fill the gaps left by the supervision provided by the health system. The findings indicate the need for an improved supervision system to enhance support and motivation and ultimately performance of APEs. Our study found disconnections between the APE programme policy and its implementation, with gaps in skills, training and support of supervisors leading to sub-optimal supervision. Improved methods of supervision could be implemented including those that maximize the opportunities during face-to-face meetings and through community-monitoring mechanisms.

9 CFR 106.1 - Biological products; exemption.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AGRICULTURE VIRUSES, SERUMS, TOXINS, AND ANALOGOUS PRODUCTS; ORGANISMS AND VECTORS EXEMPTION FOR BIOLOGICAL PRODUCTS USED IN DEPARTMENT PROGRAMS OR UNDER DEPARTMENT CONTROL OR SUPERVISION § 106.1 Biological products... 9 Animals and Animal Products 1 2012-01-01 2012-01-01 false Biological products; exemption. 106.1...

9 CFR 106.1 - Biological products; exemption.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AGRICULTURE VIRUSES, SERUMS, TOXINS, AND ANALOGOUS PRODUCTS; ORGANISMS AND VECTORS EXEMPTION FOR BIOLOGICAL PRODUCTS USED IN DEPARTMENT PROGRAMS OR UNDER DEPARTMENT CONTROL OR SUPERVISION § 106.1 Biological products... 9 Animals and Animal Products 1 2014-01-01 2014-01-01 false Biological products; exemption. 106.1...

9 CFR 106.1 - Biological products; exemption.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AGRICULTURE VIRUSES, SERUMS, TOXINS, AND ANALOGOUS PRODUCTS; ORGANISMS AND VECTORS EXEMPTION FOR BIOLOGICAL PRODUCTS USED IN DEPARTMENT PROGRAMS OR UNDER DEPARTMENT CONTROL OR SUPERVISION § 106.1 Biological products... 9 Animals and Animal Products 1 2013-01-01 2013-01-01 false Biological products; exemption. 106.1...

9 CFR 106.1 - Biological products; exemption.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AGRICULTURE VIRUSES, SERUMS, TOXINS, AND ANALOGOUS PRODUCTS; ORGANISMS AND VECTORS EXEMPTION FOR BIOLOGICAL PRODUCTS USED IN DEPARTMENT PROGRAMS OR UNDER DEPARTMENT CONTROL OR SUPERVISION § 106.1 Biological products... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Biological products; exemption. 106.1...

9 CFR 106.1 - Biological products; exemption.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AGRICULTURE VIRUSES, SERUMS, TOXINS, AND ANALOGOUS PRODUCTS; ORGANISMS AND VECTORS EXEMPTION FOR BIOLOGICAL PRODUCTS USED IN DEPARTMENT PROGRAMS OR UNDER DEPARTMENT CONTROL OR SUPERVISION § 106.1 Biological products... 9 Animals and Animal Products 1 2011-01-01 2011-01-01 false Biological products; exemption. 106.1...

Ellipsoidal fuzzy learning for smart car platoons

NASA Astrophysics Data System (ADS)

Dickerson, Julie A.; Kosko, Bart

1993-12-01

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

Sustainable vector control and management of Chagas disease in the Gran Chaco, Argentina

PubMed Central

Gürtler, Ricardo E.; Kitron, Uriel; Cecere, M. Carla; Segura, Elsa L.; Cohen, Joel E.

2007-01-01

Chagas disease remains a serious obstacle to health and economic development in Latin America, especially for the rural poor. We report the long-term effects of interventions in rural villages in northern Argentina during 1984–2006. Two community-wide campaigns of residual insecticide spraying immediately and strongly reduced domestic infestation and infection with Trypanosoma cruzi in Triatoma infestans bugs and dogs and more gradually reduced the seroprevalence of children <15 years of age. Because no effective surveillance and control actions followed the first campaign in 1985, transmission resurged in 2–3 years. Renewed interventions in 1992 followed by sustained, supervised, community-based vector control largely suppressed the reestablishment of domestic bug colonies and finally led to the interruption of local human T. cruzi transmission. Human incidence of infection was nearly an order of magnitude higher in peripheral rural areas under pulsed, unsupervised, community-based interventions, where human transmission became apparent in 2000. The sustained, supervised, community-based strategy nearly interrupted domestic transmission to dogs but did not eliminate T. infestans despite the absence of pyrethroid-insecticide resistance. T. infestans persisted in part because of the lack of major changes in housing construction and quality. Sustained community participation grew out of establishing a trusted relationship with the affected communities and the local schools. The process included health promotion and community mobilization, motivation, and supervision in close cooperation with locally nominated leaders. PMID:17913895

Perceptions on evaluative and formative functions of external supervision of Rwandan primary healthcare facilities: A qualitative study

PubMed Central

Schriver, Michael; Cubaka, Vincent Kalumire; Itangishaka, Sylvere; Nyirazinyoye, Laetitia; Kallestrup, Per

2018-01-01

Background External supervision of primary healthcare facilities in low- and middle-income countries often has a managerial main purpose in which the role of support for professional development is unclear. Aim To explore how Rwandan primary healthcare supervisors and providers (supervisees) perceive evaluative and formative functions of external supervision. Design Qualitative, exploratory study. Data Focus group discussions: three with supervisors, three with providers, and one mixed (n = 31). Findings were discussed with individual and groups of supervisors and providers. Results Evaluative activities occupied providers’ understanding of supervision, including checking, correcting, marking and performance-based financing. These were presented as sources of motivation, that in self-determination theory indicate introjected regulation. Supervisors preferred to highlight their role in formative supervision, which may mask their own and providers’ uncontested accounts that systematic performance evaluations predominated supervisors’ work. Providers strongly requested larger focus on formative and supportive functions, voiced as well by most supervisors. Impact of performance evaluation on motivation and professional development is discussed. Conclusion While external supervisors intended to support providers’ professional development, our findings indicate serious problems with this in a context of frequent evaluations and performance marking. Separating the role of supporter and evaluator does not appear as the simple solution. If external supervision is to improve health care services, it is essential that supervisors and health centre managers are competent to support providers in a way that transparently accounts for various performance pressures. This includes delivery of proper formative supervision with useful feedback, maintaining an effective supervisory relationship, as well as ensuring providers are aware of the purpose and content of evaluative and formative supervision functions. PMID:29462144

Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective

DOE PAGES

Pilania, G.; Gubernatis, J. E.; Lookman, T.

2015-12-03

The role of dynamical (or Born effective) charges in classification of octet AB-type binary compounds between four-fold (zincblende/wurtzite crystal structures) and six-fold (rocksalt crystal structure) coordinated systems is discussed. We show that the difference in the dynamical charges of the fourfold and sixfold coordinated structures, in combination with Harrison’s polarity, serves as an excellent feature to classify the coordination of 82 sp–bonded binary octet compounds. We use a support vector machine classifier to estimate the average classification accuracy and the associated variance in our model where a decision boundary is learned in a supervised manner. Lastly, we compare the out-of-samplemore » classification accuracy achieved by our feature pair with those reported previously.« less

Using Psychodrama Techniques to Promote Counselor Identity Development in Group Supervision

ERIC Educational Resources Information Center

Scholl, Mark B.; Smith-Adcock, Sondra

2007-01-01

The authors briefly introduce the concepts, techniques, and theory of identity development associated with J. L. Moreno's (1946, 1969, 1993) Psychodrama. Based upon Loganbill, Hardy, and Delworth's (1982) model, counselor identity development is conceptualized as consisting of seven developmental themes or vectors (e.g., issues of awareness and…

Modeling Time Series Data for Supervised Learning

ERIC Educational Resources Information Center

Baydogan, Mustafa Gokce

2012-01-01

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

Identifying Challenges in Supervising School Psychologists

ERIC Educational Resources Information Center

Harvey, Virginia Smith; Pearrow, Melissa

2010-01-01

Previous studies suggest that the majority of school psychologists do not believe they receive sufficient supervision, despite a growing body of research providing empirical support for supervision to maintain and improve skills. This study explores the dynamics underlying the challenges of providing adequate supervision to school psychologists.…

A global picture of pharmacy technician and other pharmacy support workforce cadres.

PubMed

Koehler, Tamara; Brown, Andrew

Understanding how pharmacy technicians and other pharmacy support workforce cadres assist pharmacists in the healthcare system will facilitate developing health systems with the ability to achieve universal health coverage as it is defined in different country contexts. The aim of this paper is to provide an overview of the present global variety in the technician and other pharmacy support workforce cadres considering; their scope, roles, supervision, education and legal framework. A structured online survey instrument was administered globally using the Survey Monkey platform, designed to address the following topic areas: roles, responsibilities, supervision, education and legislation. The survey was circulated to International Pharmaceutical Federation (FIP) member organisations and a variety of global list serves where pharmaceutical services are discussed. 193 entries from 67 countries and territories were included in the final analysis revealing a vast global variety with respect to the pharmacy support workforce. From no pharmacy technicians or other pharmacy support workforce cadres in Japan, through a variety of cadre interactions with pharmacists, to the autonomous practice of pharmacy support workforce cadres in Malawi. From strictly supervised practice with a focus on supply, through autonomous practice for a variety of responsibilities, to independent practice. From complete supervision for all tasks, through geographical varied supervision, to independent practice. From on the job training, through certificate level vocational courses, to 3-4 year diploma programs. From well-regulated and registered, through part regulation with weak implementation, to completely non-regulated contexts. This paper documents wide differences in supervision requirements, education systems and supportive legislation for pharmacy support workforce cadres globally. A more detailed understanding of specific country practice settings is required if the use of pharmacy support workforce cadres is to be optimized. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Topic detection using paragraph vectors to support active learning in systematic reviews.

PubMed

Hashimoto, Kazuma; Kontonatsios, Georgios; Miwa, Makoto; Ananiadou, Sophia

2016-08-01

Systematic reviews require expert reviewers to manually screen thousands of citations in order to identify all relevant articles to the review. Active learning text classification is a supervised machine learning approach that has been shown to significantly reduce the manual annotation workload by semi-automating the citation screening process of systematic reviews. In this paper, we present a new topic detection method that induces an informative representation of studies, to improve the performance of the underlying active learner. Our proposed topic detection method uses a neural network-based vector space model to capture semantic similarities between documents. We firstly represent documents within the vector space, and cluster the documents into a predefined number of clusters. The centroids of the clusters are treated as latent topics. We then represent each document as a mixture of latent topics. For evaluation purposes, we employ the active learning strategy using both our novel topic detection method and a baseline topic model (i.e., Latent Dirichlet Allocation). Results obtained demonstrate that our method is able to achieve a high sensitivity of eligible studies and a significantly reduced manual annotation cost when compared to the baseline method. This observation is consistent across two clinical and three public health reviews. The tool introduced in this work is available from https://nactem.ac.uk/pvtopic/. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Fuzzy classifier based support vector regression framework for Poisson ratio determination

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2013-09-01

Poisson ratio is considered as one of the most important rock mechanical properties of hydrocarbon reservoirs. Determination of this parameter through laboratory measurement is time, cost, and labor intensive. Furthermore, laboratory measurements do not provide continuous data along the reservoir intervals. Hence, a fast, accurate, and inexpensive way of determining Poisson ratio which produces continuous data over the whole reservoir interval is desirable. For this purpose, support vector regression (SVR) method based on statistical learning theory (SLT) was employed as a supervised learning algorithm to estimate Poisson ratio from conventional well log data. SVR is capable of accurately extracting the implicit knowledge contained in conventional well logs and converting the gained knowledge into Poisson ratio data. Structural risk minimization (SRM) principle which is embedded in the SVR structure in addition to empirical risk minimization (EMR) principle provides a robust model for finding quantitative formulation between conventional well log data and Poisson ratio. Although satisfying results were obtained from an individual SVR model, it had flaws of overestimation in low Poisson ratios and underestimation in high Poisson ratios. These errors were eliminated through implementation of fuzzy classifier based SVR (FCBSVR). The FCBSVR significantly improved accuracy of the final prediction. This strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. Results indicated that SVR predicted Poisson ratio values are in good agreement with measured values.

Regolith-geology mapping with support vector machine: A case study over weathered Ni-bearing peridotites, New Caledonia

NASA Astrophysics Data System (ADS)

De Boissieu, Florian; Sevin, Brice; Cudahy, Thomas; Mangeas, Morgan; Chevrel, Stéphane; Ong, Cindy; Rodger, Andrew; Maurizot, Pierre; Laukamp, Carsten; Lau, Ian; Touraivane, Touraivane; Cluzel, Dominique; Despinoy, Marc

2018-02-01

Accurate maps of Earth's geology, especially its regolith, are required for managing the sustainable exploration and development of mineral resources. This paper shows how airborne imaging hyperspectral data collected over weathered peridotite rocks in vegetated, mountainous terrane in New Caledonia were processed using a combination of methods to generate a regolith-geology map that could be used for more efficiently targeting Ni exploration. The image processing combined two usual methods, which are spectral feature extraction and support vector machine (SVM). This rationale being the spectral features extraction can rapidly reduce data complexity by both targeting only the diagnostic mineral absorptions and masking those pixels complicated by vegetation, cloud and deep shade. SVM is a supervised classification method able to generate an optimal non-linear classifier with these features that generalises well even with limited training data. Key minerals targeted are serpentine, which is considered as an indicator for hydrolysed peridotitic rock, and iron oxy-hydroxides (hematite and goethite), which are considered as diagnostic of laterite development. The final classified regolith map was assessed against interpreted regolith field sites, which yielded approximately 70% similarity for all unit types, as well as against a regolith-geology map interpreted using traditional datasets (not hyperspectral imagery). Importantly, the hyperspectral derived mineral map provided much greater detail enabling a more precise understanding of the regolith-geological architecture where there are exposed soils and rocks.

Incremental Support Vector Machine Framework for Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

2006-12-01

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

A support vector machine for spectral classification of emission-line galaxies from the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Shi, Fei; Liu, Yu-Yan; Sun, Guang-Lan; Li, Pei-Yu; Lei, Yu-Ming; Wang, Jian

2015-10-01

The emission-lines of galaxies originate from massive young stars or supermassive blackholes. As a result, spectral classification of emission-line galaxies into star-forming galaxies, active galactic nucleus (AGN) hosts, or compositions of both relates closely to formation and evolution of galaxy. To find efficient and automatic spectral classification method, especially in large surveys and huge data bases, a support vector machine (SVM) supervised learning algorithm is applied to a sample of emission-line galaxies from the Sloan Digital Sky Survey (SDSS) data release 9 (DR9) provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU). A two-step approach is adopted. (i) The SVM must be trained with a subset of objects that are known to be AGN hosts, composites or star-forming galaxies, treating the strong emission-line flux measurements as input feature vectors in an n-dimensional space, where n is the number of strong emission-line flux ratios. (ii) After training on a sample of emission-line galaxies, the remaining galaxies are automatically classified. In the classification process, we use a 10-fold cross-validation technique. We show that the classification diagrams based on the [N II]/Hα versus other emission-line ratio, such as [O III]/Hβ, [Ne III]/[O II], ([O III]λ4959+[O III]λ5007)/[O III]λ4363, [O II]/Hβ, [Ar III]/[O III], [S II]/Hα, and [O I]/Hα, plus colour, allows us to separate unambiguously AGN hosts, composites or star-forming galaxies. Among them, the diagram of [N II]/Hα versus [O III]/Hβ achieved an accuracy of 99 per cent to separate the three classes of objects. The other diagrams above give an accuracy of ˜91 per cent.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Physical isolation with virtual support: Registrars' learning via remote supervision.

PubMed

Wearne, Susan M; Teunissen, Pim W; Dornan, Tim; Skinner, Timothy

2014-08-26

Abstract Purpose: Changing the current geographical maldistribution of the medical workforce is important for global health. Research regarding programs that train doctors for work with disadvantaged, rural populations is needed. This paper explores one approach of remote supervision of registrars in isolated rural practice. Researching how learning occurs without on-site supervision may also reveal other key elements of postgraduate education. Methods: Thematic analysis of in-depth interviews exploring 11 respondents' experiences of learning via remote supervision. Results: Remote supervision created distinctive learning environments. Respondents' attributes interacted with external supports to influence whether and how their learning was promoted or impeded. Registrars with clinical and/or life experience, who were insightful and motivated to direct their learning, turned the challenges of isolated practice into opportunities that accelerated their professional development. Discussion: Remote supervision was not necessarily problematic but instead provided rich learning for doctors training in and for the context where they were needed. Registrars learnt through clinical responsibility for defined populations and longitudinal, supportive supervisory relationships. Responsibility and continuity may be as important as supervisory proximity for experienced registrars.

Target specific compound identification using a support vector machine.

PubMed

Plewczynski, Dariusz; von Grotthuss, Marcin; Spieser, Stephane A H; Rychlewski, Leszek; Wyrwicz, Lucjan S; Ginalski, Krzysztof; Koch, Uwe

2007-03-01

In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).

Research on image evidence in land supervision and GIS management

NASA Astrophysics Data System (ADS)

Li, Qiu; Wu, Lixin

2006-10-01

Land resource development and utilization brings many problems. The numbers, the scale and volume of illegal land use cases are on the increasing. Since the territory is vast, and the land violations are concealment, it is difficulty for an effective land supervision and management. In this paper, the concepts of evidence, and preservation of evidence were described first. The concepts of image evidence (IE), natural evidence (NE), natural preservation of evidence (NPE), general preservation of evidence (GPE) were proposed based on the characteristics of remote sensing image (RSI) which has a characteristic of objectiveness, truthfulness, high spatial resolution, more information included. Using MapObjects and Visual Basic 6.0, under the Access management to implement the conjunction of spatial vector database and attribute data table; taking RSI as the data sources and background layer; combining the powerful management of geographic information system (GIS) for spatial data, and visual analysis, a land supervision and GIS management system was design and implemented based on NPE. The practical use in Beijing shows that the system is running well, and solved some problems in land supervision and management.

The Common Factors Discrimination Model: An Integrated Approach to Counselor Supervision

ERIC Educational Resources Information Center

Crunk, A. Elizabeth; Barden, Sejal M.

2017-01-01

Numerous models of clinical supervision have been developed; however, there is little empirical support indicating that any one model is superior. Therefore, common factors approaches to supervision integrate essential components that are shared among counseling and supervision models. The purpose of this paper is to present an innovative model of…

Maximum margin semi-supervised learning with irrelevant data.

PubMed

Yang, Haiqin; Huang, Kaizhu; King, Irwin; Lyu, Michael R

2015-10-01

Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of the targeted labeled data. In this paper, we address a different, yet formidable scenario in semi-supervised classification, where the unlabeled data may contain irrelevant data to the labeled data. To tackle this problem, we develop a maximum margin model, named tri-class support vector machine (3C-SVM), to utilize the available training data, while seeking a hyperplane for separating the targeted data well. Our 3C-SVM exhibits several characteristics and advantages. First, it does not need any prior knowledge and explicit assumption on the data relatedness. On the contrary, it can relieve the effect of irrelevant unlabeled data based on the logistic principle and maximum entropy principle. That is, 3C-SVM approaches an ideal classifier. This classifier relies heavily on labeled data and is confident on the relevant data lying far away from the decision hyperplane, while maximally ignoring the irrelevant data, which are hardly distinguished. Second, theoretical analysis is provided to prove that in what condition, the irrelevant data can help to seek the hyperplane. Third, 3C-SVM is a generalized model that unifies several popular maximum margin models, including standard SVMs, Semi-supervised SVMs (S(3)VMs), and SVMs learned from the universum (U-SVMs) as its special cases. More importantly, we deploy a concave-convex produce to solve the proposed 3C-SVM, transforming the original mixed integer programming, to a semi-definite programming relaxation, and finally to a sequence of quadratic programming subproblems, which yields the same worst case time complexity as that of S(3)VMs. Finally, we demonstrate the effectiveness and efficiency of our proposed 3C-SVM through systematical experimental comparisons. Copyright © 2015 Elsevier Ltd. All rights reserved.

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

NASA Astrophysics Data System (ADS)

Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, AdaBoost, and Decision Tree) and is a good technique for automatic classification of exoplanet-transit-like signal.

Exploring paraprofessional and classroom factors affecting teacher supervision.

PubMed

Irvin, Dwight W; Ingram, Paul; Huffman, Jonathan; Mason, Rose; Wills, Howard

2018-02-01

Paraprofessionals serve a primary role in supporting students with disabilities in the classroom, which necessitates teachers' supervision as a means to improve their practice. Yet, little is known regarding what factors affect teacher supervision. We sought to identify how paraprofessional competence and classroom type affected the levels of teacher direction. We administered an adapted version of the Paraprofessional Needs, Knowledge & Tasks Survey and the Survey for Teachers Supervising Paraprofessionals to teachers supervising paraprofessionals in elementary schools. Structural Equation Modeling was used to examine the link between paraprofessional competence and classroom factors affecting the level of teacher supervision. Our results indicated that when teachers perceived paraprofessionals as being more skilled, they provided more supervision, and when more supervision was provided the less they thought paraprofessionals should be doing their assigned tasks. Additionally, paraprofessionals working in classrooms with more students with mild disabilities received less supervision than paraprofessionals working in classrooms with more students with moderate-to-severe disabilities. Those paraprofessionals in classrooms serving mostly children with mild disabilities were also perceived as having lower levels of skill competence than those serving in classrooms with students with more moderate-to-severe disabilities. By understanding the factors that affect teacher supervision, policy and professional development opportunities can be refined/developed to better support both supervising teachers and paraprofessionals and, in turn, improve the outcomes of children with disabilities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

Gene-Based Multiclass Cancer Diagnosis with Class-Selective Rejections

PubMed Central

Jrad, Nisrine; Grall-Maës, Edith; Beauseroy, Pierre

2009-01-01

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. PMID:19584932

Task-specific image partitioning.

PubMed

Kim, Sungwoong; Nowozin, Sebastian; Kohli, Pushmeet; Yoo, Chang D

2013-02-01

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks.

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

NASA Astrophysics Data System (ADS)

Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

2014-08-01

Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

The dynamics of Brazilian protozoology over the past century.

PubMed

Elias, M Carolina; Floeter-Winter, Lucile M; Mena-Chalco, Jesus P

2016-01-01

Brazilian scientists have been contributing to the protozoology field for more than 100 years with important discoveries of new species such as Trypanosoma cruzi and Leishmania spp. In this work, we used a Brazilian thesis database (Coordination for the Improvement of Higher Education Personnel) covering the period from 1987-2011 to identify researchers who contributed substantially to protozoology. We selected 248 advisors by filtering to obtain researchers who supervised at least 10 theses. Based on a computational analysis of the thesis databases, we found students who were supervised by these scientists. A computational procedure was developed to determine the advisors' scientific ancestors using the Lattes Platform. These analyses provided a list of 1,997 researchers who were inspected through Lattes CV examination and allowed the identification of the pioneers of Brazilian protozoology. Moreover, we investigated the areas in which researchers who earned PhDs in protozoology are now working. We found that 68.4% of them are still in protozoology, while 16.7% have migrated to other fields. We observed that support for protozoology by national or international agencies is clearly correlated with the increase of scientists in the field. Finally, we described the academic genealogy of Brazilian protozoology by formalising the "forest" of Brazilian scientists involved in the study of protozoa and their vectors over the past century.

The dynamics of Brazilian protozoology over the past century

PubMed Central

Elias, M Carolina; Floeter-Winter, Lucile M; Mena-Chalco, Jesus P

2016-01-01

Brazilian scientists have been contributing to the protozoology field for more than 100 years with important discoveries of new species such asTrypanosoma cruzi and Leishmania spp. In this work, we used a Brazilian thesis database (Coordination for the Improvement of Higher Education Personnel) covering the period from 1987-2011 to identify researchers who contributed substantially to protozoology. We selected 248 advisors by filtering to obtain researchers who supervised at least 10 theses. Based on a computational analysis of the thesis databases, we found students who were supervised by these scientists. A computational procedure was developed to determine the advisors’ scientific ancestors using the Lattes Platform. These analyses provided a list of 1,997 researchers who were inspected through Lattes CV examination and allowed the identification of the pioneers of Brazilian protozoology. Moreover, we investigated the areas in which researchers who earned PhDs in protozoology are now working. We found that 68.4% of them are still in protozoology, while 16.7% have migrated to other fields. We observed that support for protozoology by national or international agencies is clearly correlated with the increase of scientists in the field. Finally, we described the academic genealogy of Brazilian protozoology by formalising the “forest” of Brazilian scientists involved in the study of protozoa and their vectors over the past century. PMID:26814646

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J., E-mail: bje@mayo.edu

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiersmore » were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.« less

The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

PubMed

Roberts, Kirk; Shooshan, Sonya E; Rodriguez, Laritza; Abhyankar, Swapna; Kilicoglu, Halil; Demner-Fushman, Dina

2015-12-01

This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utilize a series of support vector machine models in conjunction with manually built lexicons to classify triggers specific to each risk factor. The features used for classification were quite simple, utilizing only lexical information and ignoring higher-level linguistic information such as syntax and semantics. Instead, we incorporated high-quality data to train the models by annotating additional information on top of a standard corpus. Despite the relative simplicity of the system, it achieves the highest scores (micro- and macro-F1, and micro- and macro-recall) out of the 20 participants in the 2014 i2b2/UTHealth Shared Task. This system obtains a micro- (macro-) precision of 0.8951 (0.8965), recall of 0.9625 (0.9611), and F1-measure of 0.9276 (0.9277). Additionally, we perform a series of experiments to assess the value of the annotated data we created. These experiments show how manually-labeled negative annotations can improve information extraction performance, demonstrating the importance of high-quality, fine-grained natural language annotations. Copyright © 2015 Elsevier Inc. All rights reserved.

Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies.

PubMed

Ithapu, Vamsi; Singh, Vikas; Lindner, Christopher; Austin, Benjamin P; Hinrichs, Chris; Carlsson, Cynthia M; Bendlin, Barbara B; Johnson, Sterling C

2014-08-01

Precise detection and quantification of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age-related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by texton filter banks, we provide a suite of effective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A measure of hyperintensity accumulation, referred to as normalized effective WMH volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies. Copyright © 2014 Wiley Periodicals, Inc.

Experiments on Supervised Learning Algorithms for Text Categorization

NASA Technical Reports Server (NTRS)

Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.

2005-01-01

Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

"Unscrambling what's in your head": A mixed method evaluation of clinical supervision for midwives.

PubMed

Love, Bev; Sidebotham, Mary; Fenwick, Jennifer; Harvey, Susan; Fairbrother, Greg

2017-08-01

As a strategy to promote workforce sustainability a number of midwives working in one health district in New South Wales, Australia were trained to offer a reflective model of clinical supervision. The expectation was that these midwives would then be equipped to facilitate clinical supervision for their colleagues with the organisational aim of supporting professional development and promoting emotional well-being. To identify understanding, uptake, perceptions of impact, and the experiences of midwives accessing clinical supervision. Mixed Methods. In phase one 225 midwives were invited to complete a self-administered survey. Descriptive and inferential statistics were used to analyse the data. In phase two 12 midwives were interviewed. Thematic analysis was used to deepen understanding of midwives' experiences of receiving clinical supervision. Sixty percent of midwives responding in phase one had some experience of clinical supervision. Findings from both phases were complementary with midwives reporting a positive impact on their work, interpersonal skills, situational responses and career goals. Midwives described clinical supervision as a formal, structured and confidential space for 'safe reflection' that was valued as an opportunity for self-care. Barriers included misconceptions, perceived work related pressures and a sense that taking time out was unjustifiable. Education, awareness raising and further research into reflective clinical supervision, to support emotional well-being and professional midwifery practice is needed. In addition, health organisations need to design, implement and evaluate strategies that support the embedding of clinical supervision within midwives' clinical practice. Copyright © 2016 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Michigan parents support supervised driving requirement under graduated licensing

DOT National Transportation Integrated Search

2000-03-11

Reports on a new study that finds that parents in Michigan strongly support a provision that requires them to provide extended supervised practice to their beginning drivers. Although serious reservations have been expressed because of the increased ...

Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

NASA Astrophysics Data System (ADS)

Mann, Kulwinder S.; Kaur, Sukhpreet

2017-06-01

There are various eye diseases in the patients suffering from the diabetes which includes Diabetic Retinopathy, Glaucoma, Hypertension etc. These all are the most common sight threatening eye diseases due to the changes in the blood vessel structure. The proposed method using supervised methods concluded that the segmentation of the retinal blood vessels can be performed accurately using neural networks training. It uses features which include Gray level features; Moment Invariant based features, Gabor filtering, Intensity feature, Vesselness feature for feature vector computation. Then the feature vector is calculated using only the prominent features.

Semi-automatic feedback using concurrence between mixture vectors for general databases

NASA Astrophysics Data System (ADS)

Larabi, Mohamed-Chaker; Richard, Noel; Colot, Olivier; Fernandez-Maloigne, Christine

2001-12-01

This paper describes how a query system can exploit the basic knowledge by employing semi-automatic relevance feedback to refine queries and runtimes. For general databases, it is often useless to call complex attributes, because we have not sufficient information about images in the database. Moreover, these images can be topologically very different from one to each other and an attribute that is powerful for a database category may be very powerless for the other categories. The idea is to use very simple features, such as color histogram, correlograms, Color Coherence Vectors (CCV), to fill out the signature vector. Then, a number of mixture vectors is prepared depending on the number of very distinctive categories in the database. Knowing that a mixture vector is a vector containing the weight of each attribute that will be used to compute a similarity distance. We post a query in the database using successively all the mixture vectors defined previously. We retain then the N first images for each vector in order to make a mapping using the following information: Is image I present in several mixture vectors results? What is its rank in the results? These informations allow us to switch the system on an unsupervised relevance feedback or user's feedback (supervised feedback).

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

NASA Astrophysics Data System (ADS)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

NASA Astrophysics Data System (ADS)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Promising Practices in E-Supervision: Exploring Graduate Speech-Language Pathology Interns’ Perceptions

PubMed Central

Carlin, Charles H.; Milam, Jennifer L.; Carlin, Emily L.; Owen, Ashley

2012-01-01

E-supervision has a potential role in addressing speech-language personnel shortages in rural and difficult to staff school districts. The purposes of this article are twofold: to determine how e-supervision might support graduate speech-language pathologist (SLP) interns placed in rural, remote, and difficult to staff public school districts; and, to investigate interns’ perceptions of in-person supervision compared to e-supervision. The study used a mixed methodology approach and collected data from surveys, supervision documents and records, and interviews. The results showed the use of e-supervision allowed graduate SLP interns to be adequately supervised across a variety of clients and professional activities in a manner that was similar to in-person supervision. Further, e-supervision was perceived as a more convenient and less stressful supervision format when compared to in-person supervision. Other findings are discussed and implications and limitations provided. PMID:25945201

Reflect and learn together - when two supervisors interact in the learning support process of nurse education.

PubMed

Berglund, Mia; Sjögren, Reet; Ekebergh, Margaretha

2012-03-01

To describe the importance of supervisors working together in supporting the learning process of nurse students through reflective caring science supervision. A supervision model has been developed in order to meet the need for interweaving theory and practice. The model is characterized by learning reflection in caring science. A unique aspect of the present project was that the student groups were led by a teacher and a nurse. Data were collected through interviews with the supervisors. The analysis was performed with a phenomenological approach. The results showed that theory and practice can be made more tangible and interwoven by using two supervisors in a dual supervision. The essential structure is built on the constituents 'Reflection as Learning Support', 'Interweaving Caring Science with the Patient's Narrative', 'The Student as a Learning Subject' and 'The Learning Environment of Supervision'. The study concludes that supervision in pairs provides unique possibilities for interweaving and developing theory and practice. The supervision model offers unique opportunities for cooperation, for the development of theory and practice and for the development of the professional roll of nurses and teachers. © 2012 Blackwell Publishing Ltd.

Objective coding of content and techniques in workplace-based supervision of an EBT in public mental health.

PubMed

Dorsey, Shannon; Kerns, Suzanne E U; Lucid, Leah; Pullmann, Michael D; Harrison, Julie P; Berliner, Lucy; Thompson, Kelly; Deblinger, Esther

2018-01-24

Workplace-based clinical supervision as an implementation strategy to support evidence-based treatment (EBT) in public mental health has received limited research attention. A commonly provided infrastructure support, it may offer a relatively cost-neutral implementation strategy for organizations. However, research has not objectively examined workplace-based supervision of EBT and specifically how it might differ from EBT supervision provided in efficacy and effectiveness trials. Data come from a descriptive study of supervision in the context of a state-funded EBT implementation effort. Verbal interactions from audio recordings of 438 supervision sessions between 28 supervisors and 70 clinicians from 17 public mental health organizations (in 23 offices) were objectively coded for presence and intensity coverage of 29 supervision strategies (16 content and 13 technique items), duration, and temporal focus. Random effects mixed models estimated proportion of variance in content and techniques attributable to the supervisor and clinician levels. Interrater reliability among coders was excellent. EBT cases averaged 12.4 min of supervision per session. Intensity of coverage for EBT content varied, with some discussed frequently at medium or high intensity (exposure) and others infrequently discussed or discussed only at low intensity (behavior management; assigning/reviewing client homework). Other than fidelity assessment, supervision techniques common in treatment trials (e.g., reviewing actual practice, behavioral rehearsal) were used rarely or primarily at low intensity. In general, EBT content clustered more at the clinician level; different techniques clustered at either the clinician or supervisor level. Workplace-based clinical supervision may be a feasible implementation strategy for supporting EBT implementation, yet it differs from supervision in treatment trials. Time allotted per case is limited, compressing time for EBT coverage. Techniques that involve observation of clinician skills are rarely used. Workplace-based supervision content appears to be tailored to individual clinicians and driven to some degree by the individual supervisor. Our findings point to areas for intervention to enhance the potential of workplace-based supervision for implementation effectiveness. NCT01800266 , Clinical Trials, Retrospectively Registered (for this descriptive study; registration prior to any intervention [part of phase II RCT, this manuscript is only phase I descriptive results]).

Psychiatric nursing staff members' reflections on participating in group-based clinical supervision: a semistructured interview study.

PubMed

Buus, Niels; Angel, Sanne; Traynor, Michael; Gonge, Henrik

2011-04-01

This paper is a report of an interview study exploring psychiatric hospital nursing staff members' reflections on participating in supervision. Clinical supervision is a pedagogical process designed to direct, develop, and support clinical nurses. Participation rates in clinical supervision in psychiatric settings have been reported to be relatively low. Qualitative research indicates that staff members appreciate clinical supervision, but paradoxically, do not prioritize participation and find participation emotionally challenging. Little is known about these contradictory experiences and how they influence participation rates. Twenty-two psychiatric hospital nursing staff members were interviewed with a semistructured interview guide. Interview transcripts were interpreted by means of Ricoeur's hermeneutic method. The respondents understood clinical supervision to be beneficial, but with very limited impact on their clinical practice. Neither management nor the staff effectively prioritized clinical supervision, which added to a downward spiral where low levels of participation undermined the potential benefits of clinical supervision. The respondents embraced and used alternative forums for getting emotional support among peers, but maintained that formalized supervision was the only forum for reflection that could solve the most difficult situations. © 2011 The Authors. International Journal of Mental Health Nursing © 2011 Australian College of Mental Health Nurses Inc.

Work stress among newly graduated nurses in relation to workplace and clinical group supervision.

