Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks

ERIC Educational Resources Information Center

Blikstein, Paulo; Worsley, Marcelo

2016-01-01

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics…

A Biological Basis for Generative Learning in Technology-and-Science Part I: A Theory of Learning.

ERIC Educational Resources Information Center

Schaverien, Lynette; Cosgrove, Mark

1999-01-01

Describes a theory of learning in which the brain is seen as a Darwinian machine. Argues that the generative heuristic underlying Darwinism offers considerable value for technology and science education. Contains 33 references. (Author/WRM)

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.

Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging.

PubMed

Summers, Ronald M

2018-05-05

Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image analysis and interpretation. In this editorial, I compare the expectations with the realities with particular attention to applications in abdominal oncology imaging. I explore whether these technologies will leave us at a crossroads to an exciting future or to a sustained plateau and disillusionment.

Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.

PubMed

Geilleit, R; Hen, Z Q; Chong, C Y; Loh, A P; Pang, N L; Peterson, G M; Ng, K C; Huis, A; de Korne, D F

2018-04-09

Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics. To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics. In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme. Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P < 0.001). With auditory notifications of a maximum duration of 15 s, HH performance was 80.4% (P < 0.001). Users emphasized the relevance of real-time notification and contributed to technical feasibility improvements that were implemented in the prototype. Annual running costs for the machine learning system were estimated to be 46% lower than the observational auditing programme. Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

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.

Teaching Machines and Programmed Instruction; an Introduction.

ERIC Educational Resources Information Center

Fry, Edward B.

Teaching machines and programed instruction represent new methods in education, but they are based on teaching principles established before the development of media technology. Today programed learning materials based on the new technology enjoy increasing popularity for several reasons: they apply sound psychological theories; the materials can…

Reinforcement learning in computer vision

NASA Astrophysics Data System (ADS)

Bernstein, A. V.; Burnaev, E. V.

2018-04-01

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

Application of machine learning methods in bioinformatics

NASA Astrophysics Data System (ADS)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. 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.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

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.

Summary of vulnerability related technologies based on machine learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Chen, Zhihao; Jia, Qiong

2018-04-01

As the scale of information system increases by an order of magnitude, the complexity of system software is getting higher. The vulnerability interaction from design, development and deployment to implementation stages greatly increases the risk of the entire information system being attacked successfully. Considering the limitations and lags of the existing mainstream security vulnerability detection techniques, this paper summarizes the development and current status of related technologies based on the machine learning methods applied to deal with massive and irregular data, and handling security vulnerabilities.

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.

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

NASA Astrophysics Data System (ADS)

Mølgaard, Lasse L.; Buus, Ole T.; Larsen, Jan; Babamoradi, Hamid; Thygesen, Ida L.; Laustsen, Milan; Munk, Jens Kristian; Dossi, Eleftheria; O'Keeffe, Caroline; Lässig, Lina; Tatlow, Sol; Sandström, Lars; Jakobsen, Mogens H.

2017-05-01

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.

Use of Computer Speech Technologies To Enhance Learning.

ERIC Educational Resources Information Center

Ferrell, Joe

1999-01-01

Discusses the design of an innovative learning system that uses new technologies for the man-machine interface, incorporating a combination of Automatic Speech Recognition (ASR) and Text To Speech (TTS) synthesis. Highlights include using speech technologies to mimic the attributes of the ideal tutor and design features. (AEF)

Quantum Machine Learning

NASA Technical Reports Server (NTRS)

Biswas, Rupak

2018-01-01

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

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.

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.

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.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes.

PubMed

Das, Barnan; Cook, Diane J; Krishnan, Narayanan C; Schmitter-Edgecombe, Maureen

2016-08-01

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

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.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

PubMed Central

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. PMID:29275361

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.

Machine learning and new vital signs monitoring in civilian en route care: A systematic review of the literature and future implications for the military.

PubMed

Liu, Nehemiah T; Salinas, Jose

2016-11-01

Although air transport medical services are today an integral part of trauma systems in most developed countries, to date, there are no reviews on recent innovations in civilian en route care. The purpose of this systematic review was to identify potential machine learning and new vital signs monitoring technologies in civilian en route care that could help close civilian and military capability gaps in monitoring and the early detection and treatment of various trauma injuries. MEDLINE, the Cochrane Database of Systematic Reviews, and citation review of relevant primary and review articles were searched for studies involving civilian en route care, air medical transport, and technologies from January 2005 to November 2015. Data were abstracted on study design, population, year, sponsors, innovation category, details of technologies, and outcomes. Thirteen observational studies involving civilian medical transport met inclusion criteria. Studies either focused on machine learning and software algorithms (n = 5), new vital signs monitoring (n = 6), or both (n = 2). Innovations involved continuous digital acquisition of physiologic data and parameter extraction. Importantly, all studies (n = 13) demonstrated improved outcomes where applicable and potential use during civilian and military en route care. However, almost all studies required further validation in prospective and/or randomized controlled trials. Potential machine learning technologies and monitoring of novel vital signs such as heart rate variability and complexity in civilian en route care could help enhance en route care for our nation's war fighters. In a complex global environment, they could potentially fill capability gaps such as monitoring and the early detection and treatment of various trauma injuries. However, the impact of these innovations and technologies will require further validation before widespread acceptance and prehospital use. Systematic review, level V.

Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

PubMed

Bini, Stefano A

2018-02-27

This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Using machine learning to emulate human hearing for predictive maintenance of equipment

NASA Astrophysics Data System (ADS)

Verma, Dinesh; Bent, Graham

2017-05-01

At the current time, interfaces between humans and machines use only a limited subset of senses that humans are capable of. The interaction among humans and computers can become much more intuitive and effective if we are able to use more senses, and create other modes of communicating between them. New machine learning technologies can make this type of interaction become a reality. In this paper, we present a framework for a holistic communication between humans and machines that uses all of the senses, and discuss how a subset of this capability can allow machines to talk to humans to indicate their health for various tasks such as predictive maintenance.

A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

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

Hamann, Hendrik F.

The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

Chemically intuited, large-scale screening of MOFs by machine learning techniques

NASA Astrophysics Data System (ADS)

Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria; Klontzas, Emmanuel; Tsamardinos, Ioannis; Froudakis, George E.

2017-10-01

A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

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.

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

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

PubMed

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Machine Learning in Intrusion Detection

DTIC Science & Technology

2005-07-01

machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

PubMed Central

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-01-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

NASA Astrophysics Data System (ADS)

Nawir, Mukrimah; Amir, Amiza; Lynn, Ong Bi; Yaakob, Naimah; Badlishah Ahmad, R.

2018-05-01

The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.

Effectiveness of Podcasts as Laboratory Instructional Support: Learner Perceptions of Machine Shop and Welding Students

ERIC Educational Resources Information Center

Lauritzen, Louis Dee

2014-01-01

Machine shop students face the daunting task of learning the operation of complex three-dimensional machine tools, and welding students must develop specific motor skills in addition to understanding the complexity of material types and characteristics. The use of consumer technology by the Millennial generation of vocational students, the…

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.

Challenges Facing the Semantic Web and Social Software as Communication Technology Agents in E-Learning Environments

ERIC Educational Resources Information Center

Olaniran, Bolanle A.

2010-01-01

The semantic web describes the process whereby information content is made available for machine consumption. With increased reliance on information communication technologies, the semantic web promises effective and efficient information acquisition and dissemination of products and services in the global economy, in particular, e-learning.…

Image analysis and machine learning for detecting malaria.

PubMed

Poostchi, Mahdieh; Silamut, Kamolrat; Maude, Richard J; Jaeger, Stefan; Thoma, George

2018-04-01

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis. Published by Elsevier Inc.

Automated Data Assimilation and Flight Planning for Multi-Platform Observation Missions

NASA Technical Reports Server (NTRS)

Oza, Nikunj; Morris, Robert A.; Strawa, Anthony; Kurklu, Elif; Keely, Leslie

2008-01-01

This is a progress report on an effort in which our goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advanced techniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. We describe the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

Accelerating Industrial Adoption of Metal Additive Manufacturing Technology

NASA Astrophysics Data System (ADS)

Vartanian, Kenneth; McDonald, Tom

2016-03-01

While metal additive manufacturing (AM) technology has clear benefits, there are still factors preventing its adoption by industry. These factors include the high cost of metal AM systems, the difficulty for machinists to learn and operate metal AM machines, the long approval process for part qualification/certification, and the need for better process controls; however, the high AM system cost is the main barrier deterring adoption. In this paper, we will discuss an America Makes-funded program to reduce AM system cost by combining metal AM technology with conventional computerized numerical controlled (CNC) machine tools. Information will be provided on how an Optomec-led team retrofitted a legacy CNC vertical mill with laser engineered net shaping (LENS®—LENS is a registered trademark of Sandia National Labs) AM technology, dramatically lowering deployment cost. The upgraded system, dubbed LENS Hybrid Vertical Mill, enables metal additive and subtractive operations to be performed on the same machine tool and even on the same part. Information on the LENS Hybrid system architecture, learnings from initial system deployment and continuing development work will also be provided to help guide further development activities within the materials community.

Teachers, Children and the Magical Writing Machine: Instructional Contexts for Word Processing with Learning Disabled Children. Final Report, and "I Know What to Say!" Writing Activities for the Magical Machine.

ERIC Educational Resources Information Center

Morocco, Catherine Cobb; And Others

The 2-year study investigated the use of word processing technology with 36 learning disabled (LD) intermediate grade children and 9 remedial teachers in five Massachusetts school districts. During the first year study staff documented how word processing was being used. In the second year, word processing activities hypothesized to be the most…

The Impact of an Inquiry Approach to Learning in a Technology-Rich Environment.

ERIC Educational Resources Information Center

Peck, Jacqueline K.; Hughes, Sharon V.

The impact of an inquiry approach on both teaching and learning in a technology-rich grade-1 classroom participating in the Cooperative Alliance for Gifted Education (CAGE) is described. CAGE is a partnership project that combines the resources of the Cleveland (Ohio) public schools, Kent State University, and International Business Machines Corp.…

Informatics and machine learning to define the phenotype.

PubMed

Basile, Anna Okula; Ritchie, Marylyn DeRiggi

2018-03-01

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

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.

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

NASA Technical Reports Server (NTRS)

Ambur, Manjula; Schwartz, Katherine G.; Mavris, Dimitri N.

2016-01-01

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.

Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis

NASA Astrophysics Data System (ADS)

Jia, Ningning; Y Lam, Edmund

2010-04-01

Inverse lithography technology (ILT) synthesizes photomasks by solving an inverse imaging problem through optimization of an appropriate functional. Much effort on ILT is dedicated to deriving superior masks at a nominal process condition. However, the lower k1 factor causes the mask to be more sensitive to process variations. Robustness to major process variations, such as focus and dose variations, is desired. In this paper, we consider the focus variation as a stochastic variable, and treat the mask design as a machine learning problem. The stochastic gradient descent approach, which is a useful tool in machine learning, is adopted to train the mask design. Compared with previous work, simulation shows that the proposed algorithm is effective in producing robust masks.

Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning.

PubMed

Rashidi, Mohammad; Wolkow, Robert A

2018-05-23

Atomic-scale characterization and manipulation with scanning probe microscopy rely upon the use of an atomically sharp probe. Here we present automated methods based on machine learning to automatically detect and recondition the quality of the probe of a scanning tunneling microscope. As a model system, we employ these techniques on the technologically relevant hydrogen-terminated silicon surface, training the network to recognize abnormalities in the appearance of surface dangling bonds. Of the machine learning methods tested, a convolutional neural network yielded the greatest accuracy, achieving a positive identification of degraded tips in 97% of the test cases. By using multiple points of comparison and majority voting, the accuracy of the method is improved beyond 99%.

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

NASA Astrophysics Data System (ADS)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-03-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

Cross-platform normalization of microarray and RNA-seq data for machine learning applications

PubMed Central

Thompson, Jeffrey A.; Tan, Jie

2016-01-01

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language. PMID:26844019

Applied Physics Modules Selected for Manufacturing and Metal Technologies.

ERIC Educational Resources Information Center

Waring, Gene

Designed for individualized use in an applied physics course in postsecondary vocational-technical education, this series of eighteen learning modules is equivalent to the content of two quarters of a five-credit hour class in manufacturing engineering technology, machine tool and design technology, welding technology, and industrial plastics…

Quantum machine learning for quantum anomaly detection

NASA Astrophysics Data System (ADS)

Liu, Nana; Rebentrost, Patrick

2018-04-01

Anomaly detection is used for identifying data that deviate from "normal" data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.

Simplify and Accelerate Earth Science Data Preparation to Systemize Machine Learning

NASA Astrophysics Data System (ADS)

Kuo, K. S.; Rilee, M. L.; Oloso, A.

2017-12-01

Data preparation is the most laborious and time-consuming part of machine learning. The effort required is usually more than linearly proportional to the varieties of data used. From a system science viewpoint, useful machine learning in Earth Science likely involves diverse datasets. Thus, simplifying data preparation to ease the systemization of machine learning in Earth Science is of immense value. The technologies we have developed and applied to an array database, SciDB, are explicitly designed for the purpose, including the innovative SpatioTemporal Adaptive-Resolution Encoding (STARE), a remapping tool suite, and an efficient implementation of connected component labeling (CCL). STARE serves as a universal Earth data representation that homogenizes data varieties and facilitates spatiotemporal data placement as well as alignment, to maximize query performance on massively parallel, distributed computing resources for a major class of analysis. Moreover, it converts spatiotemporal set operations into fast and efficient integer interval operations, supporting in turn moving-object analysis. Integrative analysis requires more than overlapping spatiotemporal sets. For example, meaningful comparison of temperature fields obtained with different means and resolutions requires their transformation to the same grid. Therefore, remapping has been implemented to enable integrative analysis. Finally, Earth Science investigations are generally studies of phenomena, e.g. tropical cyclone, atmospheric river, and blizzard, through their associated events, like hurricanes Katrina and Sandy. Unfortunately, except for a few high-impact phenomena, comprehensive episodic records are lacking. Consequently, we have implemented an efficient CCL tracking algorithm, enabling event-based investigations within climate data records beyond mere event presence. In summary, we have implemented the core unifying capabilities on a Big Data technology to enable systematic machine learning in Earth Science.

Impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines

NASA Astrophysics Data System (ADS)

Marulcu, Ismail; Barnett, Michael

2016-01-01

Background: Elementary Science Education is struggling with multiple challenges. National and State test results confirm the need for deeper understanding in elementary science education. Moreover, national policy statements and researchers call for increased exposure to engineering and technology in elementary science education. The basic motivation of this study is to suggest a solution to both improving elementary science education and increasing exposure to engineering and technology in it. Purpose/Hypothesis: This mixed-method study examined the impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. We hypothesize that the LEGO-engineering design unit is as successful as the inquiry-based unit in terms of students' science content learning of simple machines. Design/Method: We used a mixed-methods approach to investigate our research questions; we compared the control and the experimental groups' scores from the tests and interviews by using Analysis of Covariance (ANCOVA) and compared each group's pre- and post-scores by using paired t-tests. Results: Our findings from the paired t-tests show that both the experimental and comparison groups significantly improved their scores from the pre-test to post-test on the multiple-choice, open-ended, and interview items. Moreover, ANCOVA results show that students in the experimental group, who learned simple machines with the design-based unit, performed significantly better on the interview questions. Conclusions: Our analyses revealed that the design-based Design a people mover: Simple machines unit was, if not better, as successful as the inquiry-based FOSS Levers and pulleys unit in terms of students' science content learning.

JPRS Report Science & Technology Europe Twenty-Fourth Isata International Symposium on Automotive Technology and Automation.

DTIC Science & Technology

1991-09-05

34 Learning from Learning : Principles for Supporting Drivers" J A Groeger, MRC Applied Psychology Unit, UK "Argos: A Driver Behaviour Analysis System...Technology (CEST), UK MISCELLANEOUS "Modular Sensor System for Guiding Handling Machines " J Geit and J 423 Heinrich, TZN Forshcungs, FRG "Flexible...PUBLIC TRANSP . MANAa RESEARCH Arrrtympe PARTI "Implementation Strategl»» Systems engineering \\ PART III / Validation through Pilot

Technology for Early Detection of Depression and Anxiety in Older People.

PubMed

Andrews, Jacob A; Astell, Arlene J; Brown, Laura J E; Harrison, Robert F; Hawley, Mark S

2017-01-01

Under-diagnosis of depression and anxiety is common in older adults. This project took a mixed methods approach to explore the application of machine learning and technology for early detection of these conditions. Mood measures collected with digital technologies were used to predict depression and anxiety status according to the Geriatric Depression Scale (GDS) and the Hospital Anxiety and Depression Scale (HADS). Interactive group activities and interviews were used to explore views of older adults and healthcare professionals on this approach respectively. The results show good potential for using a machine learning approach with mood data to predict later depression, though prospective results are preliminary. Qualitative findings highlight motivators and barriers to use of mental health technologies, as well as usability issues. If consideration is given to these issues, this approach could allow alerts to be provided to healthcare staff to draw attention to service users who may go on to experience depression.

An impoverished machine: challenges to human learning and instructional technology.

PubMed

Taraban, Roman

2008-08-01

Many of the limitations to human learning and processing identified by cognitive psychologists over the last 50 years still hold true, including computational constraints, low learning rates, and unreliable processing. Instructional technology can be used in classrooms and in other learning contexts to address these limitations to learning. However, creating technological innovations is not enough. As part of psychological science, the development and assessment of instructional systems should be guided by theories and practices within the discipline. The technology we develop should become an object of research like other phenomena that are studied. In the present article, I present an informal account of my own work in assessing instructional technology for engineering thermodynamics to show not only the benefits, but also the limitations, in studying the technology we create. I conclude by considering several ways of advancing the development of instructional technology within the SCiP community, including interdisciplinary research and envisioning learning contexts that differ radically from traditional learning focused on lectures and testing.

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/

Technology and Gender: Differences in Masculine and Feminine Views.

ERIC Educational Resources Information Center

Brunner, Cornelia; Bennett, Dorothy

1997-01-01

The feminine attitude toward technology looks through Learning Environments for Today's Classroom machinery to its social function; the masculine view focuses more on the machine. Presenting technology as an end in itself turns most young women off. Exploring whether new technology solves a social problem, rather than celebrating speed or power,…

Blind Cyclostationary Feature Detection Based Spectrum Sensing for Autonomous Self-Learning Cognitive Radios

DTIC Science & Technology

2012-06-01

communication policies. Given the importance of machine learning and reconfig- urable hardware in the design of the Radiobots [1], we propose, in this paper, a...liter- ature, including, for example, the model in [9] which uses support vector machines (SVM’s). In this paper, however, we employ non-parametric...Communication Technology (ICACT ’08), vol. 1, Gangwon-Do, South Korea, Feb. 2008, pp. 481 – 485. [9] M. Ramon, T. Atwood , S. Barbin, and C

Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

NASA Astrophysics Data System (ADS)

Lary, D. J.

2013-12-01

A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

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.

Learning To Work Smarter.

ERIC Educational Resources Information Center

Baldwin, Fred D.

2001-01-01

With support from federal grants and area industry, the Alfred State College of Technology in New York's Southern Tier is training future workers for high-skill manufacturing jobs. The college offers certification and associate's degree programs in welding and machine-tool technology and is developing a training program in computer technology.…

A Framework for Structuring Learning Assessment in a Online Educational Game: Experiment Centered Design

ERIC Educational Resources Information Center

Conrad, Shawn; Clarke-Midura, Jody; Klopfer, Eric

2014-01-01

Educational games offer an opportunity to engage and inspire students to take interest in science, technology, engineering, and mathematical (STEM) subjects. Unobtrusive learning assessment techniques coupled with machine learning algorithms can be utilized to record students' in-game actions and formulate a model of the students' knowledge…

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

PubMed

Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti

2016-01-01

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

2005 Mississippi Curriculum Framework: Secondary Machine Tool Operation. (Program CIP: 48.0503 - Machine Shop Technology/Assistant)

ERIC Educational Resources Information Center

Gorman, Nathan; Parker, Ronald; Lurie, Charles; Maples, Thomas

2005-01-01

Secondary vocational-technical education programs in Mississippi are faced with many challenges resulting from sweeping educational reforms at the national and state levels. Schools and teachers are increasingly being held accountable for providing true learning activities to every student in the classroom. This accountability is measured through…

Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures.

PubMed

Kubota, Ken J; Chen, Jason A; Little, Max A

2016-09-01

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, "wearable," sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that "learn" from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Intelligent power management in a vehicular system with multiple power sources

NASA Astrophysics Data System (ADS)

Murphey, Yi L.; Chen, ZhiHang; Kiliaris, Leonidas; Masrur, M. Abul

This paper presents an optimal online power management strategy applied to a vehicular power system that contains multiple power sources and deals with largely fluctuated load requests. The optimal online power management strategy is developed using machine learning and fuzzy logic. A machine learning algorithm has been developed to learn the knowledge about minimizing power loss in a Multiple Power Sources and Loads (M_PS&LD) system. The algorithm exploits the fact that different power sources used to deliver a load request have different power losses under different vehicle states. The machine learning algorithm is developed to train an intelligent power controller, an online fuzzy power controller, FPC_MPS, that has the capability of finding combinations of power sources that minimize power losses while satisfying a given set of system and component constraints during a drive cycle. The FPC_MPS was implemented in two simulated systems, a power system of four power sources, and a vehicle system of three power sources. Experimental results show that the proposed machine learning approach combined with fuzzy control is a promising technology for intelligent vehicle power management in a M_PS&LD power system.

