An assessment of support vector machines for land cover classification

USGS Publications Warehouse

Huang, C.; Davis, L.S.; Townshend, J.R.G.

2002-01-01

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

PubMed

Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John

2017-03-15

Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P<.05). Scores did not vary between doctors who were rated as popular or innovative and those who were not rated at all (P>.05). Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor's performance. ©Chris Gibbons, Suzanne Richards, Jose Maria Valderas, John Campbell. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.03.2017.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

A Collaborative Framework for Distributed Privacy-Preserving Support Vector Machine Learning

PubMed Central

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates “privacy-insensitive” intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner. PMID:23304414

Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

PubMed

Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

2018-01-01

Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Osteoporosis risk prediction using machine learning and conventional methods.

PubMed

Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won

2013-01-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

PMLB: a large benchmark suite for machine learning evaluation and comparison.

PubMed

Olson, Randal S; La Cava, William; Orzechowski, Patryk; Urbanowicz, Ryan J; Moore, Jason H

2017-01-01

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

Sources of Evidence-of-Learning: Learning and Assessment in the Era of Big Data

ERIC Educational Resources Information Center

Cope, Bill; Kalantzis, Mary

2015-01-01

This article sets out to explore a shift in the sources of evidence-of-learning in the era of networked computing. One of the key features of recent developments has been popularly characterized as "big data". We begin by examining, in general terms, the frame of reference of contemporary debates on machine intelligence and the role of…

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

Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

PubMed

Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W

2018-03-01

The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.

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.

Machine learning for autonomous crystal structure identification.

PubMed

Reinhart, Wesley F; Long, Andrew W; Howard, Michael P; Ferguson, Andrew L; Panagiotopoulos, Athanassios Z

2017-07-21

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

NASA Astrophysics Data System (ADS)

Nelson, Kevin; Corbin, George; Blowers, Misty

2014-05-01

Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

Machine Learning in Medicine

PubMed Central

Deo, Rahul C.

2015-01-01

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games – tasks which would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in healthcare. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades – and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

PubMed Central

Ding, Fangyu; Ge, Quansheng; Fu, Jingying; Hao, Mengmeng

2017-01-01

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before. PMID:28591138

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach.

PubMed

Ding, Fangyu; Ge, Quansheng; Jiang, Dong; Fu, Jingying; Hao, Mengmeng

2017-01-01

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.

Study of Environmental Data Complexity using Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

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.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

Machine Learning in Medicine.

PubMed

Deo, Rahul C

2015-11-17

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. © 2015 American Heart Association, Inc.

BELM: Bayesian extreme learning machine.

PubMed

Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J

2011-03-01

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.

Nonlinear programming for classification problems in machine learning

NASA Astrophysics Data System (ADS)

Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

2016-10-01

We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

PubMed

Liu, Yuzhe; Gopalakrishnan, Vanathi

2017-03-01

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

A comparative analysis of support vector machines and extreme learning machines.

PubMed

Liu, Xueyi; Gao, Chuanhou; Li, Ping

2012-09-01

The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. Copyright © 2012 Elsevier Ltd. All rights reserved.

Development of a machine learning potential for graphene

NASA Astrophysics Data System (ADS)

Rowe, Patrick; Csányi, Gábor; Alfè, Dario; Michaelides, Angelos

2018-02-01

We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy

PubMed Central

2017-01-01

Background Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P<.05). Scores did not vary between doctors who were rated as popular or innovative and those who were not rated at all (P>.05). Conclusions Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance. PMID:28298265

Learning To Recognize Visual Concepts: Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning

DTIC Science & Technology

1993-01-01

Maria and My Parents, Helena and Andrzej IV ACKNOWLEDGMENTS I would like to first of all thank my advisor. Dr. Ryszard Michalski. who introduced...represent the current state of the art in machine learning methodology. The most popular method. the minimization of Bayes risk [ Duda and Hart. 1973]. is a...34 Pattern Recognition, Vol. 23, no. 3-4, pp. 291-309, 1990. Duda , O. and P. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons. 1973

Technology in Education: Its Prospects and Its Promises.

ERIC Educational Resources Information Center

Senese, Donald J.

The impact of advanced technology has increased computer usage at all levels as evidenced by the popularity of video games, increased interest on the part of students using computers to enhance learning, and business/school partnerships forming with such companies as Digital Equipment Corporation, International Business Machines, and Tandy/Radio…

Analysis of user activities on popular medical forums

NASA Astrophysics Data System (ADS)

Kamalov, M. V.; Dobrynin, V. Y.; Balykina, Y. E.; Martynov, R. S.

2017-10-01

The paper is devoted to detailed investigation of users’ behavior and level of expertise on online medical forums. Two popular forums were analyzed in terms of presence of experts who answer health related questions and participate in discussions. This study provides insight into the quality of medical information that one can get from the web resources, and also illustrates relationship between approved medical experts and popular authors of the considered forums. During experiments several machine learning and natural language processing methods were evaluated against to available web content to get further understanding of structure and distribution of information about medicine available online nowadays. As a result of this study the hypothesis of existing correlation between approved medical experts and popular authors has been rejected.

Recent advances in environmental data mining

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2016-04-01

Due to the large amount and complexity of data available nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis, modelling and visualization. An important part of these developments deals with an elaboration and application of a contemporary and coherent methodology following the process from data collection to the justification and communication of the results. Recent fundamental progress in machine learning (ML) can considerably contribute to the development of the emerging field - environmental data science. The present research highlights and investigates the different issues that can occur when dealing with environmental data mining using cutting-edge machine learning algorithms. In particular, the main attention is paid to the description of the self-consistent methodology and two efficient algorithms - Random Forest (RF, Breiman, 2001) and Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. Despite the fact that they are based on two different concepts, i.e. decision trees vs artificial neural networks, they both propose promising results for complex, high dimensional and non-linear data modelling. In addition, the study discusses several important issues of data driven modelling, including feature selection and uncertainties. The approach considered is accompanied by simulated and real data case studies from renewable resources assessment and natural hazards tasks. In conclusion, the current challenges and future developments in statistical environmental data learning are discussed. References - Breiman, L., 2001. Random Forests. Machine Learning 45 (1), 5-32. - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Machine-learning approach for local classification of crystalline structures in multiphase systems

NASA Astrophysics Data System (ADS)

Dietz, C.; Kretz, T.; Thoma, M. H.

2017-07-01

Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures.

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

NASA Astrophysics Data System (ADS)

Hannel, Mark D.; Abdulali, Aidan; O'Brien, Michael; Grier, David G.

2018-06-01

Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to be less precise, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.

Machine learning algorithms for the creation of clinical healthcare enterprise systems

NASA Astrophysics Data System (ADS)

Mandal, Indrajit

2017-10-01

Clinical recommender systems are increasingly becoming popular for improving modern healthcare systems. Enterprise systems are persuasively used for creating effective nurse care plans to provide nurse training, clinical recommendations and clinical quality control. A novel design of a reliable clinical recommender system based on multiple classifier system (MCS) is implemented. A hybrid machine learning (ML) ensemble based on random subspace method and random forest is presented. The performance accuracy and robustness of proposed enterprise architecture are quantitatively estimated to be above 99% and 97%, respectively (above 95% confidence interval). The study then extends to experimental analysis of the clinical recommender system with respect to the noisy data environment. The ranking of items in nurse care plan is demonstrated using machine learning algorithms (MLAs) to overcome the drawback of the traditional association rule method. The promising experimental results are compared against the sate-of-the-art approaches to highlight the advancement in recommendation technology. The proposed recommender system is experimentally validated using five benchmark clinical data to reinforce the research findings.

Machine Learning

NASA Astrophysics Data System (ADS)

Hoffmann, Achim; Mahidadia, Ashesh

The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules - a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for human comprehension as it is essentially a large collection of probability values. In Sect. 9, we present a generic method for improving accuracy of a given learner by generatingmultiple classifiers using variations of the training data. While this works well in most cases, the resulting classifiers have significantly increased complexity and, hence, tend to destroy the human readability of the learning result that a single learner may produce. Section 10 contains a summary, mentions briefly other techniques not discussed in this chapter and presents outlook on the potential of machine learning in the future.

Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.

PubMed

Yoo, Tae Keun; Kim, Sung Kean; Kim, Deok Won; Choi, Joon Yul; Lee, Wan Hyung; Oh, Ein; Park, Eun-Cheol

2013-11-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

Machine Learning and Data Mining Methods in Diabetes Research.

PubMed

Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

2017-01-01

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

PubMed Central

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. PMID:27213008

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography.

PubMed

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

Formation enthalpies for transition metal alloys using machine learning

NASA Astrophysics Data System (ADS)

Ubaru, Shashanka; Miedlar, Agnieszka; Saad, Yousef; Chelikowsky, James R.

2017-06-01

The enthalpy of formation is an important thermodynamic property. Developing fast and accurate methods for its prediction is of practical interest in a variety of applications. Material informatics techniques based on machine learning have recently been introduced in the literature as an inexpensive means of exploiting materials data, and can be used to examine a variety of thermodynamics properties. We investigate the use of such machine learning tools for predicting the formation enthalpies of binary intermetallic compounds that contain at least one transition metal. We consider certain easily available properties of the constituting elements complemented by some basic properties of the compounds, to predict the formation enthalpies. We show how choosing these properties (input features) based on a literature study (using prior physics knowledge) seems to outperform machine learning based feature selection methods such as sensitivity analysis and LASSO (least absolute shrinkage and selection operator) based methods. A nonlinear kernel based support vector regression method is employed to perform the predictions. The predictive ability of our model is illustrated via several experiments on a dataset containing 648 binary alloys. We train and validate the model using the formation enthalpies calculated using a model by Miedema, which is a popular semiempirical model used for the prediction of formation enthalpies of metal alloys.

The definition of insulin resistance using HOMA-IR for Americans of Mexican descent using machine learning.

PubMed

Qu, Hui-Qi; Li, Quan; Rentfro, Anne R; Fisher-Hoch, Susan P; McCormick, Joseph B

2011-01-01

The lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods. This study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measureable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance. Our study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80. Our results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population.

The Definition of Insulin Resistance Using HOMA-IR for Americans of Mexican Descent Using Machine Learning

PubMed Central

Qu, Hui-Qi; Li, Quan; Rentfro, Anne R.; Fisher-Hoch, Susan P.; McCormick, Joseph B.

2011-01-01

Objective The lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods. Methods This study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measureable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance. Results Our study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80. Conclusions Our results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population. PMID:21695082

The Influence of Rater Effects in Training Sets on the Psychometric Quality of Automated Scoring for Writing Assessments

ERIC Educational Resources Information Center

Wind, Stefanie A.; Wolfe, Edward W.; Engelhard, George, Jr.; Foltz, Peter; Rosenstein, Mark

2018-01-01

Automated essay scoring engines (AESEs) are becoming increasingly popular as an efficient method for performance assessments in writing, including many language assessments that are used worldwide. Before they can be used operationally, AESEs must be "trained" using machine-learning techniques that incorporate human ratings. However, the…

Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization.

PubMed

Podlewska, Sabina; Czarnecki, Wojciech M; Kafel, Rafał; Bojarski, Andrzej J

2017-02-27

The growing computational abilities of various tools that are applied in the broadly understood field of computer-aided drug design have led to the extreme popularity of virtual screening in the search for new biologically active compounds. Most often, the source of such molecules consists of commercially available compound databases, but they can also be searched for within the libraries of structures generated in silico from existing ligands. Various computational combinatorial approaches are based solely on the chemical structure of compounds, using different types of substitutions for new molecules formation. In this study, the starting point for combinatorial library generation was the fingerprint referring to the optimal substructural composition in terms of the activity toward a considered target, which was obtained using a machine learning-based optimization procedure. The systematic enumeration of all possible connections between preferred substructures resulted in the formation of target-focused libraries of new potential ligands. The compounds were initially assessed by machine learning methods using a hashed fingerprint to represent molecules; the distribution of their physicochemical properties was also investigated, as well as their synthetic accessibility. The examination of various fingerprints and machine learning algorithms indicated that the Klekota-Roth fingerprint and support vector machine were an optimal combination for such experiments. This study was performed for 8 protein targets, and the obtained compound sets and their characterization are publically available at http://skandal.if-pan.krakow.pl/comb_lib/ .

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

Quantum-enhanced feature selection with forward selection and backward elimination

NASA Astrophysics Data System (ADS)

He, Zhimin; Li, Lvzhou; Huang, Zhiming; Situ, Haozhen

2018-07-01

Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.

A Framework for Finding and Summarizing Product Defects, and Ranking Helpful Threads from Online Customer Forums through Machine Learning

ERIC Educational Resources Information Center

Jiao, Jian

2013-01-01

The Internet has revolutionized the way users share and acquire knowledge. As important and popular Web-based applications, online discussion forums provide interactive platforms for users to exchange information and report problems. With the rapid growth of social networks and an ever increasing number of Internet users, online forums have…

A Symbolic Approach Using Feature Construction Capable of Acquiring Information/Knowledge for Building Expert Systems.

ERIC Educational Resources Information Center

Major, Raymond L.

1998-01-01

Presents a technique for developing a knowledge-base of information to use in an expert system. Proposed approach employs a popular machine-learning algorithm along with a method for forming a finite number of features or conjuncts of at most n primitive attributes. Illustrates this procedure by examining qualitative information represented in a…

Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates

NASA Astrophysics Data System (ADS)

Borgnat, Pierre; Flandrin, Patrick; Richard, Cédric; Ferrari, André; Amoud, Hassan; Honeine, Paul

2012-03-01

Time-frequency representations provide a powerful tool for nonstationary signal analysis and classification, supporting a wide range of applications [12]. As opposed to conventional Fourier analysis, these techniques reveal the evolution in time of the spectral content of signals. In Ref. [7,38], time-frequency analysis is used to test stationarity of any signal. The proposed method consists of a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogate signals for defining the null hypothesis of stationarity and, based upon this information, to derive statistical tests. An open question remains, however, about how to choose relevant time-frequency features. Over the last decade, a number of new pattern recognition methods based on reproducing kernels have been introduced. These learning machines have gained popularity due to their conceptual simplicity and their outstanding performance [30]. Initiated by Vapnik’s support vector machines (SVM) [35], they offer now a wide class of supervised and unsupervised learning algorithms. In Ref. [17-19], the authors have shown how the most effective and innovative learning machines can be tuned to operate in the time-frequency domain. This chapter follows this line of research by taking advantage of learning machines to test and quantify stationarity. Based on one-class SVM, our approach uses the entire time-frequency representation and does not require arbitrary feature extraction. Applied to a set of surrogates, it provides the domain boundary that includes most of these stationarized signals. This allows us to test the stationarity of the signal under investigation. This chapter is organized as follows. In Section 22.2, we introduce the surrogate data method to generate stationarized signals, namely, the null hypothesis of stationarity. The concept of time-frequency learning machines is presented in Section 22.3, and applied to one-class SVM in order to derive a stationarity test in Section 22.4. The relevance of the latter is illustrated by simulation results in Section 22.5.

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users

PubMed Central

Saeedi, Sareh; Schneider, Christoph; Millán, José del R.

2018-01-01

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI–user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface—application, BCI output, and electroencephalography (EEG) neuroimaging—with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions. PMID:29746465

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users.

PubMed

Perdikis, Serafeim; Tonin, Luca; Saeedi, Sareh; Schneider, Christoph; Millán, José Del R

2018-05-01

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain-computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI-user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface-application, BCI output, and electroencephalography (EEG) neuroimaging-with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

An adaptive deep learning approach for PPG-based identification.

PubMed

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

Code Optimization and Parallelization on the Origins: Looking from Users' Perspective

NASA Technical Reports Server (NTRS)

Chang, Yan-Tyng Sherry; Thigpen, William W. (Technical Monitor)

2002-01-01

Parallel machines are becoming the main compute engines for high performance computing. Despite their increasing popularity, it is still a challenge for most users to learn the basic techniques to optimize/parallelize their codes on such platforms. In this paper, we present some experiences on learning these techniques for the Origin systems at the NASA Advanced Supercomputing Division. Emphasis of this paper will be on a few essential issues (with examples) that general users should master when they work with the Origins as well as other parallel systems.

Overview of deep learning in medical imaging.

PubMed

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

Estimation of Alpine Skier Posture Using Machine Learning Techniques

PubMed Central

Nemec, Bojan; Petrič, Tadej; Babič, Jan; Supej, Matej

2014-01-01

High precision Global Navigation Satellite System (GNSS) measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier's neck. A key issue is how to estimate other more relevant parameters of the skier's body, like the center of mass (COM) and ski trajectories. Previously, these parameters were estimated by modeling the skier's body with an inverted-pendulum model that oversimplified the skier's body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier's body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing. PMID:25313492

Parsimonious kernel extreme learning machine in primal via Cholesky factorization.

PubMed

Zhao, Yong-Ping

2016-08-01

Recently, extreme learning machine (ELM) has become a popular topic in machine learning community. By replacing the so-called ELM feature mappings with the nonlinear mappings induced by kernel functions, two kernel ELMs, i.e., P-KELM and D-KELM, are obtained from primal and dual perspectives, respectively. Unfortunately, both P-KELM and D-KELM possess the dense solutions in direct proportion to the number of training data. To this end, a constructive algorithm for P-KELM (CCP-KELM) is first proposed by virtue of Cholesky factorization, in which the training data incurring the largest reductions on the objective function are recruited as significant vectors. To reduce its training cost further, PCCP-KELM is then obtained with the application of a probabilistic speedup scheme into CCP-KELM. Corresponding to CCP-KELM, a destructive P-KELM (CDP-KELM) is presented using a partial Cholesky factorization strategy, where the training data incurring the smallest reductions on the objective function after their removals are pruned from the current set of significant vectors. Finally, to verify the efficacy and feasibility of the proposed algorithms in this paper, experiments on both small and large benchmark data sets are investigated. Copyright © 2016 Elsevier Ltd. All rights reserved.