PubMed

Blomberg, Karin; Isaksson, Ann-Kristin; Allvin, Renée; Bisholt, Birgitta; Ewertsson, Mona; Kullén Engström, Agneta; Ohlsson, Ulla; Sundler Johansson, Annelie; Gustafsson, Margareta

2016-01-01

The aim was to investigate occupational stress among newly graduated nurses in relation to the workplace and clinical group supervision. Being a newly graduated nurse is particularly stressful. What remains unclear is whether the workplace and clinical group supervision affect the stress. A cross-sectional comparative study was performed in 2012. Data were collected by means of a numerical scale measuring occupational stress, questions about workplace and clinical group supervision. One hundred and thirteen nurses who had recently graduated from three Swedish universities were included in the study. The stress was high among the newly graduated nurses but it differed significantly between workplaces, surgical departments generating the most stress. Nurses who had received clinical group supervision reported significantly less stress. The stress between workplaces remained significant also when participation in clinical group supervision was taken into account. Newly graduated nurses experience great stress and need support, especially those in surgical departments. Nurses participating in clinical group supervision reported significantly less stress. It is important to develop strategies that help to adapt the work situation so as to give nurses the necessary support. Clinical group supervision should be considered as an option for reducing stress. © 2014 John Wiley & Sons Ltd.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Reflective Supervision: A Clinical Supervision Model for Fostering Professional Growth

ERIC Educational Resources Information Center

Costello, Lisa H.; Belcaid, Erin; Arthur-Stanley, Amanda

2018-01-01

School psychologists experience a broad range of stressors in their role as school support professionals including feelings of isolation, insufficient resources, administrative pressures, and excessive caseloads (Boccio, Wiesz, & Lefkowitz, 2016). Ongoing support is necessary to help school psychologists successfully navigate these…

The Teacher's Guide to Supporting and Supervising Paraprofessionals in the Classroom. Video and Guidebook.

ERIC Educational Resources Information Center

Wallace, Teri

This videotape and accompanying guidebook are intended to help teachers as they support and supervise paraprofessionals in the classroom. Section 1 of the guidebook provides a self-assessment to help teachers evaluate their present support and supervisory skills. Sections 2 through 5 address four key knowledge and skill areas. These sections…

Supervisor Cultural Responsiveness and Unresponsiveness in Cross-Cultural Supervision

ERIC Educational Resources Information Center

Burkard, Alan W.; Johnson, Adanna J.; Madson, Michael B.; Pruitt, Nathan T.; Contreras-Tadych, Deborah A.; Kozlowski, JoEllen M.; Hess, Shirley A.; Knox, Sarah

2006-01-01

Thirteen supervisees' of color and 13 European American supervisees' experiences of culturally responsive and unresponsive cross-cultural supervision were studied using consensual qualitative research. In culturally responsive supervision, all supervisees felt supported for exploring cultural issues, which positively affected the supervisee, the…

Mental health nurses' experiences of managing work-related emotions through supervision.

PubMed

MacLaren, Jessica; Stenhouse, Rosie; Ritchie, Deborah

2016-10-01

The aim of this study was to explore emotion cultures constructed in supervision and consider how supervision functions as an emotionally safe space promoting critical reflection. Research published between 1995-2015 suggests supervision has a positive impact on nurses' emotional well-being, but there is little understanding of the processes involved in this and how styles of emotion interaction are established in supervision. A narrative approach was used to investigate mental health nurses' understandings and experiences of supervision. Eight semi-structured interviews were conducted with community mental health nurses in the UK during 2011. Analysis of audio data used features of speech to identify narrative discourse and illuminate meanings. A topic-centred analysis of interview narratives explored discourses shared between the participants. This supported the identification of feeling rules in participants' narratives and the exploration of the emotion context of supervision. Effective supervision was associated with three feeling rules: safety and reflexivity; staying professional; managing feelings. These feeling rules allowed the expression and exploration of emotions, promoting critical reflection. A contrast was identified between the emotion culture of supervision and the nurses' experience of their workplace cultures as requiring the suppression of difficult emotions. Despite this, contrast supervision functioned as an emotion micro-culture with its own distinctive feeling rules. The analytical construct of feeling rules allows us to connect individual emotional experiences to shared normative discourses, highlighting how these shape emotional processes taking place in supervision. This understanding supports an explanation of how supervision may positively influence nurses' emotion management and perhaps reduce burnout. © 2016 John Wiley & Sons Ltd.

Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments.

PubMed

Kaufhold, John P; Tsai, Philbert S; Blinder, Pablo; Kleinfeld, David

2012-08-01

A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by "learned threshold relaxation"; (2) removes spurious segments by "learning to eliminate deletion candidate strands"; and (3) enforces consistency in the joint space of learned vascular graph corrections through "consistency learning." Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with >800(3) voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5-21% and strand elimination performance by 18-57%. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. Copyright © 2012 Elsevier B.V. All rights reserved.

Vectorization of optically sectioned brain microvasculature: Learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments

PubMed Central

Kaufhold, John P.; Tsai, Philbert S.; Blinder, Pablo; Kleinfeld, David

2012-01-01

A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by “learned threshold relaxation”; (2) removes spurious segments by “learning to eliminate deletion candidate strands”; and (3) enforces consistency in the joint space of learned vascular graph corrections through “consistency learning.” Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with > 8003 voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5 to 21 % and strand elimination performance by 18 to 57 %. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. PMID:22854035

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

How Does Supervision Support Inclusive Teacherhood?

ERIC Educational Resources Information Center

Alila, Sanna; Määttä, Kaarina; Uusiautti, Satu

2016-01-01

Supervision is a multidimensional concept and phenomenon. In this study, the advantages of supervision and its development in inclusive teacherhood was studied. Inclusive teacherhood means a teacher's professional development and the school culture's change toward participatory school for all students. The study analyzed the views of supervisors…

Fast and Accurate Support Vector Machines on Large Scale Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vishnu, Abhinav; Narasimhan, Jayenthi; Holder, Larry

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminatemore » the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.« less

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

NASA Astrophysics Data System (ADS)

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-01

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Non-metallic coating thickness prediction using artificial neural network and support vector machine with time resolved thermography

NASA Astrophysics Data System (ADS)

Wang, Hongjin; Hsieh, Sheng-Jen; Peng, Bo; Zhou, Xunfei

2016-07-01

A method without requirements on knowledge about thermal properties of coatings or those of substrates will be interested in the industrial application. Supervised machine learning regressions may provide possible solution to the problem. This paper compares the performances of two regression models (artificial neural networks (ANN) and support vector machines for regression (SVM)) with respect to coating thickness estimations made based on surface temperature increments collected via time resolved thermography. We describe SVM roles in coating thickness prediction. Non-dimensional analyses are conducted to illustrate the effects of coating thicknesses and various factors on surface temperature increments. It's theoretically possible to correlate coating thickness with surface increment. Based on the analyses, the laser power is selected in such a way: during the heating, the temperature increment is high enough to determine the coating thickness variance but low enough to avoid surface melting. Sixty-one pain-coated samples with coating thicknesses varying from 63.5 μm to 571 μm are used to train models. Hyper-parameters of the models are optimized by 10-folder cross validation. Another 28 sets of data are then collected to test the performance of the three methods. The study shows that SVM can provide reliable predictions of unknown data, due to its deterministic characteristics, and it works well when used for a small input data group. The SVM model generates more accurate coating thickness estimates than the ANN model.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data.

PubMed

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-21

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

PubMed

Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Evaluation of Machine Learning Algorithms for Classification of Primary Biological Aerosol using a new UV-LIF spectrometer

NASA Astrophysics Data System (ADS)

Ruske, S. T.; Topping, D. O.; Foot, V. E.; Kaye, P. H.; Stanley, W. R.; Morse, A. P.; Crawford, I.; Gallagher, M. W.

2016-12-01

Characterisation of bio-aerosols has important implications within Environment and Public Health sectors. Recent developments in Ultra-Violet Light Induced Fluorescence (UV-LIF) detectors such as the Wideband Integrated bio-aerosol Spectrometer (WIBS) and the newly introduced Multiparameter bio-aerosol Spectrometer (MBS) has allowed for the real time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal Spores and pollen. This new generation of instruments has enabled ever-larger data sets to be compiled with the aim of studying more complex environments, yet the algorithms used for specie classification remain largely invalidated. It is therefore imperative that we validate the performance of different algorithms that can be used for the task of classification, which is the focus of this study. For unsupervised learning we test Hierarchical Agglomerative Clustering with various different linkages. For supervised learning, ten methods were tested; including decision trees, ensemble methods: Random Forests, Gradient Boosting and AdaBoost; two implementations for support vector machines: libsvm and liblinear; Gaussian methods: Gaussian naïve Bayesian, quadratic and linear discriminant analysis and finally the k-nearest neighbours algorithm. The methods were applied to two different data sets measured using a new Multiparameter bio-aerosol Spectrometer. We find that clustering, in general, performs slightly worse than the supervised learning methods correctly classifying, at best, only 72.7 and 91.1 percent for the two data sets. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 88.1 and 97.8 percent of the testing data respectively across the two data sets. We discuss the wider relevance of these results with regards to challenging existing classification in real-world environments.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PubMed Central

Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area. PMID:29554126

Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.

PubMed

Memarian, Negar; Kim, Sally; Dewar, Sandra; Engel, Jerome; Staba, Richard J

2015-09-01

This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE. Published by Elsevier Ltd.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Enablers of and barriers to high quality clinical supervision among occupational therapists across Queensland in Australia: findings from a qualitative study.

PubMed

Martin, Priya; Kumar, Saravana; Lizarondo, Lucylynn; VanErp, Ans

2015-09-24

Health professionals practising in countries with dispersed populations such as Australia rely on clinical supervision for professional support. While there are directives and guidelines in place to govern clinical supervision, little is known about how it is actually conducted and what makes it effective. The purpose of this study was to explore the enablers of and barriers to high quality clinical supervision among occupational therapists across Queensland in Australia. This qualitative study took place as part of a broader project. Individual, in-depth, semi-structured interviews were conducted with occupational therapy supervisees in Queensland. The interviews explored the enablers of and barriers to high quality clinical supervision in this group. They further explored some findings from the initial quantitative study. Content analysis of the interview data resulted in eight themes. These themes were broadly around the importance of the supervisory relationship, the impact of clinical supervision and the enablers of and barriers to high quality clinical supervision. This study identified a number of factors that were perceived to be associated with high quality clinical supervision. Supervisor-supervisee matching and fit, supervisory relationship and availability of supervisor for support in between clinical supervision sessions appeared to be associated with perceptions of higher quality of clinical supervision received. Some face-to-face contact augmented with telesupervision was found to improve perceptions of the quality of clinical supervision received via telephone. Lastly, dual roles where clinical supervision and line management were provided by the same person were not considered desirable by supervisees. A number of enablers of and barriers to high quality clinical supervision were also identified. With clinical supervision gaining increasing prominence as part of organisational and professional governance, this study provides important lessons for successful and sustainable clinical supervision in practice contexts.

Can clinical supervision sustain our workforce in the current healthcare landscape? Findings from a Queensland study of allied health professionals.

PubMed

Saxby, Christine; Wilson, Jill; Newcombe, Peter

2015-09-01

Clinical supervision is widely recognised as a mechanism for providing professional support, professional development and clinical governance for healthcare workers. There have been limited studies about the effectiveness of clinical supervision for allied health and minimal studies conducted within the Australian health context. The aim of the present study was to identify whether clinical supervision was perceived to be effective by allied health professionals and to identify components that contributed to effectiveness. Participants completed an anonymous online questionnaire, administered through the health service's intranet. A cross-sectional study was conducted with community allied health workers (n = 82) 8 months after implementation of structured clinical supervision. Demographic data (age, gender), work-related history (profession employment level, years of experience), and supervision practice (number and length of supervision sessions) were collected through an online survey. The outcome measure, clinical supervision effectiveness, was operationalised using the Manchester Clinical Supervision Scale-26 (MCSS-26). Data were analysed with Pearson correlation (r) and independent sample t-tests (t) with significance set at 0.05 (ie the probability of significant difference set at P < 0.05). The length of the supervision sessions (r(s) ≥ 0.44), the number of sessions (r(s) ≥ 0.35) and the total period supervision had been received (r(s) ≥ 0.42) were all significantly positively correlated with the MCSS-26 domains of clinical supervision effectiveness. Three individual variables, namely 'receiving clinical supervision', 'having some choice in the allocation of clinical supervisor' and 'having a completed clinical supervision agreement', were also significantly associated with higher total MCSS-26 scores (P(s) < 0.014). The results of the study demonstrate that when clinical supervision uses best practice principles, it can provide professional support for allied health workers, even during times of rapid organisational change.

Gender and Power in Counselling and Supervision.

ERIC Educational Resources Information Center

Taylor, Maye

1994-01-01

Addresses the need to reflect on how the dynamics of gender and power can be articulated together and adversely affect counseling and supervision relationships. Suggests incorporating a social analysis into supervision to help counselors clarify the political nature of some therapeutic issues, thus addressing gender stereotypes. Supports a…

Supportive supervision and constructive relationships with healthcare workers support CHW performance: Use of a qualitative framework to evaluate CHW programming in Uganda.

PubMed

Ludwick, Teralynn; Turyakira, Eleanor; Kyomuhangi, Teddy; Manalili, Kimberly; Robinson, Sheila; Brenner, Jennifer L

2018-02-13

While evidence supports community health worker (CHW) capacity to improve maternal and newborn health in less-resourced countries, key implementation gaps remain. Tools for assessing CHW performance and evidence on what programmatic components affect performance are lacking. This study developed and tested a qualitative evaluative framework and tool to assess CHW team performance in a district program in rural Uganda. A new assessment framework was developed to collect and analyze qualitative evidence based on CHW perspectives on seven program components associated with effectiveness (selection; training; community embeddedness; peer support; supportive supervision; relationship with other healthcare workers; retention and incentive structures). Focus groups were conducted with four high/medium-performing CHW teams and four low-performing CHW teams selected through random, stratified sampling. Content analysis involved organizing focus group transcripts according to the seven program effectiveness components, and assigning scores to each component per focus group. Four components, 'supportive supervision', 'good relationships with other healthcare workers', 'peer support', and 'retention and incentive structures' received the lowest overall scores. Variances in scores between 'high'/'medium'- and 'low'-performing CHW teams were largest for 'supportive supervision' and 'good relationships with other healthcare workers.' Our analysis suggests that in the Bushenyi intervention context, CHW team performance is highly correlated with the quality of supervision and relationships with other healthcare workers. CHWs identified key performance-related issues of absentee supervisors, referral system challenges, and lack of engagement/respect by health workers. Other less-correlated program components warrant further study and may have been impacted by relatively consistent program implementation within our limited study area. Applying process-oriented measurement tools are needed to better understand CHW performance-related factors and build a supportive environment for CHW program effectiveness and sustainability. Findings from a qualitative, multi-component tool developed and applied in this study suggest that factors related to (1) supportive supervision and (2) relationships with other healthcare workers may be strongly associated with variances in performance outcomes within a program. Careful consideration of supervisory structure and health worker orientation during program implementation are among strategies proposed to increase CHW performance.

Form Follows Function: A Model for Clinical Supervision of Genetic Counseling Students.

PubMed

Wherley, Colleen; Veach, Patricia McCarthy; Martyr, Meredith A; LeRoy, Bonnie S

2015-10-01

Supervision plays a vital role in genetic counselor training, yet models describing genetic counseling supervision processes and outcomes are lacking. This paper describes a proposed supervision model intended to provide a framework to promote comprehensive and consistent clinical supervision training for genetic counseling students. Based on the principle "form follows function," the model reflects and reinforces McCarthy Veach et al.'s empirically derived model of genetic counseling practice - the "Reciprocal Engagement Model" (REM). The REM consists of mutually interactive educational, relational, and psychosocial components. The Reciprocal Engagement Model of Supervision (REM-S) has similar components and corresponding tenets, goals, and outcomes. The 5 REM-S tenets are: Learning and applying genetic information are key; Relationship is integral to genetic counseling supervision; Student autonomy must be supported; Students are capable; and Student emotions matter. The REM-S outcomes are: Student understands and applies information to independently provide effective services, develop professionally, and engage in self-reflective practice. The 16 REM-S goals are informed by the REM of genetic counseling practice and supported by prior literature. A review of models in medicine and psychology confirms the REM-S contains supervision elements common in healthcare fields, while remaining unique to genetic counseling. The REM-S shows promise for enhancing genetic counselor supervision training and practice and for promoting research on clinical supervision. The REM-S is presented in detail along with specific examples and training and research suggestions.

Oceans apart, yet connected: Findings from a qualitative study on professional supervision in rural and remote allied health services

PubMed Central

Martin, Priya; Kumar, Saravana; Burge, Vanessa; Abernathy, LuJuana

2015-01-01

Abstract Objective Improving the quality and safety of health care in Australia is imperative to ensure the right treatment is delivered to the right person at the right time. Achieving this requires appropriate clinical governance and support for health professionals, including professional supervision. This study investigates the usefulness and effectiveness of and barriers to supervision in rural and remote Queensland. Design As part of the evaluation of the Allied Health Rural and Remote Training and Support program, a qualitative descriptive study was conducted involving semi‐structured interviews with 42 rural or remote allied health professionals, nine operational managers and four supervisors. The interviews explored perspectives on their supervision arrangements, including the perceived usefulness, effect on practice and barriers. Results Themes of reduced isolation; enhanced professional enthusiasm, growth and commitment to the organisation; enhanced clinical skills, knowledge and confidence; and enhanced patient safety were identified as perceived outcomes of professional supervision. Time, technology and organisational factors were identified as potential facilitators as well as potential barriers to effective supervision. Conclusions This research provides current evidence on the impact of professional supervision in rural and remote Queensland. A multidimensional model of organisational factors associated with effective supervision in rural and remote settings is proposed identifying positive supervision culture and a good supervisor–supervisee fit as key factors associated with effective arrangements. PMID:26052949

Supervision in primary health care--can it be carried out effectively in developing countries?

PubMed

Clements, C John; Streefland, Pieter H; Malau, Clement

2007-01-01

There is nothing new about supervision in primary health care service delivery. Supervision was even conducted by the Egyptian pyramid builders. Those supervising have often favoured ridicule and discipline to push individuals and communities to perform their duties. A traditional form of supervision, based on a top-down colonial model, was originally attempted as a tool to improve health service staff performance. This has recently been replaced by a more liberal "supportive supervision". While it is undoubtedly an improvement on the traditional model, we believe that even this version will not succeed to any great extent until there is a better understanding of the human interactions involved in supervision. Tremendous cultural differences exist over the globe regarding the acceptability of this form of management. While it is clear that health services in many countries have benefited from supervision of one sort or another, it is equally clear that in some countries, supervision is not carried out, or when carried out, is done inadequately. In some countries it may be culturally inappropriate, and may even be impossible to carry out supervision at all. We examine this issue with particular reference to immunization and other primary health care services in developing countries. Supported by field observations in Papua New Guinea, we conclude that supervision and its failure should be understood in a social and cultural context, being a far more complex activity than has so far been acknowledged. Social science-based research is needed to enable a third generation of culture-sensitive ideas to be developed that will improve staff performance in the field.

Healthcare students' evaluation of the clinical learning environment and supervision - a cross-sectional study.

PubMed

Pitkänen, Salla; Kääriäinen, Maria; Oikarainen, Ashlee; Tuomikoski, Anna-Maria; Elo, Satu; Ruotsalainen, Heidi; Saarikoski, Mikko; Kärsämänoja, Taina; Mikkonen, Kristina

2018-03-01

The purpose of clinical placements and supervision is to promote the development of healthcare students´ professional skills. High-quality clinical learning environments and supervision were shown to have significant influence on healthcare students´ professional development. This study aimed to describe healthcare students` evaluation of the clinical learning environment and supervision, and to identify the factors that affect these. The study was performed as a cross-sectional study. The data (n = 1973) were gathered through an online survey using the Clinical Learning Environment, Supervision and Nurse Teacher scale during the academic year 2015-2016 from all healthcare students (N = 2500) who completed their clinical placement at a certain university hospital in Finland. The data were analysed using descriptive statistics and binary logistic regression analysis. More than half of the healthcare students had a named supervisor and supervision was completed as planned. The students evaluated the clinical learning environment and supervision as 'good'. The students´ readiness to recommend the unit to other students and the frequency of separate private unscheduled sessions with the supervisor were the main factors that affect healthcare students` evaluation of the clinical learning environment and supervision. Individualized and goal-oriented supervision in which the student had a named supervisor and where supervision was completed as planned in a positive environment that supported learning had a significant impact on student's learning. The clinical learning environment and supervision support the development of future healthcare professionals' clinical competence. The supervisory relationship was shown to have a significant effect on the outcomes of students' experiences. We recommend the planning of educational programmes for supervisors of healthcare students for the enhancement of supervisors' pedagogical competencies in supervising students in the clinical practice. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development and validation of the ExPRESS instrument for primary health care providers' evaluation of external supervision.

PubMed

Schriver, Michael; Cubaka, Vincent Kalumire; Vedsted, Peter; Besigye, Innocent; Kallestrup, Per

2018-01-01

External supervision of primary health care facilities to monitor and improve services is common in low-income countries. Currently there are no tools to measure the quality of support in external supervision in these countries. To develop a provider-reported instrument to assess the support delivered through external supervision in Rwanda and other countries. "External supervision: Provider Evaluation of Supervisor Support" (ExPRESS) was developed in 18 steps, primarily in Rwanda. Content validity was optimised using systematic search for related instruments, interviews, translations, and relevance assessments by international supervision experts as well as local experts in Nigeria, Kenya, Uganda and Rwanda. Construct validity and reliability were examined in two separate field tests, the first using exploratory factor analysis and a test-retest design, the second for confirmatory factor analysis. We included 16 items in section A ('The most recent experience with an external supervisor'), and 13 items in section B ('The overall experience with external supervisors'). Item-content validity index was acceptable. In field test I, test-retest had acceptable kappa values and exploratory factor analysis suggested relevant factors in sections A and B used for model hypotheses. In field test II, models were tested by confirmatory factor analysis fitting a 4-factor model for section A, and a 3-factor model for section B. ExPRESS is a promising tool for evaluation of the quality of support of primary health care providers in external supervision of primary health care facilities in resource-constrained settings. ExPRESS may be used as specific feedback to external supervisors to help identify and address gaps in the supervision they provide. Further studies should determine optimal interpretation of scores and the number of respondents needed per supervisor to obtain precise results, as well as test the functionality of section B.

The impact of a human resource management intervention on the capacity of supervisors to support and supervise their staff at health facility level.

PubMed

Uduma, Ogenna; Galligan, Marie; Mollel, Henry; Masanja, Honorati; Bradley, Susan; McAuliffe, Eilish

2017-08-30

A systematic and structured approach to the support and supervision of health workers can strengthen the human resource management function at the district and health facility levels and may help address the current crisis in human resources for health in sub-Saharan Africa by improving health workers' motivation and retention. A supportive supervision programme including (a) a workshop, (b) intensive training and (c) action learning sets was designed to improve human resource management in districts and health facilities in Tanzania. We conducted a randomised experimental design to evaluate the impact of the intervention. Data on the same measures were collected pre and post the intervention in order to identify any changes that occurred (between baseline and end of project) in the capacity of supervisors in intervention a + b and intervention a + b + c to support and supervise their staff. These were compared to supervisors in a control group in each of Tanga, Iringa and Tabora regions (n = 9). A quantitative survey of 95 and 108 supervisors and 196 and 187 health workers sampled at baseline and end-line, respectively, also contained open-ended responses which were analysed separately. Supervisors assessed their own competency levels pre- and post-intervention. End-line samples generally scored higher compared to the corresponding baseline in both intervention groups for competence activities. Significant differences between baseline and end-line were observed in the total scores on 'maintaining high levels of performance', 'dealing with performance problems', 'counselling a troubled employee' and 'time management' in intervention a + b. In contrast, for intervention a + b + c, a significant difference in distribution of scores was only found on 'counselling a troubled employee', although the end-line mean scores were higher than their corresponding baseline mean scores in all cases. Similar trends to those in the supervisors' reports are seen in health workers data in terms of more efficient supervision processes, although the increases are not as marked. A number of different indicators were measured to assess the impact of the supportive supervision intervention on the a + b and a + b + c intervention sites. The average frequency of supervision visits and the supervisors' competency levels across the facilities increased in both intervention types. This would suggest that the intervention proved effective in raising awareness of the importance of supervision and this understanding led to action in the form of more supportive supervision.

The Practice of Supervision for Professional Learning: The Example of Future Forensic Specialists

ERIC Educational Resources Information Center

Köpsén, Susanne; Nyström, Sofia

2015-01-01

Supervision intended to support learning is of great interest in professional knowledge development. No single definition governs the implementation and enactment of supervision because of different conditions, intentions, and pedagogical approaches. Uncertainty exists at a time when knowledge and methods are undergoing constant development. This…

Possible influences on clinical supervision.

PubMed

Jones, A

This article discusses clinical supervision and suggests that, aside from helping nurses to enhance their clinical effectiveness, it could offer experiential methods of assisting nurses to identify and locate supportive networks in the workplace. The advantages and difficulties of supervision relationships are described in context, including some consideration of authority and control.

New graduate transition to practice: how can the literature inform support strategies?

PubMed

Moores, Alis; Fitzgerald, Cate

2017-07-01

Objective The transition to practice for new graduate health professionals has been identified as challenging, with health services typically adopting a range of support and management strategies to assist safe professional practice. Queensland's state-wide Occupational Therapy Clinical Education Program supporting new graduates within public sector health facilities conducted a narrative literature review to identify evidence-based recommended actions that would assist new graduate occupational therapists' to transition from student to practitioner. Method Searches of Medline, CINAHL and PubMed databases were used to locate articles describing or evaluating occupational therapy new graduate support actions. Results The themes of supervision, support and education emerged from the literature. Additionally, four interactions were identified as factors potentially influencing and being influenced by the processes and outcomes of supervision, support and education actions. The interactions identified were professional reasoning, professional identity, an active approach to learning and reflective practice. Conclusions The interactions emerging from the literature will serve to inform the delivery and focus of supervision, support and education for new graduate occupational therapists as they transition to practice. The results may have application for other health professions. What is known about the topic? The transition to practice for new graduate occupational therapists has been reported as challenging with health services implementing various actions to support and assist this transition. A previous literature review of recommended support strategies could not be found providing an impetus for this enquiry. What does this paper add? This narrative literature review identified three themes of actions supporting the transition of new graduates from student to practitioner. In addition to these themes of supervision, support and education, also emerging from the literature were factors identified as important to facilitating the transition of new graduates to the workplace. These factors, or interactions, are identified in this paper as professional reasoning, professional identity, an active approach to learning, and reflective practice. It is proposed that these interactions have an effect on and can be effected by supervision, support and education actions. The articulation between the interactions and the themes was a notable outcome emerging from this literature review. What are the implications for practitioners? This literature review will assist those planning actions to guide new graduates' transition into practice. It is proposed that the methods of implementing supervision, support and education actions are optimised by the identified interactions.

Maintaining professional resilience through group restorative supervision.

PubMed

Wallbank, Sonya

2013-08-01

Restorative clinical supervision has been delivered to over 2,500 professionals and has shown to be highly effective in reducing burnout, stress and increasing compassion satisfaction. Demand for the programme has shown that a sustainable model of implementation is needed for organisations who may not be able to invest in continued individual sessions. Following the initial six sessions, group restorative supervision has been developed and this paper reports on the programme's success in maintaining and continuing to improve compassion satisfaction, stress and burnout through the process of restorative group supervision. This means that organisations can continue to maintain the programme once the initial training has been completed and have confidence within the restorative group supervision to support professionals in managing the emotional demands of their role. The restorative groups have also had inadvertent positive benefits in workplace functioning. The paper outlines how professionals have been able to use this learning to support them in being more effective.

Clinical supervision, emotional exhaustion, and turnover intention: a study of substance abuse treatment counselors in the Clinical Trials Network of the National Institute on Drug Abuse.

PubMed

Knudsen, Hannah K; Ducharme, Lori J; Roman, Paul M

2008-12-01

An intriguing hypothesis is that clinical supervision may protect against counselor turnover. This idea has been mentioned in recent discussions of the substance abuse treatment workforce. To test this hypothesis, we extend our previous research on emotional exhaustion and turnover intention among counselors by estimating the associations between clinical supervision and these variables in a large sample (N = 823). An exploratory analysis reveals that clinical supervision was negatively associated with emotional exhaustion and turnover intention. Given our previous findings that emotional exhaustion and turnover intention were associated with job autonomy, procedural justice, and distributive justice, we estimate a structural equation model to examine whether these variables mediated clinical supervision's associations with emotional exhaustion and turnover intention. These data support the fully mediated model. We found that the perceived quality of clinical supervision is strongly associated with counselors' perceptions of job autonomy, procedural justice, and distributive justice, which are, in turn, associated with emotional exhaustion and turnover intention. These data offer support for the protective role of clinical supervision in substance abuse treatment counselors' turnover and occupational well-being.

Supporting self and others: from staff nurse to nurse consultant. Part 5: clinical supervision.

PubMed

Fowler, John

This series of articles explores various ways of supporting staff who work in the fast-moving and ever-changing health service. In previous articles, John Fowler an experienced nursing lecturer, author and consultant examined the importance of developing a supportive working culture and the role of preceptorship and mentoring. This article examines the use of clinical supervision within nursing.

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

NASA Astrophysics Data System (ADS)

Heleno, S.; Matias, M.; Pina, P.; Sousa, A. J.

2015-09-01

A method for semi-automatic landslide detection, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a Support Vector Machine classifier on a GeoEye-1 multispectral image, sensed 3 days after the major damaging landslide event that occurred in Madeira island (20 February 2010), with a pre-event LIDAR Digital Elevation Model. The testing is developed in a 15 km2-wide study area, where 95 % of the landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier east facing-slopes.

Object recognition through a multi-mode fiber

NASA Astrophysics Data System (ADS)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

SOM-based nonlinear least squares twin SVM via active contours for noisy image segmentation

NASA Astrophysics Data System (ADS)

Xie, Xiaomin; Wang, Tingting

2017-02-01

In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.

An intelligent identification algorithm for the monoclonal picking instrument

NASA Astrophysics Data System (ADS)

Yan, Hua; Zhang, Rongfu; Yuan, Xujun; Wang, Qun

2017-11-01

The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.

Machine Learning Toolkit for Extreme Scale

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-03-31

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are consideredmore » in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets« less

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Machine learning methods in chemoinformatics

PubMed Central

Mitchell, John B O

2014-01-01

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition.

PubMed

Sayago, Ana; González-Domínguez, Raúl; Beltrán, Rafael; Fernández-Recamales, Ángeles

2018-09-30

This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research. Copyright © 2018 Elsevier Ltd. All rights reserved.

Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management.

PubMed

Bellos, Christos; Papadopoulos, Athanassios; Rosso, Roberto; Fotiadis, Dimitrios I

2011-01-01

CHRONIOUS system is an integrated platform aiming at the management of chronic disease patients. One of the most important components of the system is a Decision Support System (DSS) that has been developed in a Smart Device (SD). This component decides on patient's current health status by combining several data, which are acquired either by wearable sensors or manually inputted by the patient or retrieved from the specific database. In case no abnormal situation has been tracked, the DSS takes no action and remains deactivated until next abnormal situation pack of data are being acquired or next scheduled data being transmitted. The DSS that has been implemented is an integrated classification system with two parallel classifiers, combining an expert system (rule-based system) and a supervised classifier, such as Support Vector Machines (SVM), Random Forests, artificial Neural Networks (aNN like the Multi-Layer Perceptron), Decision Trees and Naïve Bayes. The above categorized system is useful for providing critical information about the health status of the patient.

A Step Forward in Teaching Addiction Counselors How to Supervise Motivational Interviewing Using a Clinical Trials Training Approach

ERIC Educational Resources Information Center

Martino, Steve; Gallon, Steve; Ball, Samuel A.; Carroll, Kathleen M.

2007-01-01

A clinical trials training approach to supervision is a promising and empirically supported method for preparing addiction counselors to implement evidence-based behavioral treatments in community treatment programs. This supervision approach has three main components: (1) direct observation of treatment sessions; (2) structured performance…

Family Nurse Partnership: why supervision matters.

PubMed

Andrews, Lindsayws

First-time teenage mothers and their babies are likely to have increased levels of need. This article explores how supervision supports Family Nurse Partnership (FNP) nurses to undertake complex work with teenage, first-time mothers and their babies. Careful application of a supervision model can provide the structure for a safe, containing, reflective space.

Supervision to Enhance Educational and Vocational Guidance Practice: A Review

ERIC Educational Resources Information Center

Reid, Hazel L.

2010-01-01

Supervision to support the work of career practitioners is evident in many countries, but is not universal. This author presents a literature review, intending to emphasise the prime importance of developing supervision for guidance work. The author also considers the issues facing those training to develop the role of supervisors in southeast…

Clinical supervision, emotional exhaustion, and turnover intention: A study of substance abuse treatment counselors in NIDA’s Clinical Trials Network

PubMed Central

Knudsen, Hannah K.; Ducharme, Lori J.; Roman, Paul M

2008-01-01

An intriguing hypothesis is that clinical supervision may protect against counselor turnover. This idea has been mentioned in recent discussions of the substance abuse treatment workforce. To test this hypothesis, we extend our previous research on emotional exhaustion and turnover intention among counselors by estimating the associations between clinical supervision and these variables in a large sample (n = 823). An exploratory analysis reveals that clinical supervision was negatively associated with emotional exhaustion and turnover intention. Given our previous findings that emotional exhaustion and turnover intention were associated with job autonomy, procedural justice, and distributive justice, we estimate a structural equation model to examine whether these variables mediated clinical supervision’s associations with emotional exhaustion and turnover intention. These data support the fully mediated model. We found the perceived quality of clinical supervision is strongly associated with counselors’ perceptions of job autonomy, procedural justice, and distributive justice, which are, in turn, associated with emotional exhaustion and turnover intention. These data offer support for the protective role of clinical supervision in substance abuse treatment counselors’ turnover and occupational wellbeing. PMID:18424048

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

PubMed

Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

2016-01-01

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

Semi-supervised protein subcellular localization.