Behind the scenes: A medical natural language processing project.

PubMed

Wu, Joy T; Dernoncourt, Franck; Gehrmann, Sebastian; Tyler, Patrick D; Moseley, Edward T; Carlson, Eric T; Grant, David W; Li, Yeran; Welt, Jonathan; Celi, Leo Anthony

2018-04-01

Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies. Copyright © 2017. Published by Elsevier B.V.

Holography: A Transformative Technology for Learning and Human Performance Improvement

ERIC Educational Resources Information Center

Frazer, Gary W.; Stevens, George H.

2015-01-01

Most past and current learning technologies have been one- or two-dimensional in presentation. This may be fine if one is looking at a map or even a fine painting. However, to fully appreciate the detail of a statue or a machine part, it is better to be able to look at it from all sides. Use of holographic images allows an item to be shared with a…

Information Acquisition, Analysis and Integration

DTIC Science & Technology

2016-08-03

of sensing and processing, theory, applications, signal processing, image and video processing, machine learning , technology transfer. 16. SECURITY... learning . 5. Solved elegantly old problems like image and video debluring, intro- ducing new revolutionary approaches. 1 DISTRIBUTION A: Distribution...Polatkan, G. Sapiro, D. Blei, D. B. Dunson, and L. Carin, “ Deep learning with hierarchical convolution factor analysis,” IEEE 6 DISTRIBUTION A

Technology assessment of advanced automation for space missions

NASA Technical Reports Server (NTRS)

1982-01-01

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology.

Brain-Based Learning With Technological Support

ERIC Educational Resources Information Center

Miller, Anita

2004-01-01

Utilization of technology in secondary schools is varied and depends on the training and interest of the individual instructors. Even though technology has advanced way beyond its utilitarian roots of being viewed solely by educators as a useful machine for teachers to key exams and worksheets on, there are still many secondary educators who still…

Technology Transience and Distance Education in the Second Machine Age

ERIC Educational Resources Information Center

Swan, Karen

2015-01-01

In this article, the author explores how technological change is affecting most aspects of our society. In this vein, it is noted that even though education has historically been more resistant to technological change than other societal sectors, recent advances in distance education, specifically online learning, promise to radically disrupt…

Project A+ Elementary Technology Demonstration Schools 1990-91. The First Year.

ERIC Educational Resources Information Center

Marable, Paula; Frazer, Linda

Project A+ Elementary Technology Demonstration Schools is a program made possible through grants from IBM (International Business Machines Corporation) and Apple, Inc. The primary purpose of the program is to demonstrate the educational effectiveness of technology in accelerating the learning of low achieving at-risk students and enhancing the…

Toward Automating HIV Identification: Machine Learning for Rapid Identification of HIV-Related Social Media Data.

PubMed

Young, Sean D; Yu, Wenchao; Wang, Wei

2017-02-01

"Social big data" from technologies such as social media, wearable devices, and online searches continue to grow and can be used as tools for HIV research. Although researchers can uncover patterns and insights associated with HIV trends and transmission, the review process is time consuming and resource intensive. Machine learning methods derived from computer science might be used to assist HIV domain experts by learning how to rapidly and accurately identify patterns associated with HIV from a large set of social data. Using an existing social media data set that was associated with HIV and coded by an HIV domain expert, we tested whether 4 commonly used machine learning methods could learn the patterns associated with HIV risk behavior. We used the 10-fold cross-validation method to examine the speed and accuracy of these models in applying that knowledge to detect HIV content in social media data. Logistic regression and random forest resulted in the highest accuracy in detecting HIV-related social data (85.3%), whereas the Ridge Regression Classifier resulted in the lowest accuracy. Logistic regression yielded the fastest processing time (16.98 seconds). Machine learning can enable social big data to become a new and important tool in HIV research, helping to create a new field of "digital HIV epidemiology." If a domain expert can identify patterns in social data associated with HIV risk or HIV transmission, machine learning models could quickly and accurately learn those associations and identify potential HIV patterns in large social data sets.

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. PMID:28110725

Classifying smoking urges via machine learning.

PubMed

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

2017-10-01

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

PubMed

Ohsugi, Hideharu; Tabuchi, Hitoshi; Enno, Hiroki; Ishitobi, Naofumi

2017-08-25

Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep learning, a machine-learning technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra-wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.

A Geospatial Scavenger Hunt

ERIC Educational Resources Information Center

Martinez, Adriana E.; Williams, Nikki A.; Metoyer, Sandra K.; Morris, Jennifer N.; Berhane, Stephen A.

2009-01-01

With the use of technology such as Global Positioning System (GPS) units and Google Earth for a simple-machine scavenger hunt, you will transform a standard identification activity into an exciting learning experience that motivates students, incorporates practical skills in technology, and enhances students' spatial-thinking skills. In the…

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%

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

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

Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data

ERIC Educational Resources Information Center

Yang, Shuo

2017-01-01

With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with various storage formats and…

Lifelong Learning for the 21st Century.

ERIC Educational Resources Information Center

Goodnight, Ron

The Lifelong Learning Center for the 21st Century was proposed to provide personal renewal and technical training for employees at a major United States automotive manufacturing company when it implemented a new, computer-based Computer Numerical Controlled (CNC) machining, robotics, and high technology facility. The employees needed training for…

Audio Visual Technology and the Teaching of Foreign Languages.

ERIC Educational Resources Information Center

Halbig, Michael C.

Skills in comprehending the spoken language source are becoming increasingly important due to the audio-visual orientation of our culture. It would seem natural, therefore, to adjust the learning goals and environment accordingly. The video-cassette machine is an ideal means for creating this learning environment and developing the listening…

Computers in Post-Secondary Developmental Education and Learning Assistance.

ERIC Educational Resources Information Center

Christ, Frank L.; McLaughlin, Richard C.

This update on computer technology--as it affects learning assistance directors and developmental education personnel--begins by reporting on new developments and changes that have taken place during the past two years in five areas: (1) hardware (microcomputer systems, low cost PC clones, combination Apple/PC machines, lab computer controllers…

Introduction to China’s Aeronautical Engineering Institutions of Higher Learning -- Student Enrollment in 1981 in Higher Aeronautical Colleges and Schools Administered by the Third Ministry of Machine Building

DTIC Science & Technology

1981-10-13

FTD-ID(RS )T-1029-81 Si FOREIGN TECHNOLOGY DIVISION INTRODUCTION TO CHINA’S AERONAUTICAL ENGINEERING INSTITUTIONS OF HIGHER LEARNING -- STUDENT...TRODUCTION TO JWINA’ S PRONAUTICAL ,NGINEERING 7’ S- NSTITUTIONS OF •IGHER LEARNING -- 4TUDENT I-ENROLLMENT IN 1951 IN ITGHER AERONAUTICAL OLLEGES AND...BYs ADVOCATEDOR IMPLIED ARE THOSE Ot THE SOURCE AND DO NOT NECESSARILY REFLECT THE POSITION TRANSLATION DIVISION OR OPINION OF THE FOREIGN TECHNOLOGY

Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology.

PubMed

Zhang, Jing; Song, Yanlin; Xia, Fan; Zhu, Chenjing; Zhang, Yingying; Song, Wenpeng; Xu, Jianguo; Ma, Xuelei

2017-09-01

Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm. Large size of the training dataset is critical to increase the diagnostic accuracy. The performance of the trained machine could be tested by new images before clinical use. Real-time diagnosis, easy to use and potential high accuracy were the advantages of AI for IOPD. In sum, AI with deep learning technology is a promising method to help rapid and accurate IOPD. Copyright © 2017 Elsevier Ltd. All rights reserved.

Diagnosing Breast Cancer with Microwave Technology: remaining challenges and potential solutions with machine learning.

PubMed

Oliveira, Bárbara L; Godinho, Daniela; O'Halloran, Martin; Glavin, Martin; Jones, Edward; Conceição, Raquel C

2018-05-19

Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation.

Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning.

PubMed

Marella, William M; Sparnon, Erin; Finley, Edward

2017-03-01

The objective of this study was to develop a semiautomated approach to screening cases that describe hazards associated with the electronic health record (EHR) from a mandatory, population-based patient safety reporting system. Potentially relevant cases were identified through a query of the Pennsylvania Patient Safety Reporting System. A random sample of cases were manually screened for relevance and divided into training, testing, and validation data sets to develop a machine learning model. This model was used to automate screening of remaining potentially relevant cases. Of the 4 algorithms tested, a naive Bayes kernel performed best, with an area under the receiver operating characteristic curve of 0.927 ± 0.023, accuracy of 0.855 ± 0.033, and F score of 0.877 ± 0.027. The machine learning model and text mining approach described here are useful tools for identifying and analyzing adverse event and near-miss reports. Although reporting systems are beginning to incorporate structured fields on health information technology and the EHR, these methods can identify related events that reporters classify in other ways. These methods can facilitate analysis of legacy safety reports by retrieving health information technology-related and EHR-related events from databases without fields and controlled values focused on this subject and distinguishing them from reports in which the EHR is mentioned only in passing. Machine learning and text mining are useful additions to the patient safety toolkit and can be used to semiautomate screening and analysis of unstructured text in safety reports from frontline staff.

Biomechanics-machine learning system for surgical gesture analysis and development of technologies for minimal access surgery.

PubMed

Cavallo, Filippo; Sinigaglia, Stefano; Megali, Giuseppe; Pietrabissa, Andrea; Dario, Paolo; Mosca, Franco; Cuschieri, Alfred

2014-10-01

The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon-robot interactions. Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master-slave manipulator systems. © The Author(s) 2013.

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

PubMed

Janssen, Ronald J; Mourão-Miranda, Janaina; Schnack, Hugo G

2018-04-22

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine-learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine-learning-based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Rebooting Computers as Learning Machines

DOE PAGES

DeBenedictis, Erik P.

2016-06-13

Artificial neural networks could become the technological driver that replaces Moore's law, boosting computers' utlity through a process akin to automatic programming--although physics and computer architecture would are also a factor.

Rebooting Computers as Learning Machines

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

DeBenedictis, Erik P.

Artificial neural networks could become the technological driver that replaces Moore's law, boosting computers' utlity through a process akin to automatic programming--although physics and computer architecture would are also a factor.

Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

PubMed

Cario, Clinton L; Witte, John S

2018-03-15

As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.

Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation.