The need to approximate the use-case in clinical machine learning

PubMed Central

Saeb, Sohrab; Jayaraman, Arun; Mohr, David C.; Kording, Konrad P.

2017-01-01

Abstract The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists. PMID:28327985

Rapid performance modeling and parameter regression of geodynamic models

NASA Astrophysics Data System (ADS)

Brown, J.; Duplyakin, D.

2016-12-01

Geodynamic models run in a parallel environment have many parameters with complicated effects on performance and scientifically-relevant functionals. Manually choosing an efficient machine configuration and mapping out the parameter space requires a great deal of expert knowledge and time-consuming experiments. We propose an active learning technique based on Gaussion Process Regression to automatically select experiments to map out the performance landscape with respect to scientific and machine parameters. The resulting performance model is then used to select optimal experiments for improving the accuracy of a reduced order model per unit of computational cost. We present the framework and evaluate its quality and capability using popular lithospheric dynamics models.

Extended robust support vector machine based on financial risk minimization.

PubMed

Takeda, Akiko; Fujiwara, Shuhei; Kanamori, Takafumi

2014-11-01

Financial risk measures have been used recently in machine learning. For example, ν-support vector machine ν-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than ν-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting.

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices

PubMed Central

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B.

2018-01-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support. PMID:29629431

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices.

PubMed

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B

2017-06-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.

Metaheuristic Algorithms for Convolution Neural Network

PubMed Central

Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

Metaheuristic Algorithms for Convolution Neural Network.

PubMed

Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective

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

Kang, John; Schwartz, Russell; Flickinger, John

Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models.more » These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, “spam” filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the “barrier to entry” for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods—logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)—and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.« less

Artificial intelligence in healthcare: past, present and future.

PubMed

Jiang, Fei; Jiang, Yong; Zhi, Hui; Dong, Yi; Li, Hao; Ma, Sufeng; Wang, Yilong; Dong, Qiang; Shen, Haipeng; Wang, Yongjun

2017-12-01

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

A Machine Learning Method for Power Prediction on the Mobile Devices.

PubMed

Chen, Da-Ren; Chen, You-Shyang; Chen, Lin-Chih; Hsu, Ming-Yang; Chiang, Kai-Feng

2015-10-01

Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.

Using machine learning to accelerate sampling-based inversion

NASA Astrophysics Data System (ADS)

Valentine, A. P.; Sambridge, M.

2017-12-01

In most cases, a complete solution to a geophysical inverse problem (including robust understanding of the uncertainties associated with the result) requires a sampling-based approach. However, the computational burden is high, and proves intractable for many problems of interest. There is therefore considerable value in developing techniques that can accelerate sampling procedures.The main computational cost lies in evaluation of the forward operator (e.g. calculation of synthetic seismograms) for each candidate model. Modern machine learning techniques-such as Gaussian Processes-offer a route for constructing a computationally-cheap approximation to this calculation, which can replace the accurate solution during sampling. Importantly, the accuracy of the approximation can be refined as inversion proceeds, to ensure high-quality results.In this presentation, we describe and demonstrate this approach-which can be seen as an extension of popular current methods, such as the Neighbourhood Algorithm, and bridges the gap between prior- and posterior-sampling frameworks.

Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of nonsparse low-rank matrices

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Siopsis, George; Weedbrook, Christian

2018-02-01

With the significant advancement in quantum computation during the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers under certain assumptions regarding distribution of data and availability of efficient quantum access. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speedup in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of nonsparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.

Artificial intelligence in healthcare: past, present and future

PubMed Central

Jiang, Fei; Jiang, Yong; Zhi, Hui; Dong, Yi; Li, Hao; Ma, Sufeng; Wang, Yilong; Dong, Qiang; Shen, Haipeng; Wang, Yongjun

2017-01-01

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI. PMID:29507784

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

PubMed

Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution outperformed all other models (P < 0.001) with the mean absolute discrepancy of 0.62% and maximum discrepancy of 3.17% between the measured and predicted OFs. The OFs showed a small dependence on gantry angle for small and deep options while they were constant for large options. The OF decreased by 3%-4% as the field radius was reduced to 2.5 cm. Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements. © 2018 American Association of Physicists in Medicine.

Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

PubMed

Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

2015-01-01

Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

Machine learning and social network analysis applied to Alzheimer's disease biomarkers.

PubMed

Di Deco, Javier; González, Ana M; Díaz, Julia; Mato, Virginia; García-Frank, Daniel; Álvarez-Linera, Juan; Frank, Ana; Hernández-Tamames, Juan A

2013-01-01

Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological, biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of the normal ageing. Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and connections. The resulting graph-based structures are often very complex; there can be many kinds of connections between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study. This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different phenotypes in dementia, in particular, different stages of Alzheimer's disease.

The need to approximate the use-case in clinical machine learning.

PubMed

Saeb, Sohrab; Lonini, Luca; Jayaraman, Arun; Mohr, David C; Kording, Konrad P

2017-05-01

The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists. © The Author 2017. Published by Oxford University Press.

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

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

PubMed

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

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.

Disk storage management for LHCb based on Data Popularity estimator

NASA Astrophysics Data System (ADS)

Hushchyn, Mikhail; Charpentier, Philippe; Ustyuzhanin, Andrey

2015-12-01

This paper presents an algorithm providing recommendations for optimizing the LHCb data storage. The LHCb data storage system is a hybrid system. All datasets are kept as archives on magnetic tapes. The most popular datasets are kept on disks. The algorithm takes the dataset usage history and metadata (size, type, configuration etc.) to generate a recommendation report. This article presents how we use machine learning algorithms to predict future data popularity. Using these predictions it is possible to estimate which datasets should be removed from disk. We use regression algorithms and time series analysis to find the optimal number of replicas for datasets that are kept on disk. Based on the data popularity and the number of replicas optimization, the algorithm minimizes a loss function to find the optimal data distribution. The loss function represents all requirements for data distribution in the data storage system. We demonstrate how our algorithm helps to save disk space and to reduce waiting times for jobs using this data.

Question Popularity Analysis and Prediction in Community Question Answering Services

PubMed Central

Liu, Ting; Zhang, Wei-Nan; Cao, Liujuan; Zhang, Yu

2014-01-01

With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of efforts have been taken to explore the questions and answers retrieval in CQA services so as to help users to finding the similar questions or the right answers. However, to our knowledge, less attention has been paid so far to question popularity in CQA. Question popularity can reflect the attention and interest of users. Hence, predicting question popularity can better capture the users’ interest so as to improve the users’ experience. Meanwhile, it can also promote the development of the community. In this paper, we investigate the problem of predicting question popularity in CQA. We first explore the factors that have impact on question popularity by employing statistical analysis. We then propose a supervised machine learning approach to model these factors for question popularity prediction. The experimental results show that our proposed approach can effectively distinguish the popular questions from unpopular ones in the Yahoo! Answers question and answer repository. PMID:24837851

Question popularity analysis and prediction in community question answering services.

PubMed

Liu, Ting; Zhang, Wei-Nan; Cao, Liujuan; Zhang, Yu

2014-01-01

With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of efforts have been taken to explore the questions and answers retrieval in CQA services so as to help users to finding the similar questions or the right answers. However, to our knowledge, less attention has been paid so far to question popularity in CQA. Question popularity can reflect the attention and interest of users. Hence, predicting question popularity can better capture the users' interest so as to improve the users' experience. Meanwhile, it can also promote the development of the community. In this paper, we investigate the problem of predicting question popularity in CQA. We first explore the factors that have impact on question popularity by employing statistical analysis. We then propose a supervised machine learning approach to model these factors for question popularity prediction. The experimental results show that our proposed approach can effectively distinguish the popular questions from unpopular ones in the Yahoo! Answers question and answer repository.

Machine learning approaches to analysing textual injury surveillance data: a systematic review.

PubMed

Vallmuur, Kirsten

2015-06-01

To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

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

von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias

We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continuous, and differentiable with respect to atomic coordinates and nuclear charges. It is invariant with respect to translation, rotation, and nuclear permutation, and requires no preconceived knowledge about chemical bonding, topology, or electronic orbitals. As such, it meets many important criteria for a good molecular representation, suggesting its usefulness for machine learning models of molecular properties trained across chemical compound space. To assess the performance of this new descriptor, we have trained machine learning models ofmore » molecular enthalpies of atomization for training sets with up to 10 k organic molecules, drawn at random from a published set of 134 k organic molecules with an average atomization enthalpy of over 1770 kcal/mol. We validate the descriptor on all remaining molecules of the 134 k set. For a training set of 10 k molecules, the fingerprint descriptor achieves a mean absolute error of 8.0 kcal/mol. This is slightly worse than the performance attained using the Coulomb matrix, another popular alternative, reaching 6.2 kcal/mol for the same training and test sets. (c) 2015 Wiley Periodicals, Inc.« less

Machine learning of swimming data via wisdom of crowd and regression analysis.

PubMed

Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing

2017-04-01

Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.

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.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

PubMed

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

A machine learning approach to the potential-field method for implicit modeling of geological structures

NASA Astrophysics Data System (ADS)

Gonçalves, Ítalo Gomes; Kumaira, Sissa; Guadagnin, Felipe

2017-06-01

Implicit modeling has experienced a rise in popularity over the last decade due to its advantages in terms of speed and reproducibility in comparison with manual digitization of geological structures. The potential-field method consists in interpolating a scalar function that indicates to which side of a geological boundary a given point belongs to, based on cokriging of point data and structural orientations. This work proposes a vector potential-field solution from a machine learning perspective, recasting the problem as multi-class classification, which alleviates some of the original method's assumptions. The potentials related to each geological class are interpreted in a compositional data framework. Variogram modeling is avoided through the use of maximum likelihood to train the model, and an uncertainty measure is introduced. The methodology was applied to the modeling of a sample dataset provided with the software Move™. The calculations were implemented in the R language and 3D visualizations were prepared with the rgl package.

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

PubMed

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2013-06-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

PubMed Central

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2014-01-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research. PMID:22665720

Random Bits Forest: a Strong Classifier/Regressor for Big Data

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Yi; Pu, Weilin; Wen, Kathryn; Shugart, Yin Yao; Xiong, Momiao; Jin, Li

2016-07-01

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

PubMed

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. ©Jose Juan Dominguez Veiga, Martin O'Reilly, Darragh Whelan, Brian Caulfield, Tomas E Ward. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.08.2017.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

PubMed Central

O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-01-01

Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. Results With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. Conclusions The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. PMID:28778851

Efficient dual approach to distance metric learning.

PubMed

Shen, Chunhua; Kim, Junae; Liu, Fayao; Wang, Lei; van den Hengel, Anton

2014-02-01

Distance metric learning is of fundamental interest in machine learning because the employed distance metric can significantly affect the performance of many learning methods. Quadratic Mahalanobis metric learning is a popular approach to the problem, but typically requires solving a semidefinite programming (SDP) problem, which is computationally expensive. The worst case complexity of solving an SDP problem involving a matrix variable of size D×D with O(D) linear constraints is about O(D(6.5)) using interior-point methods, where D is the dimension of the input data. Thus, the interior-point methods only practically solve problems exhibiting less than a few thousand variables. Because the number of variables is D(D+1)/2, this implies a limit upon the size of problem that can practically be solved around a few hundred dimensions. The complexity of the popular quadratic Mahalanobis metric learning approach thus limits the size of problem to which metric learning can be applied. Here, we propose a significantly more efficient and scalable approach to the metric learning problem based on the Lagrange dual formulation of the problem. The proposed formulation is much simpler to implement, and therefore allows much larger Mahalanobis metric learning problems to be solved. The time complexity of the proposed method is roughly O(D(3)), which is significantly lower than that of the SDP approach. Experiments on a variety of data sets demonstrate that the proposed method achieves an accuracy comparable with the state of the art, but is applicable to significantly larger problems. We also show that the proposed method can be applied to solve more general Frobenius norm regularized SDP problems approximately.

Perceptron ensemble of graph-based positive-unlabeled learning for disease gene identification.

PubMed

Jowkar, Gholam-Hossein; Mansoori, Eghbal G

2016-10-01

Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

An Efficient Statistical Computation Technique for Health Care Big Data using R

NASA Astrophysics Data System (ADS)

Sushma Rani, N.; Srinivasa Rao, P., Dr; Parimala, P.

2017-08-01

Due to the changes in living conditions and other factors many critical health related problems are arising. The diagnosis of the problem at earlier stages will increase the chances of survival and fast recovery. This reduces the time of recovery and the cost associated for the treatment. One such medical related issue is cancer and breast cancer has been identified as the second leading cause of cancer death. If detected in the early stage it can be cured. Once a patient is detected with breast cancer tumor, it should be classified whether it is cancerous or non-cancerous. So the paper uses k-nearest neighbors(KNN) algorithm which is one of the simplest machine learning algorithms and is an instance-based learning algorithm to classify the data. Day-to -day new records are added which leds to increase in the data to be classified and this tends to be big data problem. The algorithm is implemented in R whichis the most popular platform applied to machine learning algorithms for statistical computing. Experimentation is conducted by using various classification evaluation metric onvarious values of k. The results show that the KNN algorithm out performes better than existing models.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

PubMed

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Feature and Region Selection for Visual Learning.

PubMed

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Learning locality preserving graph from data.

PubMed

Zhang, Yan-Ming; Huang, Kaizhu; Hou, Xinwen; Liu, Cheng-Lin

2014-11-01

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the aim of directly preserving the local information of the original data set. We show that the proposed objective has close connections with the popular Laplacian Eigenmap problem, and is hence well justified. The optimization turns out to be a quadratic programming problem with n(n-1)/2 variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

NASA Astrophysics Data System (ADS)

Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

2018-02-01

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

PubMed

Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

2017-12-01

This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

Mining hidden data to predict patient prognosis: texture feature extraction and machine learning in mammography

NASA Astrophysics Data System (ADS)

Leighs, J. A.; Halling-Brown, M. D.; Patel, M. N.

2018-03-01

The UK currently has a national breast cancer-screening program and images are routinely collected from a number of screening sites, representing a wealth of invaluable data that is currently under-used. Radiologists evaluate screening images manually and recall suspicious cases for further analysis such as biopsy. Histological testing of biopsy samples confirms the malignancy of the tumour, along with other diagnostic and prognostic characteristics such as disease grade. Machine learning is becoming increasingly popular for clinical image classification problems, as it is capable of discovering patterns in data otherwise invisible. This is particularly true when applied to medical imaging features; however clinical datasets are often relatively small. A texture feature extraction toolkit has been developed to mine a wide range of features from medical images such as mammograms. This study analysed a dataset of 1,366 radiologist-marked, biopsy-proven malignant lesions obtained from the OPTIMAM Medical Image Database (OMI-DB). Exploratory data analysis methods were employed to better understand extracted features. Machine learning techniques including Classification and Regression Trees (CART), ensemble methods (e.g. random forests), and logistic regression were applied to the data to predict the disease grade of the analysed lesions. Prediction scores of up to 83% were achieved; sensitivity and specificity of the models trained have been discussed to put the results into a clinical context. The results show promise in the ability to predict prognostic indicators from the texture features extracted and thus enable prioritisation of care for patients at greatest risk.

PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Olivetti, Emanuele; Fründ, Ingo; Rieger, Jochem W.; Herrmann, Christoph S.; Haxby, James V.; Hanson, Stephen José; Pollmann, Stefan

2008-01-01

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings. PMID:19212459

Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.

PubMed

Deshwar, Amit G; Vembu, Shankar; Morris, Quaid

2015-01-01

Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…

New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

NASA Astrophysics Data System (ADS)

Hancher, M.

2017-12-01

Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.

PubMed

Montagna, Fabio; Buiatti, Marco; Benatti, Simone; Rossi, Davide; Farella, Elisabetta; Benini, Luca

2017-10-01

EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5-6Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation. This limits the possibility to process the EEG on resource-constrained systems and to design smart EEG based devices for automated diagnostic. We propose an algorithm for artifact removal and automated detection of frequency tagging responses in a wide range of stimulation frequencies, which we test on a visual stimulation protocol. The algorithm is rooted on machine learning based pattern recognition techniques and it is tailored for a new generation parallel ultra low power processing platform (PULP), reaching performance of more that 90% accuracy in the frequency detection even for very low stimulation frequencies (<1Hz) with a power budget of 56mW. Copyright © 2017 Elsevier Inc. All rights reserved.

Statistical and Machine Learning forecasting methods: Concerns and ways forward

PubMed Central

Makridakis, Spyros; Assimakopoulos, Vassilios

2018-01-01

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784

Functional networks inference from rule-based machine learning models.

PubMed

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

A Typology of UK Slot Machine Gamblers: A Longitudinal Observational and Interview Study

ERIC Educational Resources Information Center

Griffiths, Mark D.

2011-01-01

Slot machine gambling is a popular leisure activity worldwide yet there has been very little research into different types of slot machine gamblers. Earlier typologies of slot machine gamblers have only concentrated on adolescents in arcade environments. This study presents a new typology of slot machine players based on over 1000 h of participant…

Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach.

PubMed

Liu, Li; Shao, Ling; Li, Xuelong; Lu, Ke

2016-01-01

Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Aliens and time in the machine age

NASA Astrophysics Data System (ADS)

Brake, Mark; Hook, Neil

2006-12-01

The 19th century saw sweeping changes for the development of astrobiology, both in the constituency of empirical science encroaching upon all aspects of life and in the evolution of ideas, with Lyell's Principles of Geology radically raising expectation of the true age of the Earth and the drama of Darwinism questioning biblically literalist accounts of natural history. This paper considers the popular culture spun on the crackling loom of the emergent aspects of astrobiology of the day: Edward Bulwer-Lytton's The Coming Race (1871), which foretold the race of the future, and satirist Samuel Butler's anticipation of machine intelligence, `Darwin Among the Machines', in his Erewhon (1872). Finally, we look at the way Darwin, Huxley and natural selection travelled into space with French astronomer Camille Flammarion's immensely popular Récits de l'infini (Stories of Infinity, 1872), and the social Darwinism of H.G. Wells' The Time Machine (1895) and The War of the Worlds (1898). These works of popular culture presented an effective and inspiring communication of science; their crucial discourse was the reducible gap between the new worlds uncovered by science and exploration and the fantastic strange worlds of the imagination. As such they exemplify a way in which the culture and science of popular astrobiology can be fused.