PubMed

Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang

2009-01-30

Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Improved Prediction of Non-methylated Islands in Vertebrates Highlights Different Characteristic Sequence Patterns

PubMed Central

Vingron, Martin

2016-01-01

Non-methylated islands (NMIs) of DNA are genomic regions that are important for gene regulation and development. A recent study of genome-wide non-methylation data in vertebrates by Long et al. (eLife 2013;2:e00348) has shown that many experimentally identified non-methylated regions do not overlap with classically defined CpG islands which are computationally predicted using simple DNA sequence features. This is especially true in cold-blooded vertebrates such as Danio rerio (zebrafish). In order to investigate how predictive DNA sequence is of a region’s methylation status, we applied a supervised learning approach using a spectrum kernel support vector machine, to see if a more complex model and supervised learning can be used to improve non-methylated island prediction and to understand the sequence properties of these regions. We demonstrate that DNA sequence is highly predictive of methylation status, and that in contrast to existing CpG island prediction methods our method is able to provide more useful predictions of NMIs genome-wide in all vertebrate organisms that were studied. Our results also show that in cold-blooded vertebrates (Anolis carolinensis, Xenopus tropicalis and Danio rerio) where genome-wide classical CpG island predictions consist primarily of false positives, longer primarily AT-rich DNA sequence features are able to identify these regions much more accurately. PMID:27984582

Peer Support in Negotiating Multiple Relationships within Supervision among Counselor Education Doctoral Students

ERIC Educational Resources Information Center

Minor, Amanda J.; Pimpleton, Asher; Stinchfield, Tracy; Stevens, Heath; Othman, Nor Asma

2013-01-01

Counselor education doctoral students (CEDSs), like other doctoral students, need assistance and support to ensure their self-care. One area markedly affecting self-care is one's relationships with others. The purpose of this article is to examine the multiple relationships involved within CEDSs supervision, the potential areas to utilize peer…

The Principles and Practices of Supervision That Supports the Development of Inclusive Teacherhood

ERIC Educational Resources Information Center

Alila, Sanna; Uusiautti, Satu; Määttä, Kaarina

2016-01-01

In this study, the purpose was to study what kind of supervision supports inclusive teacherhood the best. Inclusive teacherhood means a teacher's professional development and the school culture's change toward participatory school for all students. The study investigated the perceptions of supervisors with a teaching background. This was a…

Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.

PubMed

Liang, Zhaohui; Liu, Jun; Ou, Aihua; Zhang, Honglai; Li, Ziping; Huang, Jimmy Xiangji

2018-05-04

Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice. The purpose of this paper is to evaluate a deep learning architecture as an effective solution for CAMDM. A two-step model is applied in our study. At the first step, an optimized seven-layer deep belief network (DBN) is applied as an unsupervised learning algorithm to perform model training to acquire feature representation. Then a support vector machine model is adopted to DBN at the second step of the supervised learning. There are two data sets used in the experiments. One is a plain text data set indexed by medical experts. The other is a structured dataset on primary hypertension. The data are randomly divided to generate the training set for the unsupervised learning and the testing set for the supervised learning. The model performance is evaluated by the statistics of mean and variance, the average precision and coverage on the data sets. Two conventional shallow models (support vector machine / SVM and decision tree / DT) are applied as the comparisons to show the superiority of our proposed approach. The deep learning (DBN + SVM) model outperforms simple SVM and DT on two data sets in terms of all the evaluation measures, which confirms our motivation that the deep model is good at capturing the key features with less dependence when the index is built up by manpower. Our study shows the two-step deep learning model achieves high performance for medical information retrieval over the conventional shallow models. It is able to capture the features of both plain text and the highly-structured database of EMR data. The performance of the deep model is superior to the conventional shallow learning models such as SVM and DT. It is an appropriate knowledge-learning model for information retrieval of EMR system. Therefore, deep learning provides a good solution to improve the performance of CAMDM systems. Copyright © 2018. Published by Elsevier B.V.

Effects of coaching supervision, mentoring supervision and abusive supervision on talent development among trainee doctors in public hospitals: moderating role of clinical learning environment.

PubMed

Subramaniam, Anusuiya; Silong, Abu Daud; Uli, Jegak; Ismail, Ismi Arif

2015-08-13

Effective talent development requires robust supervision. However, the effects of supervisory styles (coaching, mentoring and abusive supervision) on talent development and the moderating effects of clinical learning environment in the relationship between supervisory styles and talent development among public hospital trainee doctors have not been thoroughly researched. In this study, we aim to achieve the following, (1) identify the extent to which supervisory styles (coaching, mentoring and abusive supervision) can facilitate talent development among trainee doctors in public hospital and (2) examine whether coaching, mentoring and abusive supervision are moderated by clinical learning environment in predicting talent development among trainee doctors in public hospital. A questionnaire-based critical survey was conducted among trainee doctors undergoing housemanship at six public hospitals in the Klang Valley, Malaysia. Prior permission was obtained from the Ministry of Health Malaysia to conduct the research in the identified public hospitals. The survey yielded 355 responses. The results were analysed using SPSS 20.0 and SEM with AMOS 20.0. The findings of this research indicate that coaching and mentoring supervision are positively associated with talent development, and that there is no significant relationship between abusive supervision and talent development. The findings also support the moderating role of clinical learning environment on the relationships between coaching supervision-talent development, mentoring supervision-talent development and abusive supervision-talent development among public hospital trainee doctors. Overall, the proposed model indicates a 26 % variance in talent development. This study provides an improved understanding on the role of the supervisory styles (coaching and mentoring supervision) on facilitating talent development among public hospital trainee doctors. Furthermore, this study extends the literature to better understand the effects of supervisory styles on trainee doctors' talent development are contigent on the trainee doctors' clinical learning environment. In summary, supervisors are stakeholders with the responsibility of facilitating learning conditions that hold sufficient structure and support to optimise the trainee doctors learning.

Oceans apart, yet connected: Findings from a qualitative study on professional supervision in rural and remote allied health services.

PubMed

Ducat, Wendy; Martin, Priya; Kumar, Saravana; Burge, Vanessa; Abernathy, LuJuana

2016-02-01

Improving the quality and safety of health care in Australia is imperative to ensure the right treatment is delivered to the right person at the right time. Achieving this requires appropriate clinical governance and support for health professionals, including professional supervision. This study investigates the usefulness and effectiveness of and barriers to supervision in rural and remote Queensland. As part of the evaluation of the Allied Health Rural and Remote Training and Support program, a qualitative descriptive study was conducted involving semi-structured interviews with 42 rural or remote allied health professionals, nine operational managers and four supervisors. The interviews explored perspectives on their supervision arrangements, including the perceived usefulness, effect on practice and barriers. Themes of reduced isolation; enhanced professional enthusiasm, growth and commitment to the organisation; enhanced clinical skills, knowledge and confidence; and enhanced patient safety were identified as perceived outcomes of professional supervision. Time, technology and organisational factors were identified as potential facilitators as well as potential barriers to effective supervision. This research provides current evidence on the impact of professional supervision in rural and remote Queensland. A multidimensional model of organisational factors associated with effective supervision in rural and remote settings is proposed identifying positive supervision culture and a good supervisor-supervisee fit as key factors associated with effective arrangements. © 2015 Commonwealth of Australia. Australian Journal of Rural Health published by Wiley Publishing Asia Pty Ltd. on behalf of National Rural Health Alliance Inc.

What influences palliative care nurses in their choice to engage in or decline clinical supervision?

PubMed

Puffett, Nick; Perkins, Paul

2017-11-02

Clinical supervision (CS) has been around since the early 1990s in the UK and has been endorsed by government and professional bodies. Levels of engagement range from 18% to 85%. To investigate what influences palliative care nurses in their choice to engage in or decline clinical supervision. A qualitative study was undertaken in an inpatient hospice in England and employed two focus groups to compare the views of participants and non-participants in CS. Data were audio recorded and transcribed verbatim by the researchers and analysed using systematic text condensation. Palliative care nurses all used informal team support for 'in the moment' support. Some engaged in formal CS to reflect 'on action' and to challenge practice. Nurses reported a lack of clarity regarding CS but, once this was overcome and engagement with CS was established, it led to changes in practice, identification of training needs and team building. The option of choice between group and individual supervision was found to be important. Group supervision led to enhanced understanding of group members which also led to team building, individual sessions were useful for individual issues. Protected time was essential for staff to be able to engage in CS. Staff who worked in larger teams reported higher levels of engagement, whereas a small team reported less need due to more informal team support. These findings are positive as they illuminate the importance of choice for support. Nurses need to be aware of their options for support and ultimately how this support affects the care they provide. The Palliative Care Nurse's Model of Support was developed, which shows the effects of each choice and how this may lead to team-building.

CREATING A "NEST" OF EMOTIONAL SAFETY: REFLECTIVE SUPERVISION IN A CHILD-PARENT PSYCHOTHERAPY CASE.

PubMed

Many, Michele M; Kronenberg, Mindy E; Dickson, Amy B

2016-11-01

Reflective supervision is considered a key practice component for any infant mental health provider to work effectively with young children and their families. This article will provide a brief history and discussion of reflective supervision followed by a case study demonstrating the importance of reflective supervision in the context of child-parent psychotherapy (CPP; A.F. Lieberman, C. Ghosh Ippen, & P. Van Horn, ; A.F. Lieberman & P. Van Horn, , 2008). Given that CPP leverages the caregiver-child relationship as the mechanism for change in young children who have been impacted by stressors and traumas, primary objectives of CPP include assisting caregivers as they understand the meaning of their child's distress and improving the caregiver-child relationship to make it a safe and supportive space in which the child can heal. As this case will demonstrate, when a clinician is emotionally triggered by a family's negative intergenerational patterns of relating, reflective supervision supports a parallel process in which the psychotherapist feels understood and contained by the supervisor so that she or he is able to support the caregiver's efforts to understand and contain the child. © 2016 Michigan Association for Infant Mental Health.

More than a checklist: a realist evaluation of supervision of mid-level health workers in rural Guatemala.

PubMed

Hernández, Alison R; Hurtig, Anna-Karin; Dahlblom, Kjerstin; San Sebastián, Miguel

2014-03-06

Mid-level health workers (MLHWs) form the front-line of service delivery in many low- and middle-income countries. Supervision is a critical institutional intervention linking their work to the health system, and it consists of activities intended to support health workers' motivation and enable them to perform. However its impact depends not only on the frequency of these activities but also how they are carried out and received. This study aims to deepen understanding of the mechanisms through which supervision activities support the performance of auxiliary nurses, a cadre of MLHWs, in rural Guatemala. A multiple case study was conducted to examine the operation of supervision of five health posts using a realist evaluation approach. A program theory was formulated describing local understanding of how supervision activities are intended to work. Data was collected through interviews and document review to test the theory. Analysis focused on comparison of activities, outcomes, mechanisms and the influence of context across cases, leading to revision of the program theory. The supervisor's orientation was identified as the main mechanism contributing to variation observed in activities and their outcomes. Managerial control was the dominant orientation, reflecting the influence of standardized performance criteria and institutional culture. Humanized support was present in one case where the auxiliary nurse was motivated by the sense that the full scope of her work was valued. This orientation reflected the supervisor's integration of her professional identity as a nurse. The nature of the support health workers received was shaped by supervisors' orientation, and in this study, nursing principles were central to humanized support. Efforts to strengthen the support that supervision provides to MLHWs should promote professional ethos as a means of developing shared performance goals and orient supervisors to a more holistic view of the health worker and their work.

More than a checklist: a realist evaluation of supervision of mid-level health workers in rural Guatemala

PubMed Central

2014-01-01

Background Mid-level health workers (MLHWs) form the front-line of service delivery in many low- and middle-income countries. Supervision is a critical institutional intervention linking their work to the health system, and it consists of activities intended to support health workers’ motivation and enable them to perform. However its impact depends not only on the frequency of these activities but also how they are carried out and received. This study aims to deepen understanding of the mechanisms through which supervision activities support the performance of auxiliary nurses, a cadre of MLHWs, in rural Guatemala. Methods A multiple case study was conducted to examine the operation of supervision of five health posts using a realist evaluation approach. A program theory was formulated describing local understanding of how supervision activities are intended to work. Data was collected through interviews and document review to test the theory. Analysis focused on comparison of activities, outcomes, mechanisms and the influence of context across cases, leading to revision of the program theory. Results The supervisor’s orientation was identified as the main mechanism contributing to variation observed in activities and their outcomes. Managerial control was the dominant orientation, reflecting the influence of standardized performance criteria and institutional culture. Humanized support was present in one case where the auxiliary nurse was motivated by the sense that the full scope of her work was valued. This orientation reflected the supervisor’s integration of her professional identity as a nurse. Conclusions The nature of the support health workers received was shaped by supervisors’ orientation, and in this study, nursing principles were central to humanized support. Efforts to strengthen the support that supervision provides to MLHWs should promote professional ethos as a means of developing shared performance goals and orient supervisors to a more holistic view of the health worker and their work. PMID:24602196

A Pilot Study Examining Factors Influencing Readiness to Progress to Indirect Supervision Among First Year Residents in a General Psychiatry Training Program.

PubMed

Touchet, Bryan; Walker, Ashley; Flanders, Sarah; McIntosh, Heather

2018-04-01

In the first year of training, psychiatry residents progress from direct supervision to indirect supervision but factors predicting time to transition between these levels of supervision are unknown. This study aimed to examine times for transition to indirect levels of supervision and to identify resident factors associated with slower progression. The authors compiled data from training files from years 2011-2015, including licensing exam scores, age, gender, medical school, month of first inpatient psychiatry rotation, and transition times between levels of supervision. Correlational analysis examined the relationship between these factors. Univariate analysis further examined the relationship between medical school training and transition times between supervision levels. Among the factors studied, only international medical school training was positively correlated with time to transition to indirect supervision and between levels of indirect supervision. International medical graduate (IMG) interns in psychiatry training may benefit from additional training and support to reach competencies required for the transition to indirect supervision.

Health visitors' needs--national perspectives from the Restorative Clinical Supervision Programme.

PubMed

Wallbank, Sonya

2012-04-01

The restorative clinical supervision programme has been delivering supervision for the last 18 months to over 600 health visitors within trusts across the UK. This opportunity has allowed the supervision team to work with individual organisations to identify the key issues with which health visiting teams are struggling, and provide effective solutions to reduce staff stress, encourage retention and increase job satisfaction. This paper offers an overview of the consistent themes that health visitors are attempting to resolve and of how restorative supervision supports professionals working with complex families to manage these difficulties.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

NASA Astrophysics Data System (ADS)

Renza, Diego; Martinez, Estibaliz; Molina, Iñigo; Ballesteros L., Dora M.

2017-04-01

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.

Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

NASA Astrophysics Data System (ADS)

Barros, Vesna; Barros, Lucas

2016-04-01

The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support-vector network to various classical learning algorithms used before in seismic detection and classification is an essential final step to analyze the advantages and disadvantages of the model.

Task sharing in rural Haiti: Qualitative assessment of a brief, structured training with and without apprenticeship supervision for community health workers

PubMed Central

McLean, Kristen E; Kaiser, Bonnie N; Hagaman, Ashley K; Wagenaar, Bradley H; Therosme, Tatiana P; Kohrt, Brandon A

2015-01-01

Despite growing support for supervision after task sharing trainings in humanitarian settings, there is limited research on the experience of trainees in apprenticeship and other supervision approaches. Studying apprenticeships from trainees’ perspectives is crucial to refine supervision and enhance motivation for service implementation. The authors implemented a multi-stage, transcultural adaptation for a pilot task sharing training in Haiti entailing three phases: 1) literature review and qualitative research to adapt a mental health and psychosocial support training; 2) implementation and qualitative process evaluation of a brief, structured group training; and 3) implementation and qualitative evaluation of an apprenticeship training, including a two year follow-up of trainees. Structured group training revealed limited knowledge acquisition, low motivation, time and resource constraints on mastery, and limited incorporation of skills into practice. Adding an apprenticeship component was associated with subjective clinical competency, increased confidence regarding utilising skills, and career advancement. Qualitative findings support the added value of apprenticeship according to trainees. PMID:26190953

Essential processes for cognitive behavioral clinical supervision: Agenda setting, problem-solving, and formative feedback.

PubMed

Cummings, Jorden A; Ballantyne, Elena C; Scallion, Laura M

2015-06-01

Clinical supervision should be a proactive and considered endeavor, not a reactive one. To that end, supervisors should choose supervision processes that are driven by theory, best available research, and clinical experience. These processes should be aimed at helping trainees develop as clinicians. We highlight 3 supervision processes we believe should be used at each supervision meeting: agenda setting, encouraging trainee problem-solving, and formative feedback. Although these are primarily cognitive-behavioral skills, they can be helpful in combination with other supervision models. We provide example dialogue from supervision exchanges, and discuss theoretical and research support for these processes. Using these processes not only encourages trainee development but also models for them how to use the same processes and approaches with clients. (c) 2015 APA, all rights reserved).

Community cooperatives and insecticide-treated materials for malaria control: a new experience in Latin America.

PubMed

Kroeger, Axel; Aviñna, Ana; Ordoñnez-Gonzalez, José; Escandon, Celia

2002-11-15

Insecticide-treated materials (ITMs) are effective in substantially reducing the burden of malaria and other vector-borne diseases; but how can high coverage rates of ITMs be achieved and maintained? In south Mexico and on the Pacific and Atlantic coasts of Colombia 14 community-based cooperatives offering three different kinds of ITM services (sale of impregnation services; sale of impregnated nets; production of nets and sale of impregnated nets) were formed and supervised by a national health service (IMSS-SOLIDARIDAD, Mexico) and by an academic institution (the Colombian Institute of Tropical Medicine) along with local district health services. The objectives of this research were to analyse the processes and results of this approach and to identify the favourable and limiting factors. The methods used for data collection and analysis were group discussions, individual and semi-structured interviews with users and non-users of ITMs, individual in-depth interviews with cooperative members and supervisors, checks of sales book and observation of impregnation services. Coverage with unimpregnated nets was above 50% in all study areas. The fastest increase of ITM coverage was achieved through the exclusive sale of impregnation services. Low-cost social marketing techniques were used to increase demand. The large-scale production of nets in two cooperatives was only possible with the aid of an international NGO which ordered impregnated bednets for their target group. A number of favourable and limiting factors relating to the success of ITM cooperatives were identified. Of particular importance for the more successful Mexican cooperatives were: a) support by health services, b) smaller size, c) lesser desire for quick returns and d) lower ITM unit costs. ITM community cooperatives supported and supervised by the health services have good potential in the Latin American context for achieving and maintaining high impregnation rates.

Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

2017-04-01

Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

How Supervisees on a Foundation Course in CBT Perceive a Supervision Session and what they Bring Forward to the Next Therapy Session.

PubMed

Törnquist, Anna; Rakovshik, Sarah; Carlsson, Jan; Norberg, Joakim

2018-05-01

There is limited research into the effect of supervision in cognitive behavioural therapy (CBT) from the supervisees' perspective. The aim of the study was to acquire knowledge from the supervisees' perspective as to what in particular in the supervision process contributes to the therapy process. Fourteen supervisees on a foundation course participated in the study. A qualitative approach was used with thematic analysis of the participants' written diaries after supervision and therapy sessions. Analyses of supervisees' experiences suggested that a variety of therapeutic interventions were easier to implement if one had the supervisor's support and felt free to decide if and when the suggested interventions could best be implemented. Evaluation in the form of positive feedback from the supervisor indicating that the supervisee was 'doing the right thing' was perceived to be important. A unifying theme when supervisees felt they were not getting anything out of the supervision was that the supervisees did not have a supervision question. The results of this research suggest that the supervisor's support during training is perceived to be important for the supervisee. Receiving positive feedback from one's supervisor in an evaluation is perceived to have a great impact on whether the therapist implements the suggested therapeutic interventions discussed in the previous supervision.

Supporting the Emotional Labour Associated with Teaching: Considering a Pluralistic Approach to Group Supervision

ERIC Educational Resources Information Center

Hanley, Terry

2017-01-01

This paper puts forward a framework for supervising teaching staff whose roles involve supporting the emotional well-being of young people and young adults. Initially, the increasing focus upon the interface between education and health is outlined and the potential for this "emotional labour" to cause distress to those in helping roles…

The Effect of Supportive Organizational Leadership, Organizational Socialization, and Satisfaction with Supervision on Turnover as Mediated by Organizational Commitment and Job Satisfaction in Faculty

ERIC Educational Resources Information Center

Lowhorn, Greg L.

2009-01-01

This study utilized a predictive, multivariate research design to test the relationship between three independent variables--supportive organizational leadership, organizational socialization, and satisfaction with supervision--and the dependent variable--turnover intent--as mediated by organizational commitment and job satisfaction. The…

Using Skype to support remote clinical supervision for health professionals delivering a sustained maternal early childhood programme: a phenomenographical study.

PubMed

Bruce, Tracey; Byrne, Fiona; Kemp, Lynn

2018-02-01

Skype technology was implemented by the Australian Maternal Early Childhood Sustained Home-visiting (MECSH) Support Service as a tool for the remote provision of clinical supervision for clinicians working in the MECSH program in Seoul, South Korea. To gain a better understanding of the processes underpinning sustainable delivery of remote clinical supervision using digital technologies. A phenomenographical study. Recorded notes and reflections on each supervision session, noting exemplars and characteristics of the experience were read and re-read to derive the characterizations of the experience. The experience has provided learnings in three domains: (1) the processes in using Skype; (2) supervisory processes; and (3) language translation, including managing clarity of, and time for translation. Skype has potential for use in remote provision of clinical supervision, including where translation is required. Further research evaluating the benefit of telesupervision from supervisor and supervisee perspectives is necessary to determine if it is a sustainable process.

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds.

PubMed

Abraham, Leandro; Bromberg, Facundo; Forradellas, Raymundo

2018-04-01

Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras. A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturbations in the point of view of the capturing device, greatly simplifying the installation process for end-users. In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g), the best variant of the proposed methodology achieved mean absolute errors of about 9.21% MVC - an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. The results prove that the correlations between the external geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ustuner, M.; Sanli, F. B.; Abdikan, S.

2016-06-01

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

Testing the Potential of Vegetation Indices for Land Use/cover Classification Using High Resolution Data

NASA Astrophysics Data System (ADS)

Karakacan Kuzucu, A.; Bektas Balcik, F.

2017-11-01

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

Nursing and health-care assistant experience of supervision in a medium secure psychiatric service for women: implications for service development.

PubMed

Long, C G; Harding, S; Payne, K; Collins, L

2014-03-01

In secure psychiatric services where the potential for 'burnout' by nurses is high, clinical supervision is viewed as a key to reflective practice to support staff in stressful working environments. Barriers to the uptake of clinical supervision in such service settings are personal and organizational. The study was prompted by the need to evaluate the effectiveness of supervision for registered nurses and health-care assistants (HCAs) and a desire to use survey findings to improve the quality and uptake of supervision. The study examined the perceived benefits, the best practice elements and the practical aspects of clinical supervision including how to improve practice. An approximate uptake of clinical supervision by 50% of staff confirmed previous findings; that HCAs were significantly less likely to engage in supervision and less likely to perceive benefit from it. Initiatives to address the training and managerial obstacles to the provision of formal supervision are described. © 2013 John Wiley & Sons Ltd.

The Perceived Benefits and Difficulties in Introducing and Maintaining Supervision Groups in a SEMH Special School

ERIC Educational Resources Information Center

Willis, Jonathan; Baines, Ed

2018-01-01

Supervision groups are often used in professional settings and are introduced to address and provide support in relation to the challenges that arise in everyday practice. Although group supervision is common amongst a range of helping professions, its use in schools is rare. Little research exists as to the merits and challenges of providing…

A Reflection on the Work of an Educational Psychologist in Providing Supervision for a Team of Community Based Support Workers, Supporting Families with Vulnerable Adolescents at Risk of Exclusion from School

ERIC Educational Resources Information Center

Maxwell, Tim

2013-01-01

The evolving role of the educational psychologist (EP) is discussed with an emphasis on the supervision provided for a team of support workers for vulnerable adolescents, working within a Local Service Team. This development is considered in the context of the Every Child Matters (DfES, 2004) agenda and the Farrell, Woods, Lewis, Rooney, Squire…

Group Supervision: Supporting Practitioners in Their Work with Children and Families in Children's Centres

ERIC Educational Resources Information Center

Soni, Anita

2013-01-01

This article discusses how group supervision can be used to support the Continuing Professional Development (CPD) of those working with children and families in early years provision in England. It is based on research conducted in 2008 with a cluster of four Children's Centres in the West Midlands in England, UK. The research evaluated group…

Improving Doctoral Support through Group Supervision: Analysing Face-to-Face and Technology-Mediated Strategies for Nurturing and Sustaining Scholarship

ERIC Educational Resources Information Center

Hutchings, Maggie

2017-01-01

The challenges of the doctoral journey can create social and academic isolation. Student support is normally facilitated through the supervisory team and research training programmes. There is little empirical evidence on the role group supervision and peer learning can play in nurturing and sustaining doctoral scholarship. This article explores…

Remote supervision of medical training via videoconference in northern Australia: a qualitative study of the perspectives of supervisors and trainees

PubMed Central

Ray, Robin; Sabesan, Sabe

2015-01-01

Objectives Telemedicine has revolutionised the ability to provide care to patients, relieve professional isolation and provide guidance and supervision to junior medical officers in rural areas. This study evaluated the Townsville teleoncology supervision model for the training of junior medical officers in rural areas of North Queensland, Australia. Specifically, the perspectives of junior and senior medical officers were explored to identify recommendations for future implementation. Design A qualitative approach incorporating observation and semistructured interviews was used to collect data. Interviews were uploaded into NVivo 10 data management software. Template analysis enabled themes to be tested and developed through consensus between researchers. Setting One tertiary level and four secondary level healthcare centres in rural and regional Queensland, Australia. Participants 10 junior medical officers (Interns, Registrars) and 10 senior medical officers (Senior Medical Officers, Consultants) who participated in the Townsville teleoncology model of remote supervision via videoconference (TTMRS) were included in the study. Primary and Secondary outcome measures Perspectives on the telemedicine experience, technology, engagement, professional support, satisfaction and limitations were examined. Perspectives on topics raised by participants were also examined as the interviews progressed. Results Four major themes with several subthemes emerged from the data: learning environment, beginning the learning relationship, stimulus for learning and practicalities of remote supervision via videoconference. While some themes were consistent with the current literature, new themes like increased professional edge, recognising non-verbal cues and physical examination challenges were identified. Conclusions Remote supervision via videoconference provides readily available guidance to trainees supporting their delivery of appropriate care to patients. However, resources required for upskilling, training in the use of supervision via videoconference, administration issues and nursing support, as well as physical barriers to examinations, must be addressed to enable more efficient implementation. PMID:25795687

Adaptive distance metric learning for diffusion tensor image segmentation.

PubMed

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

PubMed Central

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

Optimal reinforcement of training datasets in semi-supervised landmark-based segmentation

NASA Astrophysics Data System (ADS)

Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2015-03-01

During the last couple of decades, the development of computerized image segmentation shifted from unsupervised to supervised methods, which made segmentation results more accurate and robust. However, the main disadvantage of supervised segmentation is a need for manual image annotation that is time-consuming and subjected to human error. To reduce the need for manual annotation, we propose a novel learning approach for training dataset reinforcement in the area of landmark-based segmentation, where newly detected landmarks are optimally combined with reference landmarks from the training dataset and therefore enriches the training process. The approach is formulated as a nonlinear optimization problem, where the solution is a vector of weighting factors that measures how reliable are the detected landmarks. The detected landmarks that are found to be more reliable are included into the training procedure with higher weighting factors, whereas the detected landmarks that are found to be less reliable are included with lower weighting factors. The approach is integrated into the landmark-based game-theoretic segmentation framework and validated against the problem of lung field segmentation from chest radiographs.

A systematic review of professional supervision experiences and effects for allied health practitioners working in non-metropolitan health care settings

PubMed Central

Ducat, Wendy H; Kumar, Saravana

2015-01-01

Introduction In regional, rural, and remote settings, allied health professional supervision is one organizational mechanism designed to support and retain the workforce, provide clinical governance, and enhance service delivery. A systematic approach to evaluating the evidence of the experience and effects of professional supervision for non-metropolitan allied health practitioners and their service delivery is needed. Methods Studies investigating the experience and effects of professional supervision across 17 allied health disciplines in non-metropolitan health services were systematically searched for using standardized keywords across seven databases. The initial search identified 1,574 references. Of these studies, five met inclusion criteria and were subject to full methodological appraisal by both reviewers. Two studies were primarily qualitative with three studies primarily quantitative in their approach. Studies were appraised using McMaster critical appraisal tools and data were extracted and synthesized. Results Studies reported the context specific benefits and challenges of supervision in non-metropolitan areas and the importance of supervision in enhancing satisfaction and support in these areas. Comparison of findings between metropolitan and non-metropolitan settings within one study suggested that allied health in non-metropolitan settings were more satisfied with supervision though less likely to access it and preferred supervision with other non-metropolitan practitioners over access to more experienced supervisors. One study in a regional health service identified the lack of an agreed upon definition and functions of supervision when supervisors from diverse allied health disciplines were surveyed. While methodologically weak, all studies reported positive perceptions of supervision across professionals, supervisors, and managers. This is in accordance with previous research in the wider supervision literature. Discussion Considering the large pool of studies retrieved for further investigation, few of these met inclusion criteria demonstrating the paucity of primary research in this area. Increased training, policies, and implementation frameworks to ensure the definition and functions of supervision are agreed upon across the allied health disciplines in non-metropolitan areas is needed. Furthermore, systematic evaluation of supervision implementation in non-metropolitan settings, investigation of the experience and effects of distance based supervision (versus face-to-face), and increased rigor in research studies investigating non-metropolitan allied health profession supervision is needed. PMID:26347446

A Study of the Relationship of Perceived Principal Supervision and Support to the Perceived Self-Efficacy of Beginning and Experienced K-12 Teachers

ERIC Educational Resources Information Center

Spearing, Leonard M.

2013-01-01

In this quantitative study the author examined the relationship between the perceived level of principal supervision and support to the perceived self-efficacy of K-12 teachers in a suburban public school district. The impact of perceived self-efficacy upon the commitment to remain in teaching was also considered. Finally the differential…

Enforcer, manager or leader? The judicial role in family violence courts.

PubMed

King, Michael; Batagol, Becky

2010-01-01

Judicial supervision of offenders is an important component of many family violence courts. Skepticism concerning the ability of offenders to reform and a desire to protect victims has led to some judges to use supervision as a form of deterrence. Supervision is also used to hold offenders accountable for following court orders. Some family violence courts apply processes used in drug courts, such as rewards and sanctions, to promote offender rehabilitation. This article suggests that while protection and support of victims should be the prime concern of family violence courts, a form of judging that engages offenders in the development and implementation of solutions for their problems and supports their implementation is more likely to promote their positive behavioral change than other approaches to judicial supervision. The approach to judging proposed in this article draws from therapeutic jurisprudence, feminist theory, transformational leadership and solution-focused brief therapy principles. Copyright © 2010 Elsevier Ltd. All rights reserved.

Supervision for treatment of depression: An experimental study of the role of therapist gender and anxiety.

PubMed

Simpson-Southward, Chloe; Waller, Glenn; Hardy, Gillian E

2016-02-01

Psychological treatments for depression are not always delivered effectively or consistently. Clinical supervision of therapists is often assumed to keep therapy on track, ensuring positive patient outcomes. However, there is a lack of empirical evidence supporting this assumption. This experimental study explored the focus of supervision of depression cases, comparing guidance given to supervisees of different genders and anxiety levels. Participants were clinical supervisors who supervised therapists working with patients with depression. Supervisors indicated their supervision focus for three supervision case vignettes. Supervisee anxiety and gender was varied across vignettes. Supervisors focused calm female supervisees more on therapeutic techniques than state anxious female supervisees. Males were supervised in the same way, regardless of their anxiety. Both male and female supervisors had this pattern of focus. Findings indicate that supervision is influenced by supervisors' own biases towards their supervisees. These factors may cause supervisors to drift from prompting their supervisees to deliver best practice. Suggestions are made for ways to improve the effectiveness of clinical supervision and how these results may inform future research practice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effect of supportive supervision on routine immunization service delivery-a randomized post-test study in Odisha.

PubMed

Som, Meena; Panda, Bhuputra; Pati, Sanghamitra; Nallala, Srinivas; Anasuya, Anita; Chauhan, Abhimanyu Singh; Sen, Ashish Kumar; Zodpey, Sanjay

2014-06-30

Routine immunization is a key child survival intervention. Issues related to quality of service delivery pose operational challenges in delivering effective immunization services. Accumulated evidences suggest that "supportive supervision" improves the quality of health care services. During 2009-10, Govt. of Odisha (GoO) and UNICEF jointly piloted this strategy in four districts to improve routine immunization. The present study aims to assess the effect of supportive supervision strategy on improvement of knowledge and practices on routine immunization among service providers. We adopted a 'post-test only' study design to compare the knowledge and practices of frontline health workers and their supervisors in four intervention districts with that of two control districts. Altogether we interviewed 170 supervisors and supervisees (health workers), each, using semi-structured interview schedules. We also directly observed 25 ice lined refrigerator (ILR) points in both groups of districts. The findings were compared with the baseline information, available only for the intervention districts. The health workers in the intervention districts displayed a higher knowledge score in selected items than in the control group. No significant difference in knowledge was observed between control and intervention supervisors. The management practices at ILR points on key routine immunization components were found to have improved significantly in intervention districts. The observed improvements in the ILR management practices indicate positive influence of supportive supervision. Higher level of domain knowledge among intervention health workers on specific items related to routine immunization could be due to successful transfer of knowledge from supervisors. A 'pre-post' study design should be undertaken to gain insights into the effectiveness of supportive supervision in improving routine immunization services.

Implementing the supportive supervision intervention for registered nurses in a long-term care home: a feasibility study.

PubMed

McGilton, Katherine S; Profetto-McGrath, Joanne; Robinson, Angela

2013-11-01

This pilot study was conducted in response to the call in 2009 by the International Association of Gerontology and Geriatrics to focus on effective leadership structures in nursing homes and to develop leadership capacity. Few researchers have evaluated interventions aimed at enhancing the leadership ability of registered nurses in long-term care. The aim of the pilot study was to test the feasibility of a three-part supportive supervisory intervention to improve supervisory skills of registered nurses in long-term care. A repeated measures group design was used. Quantitative data were collected from healthcare aides, licensed practical nurses (i.e., supervised staff), and registered nurses (i.e., supervisors). Focus groups with care managers and supervisors examined perceptions of the intervention. There were nonsignificant changes in both the registered nurse supervisors' job satisfaction and the supervised staff's perception of their supervisors' support. Supervised staff scores indicated an increase in the use of research utilization but did not reflect an increase in job satisfaction. Focus group discussions revealed that the supervisors and care managers perceived the workshop to be valuable; however, the weekly self-reflection, coaching, and mentoring components of the intervention were rare and inconsistent. While the primary outcomes were not influenced by the Supportive Supervision Intervention, further effort is required to understand how best to enhance the supportive supervisory skills of RNs. Examples of how to improve the possibility of a successful intervention are advanced. Effective supervisory skills among registered nurses are crucial for improving the quality of care in long-term care homes. Registered nurses are receptive to interventions that will enhance their roles as supervisors. © 2013 Sigma Theta Tau International.

Nursing clinical supervision project in a Neonatal Intensive Care and a Special Care Baby Unit: a best practice implementation project.