PubMed

Kanevsky, Jonathan; Corban, Jason; Gaster, Richard; Kanevsky, Ari; Lin, Samuel; Gilardino, Mirko

2016-05-01

Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of clinically relevant health metrics, all in an effort to guide care and ensure the best possible clinical outcomes. However, as the volume and complexity of biomedical data grow, it becomes challenging to effectively process "big data" using conventional techniques. Physicians and scientists must be prepared to look beyond classic methods of data processing to extract clinically relevant information. The purpose of this article is to introduce the modern plastic surgeon to machine learning and computational interpretation of large data sets. What is machine learning? Machine learning, a subfield of artificial intelligence, can address clinically relevant problems in several domains of plastic surgery, including burn surgery; microsurgery; and craniofacial, peripheral nerve, and aesthetic surgery. This article provides a brief introduction to current research and suggests future projects that will allow plastic surgeons to explore this new frontier of surgical science.

Project BILLET Curriculum Package. Bilingual Vocational Skill Training Program 1986-1987.

ERIC Educational Resources Information Center

Community Coll. of Rhode Island, Warwick.

This document describes a project that provided vocational skills and job-specific English-as-a-second-language (ESL) training to Spanish-speaking adults in Lincoln, Rhode Island. Project BILLET (Bilingual Learning and Employment Training) offered training in five vocational skill areas: machine technology, welding technology, geriatric nursing…

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…

The Vindex Special: Learning about Technology through Advertising.

ERIC Educational Resources Information Center

Smulyan, Susan; Kosty, Carlita; Brennan, Sheila

1998-01-01

Presents a lesson plan that uses content analysis of an advertisement for an early sewing machine, the Vindex, to examine issues of marketing, new technology, and consumer economics. Includes a reproduction of an early advertisement, a list of additional readings, and several sets of questions concerning target audience, information, and image…

The Deflector Selector: A Machine Learning Framework for Prioritizing Hazardous Object Deflection Technology Development

NASA Astrophysics Data System (ADS)

Nesvold, Erika; Greenberg, Adam; Erasmus, Nicolas; Van Heerden, Elmarie; Galache, J. L.; Dahlstrom, Eric; Marchis, Franck

2018-01-01

Several technologies have been proposed for deflecting a hazardous Solar System object on a trajectory that would otherwise impact the Earth. The effectiveness of each technology depends on several characteristics of the given object, including its orbit and size. The distribution of these parameters in the likely population of Earth-impacting objects can thus determine which of the technologies are most likely to be useful in preventing a collision with the Earth. None of the proposed deflection technologies has been developed and fully tested in space. Developing every proposed technology is currently prohibitively expensive, so determining now which technologies are most likely to be effective would allow us to prioritize a subset of proposed deflection technologies for funding and development. We will present a new model, the Deflector Selector, that takes as its input the characteristics of a hazardous object or population of such objects and predicts which technology would be able to perform a successful deflection. The model consists of a machine-learning algorithm trained on data produced by N-body integrations simulating the deflections. We will describe the model and present the results of tests of the effectiveness of nuclear explosives, kinetic impactors, and gravity tractors on three simulated populations of hazardous objects.

The Deflector Selector: A machine learning framework for prioritizing hazardous object deflection technology development

NASA Astrophysics Data System (ADS)

Nesvold, E. R.; Greenberg, A.; Erasmus, N.; van Heerden, E.; Galache, J. L.; Dahlstrom, E.; Marchis, F.

2018-05-01

Several technologies have been proposed for deflecting a hazardous Solar System object on a trajectory that would otherwise impact the Earth. The effectiveness of each technology depends on several characteristics of the given object, including its orbit and size. The distribution of these parameters in the likely population of Earth-impacting objects can thus determine which of the technologies are most likely to be useful in preventing a collision with the Earth. None of the proposed deflection technologies has been developed and fully tested in space. Developing every proposed technology is currently prohibitively expensive, so determining now which technologies are most likely to be effective would allow us to prioritize a subset of proposed deflection technologies for funding and development. We present a new model, the Deflector Selector, that takes as its input the characteristics of a hazardous object or population of such objects and predicts which technology would be able to perform a successful deflection. The model consists of a machine-learning algorithm trained on data produced by N-body integrations simulating the deflections. We describe the model and present the results of tests of the effectiveness of nuclear explosives, kinetic impactors, and gravity tractors on three simulated populations of hazardous objects.

Automated Analysis of Short Responses in an Interactive Synthetic Tutoring System for Introductory Physics

ERIC Educational Resources Information Center

Nakamura, Christopher M.; Murphy, Sytil K.; Christel, Michael G.; Stevens, Scott M.; Zollman, Dean A.

2016-01-01

Computer-automated assessment of students' text responses to short-answer questions represents an important enabling technology for online learning environments. We have investigated the use of machine learning to train computer models capable of automatically classifying short-answer responses and assessed the results. Our investigations are part…

Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

NASA Astrophysics Data System (ADS)

Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

2015-03-01

Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

Representing high-dimensional data to intelligent prostheses and other wearable assistive robots: A first comparison of tile coding and selective Kanerva coding.

PubMed

Travnik, Jaden B; Pilarski, Patrick M

2017-07-01

Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches. Specifically, we examine the effect that high-dimensional sensory data has on the computation time and prediction performance of a true-online temporal-difference learning prediction method as embedded within a resource-limited upper-limb prosthesis control system. We present results comparing tile coding, the dominant linear representation for real-time prosthetic machine learning, with a newly proposed modification to Kanerva coding that we call selective Kanerva coding. In addition to showing promising results for selective Kanerva coding, our results confirm potential limitations to tile coding as the number of sensory input dimensions increases. To our knowledge, this study is the first to explicitly examine representations for realtime machine learning prosthetic devices in general terms. This work therefore provides an important step towards forming an efficient prosthesis-eye view of the world, wherein prompt and accurate representations of high-dimensional data may be provided to machine learning control systems within artificial limbs and other assistive rehabilitation technologies.

Toward an Improvement of the Analysis of Neural Coding.

PubMed

Alegre-Cortés, Javier; Soto-Sánchez, Cristina; Albarracín, Ana L; Farfán, Fernando D; Val-Calvo, Mikel; Ferrandez, José M; Fernandez, Eduardo

2017-01-01

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time-Frequency (T-F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T-F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T-F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain-machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.

Assessing Advanced Technology in CENATE

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

Tallent, Nathan R.; Barker, Kevin J.; Gioiosa, Roberto

PNNL's Center for Advanced Technology Evaluation (CENATE) is a new U.S. Department of Energy center whose mission is to assess and facilitate access to emerging computing technology. CENATE is assessing a range of advanced technologies, from evolutionary to disruptive. Technologies of interest include the processor socket (homogeneous and accelerated systems), memories (dynamic, static, memory cubes), motherboards, networks (network interface cards and switches), and input/output and storage devices. CENATE is developing a multi-perspective evaluation process based on integrating advanced system instrumentation, performance measurements, and modeling and simulation. We show evaluations of two emerging network technologies: silicon photonics interconnects and the Datamore » Vortex network. CENATE's evaluation also addresses the question of which machine is best for a given workload under certain constraints. We show a performance-power tradeoff analysis of a well-known machine learning application on two systems.« less

Needs of ergonomic design at control units in production industries.

PubMed

Levchuk, I; Schäfer, A; Lang, K-H; Gebhardt, Hj; Klussmann, A

2012-01-01

During the last decades, an increasing use of innovative technologies in manufacturing areas was monitored. A huge amount of physical workload was replaced by the change from conventional machine tools to computer-controlled units. CNC systems spread in current production processes. Because of this, machine operators today mostly have an observational function. This caused increasing of static work (e.g., standing, sitting) and cognitive demands (e.g., process observation). Machine operators have a high responsibility, because mistakes may lead to human injuries as well as to product losses - and in consequence may lead to high monetary losses (for the company) as well. Being usable often means for a CNC machine being efficient. An intuitive usability and an ergonomic organization of CNC workplaces can be an essential basis to reduce the risk of failures in operation as well as physical complaints (e.g. pain or diseases because of bad body posture during work). In contrast to conventional machines, CNC machines are equipped both with hardware and software. An intuitive and clear-sighted operating of CNC systems is a requirement for quick learning of new systems. Within this study, a survey was carried out among trainees learning the operation of CNC machines.

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.

New generation emerging technologies for neurorehabilitation and motor assistance.

PubMed

Frisoli, Antonio; Solazzi, Massimiliano; Loconsole, Claudio; Barsotti, Michele

2016-12-01

This paper illustrates the application of emerging technologies and human-machine interfaces to the neurorehabilitation and motor assistance fields. The contribution focuses on wearable technologies and in particular on robotic exoskeleton as tools for increasing freedom to move and performing Activities of Daily Living (ADLs). This would result in a deep improvement in quality of life, also in terms of improved function of internal organs and general health status. Furthermore, the integration of these robotic systems with advanced bio-signal driven human-machine interface can increase the degree of participation of patient in robotic training allowing to recognize user's intention and assisting the patient in rehabilitation tasks, thus representing a fundamental aspect to elicit motor learning.

The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning.

PubMed

Huff, Trevor J; Ludwig, Parker E; Zuniga, Jorge M

2018-05-01

3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition. The current challenges, potential solutions, and future directions for ML and 3D anatomical modeling in healthcare are discussed. Areas covered: This review covers research articles in the field of machine learning as related to 3D anatomical modeling. Topics discussed include automated image segmentation, cost reduction, and related time constraints. Expert commentary: ML-based segmentation of medical images could potentially improve the process of 3D anatomical modeling. However, until more research is done to validate these technologies in clinical practice, their impact on patient outcomes will remain unknown. We have the necessary computational tools to tackle the problems discussed. The difficulty now lies in our ability to collect sufficient data.

Unconventional Wisdom about Buying Technology

ERIC Educational Resources Information Center

Sullivan, Michael F.

2004-01-01

Conventional wisdom says that people should not buy anything in education until research is seen. The following questions should be asked: (1) Does that particular technology enhance learning? (2) Does that piece of software increase test scores? and (3) Do those machines reduce absenteeism? Of course the answer is always yes. No vendor is going…

Evolution of Advanced Learning Technologies in the 21st Century

ERIC Educational Resources Information Center

Graesser, Arthur C.

2013-01-01

The role of technology in education has mystified the contributors to "Theory Into Practice" ("TIP") during its 50-year history. In the first issue of "TIP," Guba (1962) was confident that "teaching machines are here to stay" and would help education, but raised various practical concerns, such as costs,…

The Imagination Machines. An Explanation of the Role of Computer Technology in Arts Education and the Impact of the Arts on New Electronic Learning Tools. [Videotape.

ERIC Educational Resources Information Center

1991

Narrated by actor Kadeem Hardison, this documentary videotape presents arguments and examples for using Computer Assisted Instruction (CAI) in today's classroom. Experts in education examine how individuals currently use technology and suggest how people can use technology better in the future to augment and improve education. Many programs are…

Advanced Physiological Estimation of Cognitive Status. Part 2

DTIC Science & Technology

2011-05-24

Neurofeedback Algorithms and Gaze Controller EEG Sensor System g.USBamp *, ** • internal 24-bit ADC and digital signal processor • 16 channels (expandable...SUBJECT TERMS EEG eye-tracking mental state estimation machine learning Leonard J. Trejo Pacific Development and Technology LLC 999 Commercial St. Palo...fatigue, overload) Technology Transfer Opportunity Technology from PDT – Methods to acquire various physiological signals ( EEG , EOG, EMG, ECG, etc

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

A Pythonic Approach for Computational Geosciences and Geo-Data Processing

NASA Astrophysics Data System (ADS)

Morra, G.; Yuen, D. A.; Lee, S. M.