Facility Detection and Popularity Assessment from Text Classification of Social Media and Crowdsourced Data

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

Sparks, Kevin A; Li, Roger G; Thakur, Gautam

Advances in technology have continually progressed our understanding of where people are, how they use the environment around them, and why they are at their current location. Having a better knowledge of when various locations become popular through space and time could have large impacts on research fields like urban dynamics and energy consumption. In this paper, we discuss the ability to identify and locate various facility types (e.g. restaurant, airport, stadiums) using social media, and assess methods in determining when these facilities become popular over time. We use natural language processing tools and machine learning classifiers to interpret geotaggedmore » Twitter text and determine if a user is seemingly at a location of interest when the tweet was sent. On average our classifiers are approximately 85% accurate varying across multiple facility types, with a peak precision of 98%. By using these methods to classify unstructured text, geotagged social media data can be an extremely useful tool to better understanding the composition of places and how and when people use them.« less

Deep Learning Improves Antimicrobial Peptide Recognition.

PubMed

Veltri, Daniel; Kamath, Uday; Shehu, Amarda

2018-03-24

Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. In this work we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive data set. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino-acid types. Models and data sets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at: www.ampscanner.com. amarda@gmu.edu for general inquiries and dan.veltri@gmail.com for web server information. Supplementary data are available at Bioinformatics online.

Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection.

PubMed

Lang, Congyan; Feng, Jiashi; Feng, Songhe; Wang, Jingdong; Yan, Shuicheng

2016-06-01

Saliency detection is an important procedure for machines to understand visual world as humans do. In this paper, we consider a specific saliency detection problem of predicting human eye fixations when they freely view natural images, and propose a novel dual low-rank pursuit (DLRP) method. DLRP learns saliency-aware feature transformations by utilizing available supervision information and constructs discriminative bases for effectively detecting human fixation points under the popular low-rank and sparsity-pursuit framework. Benefiting from the embedded high-level information in the supervised learning process, DLRP is able to predict fixations accurately without performing the expensive object segmentation as in the previous works. Comprehensive experiments clearly show the superiority of the proposed DLRP method over the established state-of-the-art methods. We also empirically demonstrate that DLRP provides stronger generalization performance across different data sets and inherits the advantages of both the bottom-up- and top-down-based saliency detection methods.

THE FIRST BOOK OF TEACHING MACHINES.

ERIC Educational Resources Information Center

EPSTEIN, SAM; EPSTEIN, BERYL

THE FIRST TEACHING MACHINE WAS INVENTED IN THE 1920'S BY SIDNEY L. PRESSEY AND THE FIRST MODERN TEACHING MACHINE WAS DEVELOPED AND POPULARIZED IN THE EARLY 1930'S BY B.F. SKINNER. TODAY BUSINESSMEN AND INDUSTRIALISTS AS WELL AS EDUCATORS HAVE FOUND TEACHING MACHINES USEFUL. ACTUALLY, TEACHING IS ACCOMPLISHED THROUGH THE PROGRAM, A CAREFULLY…

Using machine learning to identify structural breaks in single-group interrupted time series designs.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

PubMed Central

Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

2017-01-01

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods. PMID:28146106

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

PubMed

Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

2017-01-30

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

PubMed

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

Accelerometry-based classification of human activities using Markov modeling.

PubMed

Mannini, Andrea; Sabatini, Angelo Maria

2011-01-01

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.

Scalable Regression Tree Learning on Hadoop using OpenPlanet

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

Yin, Wei; Simmhan, Yogesh; Prasanna, Viktor

As scientific and engineering domains attempt to effectively analyze the deluge of data arriving from sensors and instruments, machine learning is becoming a key data mining tool to build prediction models. Regression tree is a popular learning model that combines decision trees and linear regression to forecast numerical target variables based on a set of input features. Map Reduce is well suited for addressing such data intensive learning applications, and a proprietary regression tree algorithm, PLANET, using MapReduce has been proposed earlier. In this paper, we describe an open source implement of this algorithm, OpenPlanet, on the Hadoop framework usingmore » a hybrid approach. Further, we evaluate the performance of OpenPlanet using realworld datasets from the Smart Power Grid domain to perform energy use forecasting, and propose tuning strategies of Hadoop parameters to improve the performance of the default configuration by 75% for a training dataset of 17 million tuples on a 64-core Hadoop cluster on FutureGrid.« less

Predicting Key Events in the Popularity Evolution of Online Information.

PubMed

Hu, Ying; Hu, Changjun; Fu, Shushen; Fang, Mingzhe; Xu, Wenwen

2017-01-01

The popularity of online information generally experiences a rising and falling evolution. This paper considers the "burst", "peak", and "fade" key events together as a representative summary of popularity evolution. We propose a novel prediction task-predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify "burst", "peak", and "fade" in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution.

Predicting Key Events in the Popularity Evolution of Online Information

PubMed Central

Fu, Shushen; Fang, Mingzhe; Xu, Wenwen

2017-01-01

The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. PMID:28046121

Mapping the spatial distribution of Aedes aegypti and Aedes albopictus.

PubMed

Ding, Fangyu; Fu, Jingying; Jiang, Dong; Hao, Mengmeng; Lin, Gang

2018-02-01

Mosquito-borne infectious diseases, such as Rift Valley fever, Dengue, Chikungunya and Zika, have caused mass human death with the transnational expansion fueled by economic globalization. Simulating the distribution of the disease vectors is of great importance in formulating public health planning and disease control strategies. In the present study, we simulated the global distribution of Aedes aegypti and Aedes albopictus at a 5×5km spatial resolution with high-dimensional multidisciplinary datasets and machine learning methods Three relatively popular and robust machine learning models, including support vector machine (SVM), gradient boosting machine (GBM) and random forest (RF), were used. During the fine-tuning process based on training datasets of A. aegypti and A. albopictus, RF models achieved the highest performance with an area under the curve (AUC) of 0.973 and 0.974, respectively, followed by GBM (AUC of 0.971 and 0.972, respectively) and SVM (AUC of 0.963 and 0.964, respectively) models. The simulation difference between RF and GBM models was not statistically significant (p>0.05) based on the validation datasets, whereas statistically significant differences (p<0.05) were observed for RF and GBM simulations compared with SVM simulations. From the simulated maps derived from RF models, we observed that the distribution of A. albopictus was wider than that of A. aegypti along a latitudinal gradient. The discriminatory power of each factor in simulating the global distribution of the two species was also analyzed. Our results provided fundamental information for further study on disease transmission simulation and risk assessment. Copyright © 2017 Elsevier B.V. All rights reserved.

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Web Mining: Machine Learning for Web Applications.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chau, Michael

2004-01-01

Presents an overview of machine learning research and reviews methods used for evaluating machine learning systems. Ways that machine-learning algorithms were used in traditional information retrieval systems in the "pre-Web" era are described, and the field of Web mining and how machine learning has been used in different Web mining…

Imbalanced learning for pattern recognition: an empirical study

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Man, Hong; Desai, Sachi; Quoraishee, Shafik

2010-10-01

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.

PubMed

Koutsoukas, Alexios; Monaghan, Keith J; Li, Xiaoli; Huan, Jun

2017-06-28

In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets.

Using Machine Learning to Advance Personality Assessment and Theory.

PubMed

Bleidorn, Wiebke; Hopwood, Christopher James

2018-05-01

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

PubMed

Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean

2018-03-30

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

Mapping the Transmission Risk of Zika Virus using Machine Learning Models.

PubMed

Jiang, Dong; Hao, Mengmeng; Ding, Fangyu; Fu, Jingying; Li, Meng

2018-06-19

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expansion fueled by increased global travel and trade. Suitability mapping of the transmission risk of Zika virus is essential for drafting public health plans and disease control strategies, which are especially important in areas where medical resources are relatively scarce. Predicting the risk of Zika virus outbreak has been studied in recent years, but the published literature rarely includes multiple model comparisons or predictive uncertainty analysis. Here, three relatively popular machine learning models including backward propagation neural network (BPNN), gradient boosting machine (GBM) and random forest (RF) were adopted to map the probability of Zika epidemic outbreak at the global level, pairing high-dimensional multidisciplinary covariate layers with comprehensive location data on recorded Zika virus infection in humans. The results show that the predicted high-risk areas for Zika transmission are concentrated in four regions: Southeastern North America, Eastern South America, Central Africa and Eastern Asia. To evaluate the performance of machine learning models, the 50 modeling processes were conducted based on a training dataset. The BPNN model obtained the highest predictive accuracy with a 10-fold cross-validation area under the curve (AUC) of 0.966 [95% confidence interval (CI) 0.965-0.967], followed by the GBM model (10-fold cross-validation AUC = 0.964[0.963-0.965]) and the RF model (10-fold cross-validation AUC = 0.963[0.962-0.964]). Based on training samples, compared with the BPNN-based model, we find that significant differences (p = 0.0258* and p = 0.0001***, respectively) are observed for prediction accuracies achieved by the GBM and RF models. Importantly, the prediction uncertainty introduced by the selection of absence data was quantified and could provide more accurate fundamental and scientific information for further study on disease transmission prediction and risk assessment. Copyright © 2018. Published by Elsevier B.V.

The Highly Adaptive Lasso Estimator

PubMed Central

Benkeser, David; van der Laan, Mark

2017-01-01

Estimation of a regression functions is a common goal of statistical learning. We propose a novel nonparametric regression estimator that, in contrast to many existing methods, does not rely on local smoothness assumptions nor is it constructed using local smoothing techniques. Instead, our estimator respects global smoothness constraints by virtue of falling in a class of right-hand continuous functions with left-hand limits that have variation norm bounded by a constant. Using empirical process theory, we establish a fast minimal rate of convergence of our proposed estimator and illustrate how such an estimator can be constructed using standard software. In simulations, we show that the finite-sample performance of our estimator is competitive with other popular machine learning techniques across a variety of data generating mechanisms. We also illustrate competitive performance in real data examples using several publicly available data sets. PMID:29094111

Intrinsic dimensionality predicts the saliency of natural dynamic scenes.

PubMed

Vig, Eleonora; Dorr, Michael; Martinetz, Thomas; Barth, Erhardt

2012-06-01

Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.

featsel: A framework for benchmarking of feature selection algorithms and cost functions

NASA Astrophysics Data System (ADS)

Reis, Marcelo S.; Estrela, Gustavo; Ferreira, Carlos Eduardo; Barrera, Junior

In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions. This framework allows the user to deal with the search space as a Boolean lattice and has its core coded in C++ for computational efficiency purposes. Moreover, featsel includes Perl scripts to add new algorithms and/or cost functions, generate random instances, plot graphs and organize results into tables. Besides, this framework already comes with dozens of algorithms and cost functions for benchmarking experiments. We also provide illustrative examples, in which featsel outperforms the popular Weka workbench in feature selection procedures on data sets from the UCI Machine Learning Repository.

Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop

DTIC Science & Technology

2007-01-01

machine learning components natural language processing, and optimization...was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting...study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Quantum machine learning.

PubMed

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Quantum machine learning

NASA Astrophysics Data System (ADS)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-01

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

The Efficacy of Machine Learning Programs for Navy Manpower Analysis

DTIC Science & Technology

1993-03-01

This thesis investigated the efficacy of two machine learning programs for Navy manpower analysis. Two machine learning programs, AIM and IXL, were...to generate models from the two commercial machine learning programs. Using a held out sub-set of the data the capabilities of the three models were...partial effects. The author recommended further investigation of AIM’s capabilities, and testing in an operational environment.... Machine learning , AIM, IXL.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Unsupervised learning on scientific ocean drilling datasets from the South China Sea

NASA Astrophysics Data System (ADS)

Tse, Kevin C.; Chiu, Hon-Chim; Tsang, Man-Yin; Li, Yiliang; Lam, Edmund Y.

2018-06-01

Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea. Compared to studies on similar datasets, but using supervised learning methods which are designed to make predictions based on sample training data, unsupervised learning methods require no a priori information and focus only on the input data. In this study, popular unsupervised learning methods including K-means, self-organizing maps, hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters. The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales. K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales. This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

Icarus Investigations: A Model for Engaging Citizen Scientists to Solve Solar Big Data Challenges

NASA Astrophysics Data System (ADS)

Winter, H. D., III; Loftus, K.

2017-12-01

Solar data is growing at an exponential rate. NASA's Atmospheric Imaging Assembly (AIA) has produced a data volume of over 6 petabytes to date, and that volume is growing. The initial suite of instruments on DKIST are expected to generate approximately 25TB of data per day, with bursts up to 50TB. Making sense of this deluge of solar data is as formidable a task as collecting it. New techniques and new ways of thinking are needed in order to optimize the value of this immense amount of data. While machine learning algorithms are a natural tool to sift through Big Data, those tools need to be carefully constructed and trained in order to provide meaningful results. Trained volunteers are needed to provide a large volume of initial classifications in order to properly train machine learning algorithms. To retain a highly trained pool of volunteers to teach machine learning algorithms, we propose to host an ever-changing array of solar-based citizen science projects under a single collaborative project banner: Icarus Investigations. Icarus Investigations would build and retain a dedicated user base within Zooniverse, the most popular citizen science website with over a million registered users. Volunteers will become increasingly comfortable with solar images and solar features of interest as they work on projects that focus on a wide array of solar phenomena. Under a unified framework, new solar citizen science projects submitted to Icarus Investigations will build on the successes, and learn from the missteps, of their predecessors. In this talk we discuss the importance and benefits of engaging the public in citizen science projects and call for collaborators on future citizen science projects. We will also demonstrate the initial Icarus Investigations project, The Where of the Flare. This demonstration will allow us to highlight the workflow of a Icarus Investigations citizen science project with a concrete example.

The Security of Machine Learning

DTIC Science & Technology

2008-04-24

Machine learning has become a fundamental tool for computer security, since it can rapidly evolve to changing and complex situations. That...adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine ...We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing

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.

Waiting to Exhale: African American Women and Adult Learning Through Movies.

ERIC Educational Resources Information Center

Rogers, Elice E.

Scholars have addressed adults and the impact of popular culture on adult learning, but little attention has been directed toward the relationship between adult learning and African Americans. Most specifically, minimal information is related to adult learning that evolves as a result of popular culture influences. Popular culture promotes…

Human action recognition with group lasso regularized-support vector machine

NASA Astrophysics Data System (ADS)

Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

2016-05-01

The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

A Machine Learning and Optimization Toolkit for the Swarm

DTIC Science & Technology

2014-11-17

Machine   Learning  and  Op0miza0on   Toolkit  for  the  Swarm   Ilge  Akkaya,  Shuhei  Emoto...3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE A Machine Learning and Optimization Toolkit for the Swarm 5a. CONTRACT NUMBER... machine   learning   methodologies  by  providing  the  right  interfaces  between   machine   learning  tools  and

Vision Guided Intelligent Robot Design And Experiments

NASA Astrophysics Data System (ADS)

Slutzky, G. D.; Hall, E. L.

1988-02-01

The concept of an intelligent robot is an important topic combining sensors, manipulators, and artificial intelligence to design a useful machine. Vision systems, tactile sensors, proximity switches and other sensors provide the elements necessary for simple game playing as well as industrial applications. These sensors permit adaption to a changing environment. The AI techniques permit advanced forms of decision making, adaptive responses, and learning while the manipulator provides the ability to perform various tasks. Computer languages such as LISP and OPS5, have been utilized to achieve expert systems approaches in solving real world problems. The purpose of this paper is to describe several examples of visually guided intelligent robots including both stationary and mobile robots. Demonstrations will be presented of a system for constructing and solving a popular peg game, a robot lawn mower, and a box stacking robot. The experience gained from these and other systems provide insight into what may be realistically expected from the next generation of intelligent machines.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

PubMed

Macesic, Nenad; Polubriaginof, Fernanda; Tatonetti, Nicholas P

2017-12-01

Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

Comparison between extreme learning machine and wavelet neural networks in data classification

NASA Astrophysics Data System (ADS)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Evaluating the Security of Machine Learning Algorithms

DTIC Science & Technology

2008-05-20

Two far-reaching trends in computing have grown in significance in recent years. First, statistical machine learning has entered the mainstream as a...computing applications. The growing intersection of these trends compels us to investigate how well machine learning performs under adversarial conditions... machine learning has a structure that we can use to build secure learning systems. This thesis makes three high-level contributions. First, we develop a

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

GTM-Based QSAR Models and Their Applicability Domains.

PubMed

Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

2015-06-01

In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantum-Enhanced Machine Learning

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

2016-09-01

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

Myths and legends in learning classification rules

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A discussion is presented of machine learning theory on empirically learning classification rules. Six myths are proposed in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, universal learning algorithms, and interactive learning. Some of the problems raised are also addressed from a Bayesian perspective. Questions are suggested that machine learning researchers should be addressing both theoretically and experimentally.

Machine Learning Based Malware Detection

DTIC Science & Technology

2015-05-18

A TRIDENT SCHOLAR PROJECT REPORT NO. 440 Machine Learning Based Malware Detection by Midshipman 1/C Zane A. Markel, USN...COVERED (From - To) 4. TITLE AND SUBTITLE Machine Learning Based Malware Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...suitably be projected into realistic performance. This work explores several aspects of machine learning based malware detection . First, we

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Machine Learning for Medical Imaging

PubMed Central

Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L.