PubMed

Johansson, Diana

2015-04-17

Clinical supervision is a process of guided reflective practice and is used in the areas of mental health and palliative care. However, within the Neonatal Intensive Care Unit setting, stressful situations may also arise, with no policies for nurses in regards to participation in clinical supervision. With critical incidents, complex family issues and loss of nursing expertise, clinical supervision could be a potential solution to this dilemma. The aims of the project were to investigate if any hospital policies supported clinical supervision. Specifically, the aims were: (i) to conduct an audit of nurses' knowledge on the topic of clinical supervision, (ii) to investigate if nurses were aware of, or had been involved in, any clinical supervision activities conducted in a Neonatal Intensive Care Unit or a Special Care Baby Unit, and (iii) to investigate if records are maintained of any clinical supervision activities held. A three-phase approach was initiated for this project: a baseline audit, implementation of education sessions, and a follow-up audit using the Joanna Briggs Institute Practical Application of Clinical Evidence System and Getting Research into Practice programs. The Neonatal Intensive Care Unit and Special Care Baby Unit have approximately 180 registered nurses working in the units where the project was conducted. The baseline audit included 37 nurses by convenience sampling and the follow-up audit included nine of these nurses. No policy on clinical supervision has been developed to support nurses in the Neonatal Intensive Care Unit and Special Care Baby Unit. The baseline audit found that nurses described clinical supervision as educational and task orientated, and did not equate clinical supervision with a process that could be also described as "guided reflective practice". Following the education sessions, there was a better understanding of what clinical supervision entailed and the benefits that could lead to improved professional practice, but there were no activities in which nurses could engage in this process. Implementation of a pilot project to test the evidence of clinical supervision in the Neonatal Intensive Care and Special Care Baby speciality units should be undertaken with strategies to assess the effectiveness of clinical supervision and the positive aspects that have been reported in the literature. The Joanna Briggs Institute.

Clinical supervision of allied health professionals in country South Australia: A mixed methods pilot study.

PubMed

Kumar, Saravana; Osborne, Kate; Lehmann, Tanya

2015-10-01

Recent times have witnessed dramatic changes in health care with overt recognition for quality and safety to underpin health care service delivery. In addition to systems-wide focus, the importance of supporting and mentoring people delivering the care has also been recognised. This can be achieved through quality clinical supervision. In 2010, Country Health South Australia Local Health Network developed a holistic allied health clinical governance structure, which was implemented in 2011. This research reports on emergent findings from the evaluation of the clinical governance structure, which included mandating clinical supervision for all allied health staff. A mixed method approach was chosen with evaluation of the impact of clinical supervision undertaken by a psychometrically sound instrument (Manchester Clinical Supervision Scale 26-item version), collected through an anonymous online survey and qualitative data collected through semistructured interviews and focus groups. Overall, 189 allied health professionals responded to the survey. Survey responses indicated allied health professionals recognised the importance of and valued receiving clinical supervision (normative domain), had levels of trust and rapport with, and were supported by supervisors (restorative domain) and positively affected their delivery of care and improvement in skills (formative domain). Qualitative data identified enablers such as profession specific gains, improved opportunities and consistency for clinical supervision and barriers such as persistent organisational issues, lack of clarity (delineation of roles) and communication issues. The findings from this research highlight that while clinical supervision has an important role to play, it is not a panacea for all the ills of the health care system. © 2015 National Rural Health Alliance Inc.

Comparative study on serum levels of macro and trace elements in schizophrenia based on supervised learning methods.

PubMed

Lin, Tong; Liu, Tiebing; Lin, Yucheng; Yan, Lailai; Chen, Zhongxue; Wang, Jingyu

2017-09-01

The etiology and pathophysiology of schizophrenia (SCZ) remain obscure. This study explored the associations between SCZ risk and serum levels of 39 macro and trace elements (MTE). A 1:1 matched case-control study was conducted among 114 schizophrenia patients and 114 healthy controls matched by age, sex and region. Blood samples were collected to determine the concentrations of 39 MTE by ICP-AES and ICP-MS. Both supervised learning methods and classical statistical testing were used to uncover the difference of MTE levels between cases and controls. The best prediction accuracies were 99.21% achieved by support vector machines in the original feature space (without dimensionality reduction), and 98.82% achieved by Naive Bayes with dimensionality reduction. More than half of MTE were found to be significantly different between SCZ patients and the controls. The presented investigation showed that there existed remarkable differences in concentrations of MTE between SCZ patients and healthy controls. The results of this study might be useful to diagnosis and prognosis of SCZ; they also indicated other promising applications in pharmacy and nutrition. However, the results should be interpreted with caution due to limited sample size and the lack of potential confounding factors, such as alcohol, smoking, body mass index (BMI), use of antipsychotics and dietary intakes. In the future the application of the analyses will be useful in designs that have larger sample sizes. Copyright © 2017 Elsevier GmbH. All rights reserved.

[A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine].

PubMed

Li, Lin; Xu, Shuo; An, Xin; Zhang, Lu-Da

2011-10-01

In near infrared spectral quantitative analysis, the precision of measured samples' chemical values is the theoretical limit of those of quantitative analysis with mathematical models. However, the number of samples that can obtain accurately their chemical values is few. Many models exclude the amount of samples without chemical values, and consider only these samples with chemical values when modeling sample compositions' contents. To address this problem, a semi-supervised LS-SVR (S2 LS-SVR) model is proposed on the basis of LS-SVR, which can utilize samples without chemical values as well as those with chemical values. Similar to the LS-SVR, to train this model is equivalent to solving a linear system. Finally, the samples of flue-cured tobacco were taken as experimental material, and corresponding quantitative analysis models were constructed for four sample compositions' content(total sugar, reducing sugar, total nitrogen and nicotine) with PLS regression, LS-SVR and S2 LS-SVR. For the S2 LS-SVR model, the average relative errors between actual values and predicted ones for the four sample compositions' contents are 6.62%, 7.56%, 6.11% and 8.20%, respectively, and the correlation coefficients are 0.974 1, 0.973 3, 0.923 0 and 0.948 6, respectively. Experimental results show the S2 LS-SVR model outperforms the other two, which verifies the feasibility and efficiency of the S2 LS-SVR model.

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice.

PubMed

Lim, Dong Kyu; Long, Nguyen Phuoc; Mo, Changyeun; Dong, Ziyuan; Cui, Lingmei; Kim, Giyoung; Kwon, Sung Won

2017-10-01

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Marine Fire Support Team as a Model for Distributed Operations Analysis: Review of Prior Analyses, Summary of Ongoing Research, and Recommendations for Future Work

DTIC Science & Technology

2008-07-17

without excessive procrastination ; to work independently and accomplish tasks without constant supervision; to take personal responsibility for completing...difficult tasks without excessive procrastination ; to work independently and accomplish tasks without constant supervision; to take personal...tasks without excessive procrastination ; to work independently and accomplish tasks without constant supervision; to take personal responsibility for

Is it possible to strengthen psychiatric nursing staff's clinical supervision? RCT of a meta-supervision intervention.

PubMed

Gonge, Henrik; Buus, Niels

2015-04-01

To test the effects of a meta-supervision intervention in terms of participation, effectiveness and benefits of clinical supervision of psychiatric nursing staff. Clinical supervision is regarded as a central component in developing mental health nursing practices, but the evidence supporting positive outcomes of clinical supervision in psychiatric nursing is not convincing. The study was designed as a randomized controlled trial. All permanently employed nursing staff members at three general psychiatric wards at a Danish university hospital (n = 83) were allocated to either an intervention group (n = 40) receiving the meta-supervision in addition to attending usual supervision or to a control group (n = 43) attending usual supervision. Self-reported questionnaire measures of clinical supervision effectiveness and benefits were collected at base line in January 2012 and at follow-up completed in February 2013. In addition, a prospective registration of clinical supervision participation was carried out over 3 months subsequent to the intervention. The main result was that it was possible to motivate staff in the intervention group to participate significantly more frequently in sessions of the ongoing supervision compared with the control group. However, more frequent participation was not reflected in the experienced effectiveness of the clinical supervision or in the general formative or restorative benefits. The intervention had a positive effect on individuals or wards already actively engaged in clinical supervision, which suggested that individuals and wards without well-established supervision practices may require more comprehensive interventions targeting individual and organizational barriers to clinical supervision. © 2014 John Wiley & Sons Ltd.

A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data.

PubMed

Stephens, David; Diesing, Markus

2014-01-01

Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for some means of feature selection.

A Semi-supervised Heat Kernel Pagerank MBO Algorithm for Data Classification

DTIC Science & Technology

2016-07-01

financial predictions, etc. and is finding growing use in text mining studies. In this paper, we present an efficient algorithm for classification of high...video data, set of images, hyperspectral data, medical data, text data, etc. Moreover, the framework provides a way to analyze data whose different...also be incorporated. For text classification, one can use tfidf (term frequency inverse document frequency) to form feature vectors for each document

Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data

NASA Astrophysics Data System (ADS)

Samsudin, Sarah Hanim; Shafri, Helmi Z. M.; Hamedianfar, Alireza

2016-04-01

Status observations of roofing material degradation are constantly evolving due to urban feature heterogeneities. Although advanced classification techniques have been introduced to improve within-class impervious surface classifications, these techniques involve complex processing and high computation times. This study integrates field spectroscopy and satellite multispectral remote sensing data to generate degradation status maps of concrete and metal roofing materials. Field spectroscopy data were used as bases for selecting suitable bands for spectral index development because of the limited number of multispectral bands. Mapping methods for roof degradation status were established for metal and concrete roofing materials by developing the normalized difference concrete condition index (NDCCI) and the normalized difference metal condition index (NDMCI). Results indicate that the accuracies achieved using the spectral indices are higher than those obtained using supervised pixel-based classification. The NDCCI generated an accuracy of 84.44%, whereas the support vector machine (SVM) approach yielded an accuracy of 73.06%. The NDMCI obtained an accuracy of 94.17% compared with 62.5% for the SVM approach. These findings support the suitability of the developed spectral index methods for determining roof degradation statuses from satellite observations in heterogeneous urban environments.

A machine learning system to improve heart failure patient assistance.

PubMed

Guidi, Gabriele; Pettenati, Maria Chiara; Melillo, Paolo; Iadanza, Ernesto

2014-11-01

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning.

PubMed

Vamsikrishna, K M; Dogra, Debi Prosad; Desarkar, Maunendra Sankar

2016-05-01

Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts.

How supportive supervision influences immunization session site practices: a quasi-experimental study in Odisha, India.

PubMed

Panda, Bhuputra; Pati, Sanghamitra; Nallala, Srinivas; Chauhan, Abhimanyu S; Anasuya, Anita; Som, Meena; Zodpey, Sanjay

2015-01-01

Routine immunization (RI) is a key child survival intervention. Ensuring acceptable standards of RI service delivery is critical for optimal outcomes. Accumulated evidences suggest that 'supportive supervision' improves the quality of health care services in general. During 2009-2010, the Government of Odisha and UNICEF jointly piloted this strategy in four districts to improve RI program outcomes. The present study aims to assess the effect of this strategy on improvement of skills and practices at immunization session sites. A quasi-experimental 'post-test only' study design was adopted to compare the opinion and practices of frontline health workers and their supervisors in four intervention districts (IDs) with two control districts (CDs). Altogether, we interviewed 111 supervisor-supervisee (health worker) pairs using semi-structured interview schedules and case vignettes. We also directly observed health workers' practices during immunization sessions at 111 sites. Data were analyzed with SPSS version 16.0. The mean knowledge score of supervisors in CDs was significantly higher than in intervention groups. Variegated responses were obtained on case vignettes. The control group performed better in solving certain hypothetically asked problems, whereas the intervention group scored better in others. Health workers in IDs gave a lower rating to their respective supervisors' knowledge, skill, and frequency of supervision. Logistics and vaccine availability were better in CDs. Notwithstanding other limitations, supportive supervision may not have independent effects on improving the quality of immunization services. Addressing systemic issues, such as the availability of essential logistics, supply chain management, timely indenting, and financial resources, could complement the supportive supervision strategy in improving immunization service delivery.

The Need for Data-Informed Clinical Supervision in Substance Use Disorder Treatment

PubMed Central

Ramsey, Alex T.; Baumann, Ana; Silver Wolf, David Patterson; Yan, Yan; Cooper, Ben; Proctor, Enola

2017-01-01

Background Effective clinical supervision is necessary for high-quality care in community-based substance use disorder treatment settings, yet little is known about current supervision practices. Some evidence suggests that supervisors and counselors differ in their experiences of clinical supervision; however, the impact of this misalignment on supervision quality is unclear. Clinical information monitoring systems may support supervision in substance use disorder treatment, but the potential use of these tools must first be explored. Aims First, this study examines the extent to which misaligned supervisor-counselor perceptions impact supervision satisfaction and emphasis on evidence-based treatments. This study also reports on formative work to develop a supervision-based clinical dashboard, an electronic information monitoring system and data visualization tool providing real-time clinical information to engage supervisors and counselors in a coordinated and data-informed manner, help align supervisor-counselor perceptions about supervision, and improve supervision effectiveness. Methods Clinical supervisors and frontline counselors (N=165) from five Midwestern agencies providing substance abuse services completed an online survey using Research Electronic Data Capture (REDCap) software, yielding a 75% response rate. Valid quantitative measures of supervision effectiveness were assessed, along with qualitative perceptions of a supervision-based clinical dashboard. Results Through within-dyad analyses, misalignment between supervisor and counselor perceptions of supervision practices was negatively associated with satisfaction of supervision and reported frequency of discussing several important clinical supervision topics, including evidence-based treatments and client rapport. Participants indicated the most useful clinical dashboard functions and reported important benefits and challenges to using the proposed tool. Discussion Clinical supervision tends to be largely an informal and unstructured process in substance abuse treatment, which may compromise the quality of care. Clinical dashboards may be a well-targeted approach to facilitate data-informed clinical supervision in community-based treatment agencies. PMID:28166480

The need for data-informed clinical supervision in substance use disorder treatment.

PubMed

Ramsey, Alex T; Baumann, Ana; Patterson Silver Wolf, David; Yan, Yan; Cooper, Ben; Proctor, Enola

2017-01-01

Effective clinical supervision is necessary for high-quality care in community-based substance use disorder treatment settings, yet little is known about current supervision practices. Some evidence suggests that supervisors and counselors differ in their experiences of clinical supervision; however, the impact of this misalignment on supervision quality is unclear. Clinical information monitoring systems may support supervision in substance use disorder treatment, but the potential use of these tools must first be explored. First, the current study examines the extent to which misaligned supervisor-counselor perceptions impact supervision satisfaction and emphasis on evidence-based treatments. This study also reports on formative work to develop a supervision-based clinical dashboard, an electronic information monitoring system and data visualization tool providing real-time clinical information to engage supervisors and counselors in a coordinated and data-informed manner, help align supervisor-counselor perceptions about supervision, and improve supervision effectiveness. Clinical supervisors and frontline counselors (N = 165) from five Midwestern agencies providing substance abuse services completed an online survey using Research Electronic Data Capture software, yielding a 75% response rate. Valid quantitative measures of supervision effectiveness were administered, along with qualitative perceptions of a supervision-based clinical dashboard. Through within-dyad analyses, misalignment between supervisor and counselor perceptions of supervision practices was negatively associated with satisfaction of supervision and reported frequency of discussing several important clinical supervision topics, including evidence-based treatments and client rapport. Participants indicated the most useful clinical dashboard functions and reported important benefits and challenges to using the proposed tool. Clinical supervision tends to be largely an informal and unstructured process in substance abuse treatment, which may compromise the quality of care. Clinical dashboards may be a well-targeted approach to facilitate data-informed clinical supervision in community-based treatment agencies.

A Model for Art Therapy-Based Supervision for End-of-Life Care Workers in Hong Kong.

PubMed

Potash, Jordan S; Chan, Faye; Ho, Andy H Y; Wang, Xiao Lu; Cheng, Carol

2015-01-01

End-of-life care workers and volunteers are particularly prone to burnout given the intense emotional and existential nature of their work. Supervision is one important way to provide adequate support that focuses on both professional and personal competencies. The inclusion of art therapy principles and practices within supervision further creates a dynamic platform for sustained self-reflection. A 6-week art therapy-based supervision group provided opportunities for developing emotional awareness, recognizing professional strengths, securing collegial relationships, and reflecting on death-related memories. The structure, rationale, and feedback are discussed.

Abusive supervision and subordinates' organization deviance.

PubMed

Tepper, Bennett J; Henle, Christine A; Lambert, Lisa Schurer; Giacalone, Robert A; Duffy, Michelle K

2008-07-01

The authors developed an integrated model of the relationships among abusive supervision, affective organizational commitment, norms toward organization deviance, and organization deviance and tested the framework in 2 studies: a 2-wave investigation of 243 supervised employees and a cross-sectional study of 247 employees organized into 68 work groups. Path analytic tests of mediated moderation provide support for the prediction that the mediated effect of abusive supervision on organization deviance (through affective commitment) is stronger when employees perceive that their coworkers are more approving of organization deviance (Study 1) and when coworkers perform more acts of organization deviance (Study 2).

The Role of Supervised Driving Requirements in Graduated Driver Licensing Programs

DOT National Transportation Integrated Search

2012-03-01

Many States require parents to certify that their teens have completed a certain amount of supervised driving practice, usually 40 to 50 hours, : before they are permitted to obtain an intermediate license. Although strongly supported by numerous gro...

Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

NASA Astrophysics Data System (ADS)

Heleno, Sandra; Matias, Magda; Pina, Pedro; Sousa, António Jorge

2016-04-01

A method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a support vector machine classifier and is tested using a GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20 February 2010), and a pre-event lidar digital terrain model. The testing is developed in a 15 km2 wide study area, where 95 % of the number of landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area, with commission errors below 26 % and omission errors below 24 %. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.

Automated Non-Alphanumeric Symbol Resolution in Clinical Texts

PubMed Central

Moon, SungRim; Pakhomov, Serguei; Ryan, James; Melton, Genevieve B.

2011-01-01

Although clinical texts contain many symbols, relatively little attention has been given to symbol resolution by medical natural language processing (NLP) researchers. Interpreting the meaning of symbols may be viewed as a special case of Word Sense Disambiguation (WSD). One thousand instances of four common non-alphanumeric symbols (‘+’, ‘–’, ‘/’, and ‘#’) were randomly extracted from a clinical document repository and annotated by experts. The symbols and their surrounding context, in addition to bag-of-Words (BoW), and heuristic rules were evaluated as features for the following classifiers: Naïve Bayes, Support Vector Machine, and Decision Tree, using 10-fold cross-validation. Accuracies for ‘+’, ‘–’, ‘/’, and ‘#’ were 80.11%, 80.22%, 90.44%, and 95.00% respectively, with Naïve Bayes. While symbol context contributed the most, BoW was also helpful for disambiguation of some symbols. Symbol disambiguation with supervised techniques can be implemented with reasonable accuracy as a module for medical NLP systems. PMID:22195157

Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multi-elemental analysis and chemometrics tools.

PubMed

Canizo, Brenda V; Escudero, Leticia B; Pérez, María B; Pellerano, Roberto G; Wuilloud, Rodolfo G

2018-03-01

The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Once the analytical data were collected, supervised pattern recognition techniques such as linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to construct classification/discrimination rules. The results indicated that nonlinear methods, RF and SVM, perform best with up to 98% and 93% accuracy rate, respectively, and therefore are excellent tools for classification of grapes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.

PubMed

Zhu, Guohun; Li, Yan; Wen, Peng Paul; Wang, Shuaifang

2015-01-01

Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

Naive scoring of human sleep based on a hidden Markov model of the electroencephalogram.

PubMed

Yaghouby, Farid; Modur, Pradeep; Sunderam, Sridhar

2014-01-01

Clinical sleep scoring involves tedious visual review of overnight polysomnograms by a human expert. Many attempts have been made to automate the process by training computer algorithms such as support vector machines and hidden Markov models (HMMs) to replicate human scoring. Such supervised classifiers are typically trained on scored data and then validated on scored out-of-sample data. Here we describe a methodology based on HMMs for scoring an overnight sleep recording without the benefit of a trained initial model. The number of states in the data is not known a priori and is optimized using a Bayes information criterion. When tested on a 22-subject database, this unsupervised classifier agreed well with human scores (mean of Cohen's kappa > 0.7). The HMM also outperformed other unsupervised classifiers (Gaussian mixture models, k-means, and linkage trees), that are capable of naive classification but do not model dynamics, by a significant margin (p < 0.05).

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Temporal assessment on land use land cover of Somalia after the effect of the civil war using remote sensing

NASA Astrophysics Data System (ADS)

Abdulle, Abdinur; Tan, Adhwa Amir; Pradhan, Biswajeet; Abdullahi, Saleh

2016-06-01

The aim of this study is to analyse land use and cover changes for the studied area during 1992-2015 and particularly evaluate the effect of civil war on these changes. Three Landsat images were used; Landsat 4 (1992), Landsat 7 (2000) and Landsat 8 (2015). Assessment of changes has been applied through three supervised classification algorithms, support vector machine, minimum classifier, and mahalanobis classifier. The result shows that SVM is providing highest overall accuracy of 98.5% for the years 2000 and 2015 with kappa coefficient of 0.9803 in year 2015. The change detection result show that the higher changes is between year 1992-2000 where vegetation land cover has dropped down to 11.1% and undeveloped area has increased to 11.4%. Whereas for year 2000-2015, higher changes belongs to build up area by 3.30% while undeveloped area and vegetation land cover keep decreasing by 2.64% and 1.93% respectively.

Predictive analysis and data mining among the employment of fresh graduate students in HEI

NASA Astrophysics Data System (ADS)

Rahman, Nor Azziaty Abdul; Tan, Kian Lam; Lim, Chen Kim

2017-10-01

Management of higher education have a problem in producing 100% of graduates who can meet the needs of industry while industry is also facing the problem of finding skilled graduates who suit their needs partly due to the lack of an effective method in assessing problem solving skills as well as weaknesses in the assessment of problem-solving skills. The purpose of this paper is to propose a suitable classification model that can be used in making prediction and assessment of the attributes of the student's dataset to meet the selection criteria of work demanded by the industry of the graduates in the academic field. Supervised and unsupervised Machine Learning Algorithms were used in this research where; K-Nearest Neighbor, Naïve Bayes, Decision Tree, Neural Network, Logistic Regression and Support Vector Machine. The proposed model will help the university management to make a better long-term plans for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well.

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter

NASA Astrophysics Data System (ADS)

Asniar; Aditya, B. R.

2017-01-01

Sentiment analysis is the process of understanding, extracting, and processing the textual data automatically to obtain information. Sentiment analysis can be used to see opinion on an issue and identify a response to something. Millions of digital data are still not used to be able to provide any information that has usefulness, especially for government. Sentiment analysis in government is used to monitor the work programs of the government such as the Government of Bandung City through social media data. The analysis can be used quickly as a tool to see the public response to the work programs, so the next strategic steps can be taken. This paper adopts Support Vector Machine as a supervised algorithm for sentiment analysis. It presents a framework for sentiment analysis implementation of Indonesian language tweet on twitter for Work Programs of Government of Bandung City. The results of this paper can be a reference for decision making in local government.

Classification Algorithms for Big Data Analysis, a Map Reduce Approach

NASA Astrophysics Data System (ADS)

Ayma, V. A.; Ferreira, R. S.; Happ, P.; Oliveira, D.; Feitosa, R.; Costa, G.; Plaza, A.; Gamba, P.

2015-03-01

Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

Using Coaching to Provide Ongoing Support and Supervision to Out-of-School Time Staff. Part 3 in a Series on Implementing Evidence-Based Practices in Out-of-School Time Programs: The Role of Frontline Staff. Research-to-Results Brief. Publication #2009-06

ERIC Educational Resources Information Center

Burkhauser, Mary; Metz, Allison J. R.

2009-01-01

Although skills needed by out-of-school time practitioners can be introduced during training, many skills can only really be learned on the job with ongoing support and supervision provided by a "coach." Research from both the education and out-of-school time fields supports the value of staff coaching as a professional development tool, and staff…

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

PubMed

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

Supportive supervision for volunteers to deliver reproductive health education: a cluster randomized trial.

PubMed

Singh, Debra; Negin, Joel; Orach, Christopher Garimoi; Cumming, Robert

2016-10-03

Community Health Volunteers (CHVs) can be effective in improving pregnancy and newborn outcomes through community education. Inadequate supervision of CHVs, whether due to poor planning, irregular visits, or ineffective supervisory methods, is, however, recognized as a weakness in many programs. There has been little research on best practice supervisory or accompaniment models. From March 2014 to February 2015 a proof of concept study was conducted to compare training alone versus training and supportive supervision by paid CHWs (n = 4) on the effectiveness of CHVs (n = 82) to deliver education about pregnancy, newborn care, family planning and hygiene. The pair-matched cluster randomized trial was conducted in eight villages (four intervention and four control) in Budondo sub-county in Jinja, Uganda. Increases in desired behaviors were seen in both the intervention and control arms over the study period. Both arms showed high retention rates of CHVs (95 %). At 1 year follow-up there was a significantly higher prevalence of installed and functioning tippy taps for hand washing (p < 0.002) in the intervention villages (47 %) than control villages (35 %). All outcome and process measures related to home-visits to homes with pregnant women and newborn babies favored the intervention villages. The CHVs in both groups implemented what they learnt and were role models in the community. A team of CHVs and CHWs can facilitate families accessing reproductive health care by addressing cultural norms and scientific misconceptions. Having a team of 2 CHWs to 40 CHVs enables close to community access to information, conversation and services. Supportive supervision involves creating a non-threatening, empowering environment in which both the CHV and the supervising CHW learn together and overcome obstacles that might otherwise demotivate the CHV. While the results seem promising for added value with supportive supervision for CHVs undertaking reproductive health activities, further research on a larger scale will be needed to substantiate the effect.

Guidelines for the training, credentialing, use, and supervision of speech-language pathology assistants. Task Force on Support Personnel.

PubMed

1996-01-01

These guidelines are an official statement of the American Speech-Language-Hearing Association. They provide guidance on the training, credentialing, use, and supervision of one category of support personnel in speech-language pathology: speech-language pathology assistants. Guidelines are not official standards of the Association. They were developed by the Task Force on Support Personnel: Dennis J. Arnst, Kenneth D. Barker, Ann Olsen Bird, Sheila Bridges, Linda S. DeYoung, Katherine Formichella, Nena M. Germany, Gilbert C. Hanke, Ann M. Horton, DeAnne M. Owre, Sidney L. Ramsey, Cathy A. Runnels, Brenda Terrell, Gerry W. Werven, Denise West, Patricia A. Mercaitis (consultant), Lisa C. O'Connor (consultant), Frederick T. Spahr (coordinator), Diane Paul-Brown (associate coordinator), Ann L. Carey (Executive Board liaison). The 1994 guidelines supersede the 1981 guidelines entitled, "Guidelines for the Employment and Utilization of Supportive Personnel" (Asha, March 1981, 165-169). Refer to the 1995 position statement on the "Training, Credentialing, Use, and Supervision of Support Personnel in Speech-Language Pathology" (Asha, 37 [Suppl. 14], 21).

CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

Mapping landslide source and transport areas in VHR images with Object-Based Analysis and Support Vector Machines

NASA Astrophysics Data System (ADS)

Heleno, Sandra; Matias, Magda; Pina, Pedro

2015-04-01

Visual interpretation of satellite imagery remains extremely demanding in terms of resources and time, especially when dealing with numerous multi-scale landslides affecting wide areas, such as is the case of rainfall-induced shallow landslides. Applying automated methods can contribute to more efficient landslide mapping and updating of existing inventories, and in recent years the number and variety of approaches is rapidly increasing. Very High Resolution (VHR) images, acquired by space-borne sensors with sub-metric precision, such as Ikonos, Quickbird, Geoeye and Worldview, are increasingly being considered as the best option for landslide mapping, but these new levels of spatial detail also present new challenges to state of the art image analysis tools, asking for automated methods specifically suited to map landslide events on VHR optical images. In this work we develop and test a methodology for semi-automatic landslide recognition and mapping of landslide source and transport areas. The method combines object-based image analysis and a Support Vector Machine supervised learning algorithm, and was tested using a GeoEye-1 multispectral image, sensed 3 days after a damaging landslide event in Madeira Island, together with a pre-event LiDAR DEM. Our approach has proved successful in the recognition of landslides on a 15 Km2-wide study area, with 81 out of 85 landslides detected in its validation regions. The classifier also showed reasonable performance (false positive rate 60% and false positive rate below 36% in both validation regions) in the internal mapping of landslide source and transport areas, in particular in the sunnier east-facing slopes. In the less illuminated areas the classifier is still able to accurately map the source areas, but performs poorly in the mapping of landslide transport areas.

Data-driven mapping of the potential mountain permafrost distribution.

PubMed

Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

2017-07-15

Existing mountain permafrost distribution models generally offer a good overview of the potential extent of this phenomenon at a regional scale. They are however not always able to reproduce the high spatial discontinuity of permafrost at the micro-scale (scale of a specific landform; ten to several hundreds of meters). To overcome this lack, we tested an alternative modelling approach using three classification algorithms belonging to statistics and machine learning: Logistic regression, Support Vector Machines and Random forests. These supervised learning techniques infer a classification function from labelled training data (pixels of permafrost absence and presence) with the aim of predicting the permafrost occurrence where it is unknown. The research was carried out in a 588km 2 area of the Western Swiss Alps. Permafrost evidences were mapped from ortho-image interpretation (rock glacier inventorying) and field data (mainly geoelectrical and thermal data). The relationship between selected permafrost evidences and permafrost controlling factors was computed with the mentioned techniques. Classification performances, assessed with AUROC, range between 0.81 for Logistic regression, 0.85 with Support Vector Machines and 0.88 with Random forests. The adopted machine learning algorithms have demonstrated to be efficient for permafrost distribution modelling thanks to consistent results compared to the field reality. The high resolution of the input dataset (10m) allows elaborating maps at the micro-scale with a modelled permafrost spatial distribution less optimistic than classic spatial models. Moreover, the probability output of adopted algorithms offers a more precise overview of the potential distribution of mountain permafrost than proposing simple indexes of the permafrost favorability. These encouraging results also open the way to new possibilities of permafrost data analysis and mapping. Copyright © 2017 Elsevier B.V. All rights reserved.

Innovation in supervision and support of community health workers for better newborn survival in southern Tanzania.

PubMed

Mkumbo, Elibariki; Hanson, Claudia; Penfold, Suzanne; Manzi, Fatuma; Schellenberg, Joanna

2014-12-01

Home visits by community health workers may help to improve newborn survival, but sustained high-quality supervision of community volunteers is challenging. To compare facility-led and community-linked supervision approaches of 824 community health volunteers working to improve newborn care in Southern Tanzania. Using a before-after design, we compared 6 months of supervision reports from each approach. During the community-linked approach over 50 times more supervision contacts were recorded than during the facility-only supervision approach (1.04 contacts per volunteer per month vs 0.02), and the volunteer-supervisor ratio reduced from 7.8 to 1.6. Involving community leaders has the potential to improve supervision of community health volunteers. ClinicalTrials.gov Identifier: NCT01022788; http://clinicaltrials.gov/ct2/show/NCT01022788?term=INSIST&rank=1. © The Author 2014. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Interprofessional supervision in an intercultural context: a qualitative study.

PubMed

Chipchase, Lucy; Allen, Shelley; Eley, Diann; McAllister, Lindy; Strong, Jenny

2012-11-01

Our understanding of the qualities and value of clinical supervision is based on uniprofessional clinical education models. There is little research regarding the role and qualities needed in the supervisor role for supporting interprofessional placements. This paper reports the views and perceptions of medical and allied heath students and supervisors on the characteristics of clinical supervision in an interprofessional, international context. A qualitative case study was used involving semi-structured interviews of eight health professional students and four clinical supervisors before and after an interprofessional, international clinical placement. Our findings suggest that supervision from educators whose profession differs from that of the students can be a beneficial and rewarding experience leading to the use of alternative learning strategies. Although all participants valued interprofessional supervision, there was agreement that profession-specific supervision was required throughout the placement. Further research is required to understand this view as interprofessional education aims to prepare graduates for collaborative practice where they may work in teams supervised by staff whose profession may differ from their own.

The crossover of psychological distress from leaders to subordinates in teams: The role of abusive supervision, psychological capital, and team performance.

PubMed

Li, Yuhui; Wang, Zhen; Yang, Liu-Qin; Liu, Songbo

2016-04-01

This study examines the underlying mechanism of the crossover process in work teams. Drawing on conservation of resources theory, we hypothesize that a leader's psychological distress positively influences subordinates' psychological distress through abusive supervision. We further hypothesize that team performance attenuates the association between a leader's psychological distress and abusive supervision. In addition, we expect that psychological capital attenuates the positive relationship between abusive supervision and subordinates' psychological distress. Participants were drawn from 86 business teams, and multisource data were collected. The hypotheses were tested with multilevel analysis. Results supported the crossover of psychological distress from leader to subordinates, and abusive supervision serves as a mediating mechanism. The positive relationship between a leader's distress and abusive supervision is stronger when team performance is lower. In addition, the positive relationship between abusive supervision and subordinates' psychological distress is stronger when subordinates' psychological capital is lower. (c) 2016 APA, all rights reserved).

District health managers' perceptions of supervision in Malawi and Tanzania.

PubMed

Bradley, Susan; Kamwendo, Francis; Masanja, Honorati; de Pinho, Helen; Waxman, Rachel; Boostrom, Camille; McAuliffe, Eilish

2013-09-05

Mid-level cadres are being used to address human resource shortages in many African contexts, but insufficient and ineffective human resource management is compromising their performance. Supervision plays a key role in performance and motivation, but is frequently characterised by periodic inspection and control, rather than support and feedback to improve performance. This paper explores the perceptions of district health management teams in Tanzania and Malawi on their role as supervisors and on the challenges to effective supervision at the district level. This qualitative study took place as part of a broader project, "Health Systems Strengthening for Equity: The Power and Potential of Mid-Level Providers". Semi-structured interviews were conducted with 20 district health management team personnel in Malawi and 37 council health team members in Tanzania. The interviews covered a range of human resource management issues, including supervision and performance assessment, staff job descriptions and roles, motivation and working conditions. Participants displayed varying attitudes to the nature and purpose of the supervision process. Much of the discourse in Malawi centred on inspection and control, while interviewees in Tanzania were more likely to articulate a paradigm characterised by support and improvement. In both countries, facility level performance metrics dominated. The lack of competency-based indicators or clear standards to assess individual health worker performance were considered problematic. Shortages of staff, at both district and facility level, were described as a major impediment to carrying out regular supervisory visits. Other challenges included conflicting and multiple responsibilities of district health team staff and financial constraints. Supervision is a central component of effective human resource management. Policy level attention is crucial to ensure a systematic, structured process that is based on common understandings of the role and purpose of supervision. This is particularly important in a context where the majority of staff are mid-level cadres for whom regulation and guidelines may not be as formalised or well-developed as for traditional cadres, such as registered nurses and medical doctors. Supervision needs to be adequately resourced and supported in order to improve performance and retention at the district level.

District health managers’ perceptions of supervision in Malawi and Tanzania

PubMed Central

2013-01-01

Background Mid-level cadres are being used to address human resource shortages in many African contexts, but insufficient and ineffective human resource management is compromising their performance. Supervision plays a key role in performance and motivation, but is frequently characterised by periodic inspection and control, rather than support and feedback to improve performance. This paper explores the perceptions of district health management teams in Tanzania and Malawi on their role as supervisors and on the challenges to effective supervision at the district level. Methods This qualitative study took place as part of a broader project, “Health Systems Strengthening for Equity: The Power and Potential of Mid-Level Providers”. Semi-structured interviews were conducted with 20 district health management team personnel in Malawi and 37 council health team members in Tanzania. The interviews covered a range of human resource management issues, including supervision and performance assessment, staff job descriptions and roles, motivation and working conditions. Results Participants displayed varying attitudes to the nature and purpose of the supervision process. Much of the discourse in Malawi centred on inspection and control, while interviewees in Tanzania were more likely to articulate a paradigm characterised by support and improvement. In both countries, facility level performance metrics dominated. The lack of competency-based indicators or clear standards to assess individual health worker performance were considered problematic. Shortages of staff, at both district and facility level, were described as a major impediment to carrying out regular supervisory visits. Other challenges included conflicting and multiple responsibilities of district health team staff and financial constraints. Conclusion Supervision is a central component of effective human resource management. Policy level attention is crucial to ensure a systematic, structured process that is based on common understandings of the role and purpose of supervision. This is particularly important in a context where the majority of staff are mid-level cadres for whom regulation and guidelines may not be as formalised or well-developed as for traditional cadres, such as registered nurses and medical doctors. Supervision needs to be adequately resourced and supported in order to improve performance and retention at the district level. PMID:24007354

Evidence-based Practices Addressed in Community-based Children’s Mental Health Clinical Supervision

PubMed Central

Accurso, Erin C.; Taylor, Robin M.; Garland, Ann F.