2016-12-01

Computational methods and data analysis play a constantly increasing role in Earth Sciences however students and professionals need to climb a steep learning curve before reaching a sufficient level that allows them to run effective models. Furthermore the recent arrival and new powerful machine learning tools such as Torch and Tensor Flow has opened new possibilities but also created a new realm of complications related to the completely different technology employed. We present here a series of examples entirely written in Python, a language that combines the simplicity of Matlab with the power and speed of compiled languages such as C, and apply them to a wide range of geological processes such as porous media flow, multiphase fluid-dynamics, creeping flow and many-faults interaction. We also explore ways in which machine learning can be employed in combination with numerical modelling. From immediately interpreting a large number of modeling results to optimizing a set of modeling parameters to obtain a desired optimal simulation. We show that by using Python undergraduate and graduate can learn advanced numerical technologies with a minimum dedicated effort, which in turn encourages them to develop more numerical tools and quickly progress in their computational abilities. We also show how Python allows combining modeling with machine learning as pieces of LEGO, therefore simplifying the transition towards a new kind of scientific geo-modelling. The conclusion is that Python is an ideal tool to create an infrastructure for geosciences that allows users to quickly develop tools, reuse techniques and encourage collaborative efforts to interpret and integrate geo-data in profound new ways.

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

PubMed

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

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 for Knowledge Extraction from PHR Big Data.

PubMed

Poulymenopoulou, Michaela; Malamateniou, Flora; Vassilacopoulos, George

2014-01-01

Cloud computing, Internet of things (IOT) and NoSQL database technologies can support a new generation of cloud-based PHR services that contain heterogeneous (unstructured, semi-structured and structured) patient data (health, social and lifestyle) from various sources, including automatically transmitted data from Internet connected devices of patient living space (e.g. medical devices connected to patients at home care). The patient data stored in such PHR systems constitute big data whose analysis with the use of appropriate machine learning algorithms is expected to improve diagnosis and treatment accuracy, to cut healthcare costs and, hence, to improve the overall quality and efficiency of healthcare provided. This paper describes a health data analytics engine which uses machine learning algorithms for analyzing cloud based PHR big health data towards knowledge extraction to support better healthcare delivery as regards disease diagnosis and prognosis. This engine comprises of the data preparation, the model generation and the data analysis modules and runs on the cloud taking advantage from the map/reduce paradigm provided by Apache Hadoop.

Building environment analysis based on temperature and humidity for smart energy systems.

PubMed

Yun, Jaeseok; Won, Kwang-Ho

2012-10-01

In this paper, we propose a new HVAC (heating, ventilation, and air conditioning) control strategy as part of the smart energy system that can balance occupant comfort against building energy consumption using ubiquitous sensing and machine learning technology. We have developed ZigBee-based wireless sensor nodes and collected realistic temperature and humidity data during one month from a laboratory environment. With the collected data, we have established a building environment model using machine learning algorithms, which can be used to assess occupant comfort level. We expect the proposed HVAC control strategy will be able to provide occupants with a consistently comfortable working or home environment.

Teaching an Old Log New Tricks with Machine Learning.

PubMed

Schnell, Krista; Puri, Colin; Mahler, Paul; Dukatz, Carl

2014-03-01

To most people, the log file would not be considered an exciting area in technology today. However, these relatively benign, slowly growing data sources can drive large business transformations when combined with modern-day analytics. Accenture Technology Labs has built a new framework that helps to expand existing vendor solutions to create new methods of gaining insights from these benevolent information springs. This framework provides a systematic and effective machine-learning mechanism to understand, analyze, and visualize heterogeneous log files. These techniques enable an automated approach to analyzing log content in real time, learning relevant behaviors, and creating actionable insights applicable in traditionally reactive situations. Using this approach, companies can now tap into a wealth of knowledge residing in log file data that is currently being collected but underutilized because of its overwhelming variety and volume. By using log files as an important data input into the larger enterprise data supply chain, businesses have the opportunity to enhance their current operational log management solution and generate entirely new business insights-no longer limited to the realm of reactive IT management, but extending from proactive product improvement to defense from attacks. As we will discuss, this solution has immediate relevance in the telecommunications and security industries. However, the most forward-looking companies can take it even further. How? By thinking beyond the log file and applying the same machine-learning framework to other log file use cases (including logistics, social media, and consumer behavior) and any other transactional data source.

Semantic Framework of Internet of Things for Smart Cities: Case Studies.

PubMed

Zhang, Ningyu; Chen, Huajun; Chen, Xi; Chen, Jiaoyan

2016-09-14

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications.

Semantic Framework of Internet of Things for Smart Cities: Case Studies

PubMed Central

Zhang, Ningyu; Chen, Huajun; Chen, Xi; Chen, Jiaoyan

2016-01-01

In recent years, the advancement of sensor technology has led to the generation of heterogeneous Internet-of-Things (IoT) data by smart cities. Thus, the development and deployment of various aspects of IoT-based applications are necessary to mine the potential value of data to the benefit of people and their lives. However, the variety, volume, heterogeneity, and real-time nature of data obtained from smart cities pose considerable challenges. In this paper, we propose a semantic framework that integrates the IoT with machine learning for smart cities. The proposed framework retrieves and models urban data for certain kinds of IoT applications based on semantic and machine-learning technologies. Moreover, we propose two case studies: pollution detection from vehicles and traffic pattern detection. The experimental results show that our system is scalable and capable of accommodating a large number of urban regions with different types of IoT applications. PMID:27649185

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy.

PubMed

Pande, Amit; Mohapatra, Prasant; Nicorici, Alina; Han, Jay J

2016-07-19

Children with physical impairments are at a greater risk for obesity and decreased physical activity. A better understanding of physical activity pattern and energy expenditure (EE) would lead to a more targeted approach to intervention. This study focuses on studying the use of machine-learning algorithms for EE estimation in children with disabilities. A pilot study was conducted on children with Duchenne muscular dystrophy (DMD) to identify important factors for determining EE and develop a novel algorithm to accurately estimate EE from wearable sensor-collected data. There were 7 boys with DMD, 6 healthy control boys, and 22 control adults recruited. Data were collected using smartphone accelerometer and chest-worn heart rate sensors. The gold standard EE values were obtained from the COSMED K4b2 portable cardiopulmonary metabolic unit worn by boys (aged 6-10 years) with DMD and controls. Data from this sensor setup were collected simultaneously during a series of concurrent activities. Linear regression and nonlinear machine-learning-based approaches were used to analyze the relationship between accelerometer and heart rate readings and COSMED values. Existing calorimetry equations using linear regression and nonlinear machine-learning-based models, developed for healthy adults and young children, give low correlation to actual EE values in children with disabilities (14%-40%). The proposed model for boys with DMD uses ensemble machine learning techniques and gives a 91% correlation with actual measured EE values (root mean square error of 0.017). Our results confirm that the methods developed to determine EE using accelerometer and heart rate sensor values in normal adults are not appropriate for children with disabilities and should not be used. A much more accurate model is obtained using machine-learning-based nonlinear regression specifically developed for this target population. ©Amit Pande, Prasant Mohapatra, Alina Nicorici, Jay J Han. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 19.07.2016.

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.

PubMed

Navarro, Pedro J; Pérez, Fernando; Weiss, Julia; Egea-Cortines, Marcos

2016-05-05

Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

Machine learning & artificial intelligence in the quantum domain: a review of recent progress

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Briegel, Hans J.

2018-07-01

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research—quantum information versus machine learning (ML) and artificial intelligence (AI)—have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our ‘big data’ world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement—exploring what ML/AI can do for quantum physics and vice versa—researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.

Machine learning & artificial intelligence in the quantum domain: a review of recent progress.

PubMed

Dunjko, Vedran; Briegel, Hans J

2018-07-01

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.

Pedagogical Praxis: The Professions as Models for Learning in the Age of the Smart Machine. WCER Working Paper No. 2003-6

ERIC Educational Resources Information Center

Shaffer, David W.

2003-01-01

Successful curricula are not collections of isolated elements; rather, effective learning environments function as coherent systems (Brown & Campione, 1996; see also Papert, 1980; Shaffer, 1998). The theory of pedagogical praxis begins with the premise that computers and other information technologies make it easier for students to become active…

Embedded Systems and TensorFlow Frameworks as Assistive Technology Solutions.

PubMed

Mulfari, Davide; Palla, Alessandro; Fanucci, Luca

2017-01-01

In the field of deep learning, this paper presents the design of a wearable computer vision system for visually impaired users. The Assistive Technology solution exploits a powerful single board computer and smart glasses with a camera in order to allow its user to explore the objects within his surrounding environment, while it employs Google TensorFlow machine learning framework in order to real time classify the acquired stills. Therefore the proposed aid can increase the awareness of the explored environment and it interacts with its user by means of audio messages.

PubMed Central

Solazzi, Massimiliano; Loconsole, Claudio; Barsotti, Michele

2016-01-01

This paper illustrates the application of emerging technologies and human-machine interfaces to the neurorehabilitation and motor assistance fields. The contribution focuses on wearable technologies and in particular on robotic exoskeleton as tools for increasing freedom to move and performing Activities of Daily Living (ADLs). This would result in a deep improvement in quality of life, also in terms of improved function of internal organs and general health status. Furthermore, the integration of these robotic systems with advanced bio-signal driven human-machine interface can increase the degree of participation of patient in robotic training allowing to recognize user's intention and assisting the patient in rehabilitation tasks, thus representing a fundamental aspect to elicit motor learning PMID:28484314

Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies.