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017 PMID:28212054

Machine Learning for Medical Imaging.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

A framework to enhance security of physically unclonable functions using chaotic circuits

NASA Astrophysics Data System (ADS)

Chen, Lanxiang

2018-05-01

As a new technique for authentication and key generation, physically unclonable function (PUF) has attracted considerable attentions, with extensive research results achieved already. To resist the popular machine learning modeling attacks, a framework to enhance the security of PUFs is proposed. The basic idea is to combine PUFs with a chaotic system of which the response is highly sensitive to initial conditions. For this framework, a specific construction which combines the common arbiter PUF circuit, a converter, and the Chua's circuit is given to implement a more secure PUF. Simulation experiments are presented to further validate the framework. Finally, some practical suggestions for the framework and specific construction are also discussed.

Deriving the expected utility of a predictive model when the utilities are uncertain.

PubMed

Cooper, Gregory F; Visweswaran, Shyam

2005-01-01

Predictive models are often constructed from clinical databases with the goal of eventually helping make better clinical decisions. Evaluating models using decision theory is therefore natural. When constructing a model using statistical and machine learning methods, however, we are often uncertain about precisely how the model will be used. Thus, decision-independent measures of classification performance, such as the area under an ROC curve, are popular. As a complementary method of evaluation, we investigate techniques for deriving the expected utility of a model under uncertainty about the model's utilities. We demonstrate an example of the application of this approach to the evaluation of two models that diagnose coronary artery disease.

The role of fMRI in cognitive neuroscience: where do we stand?

PubMed

Poldrack, Russell A

2008-04-01

Functional magnetic resonance imaging (fMRI) has quickly become the most prominent tool in cognitive neuroscience. In this article, I outline some of the limits on the kinds of inferences that can be supported by fMRI, focusing particularly on reverse inference, in which the engagement of specific mental processes is inferred from patterns of brain activation. Although this form of inference is weak, newly developed methods from the field of machine learning offer the potential to formalize and strengthen reverse inferences. I conclude by discussing the increasing presence of fMRI results in the popular media and the ethical implications of the increasing predictive power of fMRI.

Clinical Named Entity Recognition Using Deep Learning Models.

PubMed

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER.

Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

PubMed

Smith, Kevin; Horvath, Peter

2014-06-01

High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

Clinical Named Entity Recognition Using Deep Learning Models

PubMed Central

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER. PMID:29854252

Manifold Regularized Experimental Design for Active Learning.

PubMed

Zhang, Lining; Shum, Hubert P H; Shao, Ling

2016-12-02

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

Machine learning in heart failure: ready for prime time.

PubMed

Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

2018-03-01

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Human Machine Learning Symbiosis

ERIC Educational Resources Information Center

Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

2017-01-01

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

Machine learning in cardiovascular medicine: are we there yet?

PubMed

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Myths and legends in learning classification rules

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, 'universal' learning algorithms, and interactive learnings. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.

PubMed

Douglas, P K; Harris, Sam; Yuille, Alan; Cohen, Mark S

2011-05-15

Machine learning (ML) has become a popular tool for mining functional neuroimaging data, and there are now hopes of performing such analyses efficiently in real-time. Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements. We performed unsupervised dimension reduction and automated feature extraction using independent component (IC) analysis and extracted IC time courses. Optimization of classification hyperparameters across each classifier occurred prior to assessment. Maximum accuracy was achieved at 92% for Random Forest, followed by 91% for AdaBoost, 89% for Naïve Bayes, 87% for a J48 decision tree, 86% for K*, and 84% for support vector machine. For real-time decoding applications, finding a parsimonious subset of diagnostic ICs might be useful. We used a forward search technique to sequentially add ranked ICs to the feature subspace. For the current data set, we determined that approximately six ICs represented a meaningful basis set for classification. We then projected these six IC spatial maps forward onto a later scanning session within subject. We then applied the optimized ML algorithms to these new data instances, and found that classification accuracy results were reproducible. Additionally, we compared our classification method to our previously published general linear model results on this same data set. The highest ranked IC spatial maps show similarity to brain regions associated with contrasts for belief > disbelief, and disbelief < belief. Copyright © 2010 Elsevier Inc. All rights reserved.

Antibiotic Residues in Milk from Three Popular Kenyan Milk Vending Machines.

PubMed

Kosgey, Amos; Shitandi, Anakalo; Marion, Jason W

2018-05-01

Milk vending machines (MVMs) are growing in popularity in Kenya and worldwide. Milk vending machines dispense varying quantities of locally sourced, pasteurized milk. The Kenya Dairy Board has a regulatory framework, but surveillance is weak because of several factors. Milk vending machines' milk is not routinely screened for antibiotics, thereby increasing potential for antibiotic misuse. To investigate, a total of 80 milk samples from four commercial providers ( N = 25), street vendors ( N = 21), and three MVMs ( N = 34) were collected and screened in Eldoret, Kenya. Antibiotic residue surveillance occurred during December 2016 and January 2017 using Idexx SNAP ® tests for tetracyclines, sulfamethazine, beta-lactams, and gentamicin. Overall, 24% of MVM samples and 24% of street vendor samples were presumably positive for at least one antibiotic. No commercial samples were positive. Research into cost-effective screening methods and increased monitoring by food safety agencies are needed to uphold hazard analysis and critical control point for improving antibiotic stewardship throughout the Kenyan private dairy industry.

Applications of Machine Learning and Rule Induction,

DTIC Science & Technology

1995-02-15

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic

The R package "sperrorest" : Parallelized spatial error estimation and variable importance assessment for geospatial machine learning

NASA Astrophysics Data System (ADS)

Schratz, Patrick; Herrmann, Tobias; Brenning, Alexander

2017-04-01

Computational and statistical prediction methods such as the support vector machine have gained popularity in remote-sensing applications in recent years and are often compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more widely used non-spatial equivalent. The R package sperrorest by A. Brenning [IEEE International Geoscience and Remote Sensing Symposium, 1, 374 (2012)] provides a generic interface for performing (spatial) cross-validation of any statistical or machine-learning technique available in R. Since spatial statistical models as well as flexible machine-learning algorithms can be computationally expensive, parallel computing strategies are required to perform cross-validation efficiently. The most recent major release of sperrorest therefore comes with two new features (aside from improved documentation): The first one is the parallelized version of sperrorest(), parsperrorest(). This function features two parallel modes to greatly speed up cross-validation runs. Both parallel modes are platform independent and provide progress information. par.mode = 1 relies on the pbapply package and calls interactively (depending on the platform) parallel::mclapply() or parallel::parApply() in the background. While forking is used on Unix-Systems, Windows systems use a cluster approach for parallel execution. par.mode = 2 uses the foreach package to perform parallelization. This method uses a different way of cluster parallelization than the parallel package does. In summary, the robustness of parsperrorest() is increased with the implementation of two independent parallel modes. A new way of partitioning the data in sperrorest is provided by partition.factor.cv(). This function gives the user the possibility to perform cross-validation at the level of some grouping structure. As an example, in remote sensing of agricultural land uses, pixels from the same field contain nearly identical information and will thus be jointly placed in either the test set or the training set. Other spatial sampling resampling strategies are already available and can be extended by the user.

Experimental Realization of a Quantum Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Zhaokai; Liu, Xiaomei; Xu, Nanyang; Du, Jiangfeng

2015-04-01

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.

Workshop on Fielded Applications of Machine Learning

DTIC Science & Technology

1994-05-11

This report summaries the talks presented at the Workshop on Fielded Applications of Machine Learning , and draws some initial conclusions about the state of machine learning and its potential for solving real-world problems.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Machine Learning. Part 1. A Historical and Methodological Analysis.

DTIC Science & Technology

1983-05-31

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern Al systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. Part 1 of this paper presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by

Toward Harnessing User Feedback For Machine Learning

DTIC Science & Technology

2006-10-02

machine learning systems. If this resource-the users themselves-could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users? understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

PubMed Central

Zhang, Kai; Lan, Liang; Kwok, James T.; Vucetic, Slobodan; Parvin, Bahram

2014-01-01

When the amount of labeled data are limited, semi-supervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via ℓ1-regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning. PMID:25720002

Language Acquisition and Machine Learning.

DTIC Science & Technology

1986-02-01

machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, the authors propose four component tasks involved in learning from experience-aggregation, clustering, characterization, and storage. They then consider four common problems studied by machine learning researchers-learning from examples, heuristics learning, conceptual clustering, and learning macro-operators-describing each in terms of our framework. After this, they turn to the problem of grammar

Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2014-03-27

BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ...AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS Jessica R. Werling, B.S.C.S. Captain, USAF Approved

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

Machine learning in genetics and genomics

PubMed Central

Libbrecht, Maxwell W.; Noble, William Stafford

2016-01-01

The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data. PMID:25948244

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.

Addressing uncertainty in atomistic machine learning.

PubMed

Peterson, Andrew A; Christensen, Rune; Khorshidi, Alireza

2017-05-10

Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility of the predictions. In this perspective, we address the types of errors that might arise in atomistic machine learning, the unique aspects of atomistic simulations that make machine-learning challenging, and highlight how uncertainty analysis can be used to assess the validity of machine-learning predictions. We suggest this will allow researchers to more fully use machine learning for the routine acceleration of large, high-accuracy, or extended-time simulations. In our demonstrations, we use a bootstrap ensemble of neural network-based calculators, and show that the width of the ensemble can provide an estimate of the uncertainty when the width is comparable to that in the training data. Intriguingly, we also show that the uncertainty can be localized to specific atoms in the simulation, which may offer hints for the generation of training data to strategically improve the machine-learned representation.

On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

NASA Astrophysics Data System (ADS)

Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng

2017-11-01

Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Bypassing the Kohn-Sham equations with machine learning.

PubMed

Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E; Burke, Kieron; Müller, Klaus-Robert

2017-10-11

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

An Evolutionary Machine Learning Framework for Big Data Sequence Mining

ERIC Educational Resources Information Center

Kamath, Uday Krishna

2014-01-01

Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

34 Stochastic Learning Machine " Neuromorphic Target Identification * Cognitive Networks 3. Conclusions ..... ................ .. 12 4. Publications...16 5. References ...... ................... . 17 6. Appendices ....... .................. 18 I. Optoelectronic Neural Networks and...Learning Machines. II. Stochastic Optical Learning Machine. III. Learning Network for Extrapolation AccesFon For and Radar Target Identification

An iterative learning control method with application for CNC machine tools

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

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

Learning dominance relations in combinatorial search problems

NASA Technical Reports Server (NTRS)

Yu, Chee-Fen; Wah, Benjamin W.

1988-01-01

Dominance relations commonly are used to prune unnecessary nodes in search graphs, but they are problem-dependent and cannot be derived by a general procedure. The authors identify machine learning of dominance relations and the applicable learning mechanisms. A study of learning dominance relations using learning by experimentation is described. This system has been able to learn dominance relations for the 0/1-knapsack problem, an inventory problem, the reliability-by-replication problem, the two-machine flow shop problem, a number of single-machine scheduling problems, and a two-machine scheduling problem. It is considered that the same methodology can be extended to learn dominance relations in general.

IQ Tests Are Not for Machines, Yet

ERIC Educational Resources Information Center

Dowe, David L.; Hernandez-Orallo, Jose

2012-01-01

Complex, but specific, tasks--such as chess or "Jeopardy!"--are popularly seen as milestones for artificial intelligence (AI). However, they are not appropriate for evaluating the intelligence of machines or measuring the progress in AI. Aware of this delusion, Detterman has recently raised a challenge prompting AI researchers to evaluate their…

Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

DTIC Science & Technology

2016-06-01

research is being done to incorporate the field of machine learning into intrusion detection. Machine learning is a branch of artificial intelligence (AI...adversarial drift." Proceedings of the 2013 ACM workshop on Artificial intelligence and security. ACM. (2013) Kantarcioglu, M., Xi, B., and Clifton, C. "A...34 Proceedings of the 4th ACM workshop on Security and artificial intelligence . ACM. (2011) Dua, S., and Du, X. Data Mining and Machine Learning in

Large-Scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation

DTIC Science & Technology

2016-08-10

AFRL-AFOSR-JP-TR-2016-0073 Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation ...2016 4.  TITLE AND SUBTITLE Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation 5a...performances on various machine learning tasks and it naturally lends itself to fast parallel implementations . Despite this, very little work has been

ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines

DTIC Science & Technology

2014-05-16

ML-o-scope: a diagnostic visualization system for deep machine learning pipelines Daniel Bruckner Electrical Engineering and Computer Sciences... machine learning pipelines 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f...the system as a support for tuning large scale object-classification pipelines. 1 Introduction A new generation of pipelined machine learning models

WebWatcher: Machine Learning and Hypertext

DTIC Science & Technology

1995-05-29

WebWatcher: Machine Learning and Hypertext Thorsten Joachims, Tom Mitchell, Dayne Freitag, and Robert Armstrong School of Computer Science Carnegie...HTML-page about machine learning in which we in- serted a hyperlink to WebWatcher (line 6). The user follows this hyperlink and gets to a page which...AND SUBTITLE WebWatcher: Machine Learning and Hypertext 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

CW-SSIM kernel based random forest for image classification

NASA Astrophysics Data System (ADS)

Fan, Guangzhe; Wang, Zhou; Wang, Jiheng

2010-07-01

Complex wavelet structural similarity (CW-SSIM) index has been proposed as a powerful image similarity metric that is robust to translation, scaling and rotation of images, but how to employ it in image classification applications has not been deeply investigated. In this paper, we incorporate CW-SSIM as a kernel function into a random forest learning algorithm. This leads to a novel image classification approach that does not require a feature extraction or dimension reduction stage at the front end. We use hand-written digit recognition as an example to demonstrate our algorithm. We compare the performance of the proposed approach with random forest learning based on other kernels, including the widely adopted Gaussian and the inner product kernels. Empirical evidences show that the proposed method is superior in its classification power. We also compared our proposed approach with the direct random forest method without kernel and the popular kernel-learning method support vector machine. Our test results based on both simulated and realworld data suggest that the proposed approach works superior to traditional methods without the feature selection procedure.

Cross-sensor iris recognition through kernel learning.

PubMed

Pillai, Jaishanker K; Puertas, Maria; Chellappa, Rama

2014-01-01

Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.

Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

PubMed

Korvigo, Ilia; Afanasyev, Andrey; Romashchenko, Nikolay; Skoblov, Mikhail

2018-01-01

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model via the web-site: http://score.generesearch.ru/services/badmut/.

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Stacked Denoising Autoencoders Applied to Star/Galaxy Classification

NASA Astrophysics Data System (ADS)

Qin, Hao-ran; Lin, Ji-ming; Wang, Jun-yi

2017-04-01

In recent years, the deep learning algorithm, with the characteristics of strong adaptability, high accuracy, and structural complexity, has become more and more popular, but it has not yet been used in astronomy. In order to solve the problem that the star/galaxy classification accuracy is high for the bright source set, but low for the faint source set of the Sloan Digital Sky Survey (SDSS) data, we introduced the new deep learning algorithm, namely the SDA (stacked denoising autoencoder) neural network and the dropout fine-tuning technique, which can greatly improve the robustness and antinoise performance. We randomly selected respectively the bright source sets and faint source sets from the SDSS DR12 and DR7 data with spectroscopic measurements, and made preprocessing on them. Then, we randomly selected respectively the training sets and testing sets without replacement from the bright source sets and faint source sets. At last, using these training sets we made the training to obtain the SDA models of the bright sources and faint sources in the SDSS DR7 and DR12, respectively. We compared the test result of the SDA model on the DR12 testing set with the test results of the Library for Support Vector Machines (LibSVM), J48 decision tree, Logistic Model Tree (LMT), Support Vector Machine (SVM), Logistic Regression, and Decision Stump algorithm, and compared the test result of the SDA model on the DR7 testing set with the test results of six kinds of decision trees. The experiments show that the SDA has a better classification accuracy than other machine learning algorithms for the faint source sets of DR7 and DR12. Especially, when the completeness function is used as the evaluation index, compared with the decision tree algorithms, the correctness rate of SDA has improved about 15% for the faint source set of SDSS-DR7.

Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning.

PubMed

Kalyanam, Janani; Katsuki, Takeo; R G Lanckriet, Gert; Mackey, Tim K

2017-02-01

Nonmedical use of prescription medications/drugs (NMUPD) is a serious public health threat, particularly in relation to the prescription opioid analgesics abuse epidemic. While attention to this problem has been growing, there remains an urgent need to develop novel strategies in the field of "digital epidemiology" to better identify, analyze and understand trends in NMUPD behavior. We conducted surveillance of the popular microblogging site Twitter by collecting 11 million tweets filtered for three commonly abused prescription opioid analgesic drugs Percocet® (acetaminophen/oxycodone), OxyContin® (oxycodone), and Oxycodone. Unsupervised machine learning was applied on the subset of tweets for each analgesic drug to discover underlying latent themes regarding risk behavior. A two-step process of obtaining themes, and filtering out unwanted tweets was carried out in three subsequent rounds of machine learning. Using this methodology, 2.3M tweets were identified that contained content relevant to analgesic NMUPD. The underlying themes were identified for each drug and the most representative tweets of each theme were annotated for NMUPD behavioral risk factors. The primary themes identified evidence high levels of social media discussion about polydrug abuse on Twitter. This included specific mention of various polydrug combinations including use of other classes of prescription drugs, and illicit drug abuse. This study presents a methodology to filter Twitter content for NMUPD behavior, while also identifying underlying themes with minimal human intervention. Results from the study track accurately with the inclusion/exclusion criteria used to isolate NMUPD-related risk behaviors of interest and also provides insight on NMUPD behavior that has a high level of social media engagement. Results suggest that this could be a viable methodology for use in big data substance abuse surveillance, data collection, and analysis in comparison to other studies that rely upon content analysis and human coding schemes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Convolutional neural network features based change detection in satellite images

NASA Astrophysics Data System (ADS)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Machine learning for medical images analysis.

PubMed

Criminisi, A

2016-10-01

This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Machine Learning.

ERIC Educational Resources Information Center

Kirrane, Diane E.

1990-01-01

As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Quantum Machine Learning over Infinite Dimensions

DOE PAGES

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George; ...