2013-01-01

Context Clinical supervision is the principal method of training for psychotherapeutic practice, however there is virtually no research on supervision practice in community settings. Of particular interest is the role supervision might play in facilitating implementation of evidence-based (EB) care in routine care settings. Objective This study examines the format and functions of clinical supervision sessions in routine care, as well as the extent to which supervision addresses psychotherapeutic practice elements common to EB care for children with disruptive behavior problems, who represent the majority of patients served in publicly-funded routine care settings. Methods Supervisors (n=7) and supervisees (n=12) from four publicly-funded community-based child mental health clinics reported on 130 supervision sessions. Results Supervision sessions were primarily individual in-person meetings lasting one hour. The most common functions included case conceptualization and therapy interventions. Coverage of practice elements common to EB treatments was brief. Discussion Despite the fact that most children presenting to public mental health services are referred for disruptive behavior problems, supervision sessions are infrequently focused on practice elements consistent with EB treatments for this population. Supervision is a promising avenue through which training in EB practices could be supported to improve the quality of care for children in community-based “usual care” clinics. PMID:24761163

Exercise training for intermittent claudication.

PubMed

McDermott, Mary M

2017-11-01

The objective of this study was to provide an overview of evidence regarding exercise therapies for patients with lower extremity peripheral artery disease (PAD). This manuscript summarizes the content of a lecture delivered as part of the 2016 Crawford Critical Issues Symposium. Multiple randomized clinical trials demonstrate that supervised treadmill exercise significantly improves treadmill walking performance in people with PAD and intermittent claudication symptoms. A meta-analysis of 25 randomized trials demonstrated a 180-meter increase in treadmill walking distance in response to supervised exercise interventions compared with a nonexercising control group. Supervised treadmill exercise has been inaccessible to many patients with PAD because of lack of medical insurance coverage. However, in 2017, the Centers for Medicare and Medicaid Services issued a decision memorandum to support health insurance coverage of 12 weeks of supervised treadmill exercise for patients with walking impairment due to PAD. Recent evidence also supports home-based walking exercise to improve walking performance in people with PAD. Effective home-exercise programs incorporate behavioral change interventions such as a remote coach, goal setting, and self-monitoring. Supervised treadmill exercise programs preferentially improve treadmill walking performance, whereas home-based walking exercise programs preferentially improve corridor walking, such as the 6-minute walk test. Clinical trial evidence also supports arm or leg ergometry exercise to improve walking endurance in people with PAD. Treadmill walking exercise appears superior to resistance training alone for improving walking endurance. Supervised treadmill exercise significantly improves treadmill walking performance in people with PAD by approximately 180 meters compared with no exercise. Recent evidence suggests that home-based exercise is also effective and preferentially improves over-ground walking performance, such as the 6-minute walk test. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

The supervisory alliance: a half century of theory, practice, and research in critical perspective.

PubMed

Watkins, C Edward

2014-01-01

Over the course of psychotherapy supervision history, the supervisor-supervisee alliance has increasingly emerged as a variable of preeminent importance in the conceptualization and conduct of the supervision experience: It has come to be embraced as the very heart and soul of supervision. But after a half century, what evidence do we actually have to justify that highly favorable outlook afforded to the alliance? What do we really know about the supervisory alliance? What do we need to know about it? As we mark the first 50 years of supervisory alliance and look toward its future, I thought it might be useful to examine those questions and provide a current status report about the construct itself In what follows, I (a) describe the two supervisory alliance visions that have been (and remain) dominant in the supervision literature and (b) provide a review of 20 plus years of supervision alliance research. While the supervisory alliance has accumulated solid clinical support, its empirical support appears to be more tentative and less robust. I consider why that is so, identify some missing elements in the alliance research conducted thus far and propose possible remedies to move inquiry in this area forward.

Joint Sparse Recovery With Semisupervised MUSIC

NASA Astrophysics Data System (ADS)

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-01

Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a binary classification problem with respect to atoms. Meanwhile, MUSIC essentially constructs a supervised classifier based on the labeled MMVs so that its performance will heavily depend on the quality and quantity of these training samples. From this viewpoint, we develop a semisupervised MUSIC (SS-MUSIC) in the spirit of machine learning, which declares that the insufficient supervised information in the training samples can be compensated from those unlabeled atoms. Instead of constructing a classifier in a fully supervised manner, we iteratively refine a semisupervised classifier by exploiting the labeled MMVs and some reliable unlabeled atoms simultaneously. Through this way, the required conditions and iterations can be greatly relaxed and reduced. Numerical experimental results demonstrate that SS-MUSIC can achieve much better recovery performances than other MUSIC extended algorithms as well as some typical greedy algorithms for JSR in terms of iterations and recovery probability.

District nurses' experience of supervising nursing students in primary health care: A pre- and post-implementation questionnaire study.

PubMed

Bos, Elisabeth; Löfmark, Anna; Törnkvist, Lena

2009-11-01

Nursing students go through clinical supervision in primary health care settings but district nurses' (DNs) circumstances when supervising them are only briefly described in the literature. The aim of this study was to investigate DNs experience of supervising nursing students before and after the implementation of a new supervision model. Ninety-eight (74%) DNs answered a questionnaire before and 84 (65%) after implementation of the new supervision model. The study showed that DNs in most cases felt that conditions for supervision in the workplace were adequate. But about 70% lacked training for the supervisory role and 20% had no specialist district nurse training. They also experienced difficulty in keeping up-to-date with changes in nurse education programmes, in receiving support from the university and from their clinic managers, and in setting aside time for supervision. Improvements after the implementation of a new model chiefly concerned organisation; more DNs stated that one person had primary responsibility for students' clinical practice, that information packages for supervisors and students were available at the health care centres, and that conditions were in place for increasing the number of students they supervised. DNs also stated that supervisors and students benefited from supervision by more than one supervisor. To conclude, implementation of a new supervision model resulted in some improvements.

Identifying images of handwritten digits using deep learning in H2O

NASA Astrophysics Data System (ADS)

Sadhasivam, Jayakumar; Charanya, R.; Kumar, S. Harish; Srinivasan, A.

2017-11-01

Automatic digit recognition is of popular interest today. Deep learning techniques make it possible for object recognition in image data. Perceiving the digit has turned into a fundamental part as far as certifiable applications. Since, digits are composed in various styles in this way to distinguish the digit it is important to perceive and arrange it with the assistance of machine learning methods. This exploration depends on supervised learning vector quantization neural system arranged under counterfeit artificial neural network. The pictures of digits are perceived, prepared and tried. After the system is made digits are prepared utilizing preparing dataset vectors and testing is connected to the pictures of digits which are separated to each other by fragmenting the picture and resizing the digit picture as needs be for better precision.

Supporting Placement Supervision in Clinical Exercise Physiology

ERIC Educational Resources Information Center

Sealey, Rebecca M.; Raymond, Jacqueline; Groeller, Herb; Rooney, Kieron; Crabb, Meagan; Watt, Kerrianne

2015-01-01

The continued engagement of the professional workforce as supervisors is critical for the sustainability and growth of work-integrated learning activities in university degrees. This study investigated factors that influence the willingness and ability of clinicians to continue to supervise clinical exercise physiology work-integrated learning…

Professional Learning: Lessons for Supervision from Doctoral Examining

ERIC Educational Resources Information Center

Wisker, Gina; Kiley, Margaret

2014-01-01

Most research into research supervision practice focuses on functional, collegial or problematic power-related experiences. Work developing the supervisory role concentrates on new supervisors, and on taught development and support programmes. Most literature on academics' professional learning concentrates on learning to be a university teacher…

Community-Based Correctional Education

ERIC Educational Resources Information Center

Office of Vocational and Adult Education, US Department of Education, 2011

2011-01-01

Although it is known that many persons under community supervision need and eventually want correctional education programs, little is known about the providers and characteristics of these educational programs. This report provides an overview of initiatives at the national and state levels supporting new approaches to community supervision and…

A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

"Kicked out into the real world": prostate cancer patients' experiences with transitioning from hospital-based supervised exercise to unsupervised exercise in the community.

PubMed

Schmidt, Mette L K; Østergren, Peter; Cormie, Prue; Ragle, Anne-Mette; Sønksen, Jens; Midtgaard, Julie

2018-06-21

Regular exercise is recommended to mitigate the adverse effects of androgen deprivation therapy in men with prostate cancer. The purpose of this study was to explore the experience of transition to unsupervised, community-based exercise among men who had participated in a hospital-based supervised exercise programme in order to propose components that supported transition to unsupervised exercise. Participants were selected by means of purposive, criteria-based sampling. Men undergoing androgen deprivation therapy who had completed a 12-week hospital-based, supervised, group exercise intervention were invited to participate. The programme involved aerobic and resistance training using machines and included a structured transition to a community-based fitness centre. Data were collected by means of semi-structured focus group interviews and analysed using thematic analysis. Five focus group interviews were conducted with a total of 29 men, of whom 25 reported to have continued to exercise at community-based facilities. Three thematic categories emerged: Development and practice of new skills; Establishing social relationships; and Familiarising with bodily well-being. These were combined into an overarching theme: From learning to doing. Components suggested to support transition were as follows: a structured transition involving supervised exercise sessions at a community-based facility; strategies to facilitate peer support; transferable tools including an individual exercise chart; and access to 'check-ups' by qualified exercise specialists. Hospital-based, supervised exercise provides a safe learning environment. Transferring to community-based exercise can be experienced as a confrontation with the real world and can be eased through securing a structured transition, having transferable tools, sustained peer support and monitoring.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

PubMed

Yao, Chen; Zhu, Xiaojin; Weigel, Kent A

2016-11-07

Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle. We describe a self-training model that is wrapped around a support vector machine (SVM) algorithm, which enables it to use data from animals with and without measured phenotypes. Initially, a SVM model was trained using data from 792 animals with measured RFI phenotypes. Then, the resulting SVM was used to generate self-trained phenotypes for 3000 animals for which RFI measurements were not available. Finally, the SVM model was re-trained using data from up to 3792 animals, including those with measured and self-trained RFI phenotypes. Incorporation of additional animals with self-trained phenotypes enhanced the accuracy of genomic predictions compared to that of predictions that were derived from the subset of animals with measured phenotypes. The optimal ratio of animals with self-trained phenotypes to animals with measured phenotypes (2.5, 2.0, and 1.8) and the maximum increase achieved in prediction accuracy measured as the correlation between predicted and actual RFI phenotypes (5.9, 4.1, and 2.4%) decreased as the size of the initial training set (300, 400, and 500 animals with measured phenotypes) increased. The optimal number of animals with self-trained phenotypes may be smaller when prediction accuracy is measured as the mean squared error rather than the correlation between predicted and actual RFI phenotypes. Our results demonstrate that semi-supervised learning models that incorporate self-trained phenotypes can achieve genomic prediction accuracies that are comparable to those obtained with models using larger training sets that include only animals with measured phenotypes. Semi-supervised learning can be helpful for genomic prediction of novel traits, such as RFI, for which the size of reference population is limited, in particular, when the animals to be predicted and the animals in the reference population originate from the same herd-environment.

Stress relief or practice development: varied reasons for attending clinical supervision.

PubMed

Koivu, Aija; Saarinen, Pirjo I; Hyrkas, Kristiina

2011-07-01

The aim of the present study was to explore the differences in the uptake of clinical supervision on the medical and surgical units of an acute hospital relating to the nurses' background and perceptions of their work and health. Considering the varied challenges of nursing in different specialities, the reasons for attending clinical supervision may also vary. In 2003, a survey on work and health issues was conducted in a Finnish university hospital with a 3-year follow-up of the uptake of clinical supervision by the respondents. The nurses who subsequently undertook clinical supervision and their peers who decided not to undertake it were compared in five medical (n=96) and nine surgical units (n=232). On the medical units, stress management motivated the uptake of clinical supervision, whereas on the surgical units, reasons relating to practice development predominated. The reasons for attending clinical supervision can be quite different depending on the basic tasks and organizational culture of the hospital unit. If clinical and managerial supervision are meant to support and complement each other, the nurse manager should be involved in discussions about the needs and goals of clinical supervision provided for staff. © 2011 The Authors. Journal compilation © 2011 Blackwell Publishing Ltd.

Robust support vector regression networks for function approximation with outliers.

PubMed

Chuang, Chen-Chia; Su, Shun-Feng; Jeng, Jin-Tsong; Hsiao, Chih-Ching

2002-01-01

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

The Relationship between the Supervision Role and Compassion Fatigue and Burnout in Genetic Counseling.

PubMed

Allsbrook, Katlin; Atzinger, Carrie; He, Hua; Engelhard, Chalee; Yager, Geoffrey; Wusik, Katie

2016-12-01

Many genetic counselors provide supervision to students during their career. Previous studies have shown genetic counselors, in general, are at increased risk for developing compassion fatigue. The purpose of this study was to determine if there was a difference in compassion fatigue and burnout levels in genetic counselors who currently supervise compared to genetic counselors who do not. Genetic counselors who currently practice in a clinical setting (N = 391) completed an online survey containing demographic questions, the Professional Quality of Life Scale, the State-Trait Anxiety Inventory, and questions specific to the genetic counselor's experiences with supervision. Overall, when controlling for trait-anxiety, the supervision role by itself was not independently associated with the risk for compassion fatigue and burnout among genetic counselors. Within supervisors, however, there were several factors which were associated with this risk. Those with less supervision experience reported more secondary traumatic stress. Those supervisors reporting less confidence had decreased compassion satisfaction. Those with less experience or less confidence in their supervision role were most likely to be at increased risk for developing compassion fatigue. Training in supervision and support for dealing with compassion fatigue and burnout may be beneficial to supervisors with less experience.

Plumbing and Piping: Construction, Supervision, and Inspection. Course of Study.

ERIC Educational Resources Information Center

O'Meara, Lester; Turley, John

This course of study on plumbing and piping is part of a construction, supervision, and inspection series, which provides instructional materials for community or junior college technical courses in the inspection program. Material covered in this volume pertains to: uniform plumbing code; pipes, fittings, supports, and connections; sewer and…

Magazine Picture Collage in Group Supervision

ERIC Educational Resources Information Center

Shepard, Blythe C.; Guenette, Francis L.

2010-01-01

A magazine picture collage activity was used with three female counsellor education students as a vehicle to support them in processing their experience as counsellors in training. The use of magazine picture collage in group supervision is described, and the benefits and challenges are presented. The collages served as jumping-off points for…

Reflective Communication: Cultivating Mindsight through Nurturing Relationships

ERIC Educational Resources Information Center

Siegel, Daniel J.; Shahmoon-Shanok, Rebecca

2010-01-01

This article integrates ideas about mindsight, developed by Daniel Siegel, with those of reflective supervision in the zero-to-three field. The authors explore how the flow of energy and information in the context of nurturing relationships through reflective supervision supports the capacity to develop mindsight. Mindsight is the ability to have…

Good Undergraduate Dissertation Supervision: Perspectives of Supervisors and Dissertation Coordinators

ERIC Educational Resources Information Center

Roberts, Lynne D.; Seaman, Kristen

2018-01-01

There is a paucity of research, training, and material available to support supervisors of undergraduate dissertation students. This article explores what "good" supervision might look like at this level. Interviews were conducted with eight new supervisors and six dissertation coordinators using a critical incident methodology. Thematic…

Investigating the LGBTQ Responsive Model for Supervision of Group Work

ERIC Educational Resources Information Center

Luke, Melissa; Goodrich, Kristopher M.

2013-01-01

This article reports an investigation of the LGBTQ Responsive Model for Supervision of Group Work, a trans-theoretical supervisory framework to address the needs of lesbian, gay, bisexual, transgender, and questioning (LGBTQ) persons (Goodrich & Luke, 2011). Findings partially supported applicability of the LGBTQ Responsive Model for Supervision…

Characteristics of the Research Supervision of Postgraduate Teachers' Action Research

ERIC Educational Resources Information Center

Cornelissen, Frank; van den Berg, Ellen

2014-01-01

Today, many institutions of higher education support students in conducting practice-oriented research. This research refers to a broad array of approaches geared toward practitioners' practice. The supervision of such research is of crucial importance, but little is known about its nature and characteristics. This study examined what research…

Training in Reflective Supervision: Building Relationships between Supervisors and Infant Mental Health Specialists

ERIC Educational Resources Information Center

Shea, Sarah E.; Goldberg, Sheryl

2016-01-01

This article describes a unique reflective supervision training series for community-based infant mental health (IMH) specialists and their supervisors that was designed to support the relational capacities of both supervisors and supervisees and to facilitate collaborative supervisory relationships. Qualitative evaluation results of the pilot…

Supervision Training, Practices, and Interests of California Site Supervisors

ERIC Educational Resources Information Center

Uellendahl, Gail E.; Tenenbaum, Maya N.

2015-01-01

In this descriptive study, the authors surveyed 220 California school counselor site supervisors of interns about supervision training, practices, and interests. Respondents overwhelmingly (71%) felt unprepared for this role and identified the need for more formal training and support. Results indicate a crucial leadership and advocacy role for…

An Evaluation of Videoconferencing as a Supportive Technology for Practicum Supervision

ERIC Educational Resources Information Center

Dymond, Stacy K.; Renzaglia, Adelle; Halle, James W.; Chadsey, Janis; Bentz, Johnell L.

2008-01-01

In this study, the authors determine the efficacy of videoconferencing to supervise pre-service special education teachers. Efficacy is determined by (a) assessing interobserver reliability between on-site and off-site observers and (b) evaluating the feasibility and practicality of the videoconferencing technology. Data are collected in two…

Improving stressful working lives: complementary therapies, counselling and clinical supervision for staff.

PubMed

Mackereth, Peter A; White, Keven; Cawthorn, Anne; Lynch, Barbara

2005-06-01

The aim of this paper is to briefly examine the contemporary phenomenon of "burnout" within oncology and palliative care. In discussing the suitable interventions to manage stress and avoid burnout, reference will be made to counselling and clinical supervision, but more substantially the paper will report on an innovative subsidised complementary therapy service for staff. The Government's Improving Working Lives Standard will be referred as an initiative that supports the development of supportive services for NHS staff.

Training Older Siblings to be Better Supervisors: An RCT Evaluating the "Safe Sibs" Program.

PubMed

Schell, Stacey L; Morrongiello, Barbara A; Pogrebtsova, Ekaterina

2015-09-01

This study evaluated a new online training program, Safe Sibs, aimed at improving supervision knowledge and behaviors of sibling supervisors. Participants included older children (7-11 years) and their younger siblings (2-5 years). A randomized controlled trial design was used, with older siblings randomly assigned to either an intervention or wait-list control group. Before and after either the intervention or wait-list period, older siblings completed measures of supervision knowledge and their supervision behaviors were unobtrusively observed when with their younger sibling. Compared with the control group, the intervention group showed significant improvements in supervision knowledge (child development, knowledge of effective supervision practices, injury beliefs, intervention-specific knowledge) and in some aspects of supervision behavior (frequency of proactive safety behaviors to prevent supervisee access to injury hazards). Although adult supervision is ideal, this new program can support older children to become more knowledgeable and improved supervisors of younger ones. © The Author 2015. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Enhancing the intrinsic work motivation of community nutrition educators: how supportive supervision and job design foster autonomy.

PubMed

Dickin, Katherine L; Dollahite, Jamie S; Habicht, Jean-Pierre

2011-01-01

Mixed-methods research investigated the work motivation of paraprofessional community nutrition educators (CNEs) delivering a long-running public health nutrition program. In interviews, CNEs (n = 9) emphasized "freedom," supportive supervision, and "making a difference" as key sources of motivation. Community nutrition educator surveys (n = 115) confirmed high levels of autonomy, which was associated with supervisors' delegation and support, CNE decision-making on scheduling and curricula, and job satisfaction. Supervisors (n = 32) rated CNEs' job design as having inherently motivating characteristics comparable to professional jobs. Supervisory strategies can complement job design to create structured, supportive contexts that maintain fidelity, while granting autonomy to paraprofessionals to enhance intrinsic work motivation.

From new vistas to life lines: psychologists' satisfaction with supervision and confidence in supervising.

PubMed

McMahon, Aisling; Errity, Darina

2014-01-01

This study aimed to provide the first detailed survey of Irish psychologists' supervision practices as well as to identify what is related to satisfaction with supervisory support and to confidence in providing supervision. An online survey was distributed nationwide to Irish psychologists. Participants were mostly clinical and counselling psychologists. Three-quarters of the participants constituted 51% of the total population of Irish health service psychologists, the remainder working in various non-health service settings. The results showed that most Irish psychologists attend supervision but at a low frequency, typically once monthly. One-third were dissatisfied with their supervision, greater satisfaction being related to having more frequent clinical supervision and having external individual clinical supervision. Having a safe and trustworthy relationship with supervisors was a dominant issue, and two-thirds of psychologists wanted separation of their clinical and line management supervision. Although 70% were supervisors, only 40% were confident in their supervisory skills and just 16% had formal supervisor training. Independent predictors of supervisory confidence were experience as a psychologist, having formal supervisor training, experience as a supervisor and confidence as a therapist. A novel finding was that longer experience of personal therapy was related to greater confidence as a supervisor. This study indicates the need for access to more frequent clinical supervision to be facilitated for psychologists and for there to be clear separation of line management and clinical supervision. It is also essential that more resources are put into training supervisors. While most psychologists are engaged in supervision, frequency of attendance is low, with more satisfied psychologists having more frequent supervision. Most psychologists want separation of their clinical and line management supervision and have a preference for external supervision, safe and trustworthy relationships with supervisors being their primary concern. Only 16% of psychologists had formal training in supervision but having such training significantly contributed to greater confidence as a supervisor, indicating an urgent need to provide more supervisor training for psychologists. Copyright © 2013 John Wiley & Sons, Ltd.

Community cooperatives and insecticide-treated materials for malaria control: a new experience in Latin America

PubMed Central

Kroeger, Axel; Aviñna, Ana; Ordoñnez-Gonzalez, José; Escandon, Celia

2002-01-01

Background and objectives Insecticide-treated materials (ITMs) are effective in substantially reducing the burden of malaria and other vector-borne diseases; but how can high coverage rates of ITMs be achieved and maintained? In south Mexico and on the Pacific and Atlantic coasts of Colombia 14 community-based cooperatives offering three different kinds of ITM services (sale of impregnation services; sale of impregnated nets; production of nets and sale of impregnated nets) were formed and supervised by a national health service (IMSS-SOLIDARIDAD, Mexico) and by an academic institution (the Colombian Institute of Tropical Medicine) along with local district health services. The objectives of this research were to analyse the processes and results of this approach and to identify the favourable and limiting factors. Methods The methods used for data collection and analysis were group discussions, individual and semi-structured interviews with users and non-users of ITMs, individual in-depth interviews with cooperative members and supervisors, checks of sales book and observation of impregnation services. Results Coverage with unimpregnated nets was above 50% in all study areas. The fastest increase of ITM coverage was achieved through the exclusive sale of impregnation services. Low-cost social marketing techniques were used to increase demand. The large-scale production of nets in two cooperatives was only possible with the aid of an international NGO which ordered impregnated bednets for their target group. A number of favourable and limiting factors relating to the success of ITM cooperatives were identified. Of particular importance for the more successful Mexican cooperatives were: a) support by health services, b) smaller size, c) lesser desire for quick returns and d) lower ITM unit costs. Conclusions ITM community cooperatives supported and supervised by the health services have good potential in the Latin American context for achieving and maintaining high impregnation rates. PMID:12473181

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

NASA Astrophysics Data System (ADS)

Li, Manchun; Ma, Lei; Blaschke, Thomas; Cheng, Liang; Tiede, Dirk

2016-07-01

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

TWSVR: Regression via Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

2016-02-01

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

Developing psychotherapists’ competence through clinical supervision: protocol for a qualitative study of supervisory dyads

PubMed Central

2013-01-01

Background Mental health professionals face unique demands and stressors in their work, resulting in high rates of burnout and distress. Clinical supervision is a widely adopted and valued mechanism of professional support, development, and accountability, despite the very limited evidence of specific impacts on therapist or client outcomes. The current study aims to address this by exploring how psychotherapists develop competence through clinical supervision and what impact this has on the supervisees’ practice and their clients’ outcomes. This paper provides a rationale for the study and describes the protocol for an in-depth qualitative study of supervisory dyads, highlighting how it addresses gaps in the literature. Methods/Design The study of 16–20 supervisor-supervisee dyads uses a qualitative mixed method design, with two phases. In phase one, supervisors who are nominated as expert by their peers are interviewed about their supervision practice. In phase two, supervisors record a supervision session with a consenting supervisee; interpersonal process recall interviews are conducted separately with supervisor and supervisee to reflect in depth on the teaching and learning processes occurring. All interviews will be transcribed, coded and analysed to identify the processes that build competence, using a modified form of Consensual Qualitative Research (CQR) strategies. Using a theory-building case study method, data from both phases of the study will be integrated to develop a model describing the processes that build competence and support wellbeing in practising psychotherapists, reflecting the accumulated wisdom of the expert supervisors. Discussion The study addresses past study limitations by examining expert supervisors and their supervisory interactions, by reflecting on actual supervision sessions, and by using dyadic analysis of the supervisory pairs. The study findings will inform the development of future supervision training and practice and identify fruitful avenues for future research. PMID:23298408

Developing psychotherapists' competence through clinical supervision: protocol for a qualitative study of supervisory dyads.

PubMed

Schofield, Margot J; Grant, Jan

2013-01-08

Mental health professionals face unique demands and stressors in their work, resulting in high rates of burnout and distress. Clinical supervision is a widely adopted and valued mechanism of professional support, development, and accountability, despite the very limited evidence of specific impacts on therapist or client outcomes. The current study aims to address this by exploring how psychotherapists develop competence through clinical supervision and what impact this has on the supervisees' practice and their clients' outcomes. This paper provides a rationale for the study and describes the protocol for an in-depth qualitative study of supervisory dyads, highlighting how it addresses gaps in the literature. The study of 16-20 supervisor-supervisee dyads uses a qualitative mixed method design, with two phases. In phase one, supervisors who are nominated as expert by their peers are interviewed about their supervision practice. In phase two, supervisors record a supervision session with a consenting supervisee; interpersonal process recall interviews are conducted separately with supervisor and supervisee to reflect in depth on the teaching and learning processes occurring. All interviews will be transcribed, coded and analysed to identify the processes that build competence, using a modified form of Consensual Qualitative Research (CQR) strategies. Using a theory-building case study method, data from both phases of the study will be integrated to develop a model describing the processes that build competence and support wellbeing in practising psychotherapists, reflecting the accumulated wisdom of the expert supervisors. The study addresses past study limitations by examining expert supervisors and their supervisory interactions, by reflecting on actual supervision sessions, and by using dyadic analysis of the supervisory pairs. The study findings will inform the development of future supervision training and practice and identify fruitful avenues for future research.

Shifts in the ecological niche of Lutzomyia peruensis under climate change scenarios in Peru.

PubMed

Moo-Llanes, D A; Arque-Chunga, W; Carmona-Castro, O; Yañez-Arenas, C; Yañez-Trujillano, H H; Cheverría-Pacheco, L; Baak-Baak, C M; Cáceres, A G

2017-06-01

The Peruvian Andes presents a climate suitable for many species of sandfly that are known vectors of leishmaniasis or bartonellosis, including Lutzomyia peruensis (Diptera: Psychodidae), among others. In the present study, occurrences data for Lu. peruensis were compiled from several items in the scientific literature from Peru published between 1927 and 2015. Based on these data, ecological niche models were constructed to predict spatial distributions using three algorithms [Support vector machine (SVM), the Genetic Algorithm for Rule-set Prediction (GARP) and Maximum Entropy (MaxEnt)]. In addition, the environmental requirements of Lu. peruensis and three niche characteristics were modelled in the context of future climate change scenarios: (a) potential changes in niche breadth; (b) shifts in the direction and magnitude of niche centroids, and (c) shifts in elevation range. The model identified areas that included environments suitable for Lu. peruensis in most regions of Peru (45.77%) and an average altitude of 3289 m a.s.l. Under climate change scenarios, a decrease in the distribution areas of Lu. peruensis was observed for all representative concentration pathways. However, the centroid of the species' ecological niche showed a northwest direction in all climate change scenarios. The information generated in this study may help health authorities responsible for the supervision of strategies to control leishmaniasis to coordinate, plan and implement appropriate strategies for each area of risk, taking into account the geographic distribution and potential dispersal of Lu. peruensis. © 2017 The Royal Entomological Society.

Effectiveness of supportive supervision on the consistency of integrated community cases management skills of the health extension workers in 113 districts of Ethiopia.

PubMed

Ameha, Agazi; Karim, Ali Mehryar; Erbo, Amano; Ashenafi, Addis; Hailu, Mulu; Hailu, Berhan; Folla, Abebe; Bizuwork, Simret; Betemariam, Wuleta

2014-10-01

Consistency in the adherence to integrated Community Case Management (iCCM) protocols for common childhood illnesses provided by Ethiopia's Health Extension Program (HEP) frontline workers. One approach is to provide regular clinical mentoring to the frontline health workers of the HEP at their health posts (HP) through supportive supervision (SS) following the initial training. To Assess the effectiveness of visits to improve the consistency of iCCM skills (CoS) of the HEWs in 113 districts in Ethiopia. We analyzed data from 3,909 supportive supervision visits between January 2011 and June 2013 in 113 districts in Ethiopia. From case assessment registers, a health post was classified as consistent in managing pneumonia, malaria, or diarrhea cases if the disease classification, treatment, and follow-up of the last two cases managed at the health posts were consistent with the protocol. We used regression models to assess the effects of SS on CoS. All HPs (2,368) received at least one supportive supervision visit, 41% received two, and 15% received more than two. During the observation period, HP management consistency in pneumonia, malaria, and diarrhea increased by 3.0, 2.7 and 4.4-fold, respectively. After controlling for secular trend and other factors, significant dose-response relationships were observed between number of SS visits and CoS indicators. The SS visits following the initial training were effective in improving the CoS.

A Code Generation Approach for Auto-Vectorization in the Spade Compiler

NASA Astrophysics Data System (ADS)

Wang, Huayong; Andrade, Henrique; Gedik, Buğra; Wu, Kun-Lung

We describe an auto-vectorization approach for the Spade stream processing programming language, comprising two ideas. First, we provide support for vectors as a primitive data type. Second, we provide a C++ library with architecture-specific implementations of a large number of pre-vectorized operations as the means to support language extensions. We evaluate our approach with several stream processing operators, contrasting Spade's auto-vectorization with the native auto-vectorization provided by the GNU gcc and Intel icc compilers.

An exploration of undergraduate medical students' satisfaction with faculty support supervision during community placements in Uganda

PubMed Central

Mubuuke, AG; Oria, H; Dhabangi, A; Kiguli, S; Sewankambo, NK

2015-01-01

Introduction To produce health professionals who are oriented towards addressing community priority health needs, the training in medical schools has been transformed to include a component of community-based training. During this period, students spend a part of their training in the communities they are likely to serve upon graduation. They engage and empower local people in the communities to address their health needs during their placements, and at the same time learn from the people. During the community-based component, students are constantly supervised by faculty from the university to ensure that the intended objectives are achieved. The purpose of the present study was to explore student experiences of support supervision from university faculty during their community-based education, research and service (COBERS placements) and to identify ways in which the student learning can be improved through improved faculty supervision. Methods This was a cross-sectional study involving students at the College of Health Sciences, Makerere University, Uganda, who had a community-based component during their training. Data were collected using both questionnaires and focus group discussions. Quantitative data were analyzed using statistical software and thematic approaches were used for the analysis of qualitative data. Results Most students reported satisfaction with the COBERS supervision; however, junior students were less satisfied with the supervision than the more senior students with more experience of community-based training. Although many supervisors assisted students before departure to COBERS sites, a significant number of supervisors made little follow-up while students were in the community. Incorporating the use of information technology avenues such as emails and skype sessions was suggested as a potential way of enhancing supervision amidst resource constraints without faculty physically visiting the sites. Conclusions Although many students were satisfied with COBERS supervision, there are still some challenges, mostly seen with the more junior students. Using information technology could be a solution to some of these challenges. PMID:26626014

An exploration of undergraduate medical students' satisfaction with faculty support supervision during community placements in Uganda.

PubMed

Mubuuke, Aloysius G; Oria, Hussein; Dhabangi, Aggrey; Kiguli, Sarah; Sewankambo, Nelson K

2015-01-01

To produce health professionals who are oriented towards addressing community priority health needs, the training in medical schools has been transformed to include a component of community-based training. During this period, students spend a part of their training in the communities they are likely to serve upon graduation. They engage and empower local people in the communities to address their health needs during their placements, and at the same time learn from the people. During the community-based component, students are constantly supervised by faculty from the university to ensure that the intended objectives are achieved. The purpose of the present study was to explore student experiences of support supervision from university faculty during their community-based education, research and service (COBERS placements) and to identify ways in which the student learning can be improved through improved faculty supervision. This was a cross-sectional study involving students at the College of Health Sciences, Makerere University, Uganda, who had a community-based component during their training. Data were collected using both questionnaires and focus group discussions. Quantitative data were analyzed using statistical software and thematic approaches were used for the analysis of qualitative data. Most students reported satisfaction with the COBERS supervision; however, junior students were less satisfied with the supervision than the more senior students with more experience of community-based training. Although many supervisors assisted students before departure to COBERS sites, a significant number of supervisors made little follow-up while students were in the community. Incorporating the use of information technology avenues such as emails and skype sessions was suggested as a potential way of enhancing supervision amidst resource constraints without faculty physically visiting the sites. Although many students were satisfied with COBERS supervision, there are still some challenges, mostly seen with the more junior students. Using information technology could be a solution to some of these challenges.

The experience of clinical supervision for nurses and healthcare assistants in a secure adolescent service: Affecting service improvement.