PubMed

Dolecheck, K A; Silvia, W J; Heersche, G; Chang, Y M; Ray, D L; Stone, A E; Wadsworth, B A; Bewley, J M

2015-12-01

This study included 2 objectives. The first objective was to describe estrus-related changes in parameters automatically recorded by the CowManager SensOor (Agis Automatisering, Harmelen, the Netherlands), DVM bolus (DVM Systems LLC, Greeley, CO), HR Tag (SCR Engineers Ltd., Netanya, Israel), IceQube (IceRobotics Ltd., Edinburgh, UK), and Track a Cow (Animart Inc., Beaver Dam, WI). This objective was accomplished using 35 cows in 3 groups between January and June 2013 at the University of Kentucky Coldstream Dairy. We used a modified Ovsynch with G7G protocol to partially synchronize ovulation, ending after the last PGF2α injection (d 0) to allow estrus expression. Visual observation for standing estrus was conducted for four 30-min periods at 0330, 1000, 1430, and 2200h on d 2, 3, 4, and 5. Eighteen of the 35 cows stood to be mounted at least once during the observation period. These cows were used to compare differences between the 6h before and after the first standing event (estrus) and the 2wk preceding that period (nonestrus) for all technology parameters. Differences between estrus and nonestrus were observed for CowManager SensOor minutes feeding per hour, minutes of high ear activity per hour, and minutes ruminating per hour; twice daily DVM bolus reticulorumen temperature; HR Tag neck activity per 2h and minutes ruminating per 2h; IceQube lying bouts per hour, minutes lying per hour, and number of steps per hour; and Track a Cow leg activity per hour and minutes lying per hour. No difference between estrus and nonestrus was observed for CowManager SensOor ear surface temperature per hour. The second objective of this study was to explore the estrus detection potential of machine-learning techniques using automatically collected data. Three machine-learning techniques (random forest, linear discriminant analysis, and neural network) were applied to automatically collected parameter data from the 18 cows observed in standing estrus. Machine learning accuracy for all technologies ranged from 91.0 to 100.0%. When we compared visual observation with progesterone profiles of all 32 cows, we found 65.6% accuracy. Based on these results, machine-learning techniques have potential to be applied to automatically collected technology data for estrus detection. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors.

PubMed

Ferreri, Florian; Bourla, Alexis; Mouchabac, Stephane; Karila, Laurent

2018-01-01

New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping , a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past. Remote or automated interventions (web-based or smartphone-based apps), as well as virtual reality and neurofeedback, are already available or under development. These recent changes have the potential to disrupt practices, as well as practitioners' beliefs, ethics and representations, and may even call into question their professional culture. However, the impact of new technologies on health professionals' practice in addictive disorder care has yet to be determined. In the present paper, we therefore present an overview of new technology in the field of addiction medicine. Using the keywords [e-health], [m-health], [computer], [mobile], [smartphone], [wearable], [digital], [machine learning], [ecological momentary assessment], [biofeedback] and [virtual reality], we searched the PubMed database for the most representative articles in the field of assessment and interventions in substance use disorders. We screened 595 abstracts and analyzed 92 articles, dividing them into seven categories: e-health program and web-based interventions, machine learning, computerized adaptive testing, wearable devices and digital phenotyping, ecological momentary assessment, biofeedback, and virtual reality. This overview shows that new technologies can improve assessment and interventions in the field of addictive disorders. The precise role of connected devices, artificial intelligence and remote monitoring remains to be defined. If they are to be used effectively, these tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and other health professionals is essential to their design and assessment.

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…

The Impact of Machine Translation and Computer-aided Translation on Translators

NASA Astrophysics Data System (ADS)

Peng, Hao

2018-03-01

Under the context of globalization, communications between countries and cultures are becoming increasingly frequent, which make it imperative to use some techniques to help translate. This paper is to explore the influence of computer-aided translation on translators, which is derived from the field of the computer-aided translation (CAT) and machine translation (MT). Followed by an introduction to the development of machine and computer-aided translation, it then depicts the technologies practicable to translators, which are trying to analyze the demand of designing the computer-aided translation so far in translation practice, and optimize the designation of computer-aided translation techniques, and analyze its operability in translation. The findings underline the advantages and disadvantages of MT and CAT tools, and the serviceability and future development of MT and CAT technologies. Finally, this thesis probes into the impact of these new technologies on translators in hope that more translators and translation researchers can learn to use such tools to improve their productivity.

The Concept of C2 Communication and Information Support

DTIC Science & Technology

2004-06-01

communication and information literacy , • Sensors: technology and systematic development as a branch, • Military prognosis research (combat models...intelligence, • Visualization of actions, suitable forms of information presentation, • Techniques of learning CIS users communication and information ... literacy , • Sensors: technology and systematic development as a branch, • Military prognosis research (combat models), • Man - machine interface. CISu

NREL and IBM Improve Solar Forecasting with Big Data | Energy Systems

Science.gov Websites

forecasting model using deep-machine-learning technology. The multi-scale, multi-model tool, named Watt-sun the first standard suite of metrics for this purpose. Validating Watt-sun at multiple sites across the

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

NASA Astrophysics Data System (ADS)

Salehi, Hadi; Das, Saptarshi; Chakrabartty, Shantanu; Biswas, Subir; Burgueño, Rigoberto

2017-04-01

This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

Building Environment Analysis based on Temperature and Humidity for Smart Energy Systems

PubMed Central

Yun, Jaeseok; Won, Kwang-Ho

2012-01-01

In this paper, we propose a new HVAC (heating, ventilation, and air conditioning) control strategy as part of the smart energy system that can balance occupant comfort against building energy consumption using ubiquitous sensing and machine learning technology. We have developed ZigBee-based wireless sensor nodes and collected realistic temperature and humidity data during one month from a laboratory environment. With the collected data, we have established a building environment model using machine learning algorithms, which can be used to assess occupant comfort level. We expect the proposed HVAC control strategy will be able to provide occupants with a consistently comfortable working or home environment. PMID:23202004

A note on resource allocation scheduling with group technology and learning effects on a single machine

NASA Astrophysics Data System (ADS)

Lu, Yuan-Yuan; Wang, Ji-Bo; Ji, Ping; He, Hongyu

2017-09-01

In this article, single-machine group scheduling with learning effects and convex resource allocation is studied. The goal is to find the optimal job schedule, the optimal group schedule, and resource allocations of jobs and groups. For the problem of minimizing the makespan subject to limited resource availability, it is proved that the problem can be solved in polynomial time under the condition that the setup times of groups are independent. For the general setup times of groups, a heuristic algorithm and a branch-and-bound algorithm are proposed, respectively. Computational experiments show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions.

Big data bioinformatics.

PubMed

Greene, Casey S; Tan, Jie; Ung, Matthew; Moore, Jason H; Cheng, Chao

2014-12-01

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the "big data" era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both "machine learning" algorithms as well as "unsupervised" and "supervised" examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. © 2014 Wiley Periodicals, Inc.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

PubMed Central

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-01-01

Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation. PMID:28277804

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.

Applications of Deep Learning and Reinforcement Learning to Biological Data.

PubMed

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop.

PubMed

Legg, Philip A; Chung, David H S; Parry, Matthew L; Bown, Rhodri; Jones, Mark W; Griffiths, Iwan W; Chen, Min

2013-12-01

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance.

Finding New Perovskite Halides via Machine learning

NASA Astrophysics Data System (ADS)

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; Lookman, Turab

2016-04-01

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach towards rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 181 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

NASA Astrophysics Data System (ADS)

Zhou, XueFei

2018-04-01

With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

A deep learning-based multi-model ensemble method for cancer prediction.

PubMed

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Lawrence Livermore National Laboratory ULTRA-350 Test Bed

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

Hopkins, D J; Wulff, T A; Carlisle, K

2001-04-10

LLNL has many in-house designed high precision machine tools. Some of these tools include the Large Optics Diamond Turning Machine (LODTM) [1], Diamond Turning Machine No.3 (DTM-3) and two Precision Engineering Research Lathes (PERL-1 and PERL-11). These machines have accuracy in the sub-micron range and in most cases position resolution in the couple of nanometers range. All of these machines are built with similar underlying technologies. The machines use capstan drive technology, laser interferometer position feedback, tachometer velocity feedback, permanent magnet (PM) brush motors and analog velocity and position loop servo compensation [2]. The machine controller does not perform anymore » servo compensation it simply computes the differences between the commanded position and the actual position (the following error) and sends this to a D/A for the analog servo position loop. LLNL is designing a new high precision diamond turning machine. The machine is called the ULTRA 350 [3]. In contrast to many of the proven technologies discussed above, the plan for the new machine is to use brushless linear motors, high precision linear scales, machine controller motor commutation and digital servo compensation for the velocity and position loops. Although none of these technologies are new and have been in use in industry, applications of these technologies to high precision diamond turning is limited. To minimize the risks of these technologies in the new machine design, LLNL has established a test bed to evaluate these technologies for application in high precision diamond turning. The test bed is primarily composed of commercially available components. This includes the slide with opposed hydrostatic bearings, the oil system, the brushless PM linear motor, the two-phase input three-phase output linear motor amplifier and the system controller. The linear scales are not yet commercially available but use a common electronic output format. As of this writing, the final verdict for the use of these technologies is still out but the first part of the work has been completed with promising results. The goal of this part of the work was to close a servo position loop around a slide incorporating these technologies and to measure the performance. This paper discusses the tests that were setup for system evaluation and the results of the measurements made. Some very promising results include; slide positioning to nanometer level and slow speed slide direction reversal at less than 100nm/min with no observed discontinuities. This is very important for machine contouring in diamond turning. As a point of reference, at 100 nm/min it would take the slide almost 7 years to complete the full designed travel of 350 mm. This speed has been demonstrated without the use of a velocity sensor. The velocity is derived from the position sensor. With what has been learned on the test bed, the paper finishes with a brief comparison of the old and new technologies. The emphasis of this comparison will be on the servo performance as illustrated with bode plot diagrams.« less

Lawrence Livermore National Laboratory ULTRA-350 Test Bed

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

Hopkins, D J; Wulff, T A; Carlisle, K

2001-04-10

LLNL has many in-house designed high precision machine tools. Some of these tools include the Large Optics Diamond Turning Machine (LODTM) [1], Diamond Turning Machine No.3 (DTM-3) and two Precision Engineering Research Lathes (PERL-I and PERL-II). These machines have accuracy in the sub-micron range and in most cases position resolution in the couple of nanometers range. All of these machines are built with similar underlying technologies. The machines use capstan drive technology, laser interferometer position feedback, tachometer velocity feedback, permanent magnet (PM) brush motors and analog velocity and position loop servo compensation [2]. The machine controller does not perform anymore » servo compensation it simply computes the differences between the commanded position and the actual position (the following error) and sends this to a D/A for the analog servo position loop. LLNL is designing a new high precision diamond turning machine. The machine is called the ULTRA 350 [3]. In contrast to many of the proven technologies discussed above, the plan for the new machine is to use brushless linear motors, high precision linear scales, machine controller motor commutation and digital servo compensation for the velocity and position loops. Although none of these technologies are new and have been in use in industry, applications of these technologies to high precision diamond turning is limited. To minimize the risks of these technologies in the new machine design, LLNL has established a test bed to evaluate these technologies for application in high precision diamond turning. The test bed is primarily composed of commercially available components. This includes the slide with opposed hydrostatic bearings, the oil system, the brushless PM linear motor, the two-phase input three-phase output linear motor amplifier and the system controller. The linear scales are not yet commercially available but use a common electronic output format. As of this writing, the final verdict for the use of these technologies is still out but the first part of the work has been completed with promising results. The goal of this part of the work was to close a servo position loop around a slide incorporating these technologies and to measure the performance. This paper discusses the tests that were setup for system evaluation and the results of the measurements made. Some very promising results include; slide positioning to nanometer level and slow speed slide direction reversal at less than 100nm/min with no observed discontinuities. This is very important for machine contouring in diamond turning. As a point of reference, at 100 nm/min it would take the slide almost 7 years to complete the full designed travel of 350 mm. This speed has been demonstrated without the use of a velocity sensor. The velocity is derived from the position sensor. With what has been learned on the test bed, the paper finishes with a brief comparison of the old and new technologies. The emphasis of this comparison will be on the servo performance as illustrated with bode plot diagrams.« less