2017-02-21

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

Quantum Machine Learning over Infinite Dimensions

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

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

Machine learning and medicine: book review and commentary.

PubMed

Koprowski, Robert; Foster, Kenneth R

2018-02-01

This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book.

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are available at Bioinformatics online.

Approaches to Machine Learning.

DTIC Science & Technology

1984-02-16

The field of machine learning strives to develop methods and techniques to automatic the acquisition of new information, new skills, and new ways of organizing existing information. In this article, we review the major approaches to machine learning in symbolic domains, covering the tasks of learning concepts from examples, learning search methods, conceptual clustering, and language acquisition. We illustrate each of the basic approaches with paradigmatic examples. (Author)

A Qualitative Security Analysis of a New Class of 3-D Integrated Crypto Co-processors

DTIC Science & Technology

2012-01-01

and mobile phones, lottery ticket vending machines , and various electronic payment systems. The main reason for their use in such applications is that...military applications such as secure communication links. However, the proliferation of Automated Teller Machines (ATMs) in the ’80s introduced them to...commercial applications. Today many popular consumer devices have cryptographic processors in them, for example, smart- cards for pay-TV access machines

Informal in Formal: The Relationship of Informal and Formal Learning in Popular and Jazz Music Master Workshops in Conservatoires

ERIC Educational Resources Information Center

Virkkula, Esa

2016-01-01

The present article will examine informal learning in popular and jazz music education in Finland and evaluate it as a part of formal upper secondary vocational musicians' training, which is typically teacher directed. It is not necessarily the best model of working in popular and jazz music learning, which has traditionally benefitted from…

Bringing Fun and Meaning into Grammar Learning: A Case Study of a Secondary-Level EFL Class in Hong Kong

ERIC Educational Resources Information Center

Hua, Congchao; Li, Bin

2015-01-01

Popular culture materials are generally believed to have positive effects on L2 learning. This study examined the effectiveness of popular culture materials in enhancing Hong Kong EFL students' grammar learning. In a quasi-experimental design, 20 secondary school students were taught grammar in two ways: with the use of popular culture materials,…

Fast Query-Optimized Kernel-Machine Classification

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; DeCoste, Dennis

2004-01-01

A recently developed algorithm performs kernel-machine classification via incremental approximate nearest support vectors. The algorithm implements support-vector machines (SVMs) at speeds 10 to 100 times those attainable by use of conventional SVM algorithms. The algorithm offers potential benefits for classification of images, recognition of speech, recognition of handwriting, and diverse other applications in which there are requirements to discern patterns in large sets of data. SVMs constitute a subset of kernel machines (KMs), which have become popular as models for machine learning and, more specifically, for automated classification of input data on the basis of labeled training data. While similar in many ways to k-nearest-neighbors (k-NN) models and artificial neural networks (ANNs), SVMs tend to be more accurate. Using representations that scale only linearly in the numbers of training examples, while exploring nonlinear (kernelized) feature spaces that are exponentially larger than the original input dimensionality, KMs elegantly and practically overcome the classic curse of dimensionality. However, the price that one must pay for the power of KMs is that query-time complexity scales linearly with the number of training examples, making KMs often orders of magnitude more computationally expensive than are ANNs, decision trees, and other popular machine learning alternatives. The present algorithm treats an SVM classifier as a special form of a k-NN. The algorithm is based partly on an empirical observation that one can often achieve the same classification as that of an exact KM by using only small fraction of the nearest support vectors (SVs) of a query. The exact KM output is a weighted sum over the kernel values between the query and the SVs. In this algorithm, the KM output is approximated with a k-NN classifier, the output of which is a weighted sum only over the kernel values involving k selected SVs. Before query time, there are gathered statistics about how misleading the output of the k-NN model can be, relative to the outputs of the exact KM for a representative set of examples, for each possible k from 1 to the total number of SVs. From these statistics, there are derived upper and lower thresholds for each step k. These thresholds identify output levels for which the particular variant of the k-NN model already leans so strongly positively or negatively that a reversal in sign is unlikely, given the weaker SV neighbors still remaining. At query time, the partial output of each query is incrementally updated, stopping as soon as it exceeds the predetermined statistical thresholds of the current step. For an easy query, stopping can occur as early as step k = 1. For more difficult queries, stopping might not occur until nearly all SVs are touched. A key empirical observation is that this approach can tolerate very approximate nearest-neighbor orderings. In experiments, SVs and queries were projected to a subspace comprising the top few principal- component dimensions and neighbor orderings were computed in that subspace. This approach ensured that the overhead of the nearest-neighbor computations was insignificant, relative to that of the exact KM computation.

Progress with modeling activity landscapes in drug discovery.

PubMed

Vogt, Martin

2018-04-19

Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity. Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail. Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.

The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques

NASA Astrophysics Data System (ADS)

Deng, Chengbin; Wu, Changshan

2013-12-01

Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

PubMed

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Machine Learning in the Big Data Era: Are We There Yet?

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

Sukumar, Sreenivas Rangan

In this paper, we discuss the machine learning challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are machine learning algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstandingmore » challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security and healthcare to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.« less

Machine Learning

DTIC Science & Technology

1990-04-01

DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

Workshop on Fielded Applications of Machine Learning Held in Amherst, Massachusetts on 30 June-1 July 1993. Abstracts.

DTIC Science & Technology

1993-01-01

engineering has led to many AI systems that are now regularly used in industry and elsewhere. The ultimate test of machine learning , the subfield of Al that...applications of machine learning suggest the time was ripe for a meeting on this topic. For this reason, Pat Langley (Siemens Corporate Research) and Yves...Kodratoff (Universite de Paris, Sud) organized an invited workshop on applications of machine learning . The goal of the gathering was to familiarize

Explaining Support Vector Machines: A Color Based Nomogram

PubMed Central

Van Belle, Vanya; Van Calster, Ben; Van Huffel, Sabine; Suykens, Johan A. K.; Lisboa, Paulo

2016-01-01

Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. Objective In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. Results Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method. PMID:27723811

Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare.

PubMed

Mozaffari-Kermani, Mehran; Sur-Kolay, Susmita; Raghunathan, Anand; Jha, Niraj K

2015-11-01

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

Computer aided lung cancer diagnosis with deep learning algorithms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Qian, Wei

2016-03-01

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists.

PubMed

Nakai, Yasushi; Takiguchi, Tetsuya; Matsui, Gakuyo; Yamaoka, Noriko; Takada, Satoshi

2017-10-01

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders ( n = 30) and typical development ( n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.

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.

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

Multi-Stage Convex Relaxation Methods for Machine Learning

DTIC Science & Technology

2013-03-01

Many problems in machine learning can be naturally formulated as non-convex optimization problems. However, such direct nonconvex formulations have...original nonconvex formulation. We will develop theoretical properties of this method and algorithmic consequences. Related convex and nonconvex machine learning methods will also be investigated.

Machine Learning for the Knowledge Plane

DTIC Science & Technology

2006-06-01

this idea is to combine techniques from machine learning with new architectural concepts in networking to make the internet self-aware and self...work on the machine learning portion of the Knowledge Plane. This consisted of three components: (a) we wrote a document formulating the various

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Machine Learning Approaches for Clinical Psychology and Psychiatry.

PubMed

Dwyer, Dominic B; Falkai, Peter; Koutsouleris, Nikolaos

2018-05-07

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.

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.

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

A review of supervised machine learning applied to ageing research.

PubMed

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Machine learning for the automatic detection of anomalous events

NASA Astrophysics Data System (ADS)

Fisher, Wendy D.

In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97.3% overall accuracy and less than 1.4% false negatives in anomaly detection. In Chapter 4, we research using two-class and one-class support vector machines (SVMs) for an effective anomaly detection system. We again use the two different EDL data sets from experimental laboratory earth embankments (each having approximately 80% normal and 20% anomalies) to ensure our workflow is robust enough to work with multiple data sets and different types of anomalous events (e.g., cracks and piping). We apply Haar wavelet-denoising techniques and extract nine spectral features from decomposed segments of the time series data. The two-class SVM with 10-fold cross validation achieved over 94% overall accuracy and 96% F1-score. Our approach provides a means for automatically identifying anomalous events using various machine learning techniques. Detecting internal erosion events in aging EDLs, earlier than is currently possible, can allow more time to prevent or mitigate catastrophic failures. Results show that we can successfully separate normal from anomalous data observations in passive seismic data, and provide a step towards techniques for continuous real-time monitoring of EDL health. Our lightweight non-commercial BSR detection system also has promise in separating commercial from non-commercial BSR scans without the need for prior geographic location information, extensive time-lapse surveys, or a database of known commercial carriers. (Abstract shortened by ProQuest.).

Visual question answering using hierarchical dynamic memory networks

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Duan, Zhikui; Huang, Junwei

2018-04-01

Visual Question Answering (VQA) is one of the most popular research fields in machine learning which aims to let the computer learn to answer natural language questions with images. In this paper, we propose a new method called hierarchical dynamic memory networks (HDMN), which takes both question attention and visual attention into consideration impressed by Co-Attention method, which is the best (or among the best) algorithm for now. Additionally, we use bi-directional LSTMs, which have a better capability to remain more information from the question and image, to replace the old unit so that we can capture information from both past and future sentences to be used. Then we rebuild the hierarchical architecture for not only question attention but also visual attention. What's more, we accelerate the algorithm via a new technic called Batch Normalization which helps the network converge more quickly than other algorithms. The experimental result shows that our model improves the state of the art on the large COCO-QA dataset, compared with other methods.

Machine learning, social learning and the governance of self-driving cars.

PubMed

Stilgoe, Jack

2018-02-01

Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are they learning and how are they learning?' Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. 'Self-driving' or 'autonomous' cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.

Robust Fault Diagnosis in Electric Drives Using Machine Learning

DTIC Science & Technology

2004-09-08

detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various fault condition data for machine learning . The technique is viable for accurate, reliable and fast fault detection in electric drives.

Agents Technology Research

DTIC Science & Technology

2010-02-01

multi-agent reputation management. State abstraction is a technique used to allow machine learning technologies to cope with problems that have large...state abstrac- tion process to enable reinforcement learning in domains with large state spaces. State abstraction is vital to machine learning ...across a collective of independent platforms. These individual elements, often referred to as agents in the machine learning community, should exhibit both

Machine learning approaches in medical image analysis: From detection to diagnosis.

PubMed

de Bruijne, Marleen

2016-10-01

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

Testing meta tagger

DTIC Science & Technology

2017-12-21

rank , and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on...Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.[1] Arthur Samuel...an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning " in 1959 while at IBM[2]. Evolved

Cognitive learning: a machine learning approach for automatic process characterization from design

NASA Astrophysics Data System (ADS)

Foucher, J.; Baderot, J.; Martinez, S.; Dervilllé, A.; Bernard, G.

2018-03-01

Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

PubMed

Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

2016-06-21

Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension

ERIC Educational Resources Information Center

Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S.

2017-01-01

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…

Implementing Machine Learning in Radiology Practice and Research.

PubMed

Kohli, Marc; Prevedello, Luciano M; Filice, Ross W; Geis, J Raymond

2017-04-01

The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

PubMed

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

PubMed

Varshney, Kush R; Alemzadeh, Homa

2017-09-01

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. We discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

Recent developments in machine learning applications in landslide susceptibility mapping

NASA Astrophysics Data System (ADS)

Lun, Na Kai; Liew, Mohd Shahir; Matori, Abdul Nasir; Zawawi, Noor Amila Wan Abdullah

2017-11-01

While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field.

Machine vision systems using machine learning for industrial product inspection

NASA Astrophysics Data System (ADS)

Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

2002-02-01

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

The application of machine learning techniques in the clinical drug therapy.

PubMed

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Machine Learning, deep learning and optimization in computer vision

NASA Astrophysics Data System (ADS)

Canu, Stéphane

2017-03-01

As quoted in the Large Scale Computer Vision Systems NIPS workshop, computer vision is a mature field with a long tradition of research, but recent advances in machine learning, deep learning, representation learning and optimization have provided models with new capabilities to better understand visual content. The presentation will go through these new developments in machine learning covering basic motivations, ideas, models and optimization in deep learning for computer vision, identifying challenges and opportunities. It will focus on issues related with large scale learning that is: high dimensional features, large variety of visual classes, and large number of examples.

Machine Learning in Radiology: Applications Beyond Image Interpretation.

PubMed

Lakhani, Paras; Prater, Adam B; Hutson, R Kent; Andriole, Kathy P; Dreyer, Keith J; Morey, Jose; Prevedello, Luciano M; Clark, Toshi J; Geis, J Raymond; Itri, Jason N; Hawkins, C Matthew

2018-02-01

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

An Evaluative Study of Machine Translation in the EFL Scenario of Saudi Arabia

ERIC Educational Resources Information Center

Al-Tuwayrish, Raneem Khalid

2016-01-01

Artificial Intelligence or AI as it is popularly known and its corollary, Machine Translation (MT) have long engaged scientists, thinkers and linguists alike in the twenty first century. However, the wider question that lies in the relation between technology and translation is, What does technology do to language? This is an important question in…

A fast and accurate online sequential learning algorithm for feedforward networks.

PubMed

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

The Next Era: Deep Learning in Pharmaceutical Research.

PubMed

Ekins, Sean

2016-11-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Communication Idol: Using Popular Culture to Catalyse Active Learning by Engaging Students in the Development of Entertaining Teaching and Learning Resources

ERIC Educational Resources Information Center

Dune, Tinashe; Bidewell, John; Firdaus, Rubab; Kirwan, Morwenna

2016-01-01

Bringing popular culture to tertiary education can potentially increase student engagement with learning tasks and content, especially when the learning task has students producing the content. Using a single-group intervention plus post-test design, this study implemented and evaluated a purposely developed learning and teaching innovation…

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

PubMed

Luo, Wei; Phung, Dinh; Tran, Truyen; Gupta, Sunil; Rana, Santu; Karmakar, Chandan; Shilton, Alistair; Yearwood, John; Dimitrova, Nevenka; Ho, Tu Bao; Venkatesh, Svetha; Berk, Michael

2016-12-16

As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016.

Development of E-Learning Materials for Machining Safety Education

NASA Astrophysics Data System (ADS)

Nakazawa, Tsuyoshi; Mita, Sumiyoshi; Matsubara, Masaaki; Takashima, Takeo; Tanaka, Koichi; Izawa, Satoru; Kawamura, Takashi

We developed two e-learning materials for Manufacturing Practice safety education: movie learning materials and hazard-detection learning materials. Using these video and sound media, students can learn how to operate machines safely with movie learning materials, which raise the effectiveness of preparation and review for manufacturing practice. Using these materials, students can realize safety operation well. Students can apply knowledge learned in lectures to the detection of hazards and use study methods for hazard detection during machine operation using the hazard-detection learning materials. Particularly, the hazard-detection learning materials raise students‧ safety consciousness and increase students‧ comprehension of knowledge from lectures and comprehension of operations during Manufacturing Practice.

An introduction to quantum machine learning

NASA Astrophysics Data System (ADS)

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

2015-04-01

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Large-Scale Machine Learning for Classification and Search

ERIC Educational Resources Information Center

Liu, Wei

2012-01-01

With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest…

Newton Methods for Large Scale Problems in Machine Learning

ERIC Educational Resources Information Center

Hansen, Samantha Leigh

2014-01-01

The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

ERIC Educational Resources Information Center

Bone, Daniel; Goodwin, Matthew S.; Black, Matthew P.; Lee, Chi-Chun; Audhkhasi, Kartik; Narayanan, Shrikanth

2015-01-01

Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead…

An active role for machine learning in drug development

PubMed Central

Murphy, Robert F.

2014-01-01

Due to the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development. PMID:21587249

Prediction and Validation of Disease Genes Using HeteSim Scores.

PubMed

Zeng, Xiangxiang; Liao, Yuanlu; Liu, Yuansheng; Zou, Quan

2017-01-01

Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein-protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non-machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. From the analysis of the top 10 predicted genes for different diseases, we found that HSSVM avoid the disadvantage of the existing machine learning based methods, which always predict similar genes for different diseases. The data sets and Matlab code for the two methods are freely available for download at http://lab.malab.cn/data/HeteSim/index.jsp.

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

In vitro molecular machine learning algorithm via symmetric internal loops of DNA.

PubMed

Lee, Ji-Hoon; Lee, Seung Hwan; Baek, Christina; Chun, Hyosun; Ryu, Je-Hwan; Kim, Jin-Woo; Deaton, Russell; Zhang, Byoung-Tak

2017-08-01

Programmable biomolecules, such as DNA strands, deoxyribozymes, and restriction enzymes, have been used to solve computational problems, construct large-scale logic circuits, and program simple molecular games. Although studies have shown the potential of molecular computing, the capability of computational learning with DNA molecules, i.e., molecular machine learning, has yet to be experimentally verified. Here, we present a novel molecular learning in vitro model in which symmetric internal loops of double-stranded DNA are exploited to measure the differences between training instances, thus enabling the molecules to learn from small errors. The model was evaluated on a data set of twenty dialogue sentences obtained from the television shows Friends and Prison Break. The wet DNA-computing experiments confirmed that the molecular learning machine was able to generalize the dialogue patterns of each show and successfully identify the show from which the sentences originated. The molecular machine learning model described here opens the way for solving machine learning problems in computer science and biology using in vitro molecular computing with the data encoded in DNA molecules. Copyright © 2017. Published by Elsevier B.V.

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

PubMed

Taylor, Jonathan Christopher; Fenner, John Wesley

2017-11-29

Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson's disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context.

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

PubMed

Lu, Huijuan; Wei, Shasha; Zhou, Zili; Miao, Yanzi; Lu, Yi

2015-01-01

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Studying depression using imaging and machine learning methods.

PubMed

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

NASA Astrophysics Data System (ADS)

Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

2017-08-01

We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

Binary Classification using Decision Tree based Genetic Programming and Its Application to Analysis of Bio-mass Data

NASA Astrophysics Data System (ADS)

To, Cuong; Pham, Tuan D.