PubMed

McCarron, R H; Eade, J; Delmage, E

2018-04-01

WHAT IS KNOWN ON THE SUBJECT?: Regular and effective clinical supervision for mental health nurses and healthcare assistants (HCAs) is an important tool in helping to reduce stress and burnout, and in ensuring safe, effective and high-quality mental health care. Previous studies of clinical supervision within secure mental health environments have found both a low availability of clinical supervision, and a low level of staff acceptance of its value, particularly for HCAs. WHAT DOES THIS PAPER ADD TO EXISTING KNOWLEDGE?: In previous studies, the understanding shown by HCAs and nurses around the benefits of clinical supervision may have been limited by the methods used. This study was specifically designed to help them best express their views. In contrast to previous studies, both nurses and HCAs showed a good understanding of the function and value of clinical supervision. Significant improvements in the experience of, and access to, clinical supervision for nurses and HCAs working in secure mental health services may be achieved by raising staff awareness, demonstrating organizational support and increasing monitoring of clinical supervision. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Organizations should consider reviewing their approach to supervision to include raising staff awareness, multidisciplinary supervision, group supervision, and recording and tracking of supervision rates. Organizations should be mindful of the need to provide effective clinical supervision to HCAs as well as nurses. Introduction Studies have found a low availability and appreciation of clinical supervision, especially for healthcare assistants (HCAs). Qualitative research is needed to further understand this. Aims Increase understanding of nurses' and HCAs' experiences of, and access to, clinical supervision. Identify nurses' and HCAs' perceptions of the value and function of clinical supervision. Assess how interventions affect staff's experiences of clinical supervision. Methods In 2013, HCAs and nurses in a secure adolescent service were surveyed about clinical supervision. Forty-nine HCAs and 20 nurses responded. In 2014, interventions to facilitate supervision were introduced. In 2016, the study was repeated. Forty HCAs and 30 nurses responded. Responses were analysed using a mixed methods approach. Results Significantly more HCAs found supervision to be a positive experience in 2016, and both nurses and HCAs reported significantly fewer challenges in accessing supervision. HCAs and nurses understood the value of clinical supervision. Discussion Significant improvements in the experience of clinical supervision were achieved following increased staff awareness, multidisciplinary and group supervision, and recording supervision rates. HCAs and nurses understood the consequences of inadequate supervision. Implications for practice Organizations could adopt the interventions to facilitate clinical supervision. Supervision should not be overlooked for HCAs. © 2017 John Wiley & Sons Ltd.

Communication and Supervision of Alcohol in the Family: Parental Perspectives

ERIC Educational Resources Information Center

Sherriff, Nigel; Cox, Louise; Coleman, Lester; Roker, Debi

2008-01-01

It is now well acknowledged that parents can have a central role in supporting sensible alcohol use and reducing alcohol misuse amongst young people. However, little research has considered how communication and supervision in relation to alcohol actually takes place within the family. Drawing upon interviews with the parents of young people aged…

International Doctoral Students in Counselor Education: Coping Strategies in Supervision Training

ERIC Educational Resources Information Center

Woo, Hongryun; Jang, Yoo Jin; Henfield, Malik S.

2015-01-01

This study explores 8 international doctoral students' perceptions of coping strategies used in supervision training in counselor education programs. Using human agency as a conceptual framework, the authors found 3 categories: (a) personal and professional self-directed strategies as personal agency, (b) support and care from mentors as proxy…

Emotions, Social Work Practice and Supervision: An Uneasy Alliance?

PubMed Central

Ingram, Richard

2012-01-01

This paper examines the place of emotions within social work practice. The perceived tensions between emotions and rational decision making are explored and it is argued that their relationship is compatible and necessary. A model for the co-creation of emotionally intelligent supervision is developed to support this vision of practice. PMID:24764612

Evaluating the Use of Reflective Counseling Group Supervision for Military Counselors in Taiwan

ERIC Educational Resources Information Center

Jen der Pan, Peter; Deng, Liang-Yu F.; Tsai, Shiou-Ling

2008-01-01

The purpose of this study is to examine the effects of reflective counseling group supervision (RCGS) for military counselors. A convenience sampling method is adopted. Twenty-two military counselors participate in this study. Both qualitative and quantitative research methods are used for collecting and analyzing data. The results support our…

Emotions, Social Work Practice and Supervision: An Uneasy Alliance?

PubMed

Ingram, Richard

2013-03-01

This paper examines the place of emotions within social work practice. The perceived tensions between emotions and rational decision making are explored and it is argued that their relationship is compatible and necessary. A model for the co-creation of emotionally intelligent supervision is developed to support this vision of practice.

An analysis of group versus individual child health supervision.

PubMed

Rice, R L; Slater, C J

1997-12-01

This study compares the effectiveness of group health supervision (three or four families counseled simultaneously) with traditional visits in conveying knowledge of child health and development, increasing perceived maternal support, and mitigating maternal depression. Subjects were recruited from a predominantly white, middle-class, suburban/rural pediatric practice. Twenty-five families were allocated to group health supervision and 25 to individual visits. A questionnaire covering knowledge of child health and development (CHDQ), the Maternal Social Support Index (MSSI), and the Center for Epidemiologic Studies Depression Scale (CESD) were administered to both groups before their 2-month and after their 10-month visits. A subset of these charts was reviewed for problem visits between 2 and 6 months. As compared with families having traditional visits, families who received the group intervention did at least as well in acquiring knowledge of child care and development and, although not statistically significant, tended to recover from postpartum depression faster and deal better with minor illnesses. The investigators found group child health supervision to be a pleasant and effective method of health care delivery.

How do we know what makes for "best practice" in clinical supervision for psychological therapists? A content analysis of supervisory models and approaches.

PubMed

Simpson-Southward, Chloe; Waller, Glenn; Hardy, Gillian E

2017-11-01

Clinical supervision for psychotherapies is widely used in clinical and research contexts. Supervision is often assumed to ensure therapy adherence and positive client outcomes, but there is little empirical research to support this contention. Regardless, there are numerous supervision models, but it is not known how consistent their recommendations are. This review aimed to identify which aspects of supervision are consistent across models, and which are not. A content analysis of 52 models revealed 71 supervisory elements. Models focus more on supervisee learning and/or development (88.46%), but less on emotional aspects of work (61.54%) or managerial or ethical responsibilities (57.69%). Most models focused on the supervisee (94.23%) and supervisor (80.77%), rather than the client (48.08%) or monitoring client outcomes (13.46%). Finally, none of the models were clearly or adequately empirically based. Although we might expect clinical supervision to contribute to positive client outcomes, the existing models have limited client focus and are inconsistent. Therefore, it is not currently recommended that one should assume that the use of such models will ensure consistent clinician practice or positive therapeutic outcomes. There is little evidence for the effectiveness of supervision. There is a lack of consistency in supervision models. Services need to assess whether supervision is effective for practitioners and patients. Copyright © 2017 John Wiley & Sons, Ltd.

Lagged effects of family-supportive organization perceptions and supervision in relation to generalized work-related resources.

PubMed

Matthews, Russell A; Toumbeva, Tatiana H

2015-07-01

In the present study, grounded in organizational support and social exchange theory, the dynamic lagged interplay between family supportive supervision (FSS), family supportive organization perceptions (FSOP), perceived organizational support (POS), and leader-member exchange (LMX) was examined. Data were collected from 435 respondents over 3 time points with 6-week lags between assessments. Consistent with theory, FSS had a significant lagged effect on FSOP, whereas the reverse relationship was not supported. Interestingly, contrary to conservation of resources theory, we did not find significant lagged effects between POS and FSOP. Results further indicated that LMX and FSS were reciprocally related over time, suggesting the potential for a dynamic, mutually beneficial exchange relationship between subordinates and supervisors. Theoretical implications and considerations for research and practice are discussed. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine

PubMed Central

Meng, Jun; Shi, Lin; Luan, Yushi

2014-01-01

Background Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions. Results Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species. Conclusions The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided. PMID:25051153

Using remote sensing and machine learning for the spatial modelling of a bluetongue virus vector

NASA Astrophysics Data System (ADS)

Van doninck, J.; Peters, J.; De Baets, B.; Ducheyne, E.; Verhoest, N. E. C.

2012-04-01

Bluetongue is a viral vector-borne disease transmitted between hosts, mostly cattle and small ruminants, by some species of Culicoides midges. Within the Mediterranean basin, C. imicola is the main vector of the bluetongue virus. The spatial distribution of this species is limited by a number of environmental factors, including temperature, soil properties and land cover. The identification of zones at risk of bluetongue outbreaks thus requires detailed information on these environmental factors, as well as appropriate epidemiological modelling techniques. We here give an overview of the environmental factors assumed to be constraining the spatial distribution of C. imicola, as identified in different studies. Subsequently, remote sensing products that can be used as proxies for these environmental constraints are presented. Remote sensing data are then used together with species occurrence data from the Spanish Bluetongue National Surveillance Programme to calibrate a supervised learning model, based on Random Forests, to model the probability of occurrence of the C. imicola midge. The model will then be applied for a pixel-based prediction over the Iberian peninsula using remote sensing products for habitat characterization.

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

Event Recognition Based on Deep Learning in Chinese Texts

PubMed Central

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%. PMID:27501231

Event Recognition Based on Deep Learning in Chinese Texts.

PubMed

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

Abusive supervision, psychosomatic symptoms, and deviance: Can job autonomy make a difference?

PubMed

Velez, Maria João; Neves, Pedro

2016-07-01

Recently, interest in abusive supervision has grown (Tepper, 2000). However, little is still known about organizational factors that can reduce its adverse effects on employee behavior. Based on the Job Demands-Resources Model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001), we predict that job autonomy acts as a buffer of the positive relationship between abusive supervision, psychosomatic symptoms and deviance. Therefore, when job autonomy is low, a higher level of abusive supervision should be accompanied by increased psychosomatic symptoms and thus lead to higher production deviance. When job autonomy is high, abusive supervision should fail to produce increased psychosomatic symptoms and thus should not lead to higher production deviance. Our model was explored among a sample of 170 supervisor-subordinate dyads from 4 organizations. The results of the moderated mediation analysis supported our hypotheses. That is, abusive supervision was significantly related to production deviance via psychosomatic symptoms when job autonomy was low, but not when job autonomy was high. These findings suggest that job autonomy buffers the impact of abusive supervision perceptions on psychosomatic symptoms, with consequences for production deviance. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A qualitative investigation of the nature of "informal supervision" among therapists in training.

PubMed

Coren, Sidney; Farber, Barry A

2017-11-29

This study investigated how, when, why, and with whom therapists in training utilize "informal supervision"-that is, engage individuals who are not their formally assigned supervisors in significant conversations about their clinical work. Participants were 16 doctoral trainees in clinical and counseling psychology programs. Semi-structured interviews were conducted and analyzed using the Consensual Qualitative Research (CQR) method. Seven domains emerged from the analysis, indicating that, in general, participants believe that informal and formal supervision offer many of the same benefits, including validation, support, and reassurance; freedom and safety to discuss doubts, anxieties, strong personal reactions to patients, clinical mistakes and challenges; and alternative approaches to clinical interventions. However, several differences also emerged between these modes of learning-for example, formal supervision is seen as more focused on didactics per se ("what to do"), whereas informal supervision is seen as providing more of a "holding environment." Overall, the findings of this study suggest that informal supervision is an important and valuable adjunctive practice by which clinical trainees augment their professional competencies. Recommendations are proposed for clinical practice and training, including the need to further specify the ethical boundaries of this unique and essentially unregulated type of supervision.

Applying machine-learning techniques to Twitter data for automatic hazard-event classification.

NASA Astrophysics Data System (ADS)

Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.

2017-12-01

The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy is lower (83%) than the SVM-LR, since the algorithm needs a bigger training dataset to increase its accuracy. We used TensorFlow framework for applying CNN classifier to the same collection of tweets.In future we will modify both classifiers to work with other geo-hazards, use larger training datasets and apply them in real-time.

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

PubMed Central

Nagarajan, Mahesh B.; Huber, Markus B.; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2014-01-01

Objective While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. Methods and Materials We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. Results Of the feature vectors investigated, the best performance was observed with Minkowski functional ’perimeter’ while comparable performance was observed with ’area’. Of the dimension reduction algorithms tested with ’perimeter’, the best performance was observed with Sammon’s mapping (0.84 ± 0.10) while comparable performance was achieved with exploratory observation machine (0.82 ± 0.09) and principal component analysis (0.80 ± 0.10). Conclusions The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction. PMID:24355697

Learning through Supervision and Mentorship To Support the Development of Infants, Toddlers and Their Families: A Source Book.

ERIC Educational Resources Information Center

Fenichel, Emily, Ed.

Eighteen work group papers, several of which previously appeared in "Zero to Three," the Bulletin of the National Center for Infant Clinical Progams, are presented under four headings. Under the heading "Findings and Recommendations of ZERO TO THREE/National center for Clinical Infant Programs' Work Group on Supervision and…

Clinical supervision: what's going on? Results of a questionnaire.

PubMed

Bishop, V

This paper presents data obtained from a questionnaire sent to trust nurse executives in England and Scotland. While the data indicates a great deal of enthusiasm for clinical supervision, some concern must be shown for the lack of preparation and support for those involved in its implementation, a fact that will undoubtedly reflect badly in any evaluation exercise.

Emergency Department Patients Support the Use of Combat Medics in Their Clinical Care

DTIC Science & Technology

2015-07-01

indirect supervision is the growing complexity of modern medi- cine . If this level of supervised and controlled training is required of aviation and...readiness of brigade combat teams fighting the Global War on Terror . J Surg Res. 2007;138:25–31. 4. Mabry RL, Apodaca A, Penrod J, et al. Impact of

Nurse Preparation and Organizational Support for Supervision of Unlicensed Assistive Personnel in Nursing Homes: A Qualitative Exploration

ERIC Educational Resources Information Center

Siegel, Elena O.; Young, Heather M.; Mitchell, Pamela H.; Shannon, Sarah E.

2008-01-01

Purpose: Nursing supervision of the routine daily care (e.g., grooming, feeding, and toileting) that is delegated to unlicensed assistive personnel (UAP) is critical to nursing home service delivery. The conditions under which the supervisory role is organized and operationalized at the work-unit level, taking into account workloads, registered…

Zinc Finger Transcription Factors as Novel Switches to Modulate Metastatic Progression of Breast Tumors

DTIC Science & Technology

2007-05-01

Supervised College student Leanna Lagpacan, The Scripps Research Institute 2004 -Supervised Ph.D student Sharon Bergquist, The... students in the University of Montreal 1999-2004 - Research supported by Novartis Pharma fellowship (Oncology) 2005 -University Research Council...DATE: May 2007 TYPE OF REPORT: Annual PREPARED FOR: U.S. Army Medical Research and Materiel Command

Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy.

PubMed

Wang, Zhi-Long; Zhou, Zhi-Guo; Chen, Ying; Li, Xiao-Ting; Sun, Ying-Shi

The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis. Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively. The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy.

Mapping Mediterranean seagrasses with Sentinel-2 imagery.

PubMed

Traganos, Dimosthenis; Reinartz, Peter

2017-07-01

Mediterranean seagrasses have been hailed for their numerous ecosystem services, yet they are undergoing a decline in their coverage. The major complication with resolving this tendency is the sparsity of data on their overall distribution. This study addresses the suitability of the recently launched Sentinel-2 satellite for mapping the distribution of Mediterranean seagrass meadows. A comprehensive methodology is presented which applies atmospheric and analytical water column corrections and compares the performance of three different supervised classifiers. Remote sensing of the Thermaikos Gulf, northwestern Aegean Sea (Greece, eastern Mediterranean Sea) reveals that the utilization of Support Vector Machines on water column corrected reflectances yields best accuracies. Two Mediterranean seagrasses, Posidonia oceanica and Cymodocea nodosa, cover a total submerged area of 1.48km 2 between depths of 1.4-16.5m. With its 10-m spatial resolution and 5-day revisit frequency, Sentinel-2 imagery can mitigate the Mediterranean seagrass distribution data gap and allow better management and conservation in the future in a retrospective, time- and cost-effective fashion. Copyright © 2017 Elsevier Ltd. All rights reserved.

Geographical classification of apple based on hyperspectral imaging

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Zhao, Chunjiang; Peng, Yankun

2013-05-01

Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Operational algorithm for ice-water classification on dual-polarized RADARSAT-2 images

NASA Astrophysics Data System (ADS)

Zakhvatkina, Natalia; Korosov, Anton; Muckenhuber, Stefan; Sandven, Stein; Babiker, Mohamed

2017-01-01

Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures

PubMed Central

2010-01-01

Background β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. Results We have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. Conclusions We have created an accurate predictor of β-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/. PMID:20673368

Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

PubMed

Kountouris, Petros; Hirst, Jonathan D

2010-07-31

Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

A practical guide to big data research in psychology.

PubMed

Chen, Eric Evan; Wojcik, Sean P

2016-12-01

The massive volume of data that now covers a wide variety of human behaviors offers researchers in psychology an unprecedented opportunity to conduct innovative theory- and data-driven field research. This article is a practical guide to conducting big data research, covering data management, acquisition, processing, and analytics (including key supervised and unsupervised learning data mining methods). It is accompanied by walkthrough tutorials on data acquisition, text analysis with latent Dirichlet allocation topic modeling, and classification with support vector machines. Big data practitioners in academia, industry, and the community have built a comprehensive base of tools and knowledge that makes big data research accessible to researchers in a broad range of fields. However, big data research does require knowledge of software programming and a different analytical mindset. For those willing to acquire the requisite skills, innovative analyses of unexpected or previously untapped data sources can offer fresh ways to develop, test, and extend theories. When conducted with care and respect, big data research can become an essential complement to traditional research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning.

PubMed

Wei, Ning; You, Jia; Friehs, Karl; Flaschel, Erwin; Nattkemper, Tim Wilhelm

2007-08-15

Fermentation industries would benefit from on-line monitoring of important parameters describing cell growth such as cell density and viability during fermentation processes. For this purpose, an in situ probe has been developed, which utilizes a dark field illumination unit to obtain high contrast images with an integrated CCD camera. To test the probe, brewer's yeast Saccharomyces cerevisiae is chosen as the target microorganism. Images of the yeast cells in the bioreactors are captured, processed, and analyzed automatically by means of mechatronics, image processing, and machine learning. Two support vector machine based classifiers are used for separating cells from background, and for distinguishing live from dead cells afterwards. The evaluation of the in situ experiments showed strong correlation between results obtained by the probe and those by widely accepted standard methods. Thus, the in situ probe has been proved to be a feasible device for on-line monitoring of both cell density and viability with high accuracy and stability. (c) 2007 Wiley Periodicals, Inc.

Training And Supervision Did Not Meaningfully Improve Quality Of Care For Pregnant Women Or Sick Children In Sub-Saharan Africa.

PubMed

Leslie, Hannah H; Gage, Anna; Nsona, Humphreys; Hirschhorn, Lisa R; Kruk, Margaret E

2016-09-01

In-service training courses and supportive supervision of health workers are among the most common interventions to improve the quality of health care in low- and middle-income countries. Despite extensive investment from donors, evaluations of the long-term effect of these two interventions are scarce. We used nationally representative surveys of health systems in seven countries in sub-Saharan Africa to examine the association of in-service training and supervision with provider quality in antenatal and sick child care. The results of our analysis showed that observed quality of care was poor, with fewer than half of evidence-based actions completed by health workers, on average. In-service training and supervision were associated with quality of sick child care; they were associated with quality of antenatal care only when provided jointly. All associations were modest-at most, improvements related to interventions were equivalent to 2 additional provider actions out of the 18-40 actions expected per visit. In-service training and supportive supervision as delivered were not sufficient to meaningfully improve the quality of care in these countries. Greater attention to the quality of health professional education and national health system performance will be required to provide the standard of health care that patients deserve. Project HOPE—The People-to-People Health Foundation, Inc.

Enhancing the Supervision of Community Health Workers With WhatsApp Mobile Messaging: Qualitative Findings From 2 Low-Resource Settings in Kenya.

PubMed

Henry, Jade Vu; Winters, Niall; Lakati, Alice; Oliver, Martin; Geniets, Anne; Mbae, Simon M; Wanjiru, Hannah

2016-06-20

An estimated half of all mobile phone users in Kenya use WhatsApp, an instant messaging platform that provides users an affordable way to send and receive text messages, photos, and other media at the one-to-one, one-to-many, many-to-one, or many-to-many levels. A mobile learning intervention aimed at strengthening supervisory support for community health workers (CHWs) in Kibera and Makueni, Kenya, created a WhatsApp group for CHWs and their supervisors to support supervision, professional development, and team building. We analyzed 6 months of WhatsApp chat logs (from August 19, 2014, to March 1, 2015) and conducted interviews with CHWs and their supervisors to understand how they used this instant messaging tool. During the study period, 1,830 posts were made by 41participants. Photos were a key component of the communication among CHWs and their supervisors: 430 (23.4%) of all posts contained photos or other media. Of the remaining 1,400 text-based posts, 87.6% (n = 1,227) related to at least 1 of 3 defined supervision objectives: (1) quality assurance, (2) communication and information, or (3) supportive environment. This supervision took place in the context of posts about the roll out of the new mobile learning intervention and the delivery of routine health care services, as well as team-building efforts and community development. Our preliminary investigation demonstrates that with minimal training, CHWs and their supervisors tailored the multi-way communication features of this mobile instant messaging technology to enact virtual one-to-one, group, and peer-to-peer forms of supervision and support, and they switched channels of communication depending on the supervisory objectives. We encourage additional research on how health workers incorporate mobile technologies into their practices to develop and implement effective supervisory systems that will safeguard patient privacy, strengthen the formal health system, and create innovative forms of community-based, digitally supported professional development for CHWs. © Henry et al.

Enhancing the Supervision of Community Health Workers With WhatsApp Mobile Messaging: Qualitative Findings From 2 Low-Resource Settings in Kenya

PubMed Central

Henry, Jade Vu; Winters, Niall; Lakati, Alice; Oliver, Martin; Geniets, Anne; Mbae, Simon M; Wanjiru, Hannah

2016-01-01

ABSTRACT An estimated half of all mobile phone users in Kenya use WhatsApp, an instant messaging platform that provides users an affordable way to send and receive text messages, photos, and other media at the one-to-one, one-to-many, many-to-one, or many-to-many levels. A mobile learning intervention aimed at strengthening supervisory support for community health workers (CHWs) in Kibera and Makueni, Kenya, created a WhatsApp group for CHWs and their supervisors to support supervision, professional development, and team building. We analyzed 6 months of WhatsApp chat logs (from August 19, 2014, to March 1, 2015) and conducted interviews with CHWs and their supervisors to understand how they used this instant messaging tool. During the study period, 1,830 posts were made by 41participants. Photos were a key component of the communication among CHWs and their supervisors: 430 (23.4%) of all posts contained photos or other media. Of the remaining 1,400 text-based posts, 87.6% (n = 1,227) related to at least 1 of 3 defined supervision objectives: (1) quality assurance, (2) communication and information, or (3) supportive environment. This supervision took place in the context of posts about the roll out of the new mobile learning intervention and the delivery of routine health care services, as well as team-building efforts and community development. Our preliminary investigation demonstrates that with minimal training, CHWs and their supervisors tailored the multi-way communication features of this mobile instant messaging technology to enact virtual one-to-one, group, and peer-to-peer forms of supervision and support, and they switched channels of communication depending on the supervisory objectives. We encourage additional research on how health workers incorporate mobile technologies into their practices to develop and implement effective supervisory systems that will safeguard patient privacy, strengthen the formal health system, and create innovative forms of community-based, digitally supported professional development for CHWs. PMID:27353623

VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases

PubMed Central

Giraldo-Calderón, Gloria I.; Emrich, Scott J.; MacCallum, Robert M.; Maslen, Gareth; Dialynas, Emmanuel; Topalis, Pantelis; Ho, Nicholas; Gesing, Sandra; Madey, Gregory; Collins, Frank H.; Lawson, Daniel

2015-01-01

VectorBase is a National Institute of Allergy and Infectious Diseases supported Bioinformatics Resource Center (BRC) for invertebrate vectors of human pathogens. Now in its 11th year, VectorBase currently hosts the genomes of 35 organisms including a number of non-vectors for comparative analysis. Hosted data range from genome assemblies with annotated gene features, transcript and protein expression data to population genetics including variation and insecticide-resistance phenotypes. Here we describe improvements to our resource and the set of tools available for interrogating and accessing BRC data including the integration of Web Apollo to facilitate community annotation and providing Galaxy to support user-based workflows. VectorBase also actively supports our community through hands-on workshops and online tutorials. All information and data are freely available from our website at https://www.vectorbase.org/. PMID:25510499

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

PubMed

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Mental health pre-registration nursing students' experiences of group clinical supervision: a UK longitudinal qualitative study.

PubMed

Carver, Neil; Clibbens, Nicola; Ashmore, Russell; Sheldon, Julie

2014-03-01

There is widespread international interest in the use of clinical supervision in nursing as well as recognition of the need to introduce nursing students to its concepts and value. This article reports on a three-year longitudinal qualitative focus group study which explored students' views and experiences of a group clinical supervision initiative. Students attended supervision groups facilitated by teaching staff over their three year pre-registration mental health nursing course, with a main aim of developing skills, knowledge and attitudes as supervisees. The findings showed that students derived benefit from the experience, gained greater awareness of the nature of supervision and became active supervisees within their groups. These benefits took time to emerge and were not universal however. While the findings support the value of exposing students to the experience of group clinical supervision educators wishing to implement such a programme need to address a host of issues. These include; the preparation of students, structural and resource concerns, and issues relating to group dynamics. Copyright © 2013 Elsevier Ltd. All rights reserved.

Beyond the individual victim: multilevel consequences of abusive supervision in teams.

PubMed

Farh, Crystal I C; Chen, Zhijun

2014-11-01

We conceptualize a multilevel framework that examines the manifestation of abusive supervision in team settings and its implications for the team and individual members. Drawing on Hackman's (1992) typology of ambient and discretionary team stimuli, our model features team-level abusive supervision (the average level of abuse reported by team members) and individual-level abusive supervision as simultaneous and interacting forces. We further draw on team-relevant theories of social influence to delineate two proximal outcomes of abuse-members' organization-based self-esteem (OBSE) at the individual level and relationship conflict at the team level-that channel the independent and interactive effects of individual- and team-level abuse onto team members' voice, team-role performance, and turnover intentions. Results from a field study and a scenario study provided support for these multilevel pathways. We conclude that abusive supervision in team settings holds toxic consequences for the team and individual, and offer practical implications as well as suggestions for future research on abusive supervision as a multilevel phenomenon. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Model for investigating the benefits of clinical supervision in psychiatric nursing: a survey study.

PubMed

Gonge, Henrik; Buus, Niels

2011-04-01

The objective of this study was to test a model for analysing the possible benefits of clinical supervision. The model suggested a pathway from participation to effectiveness to benefits of clinical supervision, and included possible influences of individual and workplace factors. The study sample was 136 nursing staff members in permanent employment on nine general psychiatric wards and at four community mental health centres at a Danish psychiatric university hospital. Data were collected by means of a set of questionnaires. Participation in clinical supervision was associated with the effectiveness of clinical supervision, as measured by the Manchester Clinical Supervision Scale (MCSS). Furthermore, MCSS scores were associated with benefits, such as increased job satisfaction, vitality, rational coping and less stress, emotional exhaustion, and depersonalization. Multivariate analyses indicated that certain individual and workplace factors were related to subscales of the MCSS, as well as some of the benefits. The study supported the suggested model, but methodological limitations apply. © 2011 The Authors. International Journal of Mental Health Nursing © 2011 Australian College of Mental Health Nurses Inc.

Network Supervision of Adult Experience and Learning Dependent Sensory Cortical Plasticity.

PubMed

Blake, David T

2017-06-18

The brain is capable of remodeling throughout life. The sensory cortices provide a useful preparation for studying neuroplasticity both during development and thereafter. In adulthood, sensory cortices change in the cortical area activated by behaviorally relevant stimuli, by the strength of response within that activated area, and by the temporal profiles of those responses. Evidence supports forms of unsupervised, reinforcement, and fully supervised network learning rules. Studies on experience-dependent plasticity have mostly not controlled for learning, and they find support for unsupervised learning mechanisms. Changes occur with greatest ease in neurons containing α-CamKII, which are pyramidal neurons in layers II/III and layers V/VI. These changes use synaptic mechanisms including long term depression. Synaptic strengthening at NMDA-containing synapses does occur, but its weak association with activity suggests other factors also initiate changes. Studies that control learning find support of reinforcement learning rules and limited evidence of other forms of supervised learning. Behaviorally associating a stimulus with reinforcement leads to a strengthening of cortical response strength and enlarging of response area with poor selectivity. Associating a stimulus with omission of reinforcement leads to a selective weakening of responses. In some preparations in which these associations are not as clearly made, neurons with the most informative discharges are relatively stronger after training. Studies analyzing the temporal profile of responses associated with omission of reward, or of plasticity in studies with different discriminanda but statistically matched stimuli, support the existence of limited supervised network learning. © 2017 American Physiological Society. Compr Physiol 7:977-1008, 2017. Copyright © 2017 John Wiley & Sons, Inc.

Using virtual data for training deep model for hand gesture recognition

NASA Astrophysics Data System (ADS)

Nikolaev, E. I.; Dvoryaninov, P. V.; Lensky, Y. Y.; Drozdovsky, N. S.

2018-05-01

Deep learning has shown real promise for the classification efficiency for hand gesture recognition problems. In this paper, the authors present experimental results for a deeply-trained model for hand gesture recognition through the use of hand images. The authors have trained two deep convolutional neural networks. The first architecture produces the hand position as a 2D-vector by input hand image. The second one predicts the hand gesture class for the input image. The first proposed architecture produces state of the art results with an accuracy rate of 89% and the second architecture with split input produces accuracy rate of 85.2%. In this paper, the authors also propose using virtual data for training a supervised deep model. Such technique is aimed to avoid using original labelled images in the training process. The interest of this method in data preparation is motivated by the need to overcome one of the main challenges of deep supervised learning: using a copious amount of labelled data during training.

Entanglement-Based Machine Learning on a Quantum Computer

NASA Astrophysics Data System (ADS)

Cai, X.-D.; Wu, D.; Su, Z.-E.; Chen, M.-C.; Wang, X.-L.; Li, Li; Liu, N.-L.; Lu, C.-Y.; Pan, J.-W.

2015-03-01

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

NASA Astrophysics Data System (ADS)

Fusco, Terence; Bi, Yaxin; Wang, Haiying; Browne, Fiona

2016-08-01

The key issues pertaining to collection of epidemic disease data for our analysis purposes are that it is a labour intensive, time consuming and expensive process resulting in availability of sparse sample data which we use to develop prediction models. To address this sparse data issue, we present the novel Incremental Transductive methods to circumvent the data collection process by applying previously acquired data to provide consistent, confidence-based labelling alternatives to field survey research. We investigated various reasoning approaches for semi-supervised machine learning including Bayesian models for labelling data. The results show that using the proposed methods, we can label instances of data with a class of vector density at a high level of confidence. By applying the Liberal and Strict Training Approaches, we provide a labelling and classification alternative to standalone algorithms. The methods in this paper are components in the process of reducing the proliferation of the Schistosomiasis disease and its effects.

A community-based peer support service for persons with severe mental illness in China.

PubMed

Fan, Yunge; Ma, Ning; Ma, Liang; Xu, Wei; Steven Lamberti, J; Caine, Eric D

2018-06-04

Peer support services for patients with severe mental illness (SMI) originated from Western countries and have become increasingly popular during the past twenty years. The aim of this paper is to describe a peer service model and its implementation in China, including the model's feasibility and sustainability. A peer support service was developed in four Chinese communities. Implementation, feasibility and sustainability were assessed across five domains: Service process, service contents, peer training and supervision, service satisfaction, and service perceived benefit. Service process: 214 peer support activities were held between July 2013 and June 2016. No adverse events occurred during three years. Each activity ranged from 40 to 120 min; most were conducted in a community rehabilitation center or community health care center. Service content: Activities focused on eight primary topics-daily life skills, social skills, knowledge of mental disorders, entertainment, fine motor skill practice, personal perceptions, healthy life style support, emotional support. Peer training and supervision: Intensive training was provided for all peers before they started to provide services. Regular supervision and continued training were provided thereafter; online supervision supplemented face to face meetings. Service satisfaction: Nineteen consumers (79.2%) (χ 2 (1) = 12.76, p < 0.001) were satisfied with the peers and 17 consumers (70.8%) (χ 2 (1) = 8.05, p = 0.005) expressed a strong desire to continue to participate in the service. Fourteen caregivers (93.3%) (χ 2 (1) = 11.27, p = 0.001) wanted the patients to continue to organize or participate in the service. Service perceived benefit: Six peers (85.7%) (χ 2 (1) = 3.57, p = 0.059) reported an improvement of working skills. Ten consumers (41.7%) (χ 2 (1) = 0.05, p = 0.827) reported better social communication skills. Six caregivers (40%) (χ 2 (1) = 1.67, p = 0.197) observed patients' increase in social communication skills, five (33.3%) (χ 2 (1) = 1.67, p = 0.197) found their own mood had been improved. Peer support services for patients with SMI can be sustainably implemented within Chinese communities without adverse events that jeopardize safety and patient stability. Suggestions for future service development include having professionals give increased levels of support to peers at the beginning of a new program. A culturally consistent peer service manual, including peer role definition, peer training curriculum, and supervision methods, should be developed to help implement the service smoothly.

Assessing support for supervised injection services among community stakeholders in London, Canada.

PubMed

Bardwell, Geoff; Scheim, Ayden; Mitra, Sanjana; Kerr, Thomas

2017-10-01

Few qualitative studies have examined support for supervised injection services (SIS), and these have been restricted to large cities. This study aimed to assess support for SIS among a diverse representation of community stakeholders in London, a mid-sized city in southwestern Ontario, Canada. This qualitative study was undertaken as part of the Ontario Integrated Supervised Injection Services Feasibility Study. We used purposive sampling methods to recruit a diversity of key informants (n=20) from five sectors: healthcare; social services; government and municipal services; police and emergency services; and the business and community sector. Interview data, collected via one-to-one semi structured interviews, were coded and analyzed using thematic analyses through NVivo 10 software. Interview participants unanimously supported the implementation of SIS in London. However, participant support for SIS was met with some implementation-related preferences and/or conditions. These included centralization or decentralization of SIS; accessibility of SIS for people who inject drugs; proximity of SIS to interview participants; and other services and strategies offered alongside SIS. The results of this study challenge the assumptions that smaller cities like London may be unlikely to support SIS. Community stakeholders were supportive of the implementation of SIS with some preferences or conditions. Interview participants had differing perspectives, but ultimately supported similar end goals of accessibility and reducing community harms associated with injection drug use. Future research and SIS programming should consider these factors when determining optimal service delivery in ways that increase support from a diversity of community stakeholders. Copyright © 2017 Elsevier B.V. All rights reserved.

Measuring the Effectiveness of a Genetic Counseling Supervision Training Conference.

PubMed

Atzinger, Carrie L; He, Hua; Wusik, Katie

2016-08-01

Genetic counselors who receive formal training report increased confidence and competence in their supervisory roles. The effectiveness of specific formal supervision training has not been assessed previously. A day-long GC supervision conference was designed based on published supervision competencies and was attended by 37 genetic counselors. Linear Mixed Model and post-hoc paired t-test was used to compare Psychotherapy Supervisor Development Scale (PSDS) scores among/between individuals pre and post conference. Generalized Estimating Equation (GEE) model and post-hoc McNemar's test was used to determine if the conference had an effect on GC supervision competencies. PSDS scores were significantly increased 1 week (p < 0.001) and 6 months (p < 0.001) following the conference. For three supervision competencies, attendees were more likely to agree they were able to perform them after the conference than before. These effects remained significant 6 months later. For the three remaining competencies, the majority of supervisors agreed they could perform these before the conference; therefore, no change was found. This exploratory study showed this conference increased the perceived confidence and competence of the supervisors who attended and increased their self-reported ability to perform certain supervision competencies. While still preliminary, this supports the idea that a one day conference on supervision has the potential to impact supervisor development.