A fuzzy pattern matching method based on graph kernel for lithography hotspot detection

NASA Astrophysics Data System (ADS)

Nitta, Izumi; Kanazawa, Yuzi; Ishida, Tsutomu; Banno, Koji

2017-03-01

In advanced technology nodes, lithography hotspot detection has become one of the most significant issues in design for manufacturability. Recently, machine learning based lithography hotspot detection has been widely investigated, but it has trade-off between detection accuracy and false alarm. To apply machine learning based technique to the physical verification phase, designers require minimizing undetected hotspots to avoid yield degradation. They also need a ranking of similar known patterns with a detected hotspot to prioritize layout pattern to be corrected. To achieve high detection accuracy and to prioritize detected hotspots, we propose a novel lithography hotspot detection method using Delaunay triangulation and graph kernel based machine learning. Delaunay triangulation extracts features of hotspot patterns where polygons locate irregularly and closely one another, and graph kernel expresses inner structure of graphs. Additionally, our method provides similarity between two patterns and creates a list of similar training patterns with a detected hotspot. Experiments results on ICCAD 2012 benchmarks show that our method achieves high accuracy with allowable range of false alarm. We also show the ranking of the similar known patterns with a detected hotspot.

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

PubMed

Kebschull, Moritz; Papapanou, Panos N

2017-01-01

Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of -omics data.

MAS Bulletin. Papers Presented at Advisory Group for Aerospace Research and Development (AGARD) Symposium on Machine Intelligence for Aerospace Electronic Systems.

DTIC Science & Technology

1991-08-01

neural networks, and machine learning . This list ie not all 9. Future ESM Systems and the Potential for Neural Processing inclusive. This research could...U.S. CAPT James M. Skinner , USAF, Air Force Space Technology 17. Development of Tactical Doecisiont Akid. Center, and Prof. Georg* F. Luger...ntegrat11111ng Macine I~1e900enc Into the Co~pi to Aid t" Pilot 26. Integrated Communications, Navigatlion. Ideintiflocation Avionics Dr. Edward J

Bridging the gap between the technological singularity and mainstream medicine: highlighting a course on technology and the future of medicine.

PubMed

Solez, Kim; Bernier, Ashlyn; Crichton, Joel; Graves, Heather; Kuttikat, Preeti; Lockwood, Ross; Marovitz, William F; Monroe, Damon; Pallen, Mark; Pandya, Shawna; Pearce, David; Saleh, Abdullah; Sandhu, Neelam; Sergi, Consolato; Tuszynski, Jack; Waugh, Earle; White, Jonathan; Woodside, Michael; Wyndham, Roger; Zaiane, Osmar; Zakus, David

2013-09-09

The "technological singularity" is defined as that putative point in time forecasted to occur in the mid twenty-first century when machines will become smarter than humans, leading humans and machines to merge. It is hypothesized that this event will have a profound influence on medicine and population health. This work describes a new course on Technology and the Future of Medicine developed by a diverse, multi-disciplinary group of faculty members at a Canadian university. The course began as a continuous professional learning course and was later established as a recognized graduate course. We describe the philosophy of the course, the barriers encountered in course development, and some of the idiosyncratic solutions that were developed to overcome these, including the use of YouTube audience retention analytics. We hope that this report might provide a useful template for other institutions attempting to set up similar programs.

MapReduce SVM Game

DOE PAGES

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; ...

2015-08-10

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

MapReduce SVM Game

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

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

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

Advanced Technologies in Safe and Efficient Operating Rooms

DTIC Science & Technology

2009-10-01

focused on the video, not (initially) any other sensors and ii) tried to capture using machine learning techniques the ability of an expert surgeon to...plant (with humans playing the role of team leader) o a learning environment (where humans play the role of students ). As can be seen, this work...increased cognitive demands associated with the one-handed technique occur because the surgeon is providing instructions to the assistant performing

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

PubMed Central

Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system. PMID:29687000

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

PubMed

Huang, Qinghua; Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Assessing Creative Problem-Solving with Automated Text Grading

ERIC Educational Resources Information Center

Wang, Hao-Chuan; Chang, Chun-Yen; Li, Tsai-Yen

2008-01-01

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit…

A combination of HARMONIE short time direct normal irradiance forecasts and machine learning: The #hashtdim procedure

NASA Astrophysics Data System (ADS)

Gastón, Martín; Fernández-Peruchena, Carlos; Körnich, Heiner; Landelius, Tomas

2017-06-01

The present work describes the first approach of a new procedure to forecast Direct Normal Irradiance (DNI): the #hashtdim that treats to combine ground information and Numerical Weather Predictions. The system is centered in generate predictions for the very short time. It combines the outputs from the Numerical Weather Prediction Model HARMONIE with an adaptive methodology based on Machine Learning. The DNI predictions are generated with 15-minute and hourly temporal resolutions and presents 3-hourly updates. Each update offers forecasts to the next 12 hours, the first nine hours are generated with 15-minute temporal resolution meanwhile the last three hours present hourly temporal resolution. The system is proved over a Spanish emplacement with BSRN operative station in south of Spain (PSA station). The #hashtdim has been implemented in the framework of the Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies (DNICast) project, under the European Union's Seventh Programme for research, technological development and demonstration framework.

Machine learning algorithms for mode-of-action classification in toxicity assessment.

PubMed

Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can

2016-01-01

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

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.

Investigating the impact of a LEGO(TM)-based, engineering-oriented curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines

NASA Astrophysics Data System (ADS)

Marulcu, Ismail

This mixed method study examined the impact of a LEGO-based, engineering-oriented curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. This study takes a social constructivist theoretical stance that science learning involves learning scientific concepts and their relations to each other. From this perspective, students are active participants, and they construct their conceptual understanding through the guidance of their teacher. With the goal of better understanding the use of engineering education materials in classrooms the National Academy of Engineering and National Research Council in the book "Engineering in K-12 Education" conducted an in-depth review of the potential benefits of including engineering in K--12 schools as (a) improved learning and achievement in science and mathematics, (b) increased awareness of engineering and the work of engineers, (c) understanding of and the ability to engage in engineering design, (d) interest in pursuing engineering as a career, and (e) increased technological literacy (Katehi, Pearson, & Feder, 2009). However, they also noted a lack of reliable data and rigorous research to support these assertions. Data sources included identical written tests and interviews, classroom observations and videos, teacher interviews, and classroom artifacts. To investigate the impact of the design-based simple machines curriculum compared to the scientific inquiry-based simple machines curriculum on student learning outcomes, I compared the control and the experimental groups' scores on the tests and interviews by using ANCOVA. To analyze and characterize the classroom observation videotapes, I used Jordan and Henderson's (1995) method and divide them into episodes. My analyses revealed that the design-based Design a People Mover: Simple Machines unit was, if not better, as successful as the inquiry-based FOSS Levers and Pulleys unit in terms of students' content learning. I also found that students in the engineering group outperformed students in the control group in regards to their ability to answer open-ended questions when interviewed. Implications for students' science content learning and teachers' professional development are discussed.

A self-learning camera for the validation of highly variable and pseudorandom patterns

NASA Astrophysics Data System (ADS)

Kelley, Michael

2004-05-01

Reliable and productive manufacturing operations have depended on people to quickly detect and solve problems whenever they appear. Over the last 20 years, more and more manufacturing operations have embraced machine vision systems to increase productivity, reliability and cost-effectiveness, including reducing the number of human operators required. Although machine vision technology has long been capable of solving simple problems, it has still not been broadly implemented. The reason is that until now, no machine vision system has been designed to meet the unique demands of complicated pattern recognition. The ZiCAM family was specifically developed to be the first practical hardware to meet these needs. To be able to address non-traditional applications, the machine vision industry must include smart camera technology that meets its users" demands for lower costs, better performance and the ability to address applications of irregular lighting, patterns and color. The next-generation smart cameras will need to evolve as a fundamentally different kind of sensor, with new technology that behaves like a human but performs like a computer. Neural network based systems, coupled with self-taught, n-space, non-linear modeling, promises to be the enabler of the next generation of machine vision equipment. Image processing technology is now available that enables a system to match an operator"s subjectivity. A Zero-Instruction-Set-Computer (ZISC) powered smart camera allows high-speed fuzzy-logic processing, without the need for computer programming. This can address applications of validating highly variable and pseudo-random patterns. A hardware-based implementation of a neural network, Zero-Instruction-Set-Computer, enables a vision system to "think" and "inspect" like a human, with the speed and reliability of a machine.

An application of machine learning to the organization of institutional software repositories

NASA Technical Reports Server (NTRS)

Bailin, Sidney; Henderson, Scott; Truszkowski, Walt

1993-01-01

Software reuse has become a major goal in the development of space systems, as a recent NASA-wide workshop on the subject made clear. The Data Systems Technology Division of Goddard Space Flight Center has been working on tools and techniques for promoting reuse, in particular in the development of satellite ground support software. One of these tools is the Experiment in Libraries via Incremental Schemata and Cobweb (ElvisC). ElvisC applies machine learning to the problem of organizing a reusable software component library for efficient and reliable retrieval. In this paper we describe the background factors that have motivated this work, present the design of the system, and evaluate the results of its application.

Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method

NASA Astrophysics Data System (ADS)

Hsu, Roy CHaoming; Jian, Jhih-Wei; Lin, Chih-Chuan; Lai, Chien-Hung; Liu, Cheng-Ting

2013-01-01

The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.

On Textual Analysis and Machine Learning for Cyberstalking Detection.

PubMed

Frommholz, Ingo; Al-Khateeb, Haider M; Potthast, Martin; Ghasem, Zinnar; Shukla, Mitul; Short, Emma

2016-01-01

Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

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.

A machine learning approach for viral genome classification.

PubMed

Remita, Mohamed Amine; Halioui, Ahmed; Malick Diouara, Abou Abdallah; Daigle, Bruno; Kiani, Golrokh; Diallo, Abdoulaye Baniré

2017-04-11

Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families. Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .

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

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

PubMed Central

Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

PubMed

Sutphin, George L; Mahoney, J Matthew; Sheppard, Keith; Walton, David O; Korstanje, Ron

2016-11-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

Image processing and machine learning in the morphological analysis of blood cells.