2010-01-01

In machine learning, pattern recognition may be the most popular task. "Similar" patterns identification is also very important in biology because first, it is useful for prediction of patterns associated with disease, for example cancer tissue (normal or tumor); second, similarity or dissimilarity of the kinetic patterns is used to identify coordinately controlled genes or proteins involved in the same regulatory process. Third, similar genes (proteins) share similar functions. In this paper, we present an algorithm which uses genetic programming to create decision tree for binary classification problem. The application of the algorithm was implemented on five real biological databases. Base on the results of comparisons with well-known methods, we see that the algorithm is outstanding in most of cases.

Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management.

PubMed

Parameswaran, Aditya; Sarma, Akash Das; Venkataraman, Vipul

2016-12-01

Crowdsourcing is the primary means to generate training data at scale, and when combined with sophisticated machine learning algorithms, crowdsourcing is an enabler for a variety of emergent automated applications impacting all spheres of our lives. This paper surveys the emerging field of formally reasoning about and optimizing open-ended crowdsourcing, a popular and crucially important, but severely understudied class of crowdsourcing-the next frontier in crowdsourced data management. The underlying challenges include distilling the right answer when none of the workers agree with each other, teasing apart the various perspectives adopted by workers when answering tasks, and effectively selecting between the many open-ended operators appropriate for a problem. We describe the approaches that we've found to be effective for open-ended crowdsourcing, drawing from our experiences in this space.

Big Data Analytics with Datalog Queries on Spark.

PubMed

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2016-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.

Big Data Analytics with Datalog Queries on Spark

PubMed Central

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2017-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics. PMID:28626296

Machine Learning

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

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

Model-based machine learning.

PubMed

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Acceleration of saddle-point searches with machine learning.

PubMed

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

Acceleration of saddle-point searches with machine learning

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

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the numbermore » of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.« less

E-learning, Dual-task, and Cognitive Load: The Anatomy of a Failed Experiment

ERIC Educational Resources Information Center

Van Nuland, Sonya E.; Rogers, Kem A.

2016-01-01

The rising popularity of commercial anatomy e-learning tools has been sustained, in part, due to increased annual enrollment and a reduction in laboratory hours across educational institutions. While e-learning tools continue to gain popularity, the research methodologies used to investigate their impact on learning remain imprecise. As new user…

Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data.

PubMed

Wallert, John; Tomasoni, Mattia; Madison, Guy; Held, Claes

2017-07-05

Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learning algorithms trained on unselected, nation-wide population data from Sweden to solve the binary classification problem of predicting survival versus non-survival 2 years after first myocardial infarction (MI). This prospective national registry study for prognostic accuracy validation of predictive models used data from 51,943 complete first MI cases as registered during 6 years (2006-2011) in the national quality register SWEDEHEART/RIKS-HIA (90% coverage of all MIs in Sweden) with follow-up in the Cause of Death register (> 99% coverage). Primary outcome was AUROC (C-statistic) performance of each model on the untouched test set (40% of cases) after model development on the training set (60% of cases) with the full (39) predictor set. Model AUROCs were bootstrapped and compared, correcting the P-values for multiple comparisons with the Bonferroni method. Secondary outcomes were derived when varying sample size (1-100% of total) and predictor sets (39, 10, and 5) for each model. Analyses were repeated on 79,869 completed cases after multivariable imputation of predictors. A Support Vector Machine with a radial basis kernel developed on 39 predictors had the highest complete cases performance on the test set (AUROC = 0.845, PPV = 0.280, NPV = 0.966) outperforming Boosted C5.0 (0.845 vs. 0.841, P = 0.028) but not significantly higher than Logistic Regression or Random Forest. Models converged to the point of algorithm indifference with increased sample size and predictors. Using the top five predictors also produced good classifiers. Imputed analyses had slightly higher performance. Improved mortality prediction at hospital discharge after first MI is important for identifying high-risk individuals eligible for intensified treatment and care. All models performed accurately and similarly and because of the superior national coverage, the best model can potentially be used to better differentiate new patients, allowing for improved targeting of limited resources. Future research should focus on further model development and investigate possibilities for implementation.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

A Sustainable Model for Integrating Current Topics in Machine Learning Research into the Undergraduate Curriculum

ERIC Educational Resources Information Center

Georgiopoulos, M.; DeMara, R. F.; Gonzalez, A. J.; Wu, A. S.; Mollaghasemi, M.; Gelenbe, E.; Kysilka, M.; Secretan, J.; Sharma, C. A.; Alnsour, A. J.

2009-01-01

This paper presents an integrated research and teaching model that has resulted from an NSF-funded effort to introduce results of current Machine Learning research into the engineering and computer science curriculum at the University of Central Florida (UCF). While in-depth exposure to current topics in Machine Learning has traditionally occurred…

Learning as a Machine: Crossovers between Humans and Machines

ERIC Educational Resources Information Center

Hildebrandt, Mireille

2017-01-01

This article is a revised version of the keynote presented at LAK '16 in Edinburgh. The article investigates some of the assumptions of learning analytics, notably those related to behaviourism. Building on the work of Ivan Pavlov, Herbert Simon, and James Gibson as ways of "learning as a machine," the article then develops two levels of…

Computer Programmed Milling Machine Operations. High-Technology Training Module.

ERIC Educational Resources Information Center

Leonard, Dennis

This learning module for a high school metals and manufacturing course is designed to introduce the concept of computer-assisted machining (CAM). Through it, students learn how to set up and put data into the controller to machine a part. They also become familiar with computer-aided manufacturing and learn the advantages of computer numerical…

2014 Bio-Acoustics Data Challenge for the International Community on Machine Learning and Bioacoustics

DTIC Science & Technology

2014-09-30

This ONR grant promotes the development and application of advanced machine learning techniques for detection and classification of marine mammal...sounds. The objective is to engage a broad community of data scientists in the development and application of advanced machine learning techniques for detection and classification of marine mammal sounds.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Prediction of antiepileptic drug treatment outcomes using machine learning.

PubMed

Colic, Sinisa; Wither, Robert G; Lang, Min; Zhang, Liang; Eubanks, James H; Bardakjian, Berj L

2017-02-01

Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC ) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Prediction of antiepileptic drug treatment outcomes using machine learning

NASA Astrophysics Data System (ADS)

Colic, Sinisa; Wither, Robert G.; Lang, Min; Zhang, Liang; Eubanks, James H.; Bardakjian, Berj L.

2017-02-01

Objective. Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Approach. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I CFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. Main results. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Significance. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

PubMed

Brown, Andrew D; Marotta, Thomas R

2018-05-01

Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest - to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 models in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may facilitate improvements in the quality and safety of medical imaging service delivery.

Exploring Representations of "Super" Women in Popular Culture: Shaping Critical Discussions with Female College Students with Learning Exceptionalities

ERIC Educational Resources Information Center

Taber, Nancy; Woloshyn, Vera; Munn, Caitlin; Lane, Laura

2014-01-01

In this article, we discuss how our analysis of several popular culture artifacts featuring "super" women characters (superheroes and supernatural) provided the foundation for a media discussion group for female college students with learning exceptionalities. We explore the use of popular culture in discussion groups as well as discuss…

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

The Next Era: Deep Learning in Pharmaceutical Research

PubMed Central

Ekins, Sean

2016-01-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique. PMID:27599991

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

PubMed

Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2017-01-25

The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

An introduction to deep learning on biological sequence data: examples and solutions.

PubMed

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

2017-11-15

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Experimental Machine Learning of Quantum States

NASA Astrophysics Data System (ADS)

Gao, Jun; Qiao, Lu-Feng; Jiao, Zhi-Qiang; Ma, Yue-Chi; Hu, Cheng-Qiu; Ren, Ruo-Jing; Yang, Ai-Lin; Tang, Hao; Yung, Man-Hong; Jin, Xian-Min

2018-06-01

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in "big data." A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

PubMed

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p < 0.001). Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

A renaissance of neural networks in drug discovery.

PubMed

Baskin, Igor I; Winkler, David; Tetko, Igor V

2016-08-01

Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Machine Learning-Based Classification of 38 Years of Spine-Related Literature Into 100 Research Topics.

PubMed

Sing, David C; Metz, Lionel N; Dudli, Stefan

2017-06-01

Retrospective review. To identify the top 100 spine research topics. Recent advances in "machine learning," or computers learning without explicit instructions, have yielded broad technological advances. Topic modeling algorithms can be applied to large volumes of text to discover quantifiable themes and trends. Abstracts were extracted from the National Library of Medicine PubMed database from five prominent peer-reviewed spine journals (European Spine Journal [ESJ], The Spine Journal [SpineJ], Spine, Journal of Spinal Disorders and Techniques [JSDT], Journal of Neurosurgery: Spine [JNS]). Each abstract was entered into a latent Dirichlet allocation model specified to discover 100 topics, resulting in each abstract being assigned a probability of belonging in a topic. Topics were named using the five most frequently appearing terms within that topic. Significance of increasing ("hot") or decreasing ("cold") topic popularity over time was evaluated with simple linear regression. From 1978 to 2015, 25,805 spine-related research articles were extracted and classified into 100 topics. Top two most published topics included "clinical, surgeons, guidelines, information, care" (n = 496 articles) and "pain, back, low, treatment, chronic" (424). Top two hot trends included "disc, cervical, replacement, level, arthroplasty" (+0.05%/yr, P < 0.001), and "minimally, invasive, approach, technique" (+0.05%/yr, P < 0.001). By journal, the most published topics were ESJ-"operative, surgery, postoperative, underwent, preoperative"; SpineJ-"clinical, surgeons, guidelines, information, care"; Spine-"pain, back, low, treatment, chronic"; JNS- "tumor, lesions, rare, present, diagnosis"; JSDT-"cervical, anterior, plate, fusion, ACDF." Topics discovered through latent Dirichlet allocation modeling represent unbiased meaningful themes relevant to spine care. Topic dynamics can provide historical context and direction for future research for aspiring investigators and trainees interested in spine careers. Please explore https://singdc.shinyapps.io/spinetopics. N A.

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.

Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

PubMed

Narula, Sukrit; Shameer, Khader; Salem Omar, Alaa Mabrouk; Dudley, Joel T; Sengupta, Partho P

2016-11-29

Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation. Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p < 0.01), average early diastolic tissue velocity (e') (p < 0.01), and strain (p = 0.04). Because ATH were younger, adjusted analysis was undertaken in younger HCM patients and compared with ATH with left ventricular wall thickness >13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain. Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Showtime Science: Effective Use of Popular Media to Enhance Instruction of Simple Machines and Energy Transfer

ERIC Educational Resources Information Center

Bergman, Daniel

2011-01-01

The messages students receive through popular media such as movies, television, the internet, and music videos have a powerful influence and can shape their thinking. A national survey of media use by 8- to 18-year-olds found the average adolescent spends over seven and a half hours daily with entertainment media (KFF 2010). Rather than viewing…

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Learning Activity Packets for Milling Machines. Unit II--Horizontal Milling Machines.

ERIC Educational Resources Information Center

Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This learning activity packet (LAP) outlines the study activities and performance tasks covered in a related curriculum guide on milling machines. The course of study in this LAP is intended to help students learn to set up and operate a horizontal mill. Tasks addressed in the LAP include mounting style "A" or "B" arbors and adjusting arbor…

Machine learning for science: state of the art and future prospects.

PubMed

Mjolsness, E; DeCoste, D

2001-09-14

Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions.

Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

ERIC Educational Resources Information Center

Crossley, Scott A.

2013-01-01

This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

Machine Learning in the Presence of an Adversary: Attacking and Defending the SpamBayes Spam Filter

DTIC Science & Technology

2008-05-20

Machine learning techniques are often used for decision making in security critical applications such as intrusion detection and spam filtering...filter. The defenses shown in this thesis are able to work against the attacks developed against SpamBayes and are sufficiently generic to be easily extended into other statistical machine learning algorithms.

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

PubMed

Skoraczyński, G; Dittwald, P; Miasojedow, B; Szymkuć, S; Gajewska, E P; Grzybowski, B A; Gambin, A

2017-06-15

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors.

Ten quick tips for machine learning in computational biology.

PubMed

Chicco, Davide

2017-01-01

Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786

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.

Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

A review of approaches to identifying patient phenotype cohorts using electronic health records

PubMed Central

Shivade, Chaitanya; Raghavan, Preethi; Fosler-Lussier, Eric; Embi, Peter J; Elhadad, Noemie; Johnson, Stephen B; Lai, Albert M

2014-01-01

Objective To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. Materials and methods We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. Results Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. Discussion We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. Conclusions There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses. PMID:24201027

A machine learning pipeline for automated registration and classification of 3D lidar data

NASA Astrophysics Data System (ADS)

Rajagopal, Abhejit; Chellappan, Karthik; Chandrasekaran, Shivkumar; Brown, Andrew P.

2017-05-01

Despite the large availability of geospatial data, registration and exploitation of these datasets remains a persis- tent challenge in geoinformatics. Popular signal processing and machine learning algorithms, such as non-linear SVMs and neural networks, rely on well-formatted input models as well as reliable output labels, which are not always immediately available. In this paper we outline a pipeline for gathering, registering, and classifying initially unlabeled wide-area geospatial data. As an illustrative example, we demonstrate the training and test- ing of a convolutional neural network to recognize 3D models in the OGRIP 2007 LiDAR dataset using fuzzy labels derived from OpenStreetMap as well as other datasets available on OpenTopography.org. When auxiliary label information is required, various text and natural language processing filters are used to extract and cluster keywords useful for identifying potential target classes. A subset of these keywords are subsequently used to form multi-class labels, with no assumption of independence. Finally, we employ class-dependent geometry extraction routines to identify candidates from both training and testing datasets. Our regression networks are able to identify the presence of 6 structural classes, including roads, walls, and buildings, in volumes as big as 8000 m3 in as little as 1.2 seconds on a commodity 4-core Intel CPU. The presented framework is neither dataset nor sensor-modality limited due to the registration process, and is capable of multi-sensor data-fusion.

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

PubMed

Cui, De-Mi; Yan, Weizhong; Wang, Xiao-Quan; Lu, Lie-Min

2017-10-25

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

Machine Learning Based Online Performance Prediction for Runtime Parallelization and Task Scheduling

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

Li, J; Ma, X; Singh, K

2008-10-09

With the emerging many-core paradigm, parallel programming must extend beyond its traditional realm of scientific applications. Converting existing sequential applications as well as developing next-generation software requires assistance from hardware, compilers and runtime systems to exploit parallelism transparently within applications. These systems must decompose applications into tasks that can be executed in parallel and then schedule those tasks to minimize load imbalance. However, many systems lack a priori knowledge about the execution time of all tasks to perform effective load balancing with low scheduling overhead. In this paper, we approach this fundamental problem using machine learning techniques first to generatemore » performance models for all tasks and then applying those models to perform automatic performance prediction across program executions. We also extend an existing scheduling algorithm to use generated task cost estimates for online task partitioning and scheduling. We implement the above techniques in the pR framework, which transparently parallelizes scripts in the popular R language, and evaluate their performance and overhead with both a real-world application and a large number of synthetic representative test scripts. Our experimental results show that our proposed approach significantly improves task partitioning and scheduling, with maximum improvements of 21.8%, 40.3% and 22.1% and average improvements of 15.9%, 16.9% and 4.2% for LMM (a real R application) and synthetic test cases with independent and dependent tasks, respectively.« less

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.

PubMed

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

Evolving autonomous learning in cognitive networks.

PubMed

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

Using Machine Learning for Behavior-Based Access Control: Scalable Anomaly Detection on TCP Connections and HTTP Requests

DTIC Science & Technology

2013-11-01

machine learning techniques used in BBAC to make predictions about the intent of actors establishing TCP connections and issuing HTTP requests. We discuss pragmatic challenges and solutions we encountered in implementing and evaluating BBAC, discussing (a) the general concepts underlying BBAC, (b) challenges we have encountered in identifying suitable datasets, (c) mitigation strategies to cope...and describe current plans for transitioning BBAC capabilities into the Department of Defense together with lessons learned for the machine learning

Generative Modeling for Machine Learning on the D-Wave

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

Thulasidasan, Sunil

These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.

Implementing Machine Learning in the PCWG Tool

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

Clifton, Andrew; Ding, Yu; Stuart, Peter

The Power Curve Working Group (www.pcwg.org) is an ad-hoc industry-led group to investigate the performance of wind turbines in real-world conditions. As part of ongoing experience-sharing exercises, machine learning has been proposed as a possible way to predict turbine performance. This presentation provides some background information about machine learning and how it might be implemented in the PCWG exercises.

Adaptive Learning Systems: Beyond Teaching Machines

ERIC Educational Resources Information Center

Kara, Nuri; Sevim, Nese

2013-01-01

Since 1950s, teaching machines have changed a lot. Today, we have different ideas about how people learn, what instructor should do to help students during their learning process. We have adaptive learning technologies that can create much more student oriented learning environments. The purpose of this article is to present these changes and its…

Mapping Next Generation Learning Spaces as a Designed Quality Enhancement Process

ERIC Educational Resources Information Center

Leonard, Simon N.; Fitzgerald, Robert N.; Bacon, Matt; Munnerley, Danny

2017-01-01

The learning spaces of higher education are changing with collaborative, agile and technology-enabled spaces ever more popular. Despite the massive investment required to create these new spaces, current quality systems are poorly placed to account for the value they create. Such learning spaces are typically popular with students but the impact…

The Pedagogical, Linguistic, and Content Features of Popular English Language Learning Websites in China: A Framework for Analysis and Design

ERIC Educational Resources Information Center

Kettle, Margaret; Yuan, Yifeng; Luke, Allan; Ewing, Robyn; Shen, Huizhong

2012-01-01

As increasing numbers of Chinese language learners choose to learn English online, there is a need to investigate popular websites and their language learning designs. This paper reports on the first stage of a study that analyzed the pedagogical, linguistic, and content features of 25 Chinese English Language Learning (ELL) websites ranked…

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals

PubMed Central

Cyran, Krzysztof A.