Advanced psychiatric nurse practitioners' ideas and needs for supervision in private practice in South Africa.

PubMed

Temane, Annie M; Poggenpoel, Marie; Myburgh, Chris P H

2014-04-07

Supervision forms an integral part of psychiatric nursing. The value of clinicalsupervision has been demonstrated widely in research. Despite efforts made toward advancedpsychiatric nursing, supervision seems to be non-existent in this field. The aim of this study was to explore and describe advanced psychiatric nursepractitioners' ideas and needs with regard to supervision in private practice in order tocontribute to the new efforts made in advanced psychiatric nursing in South Africa. A qualitative, descriptive, exploratory, and contextual design using a phenomenological approach as research method was utilised in this study. A purposive sampling was used. Eight advanced psychiatric nurse practitioners in private practice described their ideas and needs for supervision during phenomenological interviews. Tesch's method of open coding was utilised to analyse data. After data analysis the findings were recontextualised within literature. The data analysis generated the following themes - that the supervisor should have or possess: (a) professional competencies, (b) personal competencies and (c) specificfacilitative communication skills. The findings indicated that there was a need for supervision of advanced psychiatric nurse practitioners in private practice in South Africa. This study indicates that there is need for supervision and competent supervisors in private practice. Supervision can be beneficial with regard to developing a culture of support for advanced psychiatric practitioners in private practice and also psychiatric nurse practitioners.

Challenges of Supervising Part-Time PhD Students: Towards Student-Centred Practice

ERIC Educational Resources Information Center

Watts, Jacqueline H.

2008-01-01

The supervision of part-time doctoral students is a long-term academic enterprise requiring stamina both on the part of the supervisor and the student. Because of the fractured student identity of the part-time doctoral candidate, who is usually balancing a range of work, study, and family commitments, strategies to support their progress have to…

A probabilistic topic model for clinical risk stratification from electronic health records.

PubMed

Huang, Zhengxing; Dong, Wei; Duan, Huilong

2015-12-01

Risk stratification aims to provide physicians with the accurate assessment of a patient's clinical risk such that an individualized prevention or management strategy can be developed and delivered. Existing risk stratification techniques mainly focus on predicting the overall risk of an individual patient in a supervised manner, and, at the cohort level, often offer little insight beyond a flat score-based segmentation from the labeled clinical dataset. To this end, in this paper, we propose a new approach for risk stratification by exploring a large volume of electronic health records (EHRs) in an unsupervised fashion. Along this line, this paper proposes a novel probabilistic topic modeling framework called probabilistic risk stratification model (PRSM) based on Latent Dirichlet Allocation (LDA). The proposed PRSM recognizes a patient clinical state as a probabilistic combination of latent sub-profiles, and generates sub-profile-specific risk tiers of patients from their EHRs in a fully unsupervised fashion. The achieved stratification results can be easily recognized as high-, medium- and low-risk, respectively. In addition, we present an extension of PRSM, called weakly supervised PRSM (WS-PRSM) by incorporating minimum prior information into the model, in order to improve the risk stratification accuracy, and to make our models highly portable to risk stratification tasks of various diseases. We verify the effectiveness of the proposed approach on a clinical dataset containing 3463 coronary heart disease (CHD) patient instances. Both PRSM and WS-PRSM were compared with two established supervised risk stratification algorithms, i.e., logistic regression and support vector machine, and showed the effectiveness of our models in risk stratification of CHD in terms of the Area Under the receiver operating characteristic Curve (AUC) analysis. As well, in comparison with PRSM, WS-PRSM has over 2% performance gain, on the experimental dataset, demonstrating that incorporating risk scoring knowledge as prior information can improve the performance in risk stratification. Experimental results reveal that our models achieve competitive performance in risk stratification in comparison with existing supervised approaches. In addition, the unsupervised nature of our models makes them highly portable to the risk stratification tasks of various diseases. Moreover, patient sub-profiles and sub-profile-specific risk tiers generated by our models are coherent and informative, and provide significant potential to be explored for the further tasks, such as patient cohort analysis. We hypothesize that the proposed framework can readily meet the demand for risk stratification from a large volume of EHRs in an open-ended fashion. Copyright © 2015 Elsevier Inc. All rights reserved.

Data-Driven Information Extraction from Chinese Electronic Medical Records

PubMed Central

Zhao, Tianwan; Ge, Chen; Gao, Weiguo; Wei, Jia; Zhu, Kenny Q.

2015-01-01

Objective This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event. Materials and Methods Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions. Results The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846. Discussion In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838). Conclusions The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica. PMID:26295801

Supervised classification of brain tissues through local multi-scale texture analysis by coupling DIR and FLAIR MR sequences

NASA Astrophysics Data System (ADS)

Poletti, Enea; Veronese, Elisa; Calabrese, Massimiliano; Bertoldo, Alessandra; Grisan, Enrico

2012-02-01

The automatic segmentation of brain tissues in magnetic resonance (MR) is usually performed on T1-weighted images, due to their high spatial resolution. T1w sequence, however, has some major downsides when brain lesions are present: the altered appearance of diseased tissues causes errors in tissues classification. In order to overcome these drawbacks, we employed two different MR sequences: fluid attenuated inversion recovery (FLAIR) and double inversion recovery (DIR). The former highlights both gray matter (GM) and white matter (WM), the latter highlights GM alone. We propose here a supervised classification scheme that does not require any anatomical a priori information to identify the 3 classes, "GM", "WM", and "background". Features are extracted by means of a local multi-scale texture analysis, computed for each pixel of the DIR and FLAIR sequences. The 9 textures considered are average, standard deviation, kurtosis, entropy, contrast, correlation, energy, homogeneity, and skewness, evaluated on a neighborhood of 3x3, 5x5, and 7x7 pixels. Hence, the total number of features associated to a pixel is 56 (9 textures x3 scales x2 sequences +2 original pixel values). The classifier employed is a Support Vector Machine with Radial Basis Function as kernel. From each of the 4 brain volumes evaluated, a DIR and a FLAIR slice have been selected and manually segmented by 2 expert neurologists, providing 1st and 2nd human reference observations which agree with an average accuracy of 99.03%. SVM performances have been assessed with a 4-fold cross-validation, yielding an average classification accuracy of 98.79%.

A multi-label, semi-supervised classification approach applied to personality prediction in social media.

PubMed

Lima, Ana Carolina E S; de Castro, Leandro Nunes

2014-10-01

Social media allow web users to create and share content pertaining to different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data scientists seeking to understand behaviours and trends, whilst collecting statistics for social sites. One potential application for these data is personality prediction, which aims to understand a user's behaviour within social media. Traditional personality prediction relies on users' profiles, their status updates, the messages they post, etc. Here, a personality prediction system for social media data is introduced that differs from most approaches in the literature, in that it works with groups of texts, instead of single texts, and does not take users' profiles into account. Also, the proposed approach extracts meta-attributes from texts and does not work directly with the content of the messages. The set of possible personality traits is taken from the Big Five model and allows the problem to be characterised as a multi-label classification task. The problem is then transformed into a set of five binary classification problems and solved by means of a semi-supervised learning approach, due to the difficulty in annotating the massive amounts of data generated in social media. In our implementation, the proposed system was trained with three well-known machine-learning algorithms, namely a Naïve Bayes classifier, a Support Vector Machine, and a Multilayer Perceptron neural network. The system was applied to predict the personality of Tweets taken from three datasets available in the literature, and resulted in an approximately 83% accurate prediction, with some of the personality traits presenting better individual classification rates than others. Copyright © 2014 Elsevier Ltd. All rights reserved.

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.

PubMed

Wu, Panpan; Xia, Kewen; Yu, Hengyong

2016-11-01

Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Data-Driven Information Extraction from Chinese Electronic Medical Records.

PubMed

Xu, Dong; Zhang, Meizhuo; Zhao, Tianwan; Ge, Chen; Gao, Weiguo; Wei, Jia; Zhu, Kenny Q

2015-01-01

This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event. Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions. The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846. In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838). The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica.

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

PubMed

Le, Hoang-Quynh; Tran, Mai-Vu; Dang, Thanh Hai; Ha, Quang-Thuy; Collier, Nigel

2016-07-01

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system's performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a 'silver' CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID-The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530). © The Author(s) 2016. Published by Oxford University Press.

Towards automatic lithological classification from remote sensing data using support vector machines

NASA Astrophysics Data System (ADS)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14 principal component bands, 14 independent component bands, 3 band ratios, 3 DEM derivatives: slope/curvatureroughness and 2 aeromagnetic derivatives: mean and variance of susceptibility) extracted from the ASTER, DEM and aeromagnetic data, in order to determine the optimal inputs that provide the highest classification accuracy. It was found that a combination of ASTER-derived independent components, principal components and band ratios, DEM-derived slope, curvature and roughness, and aeromagnetic-derived mean and variance of magnetic susceptibility provide the highest classification accuracy of 93.4% on independent test samples. A comparison of the classification results of the SVM with those of maximum likelihood (84.9%) and minimum distance (38.4%) classifiers clearly show that the SVM algorithm returns much higher classification accuracy. Therefore, the SVM method can be used to produce quick and reliable geological maps from scarce geological information, which is still the case with many under-developed frontier regions of the world.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure☆

PubMed Central

Frick, Andreas; Gingnell, Malin; Marquand, Andre F.; Howner, Katarina; Fischer, Håkan; Kristiansson, Marianne; Williams, Steven C.R.; Fredrikson, Mats; Furmark, Tomas

2014-01-01

Functional neuroimaging of social anxiety disorder (SAD) support altered neural activation to threat-provoking stimuli focally in the fear network, while structural differences are distributed over the temporal and frontal cortices as well as limbic structures. Previous neuroimaging studies have investigated the brain at the voxel level using mass-univariate methods which do not enable detection of more complex patterns of activity and structural alterations that may separate SAD from healthy individuals. Support vector machine (SVM) is a supervised machine learning method that capitalizes on brain activation and structural patterns to classify individuals. The aim of this study was to investigate if it is possible to discriminate SAD patients (n = 14) from healthy controls (n = 12) using SVM based on (1) functional magnetic resonance imaging during fearful face processing and (2) regional gray matter volume. Whole brain and region of interest (fear network) SVM analyses were performed for both modalities. For functional scans, significant classifications were obtained both at whole brain level and when restricting the analysis to the fear network while gray matter SVM analyses correctly classified participants only when using the whole brain search volume. These results support that SAD is characterized by aberrant neural activation to affective stimuli in the fear network, while disorder-related alterations in regional gray matter volume are more diffusely distributed over the whole brain. SVM may thus be useful for identifying imaging biomarkers of SAD. PMID:24239689

Does power distance exacerbate or mitigate the effects of abusive supervision? It depends on the outcome.

PubMed

Lian, Huiwen; Ferris, D Lance; Brown, Douglas J

2012-01-01

We predicted that the effects of abusive supervision are likely to be moderated by subordinate power distance orientation and that the nature of the moderating effect will depend on the outcome. Drawing upon work suggesting that high power distance orientation subordinates are more tolerant of supervisory mistreatment, we posited that high power distance orientation subordinates would be less likely to view abusive supervision as interpersonally unfair. Drawing upon social learning theory suggestions that high power distance orientation subordinates are more likely to view supervisors as role models, we posited that high power distance orientation subordinates would be more likely to pattern their own interpersonally deviant behavior after that of abusive supervisors. Across 3 samples we found support for our predicted interactions, culminating in a mediated moderation model demonstrating that social learning mediates the interaction of abusive supervision and power distance on subordinate interpersonal deviance, while ruling out alternate self-regulation impairment or displaced aggression explanations. Implications for the abusive supervision literature are discussed.

Supervisory needs of research doctoral students in a university teaching hospital setting.

PubMed

Caldwell, Patrina Hy; Oldmeadow, Wendy; Jones, Cheryl A

2012-10-01

Teaching hospitals affiliated with universities are now common sites for research higher degree supervision. We hypothesised that the hospital environment poses unique challenges to supervision compared with the traditional university research institute setting. This study aimed to identify and rank important supervision issues in a clinical setting from the students' perspective. Using the Delphi method to explore issues and facilitate consensus, small group discussions were conducted with 10 research doctoral students from a tertiary teaching hospital. We identified supervision issues that are unique to the hospital-based context. These include the demands placed on supervisors combining clinical and supervisory roles, the challenges of academic medical/scientific writing and career issues for students who are already established in their professions. Other issues identified, common to all doctoral students, include differing expectations between students and supervisors (with students wanting support for their career plans, training in research skills and increasing autonomy and responsibility), supervisor access, quality and frequency of meetings, lack of training in writing and dealing with conflicts. Our research identified that postgraduate students of supervisors who combine clinical and supervisory roles report significant issues with supervision, some of which are unique to the clinical setting. Clinician researchers who supervise postgraduate students need to balance clinical and supervisory responsibilities, identify and negotiate student expectations early in candidature and provide career counselling to students who are already highly experienced. Furthermore, clinician supervisors should undertake postgraduate supervisor training programme tailored to the hospital setting to better support their students. © 2012 The Authors. Journal of Paediatrics and Child Health © 2012 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

Paediatric specialist registrars' views of educational supervision and how it can be improved: a questionnaire study.

PubMed

Lloyd, B W; Becker, D

2007-08-01

To determine what paediatric specialist registrars think of the educational supervision they have received and what advice they would give to a consultant who wanted to be a more effective educational supervisor. A questionnaire study. The North Thames Deanery, UK. 129 year 3, 4 or 5 paediatric specialist registrars in the North Thames Deanery. Reported value of educational supervision on a Likert scale; what elements of educational supervision were reported to be most useful; what elements of educational supervision were reported to be done poorly; what advice would specialist registrars give to a consultant who wanted to be a more effective educational supervisor. 86/129 specialist registrars responded (67%). The mean score on the Likert scale (0-a complete waste of time; 100-excellent) was 57 with 37% of respondents giving a score of less than 50. The most valued aspects of educational supervision were: feedback on performance--cited by 50 respondents (56% of respondents); career advice--cited by 43 (48%); objective setting--cited by 36 (40%); pastoral support--cited by 25 (28%). Aspects of educational supervision that were reported to be often not done well were: commitment to educational supervision--cited by 44 respondents (49% of respondents); ensuring sessions are bleep-free-cited by 43 (48%); listening rather than talking--cited by 23 (26%); being encouraging--cited by 18 (20%). Advice to consultants about how to improve educational supervision included: listen rather than talk; be encouraging; treat the trainee as an individual with individual needs. We can find no other study of trainees' views about how educational supervision can be improved. Although some trainees found educational supervision very valuable, many did not. Educational supervision should only be carried out by consultants who are committed to the task. An educational supervisor should listen carefully in order to understand the trainee's individual ambitions and needs, should provide specific feedback on performance and should be encouraging.

Improving practice in community-based settings: a randomized trial of supervision - study protocol.

PubMed

Dorsey, Shannon; Pullmann, Michael D; Deblinger, Esther; Berliner, Lucy; Kerns, Suzanne E; Thompson, Kelly; Unützer, Jürgen; Weisz, John R; Garland, Ann F

2013-08-10

Evidence-based treatments for child mental health problems are not consistently available in public mental health settings. Expanding availability requires workforce training. However, research has demonstrated that training alone is not sufficient for changing provider behavior, suggesting that ongoing intervention-specific supervision or consultation is required. Supervision is notably under-investigated, particularly as provided in public mental health. The degree to which supervision in this setting includes 'gold standard' supervision elements from efficacy trials (e.g., session review, model fidelity, outcome monitoring, skill-building) is unknown. The current federally-funded investigation leverages the Washington State Trauma-focused Cognitive Behavioral Therapy Initiative to describe usual supervision practices and test the impact of systematic implementation of gold standard supervision strategies on treatment fidelity and clinical outcomes. The study has two phases. We will conduct an initial descriptive study (Phase I) of supervision practices within public mental health in Washington State followed by a randomized controlled trial of gold standard supervision strategies (Phase II), with randomization at the clinician level (i.e., supervisors provide both conditions). Study participants will be 35 supervisors and 130 clinicians in community mental health centers. We will enroll one child per clinician in Phase I (N = 130) and three children per clinician in Phase II (N = 390). We use a multi-level mixed within- and between-subjects longitudinal design. Audio recordings of supervision and therapy sessions will be collected and coded throughout both phases. Child outcome data will be collected at the beginning of treatment and at three and six months into treatment. This study will provide insight into how supervisors can optimally support clinicians delivering evidence-based treatments. Phase I will provide descriptive information, currently unavailable in the literature, about commonly used supervision strategies in community mental health. The Phase II randomized controlled trial of gold standard supervision strategies is, to our knowledge, the first experimental study of gold standard supervision strategies in community mental health and will yield needed information about how to leverage supervision to improve clinician fidelity and client outcomes. ClinicalTrials.gov NCT01800266.

Improving practice in community-based settings: a randomized trial of supervision – study protocol

PubMed Central

2013-01-01

Background Evidence-based treatments for child mental health problems are not consistently available in public mental health settings. Expanding availability requires workforce training. However, research has demonstrated that training alone is not sufficient for changing provider behavior, suggesting that ongoing intervention-specific supervision or consultation is required. Supervision is notably under-investigated, particularly as provided in public mental health. The degree to which supervision in this setting includes ‘gold standard’ supervision elements from efficacy trials (e.g., session review, model fidelity, outcome monitoring, skill-building) is unknown. The current federally-funded investigation leverages the Washington State Trauma-focused Cognitive Behavioral Therapy Initiative to describe usual supervision practices and test the impact of systematic implementation of gold standard supervision strategies on treatment fidelity and clinical outcomes. Methods/Design The study has two phases. We will conduct an initial descriptive study (Phase I) of supervision practices within public mental health in Washington State followed by a randomized controlled trial of gold standard supervision strategies (Phase II), with randomization at the clinician level (i.e., supervisors provide both conditions). Study participants will be 35 supervisors and 130 clinicians in community mental health centers. We will enroll one child per clinician in Phase I (N = 130) and three children per clinician in Phase II (N = 390). We use a multi-level mixed within- and between-subjects longitudinal design. Audio recordings of supervision and therapy sessions will be collected and coded throughout both phases. Child outcome data will be collected at the beginning of treatment and at three and six months into treatment. Discussion This study will provide insight into how supervisors can optimally support clinicians delivering evidence-based treatments. Phase I will provide descriptive information, currently unavailable in the literature, about commonly used supervision strategies in community mental health. The Phase II randomized controlled trial of gold standard supervision strategies is, to our knowledge, the first experimental study of gold standard supervision strategies in community mental health and will yield needed information about how to leverage supervision to improve clinician fidelity and client outcomes. Trial registration ClinicalTrials.gov NCT01800266 PMID:23937766

Professional Supervision as Storied Experience: Narrative Analysis Findings for Australian-Based Registered Music Therapists.

PubMed

Kennelly, Jeanette D; Baker, Felicity A; Daveson, Barbara A

2017-03-01

Limited research exists to inform a music therapist's supervision story from their pre-professional training to their practice as a professional. Evidence is needed to understand the complex nature of supervision experiences and their impact on professional practice. This qualitative study explored the supervisory experiences of Australian-based Registered Music Therapists, according to the: 1) themes that characterize their experiences, 2) influences of the supervisor's professional background, 3) outcomes of supervision, and 4) roles of the employer, the professional music therapy association, and the university in supervision standards and practice. Seven professionals were interviewed for this study. Five stages of narrative analysis were used to create their supervision stories: a life course graph, narrative psychological analysis, component story framework and narrative analysis, analysis of narratives, and final integration of the seven narrative summaries. Findings revealed that supervision practice is influenced by a supervisee's personal and professional needs. A range of supervision models or approaches is recommended, including the access of supervisors from different professional backgrounds to support each stage of learning and development. A quality supervisory experience facilitates shifts in awareness and insight, which results in improved or increased skills, confidence, and accountability of practice. Participants' concern about stakeholders included a limited understanding of the role of the supervisor, a lack of clarity about accountability of supervisory practice, and minimal guidelines, which monitor professional competencies. The benefits of supervision in music therapy depend on the quality of the supervision provided, and clarity about the roles of those involved. Research and guidelines are recommended to target these areas. © the American Music Therapy Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Experiences of registered nurses who supervise international nursing students in the clinical and classroom setting: an integrative literature review.

PubMed

Newton, Louise; Pront, Leeanne; Giles, Tracey M

2016-06-01

To examine the literature reporting the experiences and perceptions of registered nurses who supervise international nursing students in the clinical and classroom setting. Nursing education relies on clinical experts to supervise students during classroom and clinical education, and the quality of that supervision has a significant impact on student development and learning. Global migration and internationalisation of nursing education have led to increasing numbers of registered nurses supervising international nursing students. However, a paucity of relevant literature limits our understanding of these experiences. An integrative literature review. Comprehensive database searches of CINAHL, Informit, PubMed, Journals@Ovid, Findit@flinders and Medline were undertaken. Screening of 179 articles resulted in 10 included for review. Appraisal and analysis using Whittemore and Knafl's (Journal of Advanced Nursing, 52, 2005, 546) five stage integrative review recommendations was undertaken. This review highlighted some unique challenges for registered nurses supervising international nursing students. Identified issues were, a heightened sense of responsibility, additional pastoral care challenges, considerable time investments, communication challenges and cultural differences between teaching and learning styles. It is possible that these unique challenges could be minimised by implementing role preparation programmes specific to international nursing student supervision. Further research is needed to provide an in-depth exploration of current levels of preparation and support to make recommendations for future practice, education and policy development. An awareness of the specific cultural learning needs of international nursing students is an important first step to the provision of culturally competent supervision for this cohort of students. There is an urgent need for education and role preparation for all registered nurses supervising international nursing students, along with adequate recognition of the additional time required to effectively supervise these students. © 2016 John Wiley & Sons Ltd.

Phytoplankton global mapping from space with a support vector machine algorithm

NASA Astrophysics Data System (ADS)

de Boissieu, Florian; Menkes, Christophe; Dupouy, Cécile; Rodier, Martin; Bonnet, Sophie; Mangeas, Morgan; Frouin, Robert J.

2014-11-01

In recent years great progress has been made in global mapping of phytoplankton from space. Two main trends have emerged, the recognition of phytoplankton functional types (PFT) based on reflectance normalized to chlorophyll-a concentration, and the recognition of phytoplankton size class (PSC) based on the relationship between cell size and chlorophyll-a concentration. However, PFTs and PSCs are not decorrelated, and one approach can complement the other in a recognition task. In this paper, we explore the recognition of several dominant PFTs by combining reflectance anomalies, chlorophyll-a concentration and other environmental parameters, such as sea surface temperature and wind speed. Remote sensing pixels are labeled thanks to coincident in-situ pigment data from GeP&CO, NOMAD and MAREDAT datasets, covering various oceanographic environments. The recognition is made with a supervised Support Vector Machine classifier trained on the labeled pixels. This algorithm enables a non-linear separation of the classes in the input space and is especially adapted for small training datasets as available here. Moreover, it provides a class probability estimate, allowing one to enhance the robustness of the classification results through the choice of a minimum probability threshold. A greedy feature selection associated to a 10-fold cross-validation procedure is applied to select the most discriminative input features and evaluate the classification performance. The best classifiers are finally applied on daily remote sensing datasets (SeaWIFS, MODISA) and the resulting dominant PFT maps are compared with other studies. Several conclusions are drawn: (1) the feature selection highlights the weight of temperature, chlorophyll-a and wind speed variables in phytoplankton recognition; (2) the classifiers show good results and dominant PFT maps in agreement with phytoplankton distribution knowledge; (3) classification on MODISA data seems to perform better than on SeaWIFS data, (4) the probability threshold screens correctly the areas of smallest confidence such as the interclass regions.

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning.

PubMed

Månsson, K N T; Frick, A; Boraxbekk, C-J; Marquand, A F; Williams, S C R; Carlbring, P; Andersson, G; Furmark, T

2015-03-17

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a substantial proportion relapse after treatment has ended. Predicting an individual's long-term clinical response therefore remains an important challenge. This study aimed at assessing neural predictors of long-term treatment outcome in participants with SAD 1 year after completion of Internet-delivered CBT (iCBT). Twenty-six participants diagnosed with SAD underwent iCBT including attention bias modification for a total of 13 weeks. Support vector machines (SVMs), a supervised pattern recognition method allowing predictions at the individual level, were trained to separate long-term treatment responders from nonresponders based on blood oxygen level-dependent (BOLD) responses to self-referential criticism. The Clinical Global Impression-Improvement scale was the main instrument to determine treatment response at the 1-year follow-up. Results showed that the proportion of long-term responders was 52% (12/23). From multivariate BOLD responses in the dorsal anterior cingulate cortex (dACC) together with the amygdala, we were able to predict long-term response rate of iCBT with an accuracy of 92% (confidence interval 95% 73.2-97.6). This activation pattern was, however, not predictive of improvement in the continuous Liebowitz Social Anxiety Scale-Self-report version. Follow-up psychophysiological interaction analyses revealed that lower dACC-amygdala coupling was associated with better long-term treatment response. Thus, BOLD response patterns in the fear-expressing dACC-amygdala regions were highly predictive of long-term treatment outcome of iCBT, and the initial coupling between these regions differentiated long-term responders from nonresponders. The SVM-neuroimaging approach could be of particular clinical value as it allows for accurate prediction of treatment outcome at the level of the individual.

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods.

PubMed

Lise, Stefano; Archambeau, Cedric; Pontil, Massimiliano; Jones, David T

2009-10-30

Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (DeltaDeltaG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which DeltaDeltaG >or= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration.

Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set.

PubMed

Ma, Yuefeng; Liang, Xun; Kwok, James T; Li, Jianping; Zhou, Xiaoping; Zhang, Haiyan

2018-04-01

The semisupervised least squares support vector machine (LS-S 3 VM) is an important enhancement of least squares support vector machines in semisupervised learning. Given that most data collected from the real world are without labels, semisupervised approaches are more applicable than standard supervised approaches. Although a few training methods for LS-S 3 VM exist, the problem of deriving the optimal decision hyperplane efficiently and effectually has not been solved. In this paper, a fully weighted model of LS-S 3 VM is proposed, and a simple integer programming (IP) model is introduced through an equivalent transformation to solve the model. Based on the distances between the unlabeled data and the decision hyperplane, a new indicator is designed to represent the possibility that the label of an unlabeled datum should be reversed in each iteration during training. Using the indicator, we construct an extended candidate set consisting of the indices of unlabeled data with high possibilities, which integrates more information from unlabeled data. Our algorithm is degenerated into a special scenario of the previous algorithm when the extended candidate set is reduced into a set with only one element. Two strategies are utilized to determine the descent directions based on the extended candidate set. Furthermore, we developed a novel method for locating a good starting point based on the properties of the equivalent IP model. Combined with the extended candidate set and the carefully computed starting point, a fast algorithm to solve LS-S 3 VM quasi-optimally is proposed. The choice of quasi-optimal solutions results in low computational cost and avoidance of overfitting. Experiments show that our algorithm equipped with the two designed strategies is more effective than other algorithms in at least one of the following three aspects: 1) computational complexity; 2) generalization ability; and 3) flexibility. However, our algorithm and other algorithms have similar levels of performance in the remaining aspects.

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

NASA Astrophysics Data System (ADS)

Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

2016-07-01

Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification technique to the Global Ionospheric Map (GIM) TEC data which is provided by the NASA Jet Propulsion Laboratory (JPL), it will be shown that SVM can be a suitable learning method to detect the anomalies in Total Electron Content (TEC) variations. This study is supported by TUBITAK 114E541 project as a part of the Scientific and Technological Research Projects Funding Program (1001).

Impact of a supervised exercise program supporting autonomy on the adoption of an active lifestyle among obese adolescents: a Self-Determination Theory perspective.

PubMed

Gourlan, M; Sant, F; Boiche, J

2014-12-01

Regular physical activity (PA) practice represents a key component of obesity treatment. Drawing upon Self-Determination Theory, the purpose of this study was twofold. The first aim was to evaluate among obese adolescents the impact of a supervised exercise program supporting autonomy on their motivation to practice PA at the end of the intervention. The second aim was to evaluate the impact of the program on their level of PA one month after the end of the intervention. Eighteen obese adolescents (mean age=14.3 years, mean BMI=33.47 kg/m²) were recruited to participate in an 11-week residential obesity treatment program. They received a 45-minute supervised exercise session each week. Motivational regulations were assessed at baseline and at the end of the intervention (via the Exercise Motivation Scale). PA practice was assessed at baseline and one month after the end of the intervention (via the 7-day PA recall interview). The analyses revealed that adolescents' levels of autonomy increased, that their levels of intrinsic motivation tended to increase, and that their level of external regulation tended to decrease. In addition, the participants increased their habitual PA practice one month after the end of the intervention in comparison to baseline. This study highlights that supporting autonomy during supervised exercise sessions appears as an effective strategy to promote PA among obese adolescents because it fosters internalization of the behavior.

'It gave me a new lease of life … ': GPs' views and experiences of supervising foundation doctors in general practice.

PubMed

Sabey, Abigail; Harris, Michael; van Hamel, Clare

2016-03-01

General practice is a popular placement in the second year of Foundation training. Evaluations suggest this is a positive experience for most trainee doctors and benefits their perceptions of primary care, but the impact on primary care supervisors has not been considered. At a time when placements may need to increase, understanding the experience of the GP supervisors responsible for these placements is important. To explore the views, experiences and needs of GPs who supervise F2 doctors in their practices including their perceptions of the benefits to individuals and practices. A qualitative approach with GPs from across Severn Postgraduate Medical Education who supervise F2 doctors. Semi-structured interviews with 15 GPs between December 2012 and April 2013. GP supervisors are enthusiastic about helping F2 doctors to appreciate the uniqueness of primary care. Workload and responsibility around supervision is considerable making a supportive team important. Working with young, enthusiastic doctors boosts morale in the team. The presence of freshly trained minds prompts GPs to consider their own learning needs. Being a supervisor can increase job satisfaction; the teaching role gives respite from the demanding nature of GP work. Supervisors are positive about working with F2s, who lift morale in the team and challenge GPs in their own practice and learning. This boosts job and personal satisfaction. Nonetheless, consideration should be given to managing teaching workload and team support for supervision.

A Subdivision-Based Representation for Vector Image Editing.

PubMed

Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

2012-11-01

Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

Clinical supervision of nurses working with patients with borderline personality disorder.

PubMed

Bland, Ann R; Rossen, Eileen K

2005-06-01

Some nurses describe individuals diagnosed with borderline personality disorder (BPD) as among the most challenging and difficult patients encountered in their practice. As a result, the argument has been made for nursing staff to receive clinical supervision to enhance therapeutic effectiveness and treatment outcomes for individuals with BPD. Formal clinical supervision can focus on the stresses of working in a demanding environment within the work place and enable nurses to accept accountability for their own practice and development (Pesut & Herman, 1999). A psychiatric-mental health clinical nurse specialist can provide individual and/or group supervision for the nursing staff, including education about patient dynamics, staff responses, and treatment team decisions. A clinical nurse specialist also can provide emotional support to nursing staff, which enhances job satisfaction, as they struggle to maintain professional therapeutic behavior with these individuals.

CNN: a speaker recognition system using a cascaded neural network.

PubMed

Zaki, M; Ghalwash, A; Elkouny, A A

1996-05-01

The main emphasis of this paper is to present an approach for combining supervised and unsupervised neural network models to the issue of speaker recognition. To enhance the overall operation and performance of recognition, the proposed strategy integrates the two techniques, forming one global model called the cascaded model. We first present a simple conventional technique based on the distance measured between a test vector and a reference vector for different speakers in the population. This particular distance metric has the property of weighting down the components in those directions along which the intraspeaker variance is large. The reason for presenting this method is to clarify the discrepancy in performance between the conventional and neural network approach. We then introduce the idea of using unsupervised learning technique, presented by the winner-take-all model, as a means of recognition. Due to several tests that have been conducted and in order to enhance the performance of this model, dealing with noisy patterns, we have preceded it with a supervised learning model--the pattern association model--which acts as a filtration stage. This work includes both the design and implementation of both conventional and neural network approaches to recognize the speakers templates--which are introduced to the system via a voice master card and preprocessed before extracting the features used in the recognition. The conclusion indicates that the system performance in case of neural network is better than that of the conventional one, achieving a smooth degradation in respect of noisy patterns, and higher performance in respect of noise-free patterns.

Improving the quality of hospital care for children by supportive supervision: a cluster randomized trial, Kyrgyzstan

PubMed Central

Shukurova, Venera; Davletbaeva, Marina; Monolbaev, Kubanychbek; Kulichenko, Tatiana; Akoev, Yuri; Bakradze, Maya; Margieva, Tea; Mityushino, Ilya; Namazova-Baranova, Leyla; Boronbayeva, Elnura; Kuttumuratova, Aigul; Weber, Martin Willy; Tamburlini, Giorgio

2017-01-01

Abstract Objective To determine whether periodic supportive supervision after a training course improved the quality of paediatric hospital care in Kyrgyzstan, where inappropriate care was common but in-hospital postnatal mortality was low. Methods In a cluster, randomized, parallel-group trial, 10 public hospitals were allocated to a 4-day World Health Organization (WHO) course on hospital care for children followed by periodic supportive supervision by paediatricians for 1 year, while 10 hospitals had no intervention. We assessed prospectively 10 key indicators of inappropriate paediatric case management, as indicated by WHO guidelines. The primary indicator was the combination of the three indicators: unnecessary hospitalization, increased iatrogenic risk and unnecessary painful procedures. An independent team evaluated the overall quality of care. Findings We prospectively reviewed the medical records of 4626 hospitalized children aged 2 to 60 months. In the intervention hospitals, the mean proportion of the primary indicator decreased from 46.9% (95% confidence interval, CI: 24.2 to 68.9) at baseline to 6.8% (95% CI: 1.1 to 12.1) at 1 year, but was unchanged in the control group (45.5%, 95% CI: 25.2 to 67.9, to 64.7%, 95% CI: 43.3 to 86.1). At 1 year, the risk ratio for the primary indicator in the intervention versus the control group was 0.09 (95% CI: 0.06 to 0.13). The proportions of the other nine indicators also decreased in the intervention group (P < 0.0001 for all). Overall quality of care improved significantly in intervention hospitals. Conclusion Periodic supportive supervision for 1 year after a training course improved both adherence to WHO guidelines on hospital care for children and the overall quality of paediatric care. PMID:28603306

32 CFR 2003.7 - Support Staff (Article VII).

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 6 2014-07-01 2014-07-01 false Support Staff (Article VII). 2003.7 Section 2003... (ISCAP) BYLAWS, RULES, AND APPEAL PROCEDURES Bylaws § 2003.7 Support Staff (Article VII). The staff of..., provides program and administrative support for the Panel. The Executive Secretary supervises the staff in...

32 CFR 2003.7 - Support Staff (Article VII).

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 6 2013-07-01 2013-07-01 false Support Staff (Article VII). 2003.7 Section 2003... (ISCAP) BYLAWS, RULES, AND APPEAL PROCEDURES Bylaws § 2003.7 Support Staff (Article VII). The staff of..., provides program and administrative support for the Panel. The Executive Secretary supervises the staff in...