2018-01-01

This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks. PMID:29849544

Automatic detection of atrial fibrillation in cardiac vibration signals.

PubMed

Brueser, C; Diesel, J; Zink, M D H; Winter, S; Schauerte, P; Leonhardt, S

2013-01-01

We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.

Quantum neural network based machine translator for Hindi to English.

PubMed

Narayan, Ravi; Singh, V P; Chakraverty, S

2014-01-01

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

Managing the data deluge: data-driven GO category assignment improves while complexity of functional annotation increases.

PubMed

Gobeill, Julien; Pasche, Emilie; Vishnyakova, Dina; Ruch, Patrick

2013-01-01

The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based--or dictionary-based--approaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists. DATABASE URL: http://eagl.unige.ch/GOCat/

Energy landscapes for machine learning

NASA Astrophysics Data System (ADS)

Ballard, Andrew J.; Das, Ritankar; Martiniani, Stefano; Mehta, Dhagash; Sagun, Levent; Stevenson, Jacob D.; Wales, David J.

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.

Motor-response learning at a process control panel by an autonomous robot

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

Spelt, P.F.; de Saussure, G.; Lyness, E.

1988-01-01

The Center for Engineering Systems Advanced Research (CESAR) was founded at Oak Ridge National Laboratory (ORNL) by the Department of Energy's Office of Energy Research/Division of Engineering and Geoscience (DOE-OER/DEG) to conduct basic research in the area of intelligent machines. Therefore, researchers at the CESAR Laboratory are engaged in a variety of research activities in the field of machine learning. In this paper, we describe our approach to a class of machine learning which involves motor response acquisition using feedback from trial-and-error learning. Our formulation is being experimentally validated using an autonomous robot, learning tasks of control panel monitoring andmore » manipulation for effect process control. The CLIPS Expert System and the associated knowledge base used by the robot in the learning process, which reside in a hypercube computer aboard the robot, are described in detail. Benchmark testing of the learning process on a robot/control panel simulation system consisting of two intercommunicating computers is presented, along with results of sample problems used to train and test the expert system. These data illustrate machine learning and the resulting performance improvement in the robot for problems similar to, but not identical with, those on which the robot was trained. Conclusions are drawn concerning the learning problems, and implications for future work on machine learning for autonomous robots are discussed. 16 refs., 4 figs., 1 tab.« less

The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

PubMed

Kruse, Christian

2018-06-01

To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature. Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue. Unsupervised machine learning can allow us to understand patterns in data between diabetic pathophysiology and altered bone metabolism. Image analysis using deep learning can allow us to be less dependent on surrogate predictors and use large volumes of images to classify diabetes-induced osteoporosis and predict future outcomes directly from images. "Big Data" techniques herald new possibilities to understand diabetes-induced osteoporosis and ascertain our current ability to classify, understand, and predict this condition.

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Machine Learning Approaches in Cardiovascular Imaging.

PubMed

Henglin, Mir; Stein, Gillian; Hushcha, Pavel V; Snoek, Jasper; Wiltschko, Alexander B; Cheng, Susan

2017-10-01

Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging. © 2017 American Heart Association, Inc.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.

PubMed

Braithwaite, Scott R; Giraud-Carrier, Christophe; West, Josh; Barnes, Michael D; Hanson, Carl Lee

2016-05-16

One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

PubMed Central

2016-01-01

Background One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. PMID:27185366

Radar detection with the Neyman-Pearson criterion using supervised-learning-machines trained with the cross-entropy error

NASA Astrophysics Data System (ADS)

Jarabo-Amores, María-Pilar; la Mata-Moya, David de; Gil-Pita, Roberto; Rosa-Zurera, Manuel

2013-12-01

The application of supervised learning machines trained to minimize the Cross-Entropy error to radar detection is explored in this article. The detector is implemented with a learning machine that implements a discriminant function, which output is compared to a threshold selected to fix a desired probability of false alarm. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition for a discriminant function to be used to approximate the optimum Neyman-Pearson (NP) detector. In this article, the function a supervised learning machine approximates to after being trained to minimize the Cross-Entropy error is obtained. This discriminant function can be used to implement the NP detector, which maximizes the probability of detection, maintaining the probability of false alarm below or equal to a predefined value. Some experiments about signal detection using neural networks are also presented to test the validity of the study.

AstroML: Python-powered Machine Learning for Astronomy

NASA Astrophysics Data System (ADS)

Vander Plas, Jake; Connolly, A. J.; Ivezic, Z.

2014-01-01

As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.org) provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in Python. The astroML module contains a host of general-purpose data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines being used for analysis of real astronomical datasets, while the associated textbook provides a curriculum resource for graduate-level courses focusing on practical statistics, machine learning, and data mining approaches within Astronomical research. This poster will highlight several of the more powerful and unique examples of analysis performed with astroML, all of which can be reproduced in their entirety on any computer with the proper packages installed.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

PubMed

Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

2017-09-01

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller.

PubMed

Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

2014-06-01

Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance--competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

NASA Astrophysics Data System (ADS)

Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

2014-06-01

Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

DTIC Science & Technology

2010-04-01

that state-of-the-art machine learning techniques when integrated with novel non-invasive monitoring technologies could detect subtle, physiological...decompensation. Continuous, non-invasively measured hemodynamic signals (e.g., ECG, blood pressures, stroke volume) were used for the development of machine ... learning algorithms. Accuracy estimates were obtained by building models using 27 subjects and testing on the 28th. This process was repeated 28 times

A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)

DTIC Science & Technology

2008-11-01

retrieve relevant documents. For the Opinion Retrieval subtask, we propose a hybrid model of lexicon-based approach and machine learning approach for...estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity...and linear combination technique for ranking polar documents. The hybrid model which utilize both lexicon-based approach and machine learning approach

Time of Flight Estimation in the Presence of Outliers: A Biosonar-Inspired Machine Learning Approach

DTIC Science & Technology

2013-08-29

REPORT Time of Flight Estimation in the Presence of Outliers: A biosonar -inspired machine learning approach 14. ABSTRACT 16. SECURITY CLASSIFICATION OF...installations, biosonar , remote sensing, sonar resolution, sonar accuracy, sonar energy consumption Nathan Intrator, Leon N Cooper Brown University...Presence of Outliers: A biosonar -inspired machine learning approach Report Title ABSTRACT When the Signal-to-Noise Ratio (SNR) falls below a certain

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.

PubMed

Liu, Nehemiah T; Holcomb, John B; Wade, Charles E; Batchinsky, Andriy I; Cancio, Leopoldo C; Darrah, Mark I; Salinas, José

2014-02-01

Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82 h of trauma patient data showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Machine learning enhanced optical distance sensor

NASA Astrophysics Data System (ADS)

Amin, M. Junaid; Riza, N. A.

2018-01-01

Presented for the first time is a machine learning enhanced optical distance sensor. The distance sensor is based on our previously demonstrated distance measurement technique that uses an Electronically Controlled Variable Focus Lens (ECVFL) with a laser source to illuminate a target plane with a controlled optical beam spot. This spot with varying spot sizes is viewed by an off-axis camera and the spot size data is processed to compute the distance. In particular, proposed and demonstrated in this paper is the use of a regularized polynomial regression based supervised machine learning algorithm to enhance the accuracy of the operational sensor. The algorithm uses the acquired features and corresponding labels that are the actual target distance values to train a machine learning model. The optimized training model is trained over a 1000 mm (or 1 m) experimental target distance range. Using the machine learning algorithm produces a training set and testing set distance measurement errors of <0.8 mm and <2.2 mm, respectively. The test measurement error is at least a factor of 4 improvement over our prior sensor demonstration without the use of machine learning. Applications for the proposed sensor include industrial scenario distance sensing where target material specific training models can be generated to realize low <1% measurement error distance measurements.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

PubMed Central

Goldstein, Benjamin A.; Navar, Ann Marie; Carter, Rickey E.

2017-01-01

Abstract Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. PMID:27436868

Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology

PubMed Central

Swan, Anna Louise; Mobasheri, Ali; Allaway, David; Liddell, Susan

2013-01-01

Abstract Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes. PMID:24116388

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

PubMed Central

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. PMID:28060903

Creating Situational Awareness in Spacecraft Operations with the Machine Learning Approach

NASA Astrophysics Data System (ADS)

Li, Z.

2016-09-01

This paper presents a machine learning approach for the situational awareness capability in spacecraft operations. There are two types of time dependent data patterns for spacecraft datasets: the absolute time pattern (ATP) and the relative time pattern (RTP). The machine learning captures the data patterns of the satellite datasets through the data training during the normal operations, which is represented by its time dependent trend. The data monitoring compares the values of the incoming data with the predictions of machine learning algorithm, which can detect any meaningful changes to a dataset above the noise level. If the difference between the value of incoming telemetry and the machine learning prediction are larger than the threshold defined by the standard deviation of datasets, it could indicate the potential anomaly that may need special attention. The application of the machine-learning approach to the Advanced Himawari Imager (AHI) on Japanese Himawari spacecraft series is presented, which has the same configuration as the Advanced Baseline Imager (ABI) on Geostationary Environment Operational Satellite (GOES) R series. The time dependent trends generated by the data-training algorithm are in excellent agreement with the datasets. The standard deviation in the time dependent trend provides a metric for measuring the data quality, which is particularly useful in evaluating the detector quality for both AHI and ABI with multiple detectors in each channel. The machine-learning approach creates the situational awareness capability, and enables engineers to handle the huge data volume that would have been impossible with the existing approach, and it leads to significant advances to more dynamic, proactive, and autonomous spacecraft operations.

Chemical Entity Recognition and Resolution to ChEBI

PubMed Central

Grego, Tiago; Pesquita, Catia; Bastos, Hugo P.; Couto, Francisco M.

2012-01-01

Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the entity recognition task, 2–5% for the entity-resolution task, and 15% for combined entity recognition and resolution tasks. PMID:25937941

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Training English Language Pre-Service Teachers Using a Team Based Learning Approach

ERIC Educational Resources Information Center

Samad, Arshad Abd; Husein, Hawanum; Rashid, Juridah Md; Rahman, Sharifah Zainab Syd Abd

2015-01-01

Team Based Learning which focuses on enhancing positive group dynamics is a relatively popular instructional approach in several disciplines such as Health Sciences and Business but has yet to gain popularity in Education. This paper examines the use of Team Based Learning in teacher training as well as the receptiveness towards the approach as…

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

NASA Astrophysics Data System (ADS)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

PubMed Central

Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-01-01

Background To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets. Objective This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. Methods This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. Results We are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022. Conclusions Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. PMID:28851678

Reconstruction of initial pressure from limited view photoacoustic images using deep learning

NASA Astrophysics Data System (ADS)

Waibel, Dominik; Gröhl, Janek; Isensee, Fabian; Kirchner, Thomas; Maier-Hein, Klaus; Maier-Hein, Lena

2018-02-01

Quantification of tissue properties with photoacoustic (PA) imaging typically requires a highly accurate representation of the initial pressure distribution in tissue. Almost all PA scanners reconstruct the PA image only from a partial scan of the emitted sound waves. Especially handheld devices, which have become increasingly popular due to their versatility and ease of use, only provide limited view data because of their geometry. Owing to such limitations in hardware as well as to the acoustic attenuation in tissue, state-of-the-art reconstruction methods deliver only approximations of the initial pressure distribution. To overcome the limited view problem, we present a machine learning-based approach to the reconstruction of initial pressure from limited view PA data. Our method involves a fully convolutional deep neural network based on a U-Net-like architecture with pixel-wise regression loss on the acquired PA images. It is trained and validated on in silico data generated with Monte Carlo simulations. In an initial study we found an increase in accuracy over the state-of-the-art when reconstructing simulated linear-array scans of blood vessels.

Estimation of the prevalence of adverse drug reactions from social media.

PubMed

Nguyen, Thin; Larsen, Mark E; O'Dea, Bridianne; Phung, Dinh; Venkatesh, Svetha; Christensen, Helen

2017-06-01

This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora. Agreement between these measures for a sample of ten popular psychiatric drugs was evaluated using the Pearson correlation coefficient, r, with values between 0.08 and 0.50. Word2vec, a novel neural learning framework, was utilized to improve the coverage of variants of ADR terms in the unstructured text by identifying syntactically or semantically similar terms. Improved correlation coefficients, between 0.29 and 0.59, demonstrates the capability of advanced techniques in machine learning to aid in the discovery of meaningful patterns from medical data, and social media data, at scale. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling perceived stress via HRV and accelerometer sensor streams.

PubMed

Wu, Min; Cao, Hong; Nguyen, Hai-Long; Surmacz, Karl; Hargrove, Caroline

2015-08-01

Discovering and modeling of stress patterns of human beings is a key step towards achieving automatic stress monitoring, stress management and healthy lifestyle. As various wearable sensors become popular, it becomes possible for individuals to acquire their own relevant sensory data and to automatically assess their stress level on the go. Previous studies for stress analysis were conducted in the controlled laboratory and clinic settings. These studies are not suitable for stress monitoring in one's daily life as various physical activities may affect the physiological signals. In this paper, we address such issue by integrating two modalities of sensors, i.e., HRV sensors and accelerometers, to monitor the perceived stress levels in daily life. We gathered both the heart and the motion data from 8 participants continuously for about 2 weeks. We then extracted features from both sensory data and compared the existing machine learning methods for learning personalized models to interpret the perceived stress levels. Experimental results showed that Bagging classifier with feature selection is able to achieve a prediction accuracy 85.7%, indicating our stress monitoring on daily basis is fairly practical.

Galaxy Classifications with Deep Learning

NASA Astrophysics Data System (ADS)

Lukic, Vesna; Brüggen, Marcus

2017-06-01

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.

Quantum Neural Network Based Machine Translator for Hindi to English

PubMed Central

Singh, V. P.; Chakraverty, S.

2014-01-01

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. PMID:24977198

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Predicting the dissolution kinetics of silicate glasses using machine learning

NASA Astrophysics Data System (ADS)

Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

2018-05-01

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

Identifying product order with restricted Boltzmann machines

NASA Astrophysics Data System (ADS)

Rao, Wen-Jia; Li, Zhenyu; Zhu, Qiong; Luo, Mingxing; Wan, Xin

2018-03-01

Unsupervised machine learning via a restricted Boltzmann machine is a useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from nonergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity of the product form, which resembles the conventional product order parameter.

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

PubMed

van Ginneken, Bram

2017-03-01

Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

Man Machine Systems in Education.

ERIC Educational Resources Information Center

Sall, Malkit S.

This review of the research literature on the interaction between humans and computers discusses how man machine systems can be utilized effectively in the learning-teaching process, especially in secondary education. Beginning with a definition of man machine systems and comments on the poor quality of much of the computer-based learning material…

Learning Machine, Vietnamese Based Human-Computer Interface.

ERIC Educational Resources Information Center

Northwest Regional Educational Lab., Portland, OR.

The sixth session of IT@EDU98 consisted of seven papers on the topic of the learning machine--Vietnamese based human-computer interface, and was chaired by Phan Viet Hoang (Informatics College, Singapore). "Knowledge Based Approach for English Vietnamese Machine Translation" (Hoang Kiem, Dinh Dien) presents the knowledge base approach,…

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Machine Learning: A Crucial Tool for Sensor Design

PubMed Central

Zhao, Weixiang; Bhushan, Abhinav; Santamaria, Anthony D.; Simon, Melinda G.; Davis, Cristina E.

2009-01-01

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies. PMID:20191110

Feasibility of Active Machine Learning for Multiclass Compound Classification.

PubMed

Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

2016-01-25

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Paradigms for machine learning

NASA Technical Reports Server (NTRS)

Schlimmer, Jeffrey C.; Langley, Pat

1991-01-01

Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

NASA Astrophysics Data System (ADS)

Paradis, Daniel; Lefebvre, René; Gloaguen, Erwan; Rivera, Alfonso

2015-01-01

The spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products

PubMed Central

Myslín, Mark; Zhu, Shu-Hong; Chapman, Wendy

2013-01-01

Background Social media platforms such as Twitter are rapidly becoming key resources for public health surveillance applications, yet little is known about Twitter users’ levels of informedness and sentiment toward tobacco, especially with regard to the emerging tobacco control challenges posed by hookah and electronic cigarettes. Objective To develop a content and sentiment analysis of tobacco-related Twitter posts and build machine learning classifiers to detect tobacco-relevant posts and sentiment towards tobacco, with a particular focus on new and emerging products like hookah and electronic cigarettes. Methods We collected 7362 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012. Each tweet was manually classified using a triaxial scheme, capturing genre, theme, and sentiment. Using the collected data, machine-learning classifiers were trained to detect tobacco-related vs irrelevant tweets as well as positive vs negative sentiment, using Naïve Bayes, k-nearest neighbors, and Support Vector Machine (SVM) algorithms. Finally, phi contingency coefficients were computed between each of the categories to discover emergent patterns. Results The most prevalent genres were first- and second-hand experience and opinion, and the most frequent themes were hookah, cessation, and pleasure. Sentiment toward tobacco was overall more positive (1939/4215, 46% of tweets) than negative (1349/4215, 32%) or neutral among tweets mentioning it, even excluding the 9% of tweets categorized as marketing. Three separate metrics converged to support an emergent distinction between, on one hand, hookah and electronic cigarettes corresponding to positive sentiment, and on the other hand, traditional tobacco products and more general references corresponding to negative sentiment. These metrics included correlations between categories in the annotation scheme (phihookah-positive=0.39; phie-cigs-positive=0.19); correlations between search keywords and sentiment (χ2 4=414.50, P<.001, Cramer’s V=0.36), and the most discriminating unigram features for positive and negative sentiment ranked by log odds ratio in the machine learning component of the study. In the automated classification tasks, SVMs using a relatively small number of unigram features (500) achieved best performance in discriminating tobacco-related from unrelated tweets (F score=0.85). Conclusions Novel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment. This positive sentiment is correlated in complex ways with social image, personal experience, and recently popular products such as hookah and electronic cigarettes. Several apparent perceptual disconnects between these products and their health effects suggest opportunities for tobacco control education. Finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, yielding an improved signal-to-noise ratio in Twitter data and paving the way for automated tobacco surveillance applications. PMID:23989137

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Teaching learning algorithm based optimization of kerf deviations in pulsed Nd:YAG laser cutting of Kevlar-29 composite laminates

NASA Astrophysics Data System (ADS)

Gautam, Girish Dutt; Pandey, Arun Kumar

2018-03-01

Kevlar is the most popular aramid fiber and most commonly used in different technologically advanced industries for various applications. But the precise cutting of Kevlar composite laminates is a difficult task. The conventional cutting methods face various defects such as delamination, burr formation, fiber pullout with poor surface quality and their mechanical performance is greatly affected by these defects. The laser beam machining may be an alternative of the conventional cutting processes due to its non-contact nature, requirement of low specific energy with higher production rate. But this process also faces some problems that may be minimized by operating the machine at optimum parameters levels. This research paper examines the effective utilization of the Nd:YAG laser cutting system on difficult-to-cut Kevlar-29 composite laminates. The objective of the proposed work is to find the optimum process parameters settings for getting the minimum kerf deviations at both sides. The experiments have been conducted on Kevlar-29 composite laminates having thickness 1.25 mm by using Box-Benkhen design with two center points. The experimental data have been used for the optimization by using the proposed methodology. For the optimization, Teaching learning Algorithm based approach has been employed to obtain the minimum kerf deviation at bottom and top sides. A self coded Matlab program has been developed by using the proposed methodology and this program has been used for the optimization. Finally, the confirmation tests have been performed to compare the experimental and optimum results obtained by the proposed methodology. The comparison results show that the machining performance in the laser beam cutting process has been remarkably improved through proposed approach. Finally, the influence of different laser cutting parameters such as lamp current, pulse frequency, pulse width, compressed air pressure and cutting speed on top kerf deviation and bottom kerf deviation during the Nd:YAG laser cutting of Kevlar-29 laminates have been discussed.