Supervised space robots are needed in space exploration

NASA Technical Reports Server (NTRS)

Erickson, Jon D.

1994-01-01

High level systems engineering models were developed to simulate and analyze the types, numbers, and roles of intelligent systems, including supervised autonomous robots, which will be required to support human space exploration. Conventional and intelligent systems were compared for two missions: (1) a 20-year option 5A space exploration; and (2) the First Lunar Outpost (FLO). These studies indicate that use of supervised intelligent systems on planet surfaces will 'enable' human space exploration. The author points out that space robotics can be considered a form of the emerging technology of field robotics and solutions to many space applications will apply to problems relative to operating in Earth-based hazardous environments.

Examining clinical supervision as a mechanism for changes in practice: a research protocol.

PubMed

Dilworth, Sophie; Higgins, Isabel; Parker, Vicki; Kelly, Brian; Turner, Jane

2014-02-01

This paper describes the research protocol for a study exploring if and how clinical supervision facilitates change in practice relating to psychosocial aspects of care for Health Professionals, who have been trained to deliver a psychosocial intervention to adults with cancer. There is a recognized need to implement care that is in line with clinical practice guidelines for the psychosocial care of adults with cancer. Clinical supervision is recommended as a means to support Health Professionals in providing the recommended psychosocial care. A qualitative design embedded within an experimental, stepped wedge randomized control trial. The study will use discourse analysis to analyse audio-recorded data collected in clinical supervision sessions that are being delivered as one element of a large randomized control trial. The sessions will be attended primarily by nurses, but including physiotherapists, radiation therapists, occupational therapists. The Health Professionals are participants in a randomized control trial designed to reduce anxiety and depression of distressed adults with cancer. The sessions will be facilitated by psychiatrists experienced in psycho-oncology and the provision of clinical supervision. The proposed research is designed specifically to facilitate exploration of the mechanisms by which clinical supervision enables Health Professionals to deliver a brief, tailored psychosocial intervention in the context of their everyday practice. This is the first study to use discourse analysis embedded within an experimental randomized control trial to explore the mechanisms of change generated within clinical supervision by analysing the discourse within the clinical supervision sessions. © 2013 John Wiley & Sons Ltd.

The influence of supervision on manual adherence and therapeutic processes.

PubMed

Anderson, Timothy; Crowley, Mary Ellen J; Patterson, Candace L; Heckman, Bernadette D

2012-09-01

To identify the effectiveness of psychotherapy supervision on therapists' immediate (next session) and long-term (1 year) adherence to time-limited dynamic psychotherapy (TLDP). Sixteen therapists from the Vanderbilt II psychotherapy project were assigned new cases in pretraining, training, and booster/posttraining year-long cohorts. Technical adherence to the manual, as well as general therapeutic relational processes, were rated for clinical supervisory sessions in which the third therapy session was discussed. The therapy sessions immediately before and after the supervisory sessions were also rated for technical adherence and relational processes. Postsupervision adherence increased from the presupervision session during the training cohort. In supervision, therapists' discussion of techniques and strategies from the manual in supervision was significantly related to technical adherence in the session prior to (but not after) supervision. However, supervisors' discussion of specific techniques predicted therapists' total technical adherence in the therapy session after (but not before) supervision. In terms of the type of techniques, supervisors' influenced postsupervision therapy adherence on TLDP's unique approach to formulation, the cyclical maladaptive pattern, but did not influence technical adherence on the therapeutic relationship. In supervision, therapists tend to focus on how they adhered to techniques from the previous session, whereas supervisors' comments about specific techniques predicted how the therapist would adhere to techniques in the next therapy session. The findings provide support for the immediate effects of supervision in shaping therapist techniques as well as highlighting the challenges of altering common relational processes through technical training. © 2012 Wiley Periodicals, Inc.

Insect cell transformation vectors that support high level expression and promoter assessment in insect cell culture

USDA-ARS?s Scientific Manuscript database

A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus th...

Helping Hands: Using Augmented Reality to Provide Remote Guidance to Health Professionals.

PubMed

Mather, Carey; Barnett, Tony; Broucek, Vlasti; Saunders, Annette; Grattidge, Darren; Huang, Weidong

2017-01-01

Access to expert practitioners or geographic distance can compound the capacity for appropriate supervision of health professionals in the workplace. Guidance and support of clinicians and students to undertake new or infrequent procedures can be resource intensive. The Helping Hands remote augmented reality system is an innovation to support the development of, and oversee the acquisition of procedural skills through remote learning and teaching supervision while in clinical practice. Helping Hands is a wearable, portable, hands-free, low cost system comprised of two networked laptops, a head-mounted display worn by the recipient and a display screen used remotely by the instructor. Hand hygiene was used as the test procedure as it is a foundation skill learned by all health profession students. The technology supports unmediated remote gesture guidance by augmenting the object with the Helping Hands of a health professional. A laboratory-based study and field trial tested usability and feasibility of the remote guidance system. The study found the Helping Hands system did not compromise learning outcomes. This innovation has the potential to transform remote learning and teaching supervision by enabling health professionals and students opportunities to develop and improve their procedural performance at the workplace.

Health visitor professional education and post-qualification clinical supervision: how well does it equip practitioners for dealing with ethical tensions associated with promoting the public health agenda to individual clients?

PubMed

Greenway, Julie C; Entwistle, Vikki A; Termeulen, Ruud

2013-01-01

To explore how well professional education and post-qualification clinical supervision support equips health visitors to deal with ethical tensions associated with implementing the public health agenda while also being responsive to individual clients. Current health policy in England gives health visitors a key role in implementing the government's public health agenda. Health visitors are also required by their Professional Code to respond to the health-related concerns and preferences of their individual clients. This generates a number of public health-related ethical tensions. Exploratory cross-sectional qualitative (interpretive) study using 29 semi-structured individual interviews with health visitors, practice teachers and university lecturers exploring how well health visitors' professional education and post-qualification clinical supervision support equips them for dealing with these ethical tensions and whether they thought further ethics education was needed. Interviews were audio-recorded, transcribed and analysed thematically using a Framework approach. Health visitors' professional education did not always equip them to deal with ethical tensions, which arose from delivering public health interventions to their clients. However, the majority of participants thought that ethics could not be taught in a way that would equip health visitors for every situation and that ongoing post-qualification clinical supervision support was also needed, particularly in the first year after qualifying. The amount of post-qualification support available to practising health visitors was variable with some health visitors unable to access such support due to their working circumstances and pressures on staff time. Literature on the ethical tensions associated with evidence-based practice; public health ethics and ethics of care might be useful for health visitors in gaining greater understanding of the ethical tensions they face. This could be introduced as part of health visitors' professional education or on post-qualification study days.

A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Sainani, Varsha

The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

Parental influences on adolescent sexual behaviors.

PubMed

Rupp, Richard; Rosenthal, Susan L

2007-12-01

Parents play a significant role in the sexual development and behaviors of their children. Parental monitoring and supervision are important avenues for keeping adolescents from risky situations and activities while the teen develops responsible decision-making skills. A supportive relationship between the parent and adolescent is important for enhancing communication and supervision. In this article we discuss programs that were designed to improve parenting skills to decrease adolescent sexual risk behaviors.

[Support vector machine?assisted diagnosis of human malignant gastric tissues based on dielectric properties].

PubMed

Zhang, Sa; Li, Zhou; Xin, Xue-Gang

2017-12-20

To achieve differential diagnosis of normal and malignant gastric tissues based on discrepancies in their dielectric properties using support vector machine. The dielectric properties of normal and malignant gastric tissues at the frequency ranging from 42.58 to 500 MHz were measured by coaxial probe method, and the Cole?Cole model was used to fit the measured data. Receiver?operating characteristic (ROC) curve analysis was used to evaluate the discrimination capability with respect to permittivity, conductivity, and Cole?Cole fitting parameters. Support vector machine was used for discriminating normal and malignant gastric tissues, and the discrimination accuracy was calculated using k?fold cross? The area under the ROC curve was above 0.8 for permittivity at the 5 frequencies at the lower end of the measured frequency range. The combination of the support vector machine with the permittivity at all these 5 frequencies combined achieved the highest discrimination accuracy of 84.38% with a MATLAB runtime of 3.40 s. The support vector machine?assisted diagnosis is feasible for human malignant gastric tissues based on the dielectric properties.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

NASA Astrophysics Data System (ADS)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

The association of health and voluntary early retirement pension and the modifying effect of quality of supervision: Results from a Danish register-based follow-up study.

PubMed

Wind, Astrid de; Burr, Hermann; Pohrt, Anne; Hasselhorn, Hans Martin; Van der Beek, Allard Johan; Rugulies, Reiner

2017-07-01

The aims of this article are to (1) determine whether and to what extent general perceived health and quality of supervision predict voluntary early retirement pension (VERP) and (2) assess whether quality of supervision modifies the association between general perceived health and VERP. Employees aged 49-64 years who participated in the Danish Work Environment Cohort Study in 2000 were selected. Their questionnaire data about health and work were linked to register data on social transfer payments, among others VERP, from 2001 to 2012 in the Danish Register for Evaluation of Marginalization ( N=1167). Cox proportional hazards analyses were performed to identify the prospective association of general perceived health and quality of supervision on VERP. Relative excess risks due to interaction (RERIs) were calculated to assess whether quality of supervision modified the association between health and VERP. Employees with poor health at baseline had an increased risk of VERP during follow-up (hazard ratio [HR]=1.23; 95% confidence interval [CI] 1.02-1.49). Quality of supervision at baseline was not associated to VERP during follow-up (HR=1.04; 95% CI 0.90-1.21). There was no statistically significant interaction of poor health and poor quality of supervision with regard to risk of VERP (RERI=-0.33; 95% CI -1.79 to 1.14). This study did not support the notion that quality of supervision buffers the association between poor health and VERP. Future research is needed to determine whether other aspects of supervision, for example supervisors' opportunities to effectuate workplace adjustments, may modify the association of poor health and VERP.

The association of health and voluntary early retirement pension and the modifying effect of quality of supervision: Results from a Danish register-based follow-up study

PubMed Central

de Wind, Astrid; Burr, Hermann; Pohrt, Anne; Hasselhorn, Hans Martin; Van der Beek, Allard Johan; Rugulies, Reiner

2017-01-01

Objectives: The aims of this article are to (1) determine whether and to what extent general perceived health and quality of supervision predict voluntary early retirement pension (VERP) and (2) assess whether quality of supervision modifies the association between general perceived health and VERP. Methods: Employees aged 49–64 years who participated in the Danish Work Environment Cohort Study in 2000 were selected. Their questionnaire data about health and work were linked to register data on social transfer payments, among others VERP, from 2001 to 2012 in the Danish Register for Evaluation of Marginalization (N=1167). Cox proportional hazards analyses were performed to identify the prospective association of general perceived health and quality of supervision on VERP. Relative excess risks due to interaction (RERIs) were calculated to assess whether quality of supervision modified the association between health and VERP. Results: Employees with poor health at baseline had an increased risk of VERP during follow-up (hazard ratio [HR]=1.23; 95% confidence interval [CI] 1.02–1.49). Quality of supervision at baseline was not associated to VERP during follow-up (HR=1.04; 95% CI 0.90–1.21). There was no statistically significant interaction of poor health and poor quality of supervision with regard to risk of VERP (RERI=−0.33; 95% CI −1.79 to 1.14). Conclusions: This study did not support the notion that quality of supervision buffers the association between poor health and VERP. Future research is needed to determine whether other aspects of supervision, for example supervisors’ opportunities to effectuate workplace adjustments, may modify the association of poor health and VERP. PMID:28381121

Supervising and Supporting Student Nurses in Clinical Placements: The Peer Support Initiative.

ERIC Educational Resources Information Center

Aston, Liz; Molassiotis, Alexander

2003-01-01

A program in which senior nursing students (n=31) supported junior students (n=27) in clinical placements was evaluated. Peer support was considered valuable, but both groups desired more preparation for their roles. Seniors felt their teaching and mentoring skills were enhanced. Juniors reduced anxiety about placements. (Contains 26 references.)…

The Design of a Templated C++ Small Vector Class for Numerical Computing

NASA Technical Reports Server (NTRS)

Moran, Patrick J.

2000-01-01

We describe the design and implementation of a templated C++ class for vectors. The vector class is templated both for vector length and vector component type; the vector length is fixed at template instantiation time. The vector implementation is such that for a vector of N components of type T, the total number of bytes required by the vector is equal to N * size of (T), where size of is the built-in C operator. The property of having a size no bigger than that required by the components themselves is key in many numerical computing applications, where one may allocate very large arrays of small, fixed-length vectors. In addition to the design trade-offs motivating our fixed-length vector design choice, we review some of the C++ template features essential to an efficient, succinct implementation. In particular, we highlight some of the standard C++ features, such as partial template specialization, that are not supported by all compilers currently. This report provides an inventory listing the relevant support currently provided by some key compilers, as well as test code one can use to verify compiler capabilities.

Stochastic-Constraints Method in Nonstationary Hot-Clutter Cancellation Part I: Fundamentals and Supervised Training Applications

DTIC Science & Technology

2003-04-01

any of the P interfering sources, and Hkt i (1) (P)] T is defined below. The P-variate vector = t kt , • t J consists of complex waveforms radiated by...line. More precisely, the (i, j ) t element of the matrix Hke is a complex 4-4 coefficient which is practically constant over the kth PRI, and is a...multivariate auto-regressive (AR) model of order n: Ykt + Z Bj Yk- j , t = tkt (25) j =l In the above equation, Bj are the M-variate matrices which are the

An Efficient Wait-Free Vector

DOE PAGES

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

2016-03-01

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

Work-supportive family, family-supportive supervision, use of organizational benefits, and problem-focused coping: implications for work-family conflict and employee well-being.

PubMed

Lapierre, Laurent M; Allen, Tammy D

2006-04-01

Employees (n = 230) from multiple organizations and industries were involved in a study assessing how work-family conflict avoidance methods stemming from the family domain (emotional sustenance and instrumental assistance from the family), the work domain (family-supportive supervision, use of telework and flextime), and the individual (use of problem-focused coping) independently relate to different dimensions of work-family conflict and to employees' affective and physical well-being. Results suggest that support from one's family and one's supervisor and the use of problem-focused coping seem most promising in terms of avoiding work-family conflict and/or decreased well-being. Benefits associated with the use of flextime, however, are relatively less evident, and using telework may potentially increase the extent to which family time demands interfere with work responsibilities. (c) 2006 APA, all rights reserved.

Factors Supporting the Employment of Young Adult Peer Providers: Perspectives of Peers and Supervisors.

PubMed

Delman, Jonathan; Klodnick, Vanessa V

2017-10-01

Peer providers are a promising practice for transition-age youth community mental health treatment engagement and support, yet little is known about the experience of being a young adult peer provider or what helps to make an individual in this role successful. Utilizing a capital theory lens, this study uses data from focus groups (two with young adult peer providers and two with their supervisors) to examine facilitators of young adult peer provider success in community mental health treatment settings. Eight factors were identified as critical to young adult peer provider on-the-job success: persistence, job confidence, resilience, job training, skilled communications with colleagues, regular and individualized supervision, support from colleagues, and family support. Findings suggest that young adult peer providers may benefit immensely from an agency level focus on fostering social organizational capital as well as more individualized efforts to increase cultural, social, and psychological capital through training and supervision.

A device for automatically measuring and supervising the critical care patient's urine output.

PubMed

Otero, Abraham; Palacios, Francisco; Akinfiev, Teodor; Fernández, Roemi

2010-01-01

Critical care units are equipped with commercial monitoring devices capable of sensing patients' physiological parameters and supervising the achievement of the established therapeutic goals. This avoids human errors in this task and considerably decreases the workload of the healthcare staff. However, at present there still is a very relevant physiological parameter that is measured and supervised manually by the critical care units' healthcare staff: urine output. This paper presents a patent-pending device capable of automatically recording and supervising the urine output of a critical care patient. A high precision scale is used to measure the weight of a commercial urine meter. On the scale's pan there is a support frame made up of Bosch profiles that isolates the scale from force transmission from the patient's bed, and guarantees that the urine flows properly through the urine meter input tube. The scale's readings are sent to a PC via Bluetooth where an application supervises the achievement of the therapeutic goals. The device is currently undergoing tests at a research unit associated with the University Hospital of Getafe in Spain.

Radio-ecological characterization and radiological assessment in support of regulatory supervision of legacy sites in northwest Russia.

PubMed

Sneve, M K; Kiselev, M; Shandala, N K

2014-05-01

The Norwegian Radiation Protection Authority has been implementing a regulatory cooperation program in the Russian Federation for over 10 years, as part of the Norwegian government's Plan of Action for enhancing nuclear and radiation safety in northwest Russia. The overall long-term objective has been the enhancement of safety culture and includes a special focus on regulatory supervision of nuclear legacy sites. The initial project outputs included appropriate regulatory threat assessments, to determine the hazardous situations and activities which are most in need of enhanced regulatory supervision. In turn, this has led to the development of new and updated norms and standards, and related regulatory procedures, necessary to address the often abnormal conditions at legacy sites. This paper presents the experience gained within the above program with regard to radio-ecological characterization of Sites of Temporary Storage for spent nuclear fuel and radioactive waste at Andreeva Bay and Gremikha in the Kola Peninsula in northwest Russia. Such characterization is necessary to support assessments of the current radiological situation and to support prospective assessments of its evolution. Both types of assessments contribute to regulatory supervision of the sites. Accordingly, they include assessments to support development of regulatory standards and guidance concerning: control of radiation exposures to workers during remediation operations; emergency preparedness and response; planned radionuclide releases to the environment; development of site restoration plans, and waste treatment and disposal. Examples of characterization work are presented which relate to terrestrial and marine environments at Andreeva Bay. The use of this data in assessments is illustrated by means of the visualization and assessment tool (DATAMAP) developed as part of the regulatory cooperation program, specifically to help control radiation exposure in operations and to support regulatory analysis of management options. For assessments of the current radiological situation, the types of data needed include information about the distribution of radionuclides in environmental media. For prognostic assessments, additional data are needed about the landscape features, on-shore and off-shore hydrology, geochemical properties of soils and sediments, and possible continuing source terms from continuing operations and on-site disposal. It is anticipated that shared international experience in legacy site characterization can be useful in the next steps. Although the output has been designed to support regulatory evaluation of these particular sites in northwest Russia, the methods and techniques are considered useful examples for application elsewhere, as well as providing relevant input to the International Atomic Energy Agency's international Working Forum for the Regulatory Supervision of Legacy Sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

Best practices in nursing homes. Clinical supervision, management, and human resource practices.

PubMed

Dellefield, Mary Ellen

2008-07-01

Human resource practices including supervision and management are associated with organizational performance. Evidence supportive of such an association in nursing homes is found in the results of numerous research studies conducted during the past 17 years. In this article, best practices related to this topic have been culled from descriptive, explanatory, and intervention studies in a range of interdisciplinary research journals published between 1990 and 2007. Identified best practices include implementation of training programs on supervision and management for licensed nurses, certified nursing assistant job enrichment programs, implementation of consistent nursing assignments, and the use of electronic documentation. Organizational barriers and facilitators of these best practices are described. Copyright 2009, SLACK Incorporated.

Supervision for superheroes: the case for reflective professional supervision for senior doctors.

PubMed

Austin, Helen

2016-05-06

The practice of medicine is inherently stressful with regular exposure to trauma and the distress of others. There is a culture in medicine that doctors should not be affected by such things, although it is well recognised that doctors have higher rates of depression, anxiety, suicide, and substance abuse than the general public. Reflective professional supervision is a forum where the complexities of the interpersonal interactions that underpin the provision of healthcare can be explored in a supportive and confidential setting. It is argued that this is a process that should continue for the duration of a doctor's career, with potential benefits including enhanced job satisfaction and resilience, better workplace communication and improved interpersonal skills.

Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach

PubMed Central

Jagannatha, Abhyuday N; Fodeh, Samah J; Yu, Hong

2017-01-01

Background Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first. Objective We aimed to develop an NLP system, called adapted distant supervision (ADS), to rank candidate terms mined from EHR corpora. We will give EHR terms ranked as high by ADS a higher priority for lay language annotation—that is, creating lay definitions for these terms. Methods Adapted distant supervision uses distant supervision from consumer health vocabulary and transfer learning to adapt itself to solve the problem of ranking EHR terms in the target domain. We investigated 2 state-of-the-art transfer learning algorithms (ie, feature space augmentation and supervised distant supervision) and designed 5 types of learning features, including distributed word representations learned from large EHR data for ADS. For evaluating ADS, we asked domain experts to annotate 6038 candidate terms as important or nonimportant for EHR comprehension. We then randomly divided these data into the target-domain training data (1000 examples) and the evaluation data (5038 examples). We compared ADS with 2 strong baselines, including standard supervised learning, on the evaluation data. Results The ADS system using feature space augmentation achieved the best average precision, 0.850, on the evaluation set when using 1000 target-domain training examples. The ADS system using supervised distant supervision achieved the best average precision, 0.819, on the evaluation set when using only 100 target-domain training examples. The 2 ADS systems both performed significantly better than the baseline systems (P<.001 for all measures and all conditions). Using a rich set of learning features contributed to ADS’s performance substantially. Conclusions ADS can effectively rank terms mined from EHRs. Transfer learning improved ADS’s performance even with a small number of target-domain training examples. EHR terms prioritized by ADS were used to expand a lay language resource that supports patient EHR comprehension. The top 10,000 EHR terms ranked by ADS are available upon request. PMID:29089288

Minimal supervision out-patient clinical teaching.

PubMed

Figueiró-Filho, Ernesto Antonio; Amaral, Eliana; McKinley, Danette; Bezuidenhout, Juanita; Tekian, Ara

2014-08-01

Minimal faculty member supervision of students refers to a method of instruction in which the patient-student encounter is not directly supervised by a faculty member, and presents a feasible solution in clinical teaching. It is unclear, however, how such practices are perceived by patients and how they affect student learning. We aimed to assess patient and medical student perceptions of clinical teaching with minimal faculty member supervision. Questionnaires focusing on the perception of students' performance were administered to patients pre- and post-consultation. Students' self-perceptions on their performance were obtained using a questionnaire at the end of the consultation. Before encounters with students, 22 per cent of the 95 patients were not sure if they would feel comfortable or trust the students; after the consultation, almost all felt comfortable (97%) and relied on the students (99%). The 81 students surveyed agreed that instruction with minimal faculty member supervision encouraged their participation and engagement (86%). They expressed interest in knowing patients' opinions about their performance (94%), and they felt comfortable about being assessed by the patients (86%). The minimal faculty member supervision model was well accepted by patients. Responses from the final-year students support the use of assessments that incorporate feedback from patients in their overall clinical evaluations. © 2014 John Wiley & Sons Ltd.

Facilitating the learning process in design-based learning practices: an investigation of teachers' actions in supervising students

NASA Astrophysics Data System (ADS)

Gómez Puente, S. M.; van Eijck, M.; Jochems, W.

2013-11-01

Background: In research on design-based learning (DBL), inadequate attention is paid to the role the teacher plays in supervising students in gathering and applying knowledge to design artifacts, systems, and innovative solutions in higher education. Purpose: In this study, we examine whether teacher actions we previously identified in the DBL literature as important in facilitating learning processes and student supervision are present in current DBL engineering practices. Sample: The sample (N=16) consisted of teachers and supervisors in two engineering study programs at a university of technology: mechanical and electrical engineering. We selected randomly teachers from freshman and second-year bachelor DBL projects responsible for student supervision and assessment. Design and method: Interviews with teachers, and interviews and observations of supervisors were used to examine how supervision and facilitation actions are applied according to the DBL framework. Results: Major findings indicate that formulating questions is the most common practice seen in facilitating learning in open-ended engineering design environments. Furthermore, other DBL actions we expected to see based upon the literature were seldom observed in the coaching practices within these two programs. Conclusions: Professionalization of teachers in supervising students need to include methods to scaffold learning by supporting students in reflecting and in providing formative feedback.

Intellectual Production Supervision Perform based on RFID Smart Electricity Meter

NASA Astrophysics Data System (ADS)

Chen, Xiangqun; Huang, Rui; Shen, Liman; chen, Hao; Xiong, Dezhi; Xiao, Xiangqi; Liu, Mouhai; Xu, Renheng

2018-03-01

This topic develops the RFID intelligent electricity meter production supervision project management system. The system is designed for energy meter production supervision in the management of the project schedule, quality and cost information management requirements in RFID intelligent power, and provide quantitative information more comprehensive, timely and accurate for supervision engineer and project manager management decisions, and to provide technical information for the product manufacturing stage file. From the angle of scheme analysis, design, implementation and test, the system development of production supervision project management system for RFID smart meter project is discussed. Focus on the development of the system, combined with the main business application and management mode at this stage, focuses on the energy meter to monitor progress information, quality information and cost based information on RFID intelligent power management function. The paper introduces the design scheme of the system, the overall client / server architecture, client oriented graphical user interface universal, complete the supervision of project management and interactive transaction information display, the server system of realizing the main program. The system is programmed with C# language and.NET operating environment, and the client and server platforms use Windows operating system, and the database server software uses Oracle. The overall platform supports mainstream information and standards and has good scalability.

The Nexus between Instructional Supervision, Supervisors' and Teachers': The Practical Paradox and Its Effect on Quality Education a Case of Woliat Zone Administration Elementary and Secondary Schools (1-8)

ERIC Educational Resources Information Center

Dea, Mulatu

2016-01-01

Even though the new instructional supervision practices materialized in the schools level, teachers were not properly supported well, so that the students achievements was decreasing in national exams than before as the regional grade report documents revealed and quality is deteriorating from time to times. Hence, the main objective of this study…

45 CFR 95.505 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., and allocating all State agency costs incurred in support of all programs administered or supervised... Department of Health and Human Services (HHS) organizational components responsible for administering public... Services, Office of Child Support Enforcement,Centers for Medicare & Medicaid Services, and Office of...

NetVLAD: CNN Architecture for Weakly Supervised Place Recognition.

PubMed

Arandjelovic, Relja; Gronat, Petr; Torii, Akihiko; Pajdla, Tomas; Sivic, Josef

2018-06-01

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following four principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we create a new weakly supervised ranking loss, which enables end-to-end learning of the architecture's parameters from images depicting the same places over time downloaded from Google Street View Time Machine. Third, we develop an efficient training procedure which can be applied on very large-scale weakly labelled tasks. Finally, we show that the proposed architecture and training procedure significantly outperform non-learnt image representations and off-the-shelf CNN descriptors on challenging place recognition and image retrieval benchmarks.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

Harvesting geographic features from heterogeneous raster maps

NASA Astrophysics Data System (ADS)

Chiang, Yao-Yi

2010-11-01

Raster maps offer a great deal of geospatial information and are easily accessible compared to other geospatial data. However, harvesting geographic features locked in heterogeneous raster maps to obtain the geospatial information is challenging. This is because of the varying image quality of raster maps (e.g., scanned maps with poor image quality and computer-generated maps with good image quality), the overlapping geographic features in maps, and the typical lack of metadata (e.g., map geocoordinates, map source, and original vector data). Previous work on map processing is typically limited to a specific type of map and often relies on intensive manual work. In contrast, this thesis investigates a general approach that does not rely on any prior knowledge and requires minimal user effort to process heterogeneous raster maps. This approach includes automatic and supervised techniques to process raster maps for separating individual layers of geographic features from the maps and recognizing geographic features in the separated layers (i.e., detecting road intersections, generating and vectorizing road geometry, and recognizing text labels). The automatic technique eliminates user intervention by exploiting common map properties of how road lines and text labels are drawn in raster maps. For example, the road lines are elongated linear objects and the characters are small connected-objects. The supervised technique utilizes labels of road and text areas to handle complex raster maps, or maps with poor image quality, and can process a variety of raster maps with minimal user input. The results show that the general approach can handle raster maps with varying map complexity, color usage, and image quality. By matching extracted road intersections to another geospatial dataset, we can identify the geocoordinates of a raster map and further align the raster map, separated feature layers from the map, and recognized features from the layers with the geospatial dataset. The road vectorization and text recognition results outperform state-of-art commercial products, and with considerably less user input. The approach in this thesis allows us to make use of the geospatial information of heterogeneous maps locked in raster format.

Community-based biological control of malaria mosquitoes using Bacillus thuringiensis var. israelensis (Bti) in Rwanda: community awareness, acceptance and participation.

PubMed

Ingabire, Chantal Marie; Hakizimana, Emmanuel; Rulisa, Alexis; Kateera, Fredrick; Van Den Borne, Bart; Muvunyi, Claude Mambo; Mutesa, Leon; Van Vugt, Michelle; Koenraadt, Constantianus J M; Takken, Willem; Alaii, Jane

2017-10-03

Targeting the aquatic stages of malaria vectors via larval source management (LSM) in collaboration with local communities could accelerate progress towards malaria elimination when deployed in addition to existing vector control strategies. However, the precise role that communities can assume in implementing such an intervention has not been fully investigated. This study investigated community awareness, acceptance and participation in a study that incorporated the socio-economic and entomological impact of LSM using Bacillus thuringiensis var. israelensis (Bti) in eastern Rwanda, and identified challenges and recommendations for future scale-up. The implementation of the community-based LSM intervention took place in Ruhuha, Rwanda, from February to July 2015. The intervention included three arms: control, community-based (CB) and project-supervised (PS). Mixed methods were used to collect baseline and endline socio-economic data in January and October 2015. A high perceived safety and effectiveness of Bti was reported at the start of the intervention. Being aware of malaria symptoms and perceiving Bti as safe on other living organisms increased the likelihood of community participation through investment of labour time for Bti application. On the other hand, the likelihood for community participation was lower if respondents: (1) perceived rice farming as very profitable; (2) provided more money to the cooperative as a capital; and, (3) were already involved in rice farming for more than 6 years. After 6 months of implementation, an increase in knowledge and skills regarding Bti application was reported. The community perceived a reduction in mosquito density and nuisance biting on treated arms. Main operational, seasonal and geographical challenges included manual application of Bti, long working hours, and need for transportation for reaching the fields. Recommendations were made for future scale-up, including addressing above-mentioned concerns and government adoption of LSM as part of its vector control strategies. Community awareness and support for LSM increased following Bti application. A high effectiveness of Bti in terms of reduction of mosquito abundance and nuisance biting was perceived. The study confirmed the feasibility of community-based LSM interventions and served as evidence for future scale-up of Bti application and adoption into Rwandan malaria vector control strategies.

Deep Learning in Label-free Cell Classification

PubMed Central

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-01-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

Robust prediction of protein subcellular localization combining PCA and WSVMs.

PubMed

Tian, Jiang; Gu, Hong; Liu, Wenqi; Gao, Chiyang

2011-08-01

Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Deep Learning in Label-free Cell Classification

NASA Astrophysics Data System (ADS)

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-03-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

Identifying Green Infrastructure from Social Media and Crowdsourcing- An Image Based Machine-Learning Approach.

NASA Astrophysics Data System (ADS)

Rai, A.; Minsker, B. S.

2016-12-01

In this work we introduce a novel dataset GRID: GReen Infrastructure Detection Dataset and a framework for identifying urban green storm water infrastructure (GI) designs (wetlands/ponds, urban trees, and rain gardens/bioswales) from social media and satellite aerial images using computer vision and machine learning methods. Along with the hydrologic benefits of GI, such as reducing runoff volumes and urban heat islands, GI also provides important socio-economic benefits such as stress recovery and community cohesion. However, GI is installed by many different parties and cities typically do not know where GI is located, making study of its impacts or siting new GI difficult. We use object recognition learning methods (template matching, sliding window approach, and Random Hough Forest method) and supervised machine learning algorithms (e.g., support vector machines) as initial screening approaches to detect potential GI sites, which can then be investigated in more detail using on-site surveys. Training data were collected from GPS locations of Flickr and Instagram image postings and Amazon Mechanical Turk identification of each GI type. Sliding window method outperformed other methods and achieved an average F measure, which is combined metric for precision and recall performance measure of 0.78.

Inverse Problems in Geodynamics Using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

2018-01-01

During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-11-30

Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

The effects of pre-processing strategies in sentiment analysis of online movie reviews

NASA Astrophysics Data System (ADS)

Zin, Harnani Mat; Mustapha, Norwati; Murad, Masrah Azrifah Azmi; Sharef, Nurfadhlina Mohd

2017-10-01

With the ever increasing of internet applications and social networking sites, people nowadays can easily express their feelings towards any products and services. These online reviews act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like sentiment analysis and classification to provide a meaningful information for future uses. In text analysis tasks, the appropriate selection of words/features will have a huge impact on the effectiveness of the classifier. Thus, this paper explores the effect of the pre-processing strategies in the sentiment analysis of online movie reviews. In this paper, supervised machine learning method was used to classify the reviews. The support vector machine (SVM) with linear and non-linear kernel has been considered as classifier for the classification of the reviews. The performance of the classifier is critically examined based on the results of precision, recall, f-measure, and accuracy. Two different features representations were used which are term frequency and term frequency-inverse document frequency. Results show that the pre-processing strategies give a significant impact on the classification process.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs.

PubMed

Balamurugan, P; Shevade, Shirish; Sundararajan, S

2015-10-01

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier.

PubMed

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Rao, A R

2016-11-05

DNA barcoding is a molecular diagnostic method that allows automated and accurate identification of species based on a short and standardized fragment of DNA. To this end, an attempt has been made in this study to develop a computational approach for identifying the species by comparing its barcode with the barcode sequence of known species present in the reference library. Each barcode sequence was first mapped onto a numeric feature vector based on k-mer frequencies and then Random forest methodology was employed on the transformed dataset for species identification. The proposed approach outperformed similarity-based, tree-based, diagnostic-based approaches and found comparable with existing supervised learning based approaches in terms of species identification success rate, while compared using real and simulated datasets. Based on the proposed approach, an online web interface SPIDBAR has also been developed and made freely available at http://cabgrid.res.in:8080/spidbar/ for species identification by the taxonomists. Copyright © 2016 Elsevier B.V. All rights reserved.

Image segmentation using hidden Markov Gauss mixture models.

PubMed

Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M

2007-07-01

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

Machine Learning Methods for Attack Detection in the Smart Grid.

PubMed

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

A new clustering algorithm applicable to multispectral and polarimetric SAR images

NASA Technical Reports Server (NTRS)

Wong, Yiu-Fai; Posner, Edward C.

1993-01-01

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Resilient Systemics to Telehealth Support for Clinical Psychiatry and Psychology.

PubMed

Fiorini, Rodolfo A; De Giacomo, Piero; L'Abate, Luciano

2015-01-01

Reliably expanding our clinical practice and lowering our overhead with telepsychiatry, telepsychology, distance counseling and online therapy, requires resilient and antifragile system and tools. When utilized appropriately these technologies may provide greater access to needed services to include more reliable treatment, consultation, supervision, and training. The wise and proper use of technology is fundamental to create and boost outstanding social results. We present, as an example, the main steps to achieve application resilience and antifragility at system level, for diagnostic and therapeutic telepractice and telehealth support, devoted to psychiatry and psychology application. This article presents a number of innovations that can take psychotherapy treatment, supervision, training, and research forward, towards increased effectiveness application.