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

2012-01-01

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Study on Electro-polymerization Nano-micro Wiring System Imitating Axonal Growth of Artificial Neurons towards Machine Learning

NASA Astrophysics Data System (ADS)

Dang, Nguyen Tuan; Akai-Kasada, Megumi; Asai, Tetsuya; Saito, Akira; Kuwahara, Yuji; Hokkaido University Collaboration

2015-03-01

Machine learning using the artificial neuron network research is supposed to be the best way to understand how the human brain trains itself to process information. In this study, we have successfully developed the programs using supervised machine learning algorithm. However, these supervised learning processes for the neuron network required the very strong computing configuration. Derivation from the necessity of increasing in computing ability and in reduction of power consumption, accelerator circuits become critical. To develop such accelerator circuits using supervised machine learning algorithm, conducting polymer micro/nanowires growing process was realized and applied as a synaptic weigh controller. In this work, high conductivity Polypyrrole (PPy) and Poly (3, 4 - ethylenedioxythiophene) PEDOT wires were potentiostatically grown crosslinking the designated electrodes, which were prefabricated by lithography, when appropriate square wave AC voltage and appropriate frequency were applied. Micro/nanowire growing process emulated the neurotransmitter release process of synapses inside a biological neuron and wire's resistance variation during the growing process was preferred to as the variation of synaptic weigh in machine learning algorithm. In a cooperation with Graduate School of Information Science and Technology, Hokkaido University.

MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a

PubMed Central

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S.; Leswing, Karl

2017-01-01

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm. PMID:29629118

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

PubMed

Madsen, Kristoffer H; Krohne, Laerke G; Cai, Xin-Lu; Wang, Yi; Chan, Raymond C K

2018-03-15

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

A parallel-machine scheduling problem with two competing agents

NASA Astrophysics Data System (ADS)

Lee, Wen-Chiung; Chung, Yu-Hsiang; Wang, Jen-Ya

2017-06-01

Scheduling with two competing agents has become popular in recent years. Most of the research has focused on single-machine problems. This article considers a parallel-machine problem, the objective of which is to minimize the total completion time of jobs from the first agent given that the maximum tardiness of jobs from the second agent cannot exceed an upper bound. The NP-hardness of this problem is also examined. A genetic algorithm equipped with local search is proposed to search for the near-optimal solution. Computational experiments are conducted to evaluate the proposed genetic algorithm.

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

PubMed

Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

2016-04-01

Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Machine learning approaches to the social determinants of health in the health and retirement study.

PubMed

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Detection of longitudinal visual field progression in glaucoma using machine learning.

PubMed

Yousefi, Siamak; Kiwaki, Taichi; Zheng, Yuhui; Suigara, Hiroki; Asaoka, Ryo; Murata, Hiroshi; Lemij, Hans; Yamanishi, Kenji

2018-06-16

Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine-learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices. Development and comparison of a prognostic index. Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucoma patients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucoma patients and survival analysis were utilized to compare methods. The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 years (95% confidence interval, 4.1 - 6.5 years); 4.5 years (4.0 - 5.5) using region-wise, 3.9 years (3.5 - 4.6) using point-wise, and 3.5 years (3.1 - 4.0) using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after two additional visits were included were 6.6 years (5.6 - 7.4 years), 5.7 years (4.8 - 6.7), 5.6 years (4.7 - 6.5), and 5.1 years (4.5 - 6.0) for global, region-wise, point-wise, and machine learning analyses, respectively. Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods. Copyright © 2018 Elsevier Inc. All rights reserved.

Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.

PubMed

Li, Lingli; Fan, Wenliang; Li, Jun; Li, Quanlin; Wang, Jin; Fan, Yang; Ye, Tianhe; Guo, Jialun; Li, Sen; Zhang, Youpeng; Cheng, Yongbiao; Tang, Yong; Zeng, Hanqing; Yang, Lian; Zhu, Zhaohui

2018-03-29

To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a machine learning classification. 45 VED patients and 50 healthy controls were included. Voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and correlation analyses of VED patients and clinical variables were performed. The machine learning classification method was adopted to confirm its effectiveness in distinguishing VED patients from healthy controls. Compared to healthy control subjects, VED patients showed significantly decreased cortical volumes in the left postcentral gyrus and precentral gyrus, while only the right middle temporal gyrus showed a significant increase in cortical volume. Increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) values were observed in widespread brain regions. Certain regions of these alterations related to VED patients showed significant correlations with clinical symptoms and disorder durations. Machine learning analyses discriminated patients from controls with overall accuracy 96.7%, sensitivity 93.3% and specificity 99.0%. Cortical volume and white matter (WM) microstructural changes were observed in VED patients, and showed significant correlations with clinical symptoms and dysfunction durations. Various DTI-derived indices of some brain regions could be regarded as reliable discriminating features between VED patients and healthy control subjects, as shown by machine learning analyses. • Multimodal magnetic resonance imaging helps clinicians to assess patients with VED. • VED patients show cerebral structural alterations related to their clinical symptoms. • Machine learning analyses discriminated VED patients from controls with an excellent performance. • Machine learning classification provided a preliminary demonstration of DTI's clinical use.

Machine learning in laboratory medicine: waiting for the flood?

PubMed

Cabitza, Federico; Banfi, Giuseppe

2018-03-28

This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to laboratory data for both diagnostic and prognostic purposes deserves more attention by the readership of this journal, as well as by physician-scientists who will want to take advantage of this new computer-based support in pathology and laboratory medicine.

Speckle-learning-based object recognition through scattering media.

PubMed

Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

2015-12-28

We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning.

Applying machine learning to identify autistic adults using imitation: An exploratory study.

PubMed

Li, Baihua; Sharma, Arjun; Meng, James; Purushwalkam, Senthil; Gowen, Emma

2017-01-01

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

PubMed

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Learn about Physical Science: Simple Machines. [CD-ROM].

ERIC Educational Resources Information Center

2000

This CD-ROM, designed for students in grades K-2, explores the world of simple machines. It allows students to delve into the mechanical world and learn the ways in which simple machines make work easier. Animated demonstrations are provided of the lever, pulley, wheel, screw, wedge, and inclined plane. Activities include practical matching and…

Learning molecular energies using localized graph kernels.

PubMed

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-21

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels

NASA Astrophysics Data System (ADS)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Modeling Geomagnetic Variations using a Machine Learning Framework

NASA Astrophysics Data System (ADS)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

Health Informatics via Machine Learning for the Clinical Management of Patients.

PubMed

Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

2015-08-13

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

How much information is in a jet?

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew

2017-06-01

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N-body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.

Feature Discovery by Competitive Learning.

DTIC Science & Technology

1984-06-01

Probably the first such attempt occurred in 1951 when Dean Edmonds and Marvin Minsky built their learning machine. The flavor of this machine and...Bernstein, J. (1961). Profiles: Al, Marvin Minsky . The New Yorker. 57, 50-126. Bienenstock, E. L., Cooper, L. N., & Munro, P. W. (1982). Theory for the...This machine actually worked and was so fascinating to watch that Minsky remembers: We sort of quit science for awhile to watch the machine. We were

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

PubMed

Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin

2017-01-01

Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

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

Xu, Songhua; Tourassi, Georgia

2012-01-01

The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using themore » BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.« less

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

PubMed

Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen; Stoyanov, Danail; Giannarou, Stamatia; Metzen, Jan Hendrik; Vander Poorten, Emmanuel

2016-04-01

Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments.

Ship localization in Santa Barbara Channel using machine learning classifiers.

PubMed

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

2017-11-01

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

ICTNET at Web Track 2012 Ad-hoc Task

DTIC Science & Technology

2012-11-01

Model and use it as baseline this year. 3.2 Learning to rank Learning to rank (LTR) introduces machine learning to retrieval ranking problem. It...Yoram Singer. An efficient boosting algorithm  for  combining preferences [J]. The Journal of  Machine   Learning  Research. 2003. 

The Value Simulation-Based Learning Added to Machining Technology in Singapore

ERIC Educational Resources Information Center

Fang, Linda; Tan, Hock Soon; Thwin, Mya Mya; Tan, Kim Cheng; Koh, Caroline

2011-01-01

This study seeks to understand the value simulation-based learning (SBL) added to the learning of Machining Technology in a 15-week core subject course offered to university students. The research questions were: (1) How did SBL enhance classroom learning? (2) How did SBL help participants in their test? (3) How did SBL prepare participants for…

Applications of Machine Learning for Radiation Therapy.

PubMed

Arimura, Hidetaka; Nakamoto, Takahiro

2016-01-01

Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.

Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data.

PubMed

Richardson, Alice; Signor, Ben M; Lidbury, Brett A; Badrick, Tony

2016-11-01

Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

PubMed

Hansen, Katja; Montavon, Grégoire; Biegler, Franziska; Fazli, Siamac; Rupp, Matthias; Scheffler, Matthias; von Lilienfeld, O Anatole; Tkatchenko, Alexandre; Müller, Klaus-Robert

2013-08-13

The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques

PubMed Central

Cui, De-Mi; Wang, Xiao-Quan; Lu, Lie-Min

2017-01-01

Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT’s turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts’ interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology’s effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction. PMID:29068431

Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data.

PubMed

Humphries, Grant Richard Woodrow

2015-01-01

Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori 'muttonbirder' diaries were obtained and used as response variables in a gridded spatial model. It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as "flyways" by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest.

Public Pedagogy and Representations of Higher Education in Popular Film: New Ground for the Scholarship of Teaching and Learning

ERIC Educational Resources Information Center

Johnstone, Katelyn; Marquis, Elizabeth; Puri, Varun

2018-01-01

Constructions of teaching, learning, and the university within popular culture can exert an important influence on public understandings of higher education, including those held by faculty and students. As such, they constitute a rich site of inquiry for the scholarship of teaching and learning. Drawing on the notion of film as 'public pedagogy,'…

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

PubMed

Luo, Gang; Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-08-29

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets. This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. We are currently writing Auto-ML's design document. We intend to finish our study by around the year 2022. Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. ©Gang Luo, Bryan L Stone, Michael D Johnson, Peter Tarczy-Hornoch, Adam B Wilcox, Sean D Mooney, Xiaoming Sheng, Peter J Haug, Flory L Nkoy. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.08.2017.

Using machine learning algorithms to guide rehabilitation planning for home care clients.

PubMed

Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul

2007-12-20

Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

Exploring the Function Space of Deep-Learning Machines

NASA Astrophysics Data System (ADS)

Li, Bo; Saad, David

2018-06-01

The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine. Using physics-inspired methods we study both sparsely and densely connected architectures to discover a layerwise convergence of candidate functions, marked by a corresponding reduction in entropy when approaching the reference function, gain insight into the importance of having a large number of layers, and observe phase transitions as the error increases.

Designing Contestability: Interaction Design, Machine Learning, and Mental Health

PubMed Central

Hirsch, Tad; Merced, Kritzia; Narayanan, Shrikanth; Imel, Zac E.; Atkins, David C.

2017-01-01

We describe the design of an automated assessment and training tool for psychotherapists to illustrate challenges with creating interactive machine learning (ML) systems, particularly in contexts where human life, livelihood, and wellbeing are at stake. We explore how existing theories of interaction design and machine learning apply to the psychotherapy context, and identify “contestability” as a new principle for designing systems that evaluate human behavior. Finally, we offer several strategies for making ML systems more accountable to human actors. PMID:28890949

Comparison of Automated and Manual Recording of Brief Episodes of Intracranial Hypertension and Cerebral Hypoperfusion and Their Association with Outcome After Severe Traumatic Brain Injury

DTIC Science & Technology

2017-03-01

neuro ICP care beyond trauma care. 15. SUBJECT TERMS Advanced machine learning techniques, intracranial pressure, vital signs, monitoring...death and disability in combat casualties [1,2]. Approximately 2 million head injuries occur annually in the United States, resulting in more than...editor. Machine learning and data mining in pattern recognition. Proceedings of the 8th International Workshop on Machine Learning and Data Mining in

Machine learning with quantum relative entropy

NASA Astrophysics Data System (ADS)

Tsuda, Koji

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Case Studies in Environmental Adult and Popular Education.

ERIC Educational Resources Information Center

Clover, Darlene E., Ed.; Follen, Shirley, Ed.

Following an introduction by Darlene E. Clover and Rene Karottki, this booklet provides 16 case studies about nonformal environmental adult education: "Environment and Development in Argentina: Innovative Experiences in Adult Learning" (Raul A. Montenegro); "Learning for Environmental Action: Environmental Adult and Popular Education in Canada"…

News | Argonne National Laboratory

Science.gov Websites

Highlights In the News Photos Videos News News Transforming transportation with machine learning Full Story  » From individual vehicle components to entire metropolitan areas, Argonne uses machine learning to

An Investigation of Data Privacy and Utility Using Machine Learning as a Gauge

ERIC Educational Resources Information Center

Mivule, Kato

2014-01-01

The purpose of this investigation is to study and pursue a user-defined approach in preserving data privacy while maintaining an acceptable level of data utility using machine learning classification techniques as a gauge in the generation of synthetic data sets. This dissertation will deal with data privacy, data utility, machine learning…

Learning Activity Packets for Grinding Machines. Unit I--Grinding Machines.

ERIC Educational Resources Information Center

Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This learning activity packet (LAP) is one of three that accompany the curriculum guide on grinding machines. It outlines the study activities and performance tasks for the first unit of this curriculum guide. Its purpose is to aid the student in attaining a working knowledge of this area of training and in achieving a skilled or moderately…

Classification of older adults with/without a fall history using machine learning methods.

PubMed

Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

2015-01-01

Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

PubMed

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Line-drawing algorithms for parallel machines

NASA Technical Reports Server (NTRS)

Pang, Alex T.

1990-01-01

The fact that conventional line-drawing algorithms, when applied directly on parallel machines, can lead to very inefficient codes is addressed. It is suggested that instead of modifying an existing algorithm for a parallel machine, a more efficient implementation can be produced by going back to the invariants in the definition. Popular line-drawing algorithms are compared with two alternatives; distance to a line (a point is on the line if sufficiently close to it) and intersection with a line (a point on the line if an intersection point). For massively parallel single-instruction-multiple-data (SIMD) machines (with thousands of processors and up), the alternatives provide viable line-drawing algorithms. Because of the pixel-per-processor mapping, their performance is independent of the line length and orientation.

A solution to the single-question crowd wisdom problem.

PubMed

Prelec, Dražen; Seung, H Sebastian; McCoy, John

2017-01-25

Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard 'most popular' or 'most confident' principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.

Learning Machine Learning: A Case Study

ERIC Educational Resources Information Center

Lavesson, N.

2010-01-01

This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…

Machine Shop. Student Learning Guide.

ERIC Educational Resources Information Center

Palm Beach County Board of Public Instruction, West Palm Beach, FL.

This student learning guide contains eight modules for completing a course in machine shop. It is designed especially for use in Palm Beach County, Florida. Each module covers one task, and consists of a purpose, performance objective, enabling objectives, learning activities and resources, information sheets, student self-check with answer key,…

A Flexible Approach to Quantifying Various Dimensions of Environmental Complexity

DTIC Science & Technology

2004-08-01

dissertation, Cambridge University, Cambridge, England, 1989. [15] C. J. C. H. Watkins and P. Dayan, “Q-learning,” Machine Learning , vol. 8, pp. 279–292, 1992...16] I. Szita, B. Takács, and A. Lörincz, “²-MDPs: Learning in varying environments,” Journal of Machine Learning Research, vol. 3, pp. 145–174, 2002

Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

PubMed

Jaspers, Arne; De Beéck, Tim Op; Brink, Michel S; Frencken, Wouter G P; Staes, Filip; Davis, Jesse J; Helsen, Werner F

2018-05-01

Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models' performance on predicting the reported RPE values for future training sessions was compared with the naive baseline's performance. Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.