PubMed

Rodellar, J; Alférez, S; Acevedo, A; Molina, A; Merino, A

2018-05-01

This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies. © 2018 John Wiley & Sons Ltd.

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods.

PubMed

Gonzalez-Navarro, Felix F; Stilianova-Stoytcheva, Margarita; Renteria-Gutierrez, Livier; Belanche-Muñoz, Lluís A; Flores-Rios, Brenda L; Ibarra-Esquer, Jorge E

2016-10-26

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

Overview and fundamentals of urologic robot-integrated systems.

PubMed

Allaf, Mohamad; Patriciu, Alexandru; Mazilu, Dumitru; Kavoussi, Louis; Stoianovici, Dan

2004-11-01

Advances in technology have revolutionized urology. Minimally invasive tools now form the core of the urologist's armamentarium. Laparoscopic surgery has become the favored approach for treating many complicated urologic ailments. Surgical robots represent the next evolutionary step in the fruitful man-machine partnership. The introduction of robotic technology in urology changes how urologists learn, teach, plan, and operate. As technology evolves, robots not only will improve performance in minimally invasive procedures, but also enhance other procedures or enable new kinds of operations.

Anomaly detection using temporal data mining in a smart home environment.

PubMed

Jakkula, V; Cook, D J

2008-01-01

To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.

PubMed

VoPham, Trang; Hart, Jaime E; Laden, Francine; Chiang, Yao-Yi

2018-04-17

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

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

Learning about (Not by) Osmosis.

ERIC Educational Resources Information Center

Borovoy, Alexander

1991-01-01

Describes the process of osmosis from its discovery by Nollet in 1848 to modern applications. Uses experimental descriptions, illustrations, and photographs to explain osmosis. Discusses the technology of producing perfect filters and their applications in reverse osmosis to purify salt water and to filter blood in kidney machines. (PR)

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.

Refining fuzzy logic controllers with machine learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1994-01-01

In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

Finding new perovskite halides via machine learning

DOE PAGES

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; ...

2016-04-26

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vectormore » machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.« less

Finding new perovskite halides via machine learning

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

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vectormore » machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.« less

A New Look at NASA: Strategic Research In Information Technology

NASA Technical Reports Server (NTRS)

Alfano, David; Tu, Eugene (Technical Monitor)

2002-01-01

This viewgraph presentation provides information on research undertaken by NASA to facilitate the development of information technologies. Specific ideas covered here include: 1) Bio/nano technologies: biomolecular and nanoscale systems and tools for assembly and computing; 2) Evolvable hardware: autonomous self-improving, self-repairing hardware and software for survivable space systems in extreme environments; 3) High Confidence Software Technologies: formal methods, high-assurance software design, and program synthesis; 4) Intelligent Controls and Diagnostics: Next generation machine learning, adaptive control, and health management technologies; 5) Revolutionary computing: New computational models to increase capability and robustness to enable future NASA space missions.

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

PubMed

Borchers, M R; Chang, Y M; Proudfoot, K L; Wadsworth, B A; Stone, A E; Bewley, J M

2017-07-01

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

Scalable Kernel Methods and Algorithms for General Sequence Analysis

ERIC Educational Resources Information Center

Kuksa, Pavel

2011-01-01

Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…

Challenging Gifted Learners through Children's Literature.

ERIC Educational Resources Information Center

Bryant, Margaret A.

1989-01-01

Gifted learners can be challenged by extending and enriching the mandated curriculum through the use of children's literature. Demonstrated is the use of the book "Mike Mulligan and His Steam Shovel" as a mechanism for learning about authorship, research skills, story evaluation, simple machines, problem solving, and technological change. (PB)

Robots as Language Learning Tools

ERIC Educational Resources Information Center

Collado, Ericka

2017-01-01

Robots are machines that resemble different forms, usually those of humans or animals, that can perform preprogrammed or autonomous tasks (Robot, n.d.). With the emergence of STEM programs, there has been a rise in the use of robots in educational settings. STEM programs are those where students study science, technology, engineering and…

Implementation of machine learning for high-volume manufacturing metrology challenges (Conference Presentation)

NASA Astrophysics Data System (ADS)

Timoney, Padraig; Kagalwala, Taher; Reis, Edward; Lazkani, Houssam; Hurley, Jonathan; Liu, Haibo; Kang, Charles; Isbester, Paul; Yellai, Naren; Shifrin, Michael; Etzioni, Yoav

2018-03-01

In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level across 3 products. In the case of product C, it is found that the predicted Rs correlation to the e-test value is significantly improved utilizing spectra acquired at the e-test structure. This paper will explore the considerations required to enable use of machine learning derived metrology output to enable improved process monitoring and control. Further results from the FEOL and BEOL sectors will be presented, together with further discussion on future proliferation of machine learning based metrology solutions in high volume manufacturing.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

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.

Model-Driven Engineering of Machine Executable Code

NASA Astrophysics Data System (ADS)

Eichberg, Michael; Monperrus, Martin; Kloppenburg, Sven; Mezini, Mira

Implementing static analyses of machine-level executable code is labor intensive and complex. We show how to leverage model-driven engineering to facilitate the design and implementation of programs doing static analyses. Further, we report on important lessons learned on the benefits and drawbacks while using the following technologies: using the Scala programming language as target of code generation, using XML-Schema to express a metamodel, and using XSLT to implement (a) transformations and (b) a lint like tool. Finally, we report on the use of Prolog for writing model transformations.

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

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.

PubMed

Zhang, Jie; Xiao, Wendong; Zhang, Sen; Huang, Shoudong

2017-04-17

Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.

Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

PubMed Central

Zhang, Jie; Xiao, Wendong; Zhang, Sen; Huang, Shoudong

2017-01-01

Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach. PMID:28420187

Integrating artificial and human intelligence into tablet production process.

PubMed

Gams, Matjaž; Horvat, Matej; Ožek, Matej; Luštrek, Mitja; Gradišek, Anton

2014-12-01

We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning.

PubMed

Caiazzo, Fabrizia; Caggiano, Alessandra

2018-03-19

Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

PubMed Central

2018-01-01

Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace. PMID:29562682

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.

Open source tools for large-scale neuroscience.

PubMed

Freeman, Jeremy

2015-06-01

New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

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

Chen, Barry Y.

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patternsmore » are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.« less

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.

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT.

PubMed

Wang, Xiaoxin; Hu, Hongli; Jia, Huiqin; Tang, Kaihao

2017-12-09

Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object.

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT

PubMed Central

Wang, Xiaoxin; Hu, Hongli; Jia, Huiqin; Tang, Kaihao

2017-01-01

Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object. PMID:29232850

Census of the Local Universe (CLU): Classification of Galaxy Candidates in Narrowband Images Using Machine Learning

NASA Astrophysics Data System (ADS)

Zhang, Chaoran; Van Sistine, Anglea; Kaplan, David; Brady, Patrick; Cook, David O.; Kasliwal, Mansi

2018-01-01

A complete catalog of galaxies in the local universe is critical for efficient electromagnetic follow-up of gravitational wave events (EMGW). The Census of the Local Universe (CLU; Cook et al. 2017, in preparation) aims to provide a galaxy catalog out to 200 Mpc that is as complete as possible. CLU has recently completed an Hα survey of ~3π of the sky with the goal of cataloging those galaxies that are likely hosts of EMGW events. Here, we present a tool we developed using machine learning technology to classify sources extracted from the Hα narrowband images within 200Mpc. In this analysis we find we are able to recover more galaxies compared to selections based on Hα colors alone.

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

Interpreting Medical Information Using Machine Learning and Individual Conditional Expectation.

PubMed

Nohara, Yasunobu; Wakata, Yoshifumi; Nakashima, Naoki

2015-01-01

Recently, machine-learning techniques have spread many fields. However, machine-learning is still not popular in medical research field due to difficulty of interpreting. In this paper, we introduce a method of interpreting medical information using machine learning technique. The method gave new explanation of partial dependence plot and individual conditional expectation plot from medical research field.

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.

A Survey of Research in Supervisory Control and Data Acquisition (SCADA)

DTIC Science & Technology

2014-09-01

distance learning .2 The data acquired may be operationally oriented and used to better run the system, or it could be strategic in nature and used to...Technically the SCADA system is composed of the information technology (IT) that provides the human- machine interface (HMI) and stores and analyzes the data...systems work by learning what normal or benign traffic is and reporting on any abnormal traffic. These systems have the potential to detect zero-day

Learning and Adaptive Hybrid Systems for Nonlinear Control

DTIC Science & Technology

1991-05-01

34 Invention Report, S81-64, File 1, Office of Technology Liscensirig, Stanford University, 1982. [Ros62J Rosenblatt, F., Principles of Neurodynamics ...Explorations in the Microstructure of Cognition , vol. 1, Rumelhart, D., and J. McClelland, ed., MIT Press, Carbnbdge, MA, 1986. [RI-1W86] Rumnelhart, D., 0...Microstructure of Cognition , vol. 1, Rumelhart, D., and J. McClelland, ed., MIT Pres, Cambridge, MA, 1986. [Sain67] Samuel, A., "Some Studies in Machine Learning

2016 New Horizons Lecture: Beyond Imaging-Radiology of Tomorrow.

PubMed

Hricak, Hedvig

2018-03-01

This article is based on the New Horizons lecture delivered at the 2016 Radiological Society of North America Annual Meeting. It addresses looming changes for radiology, many of which stem from the disruptive effects of the Fourth Industrial Revolution. This is an emerging era of unprecedented rapid innovation marked by the integration of diverse disciplines and technologies, including data science, machine learning, and artificial intelligence-technologies that narrow the gap between man and machine. Technologic advances and the convergence of life sciences, physical sciences, and bioengineering are creating extraordinary opportunities in diagnostic radiology, image-guided therapy, targeted radionuclide therapy, and radiology informatics, including radiologic image analysis. This article uses the example of oncology to make the case that, if members in the field of radiology continue to be innovative and continuously reinvent themselves, radiology can play an ever-increasing role in both precision medicine and value-driven health care. © RSNA, 2018.

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.

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.

Automated assessment of cognitive health using smart home technologies.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2013-01-01

The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

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.

Automated Assessment of Cognitive Health Using Smart Home Technologies

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2014-01-01

BACKGROUND The goal of this work is to develop intelligent systems to monitor the well being of individuals in their home environments. OBJECTIVE This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve = 0.80, g-mean = 0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained. PMID:23949177

Advanced warfighter machine interface (Invited Paper)

NASA Astrophysics Data System (ADS)

Franks, Erin

2005-05-01

Future military crewmen may have more individual and shared tasks to complete throughout a mission as a result of smaller crew sizes and an increased number of technology interactions. To maintain reasonable workload levels, the Warfighter Machine Interface (WMI) must provide information in a consistent, logical manner, tailored to the environment in which the soldier will be completing their mission. This paper addresses design criteria for creating an advanced, multi-modal warfighter machine interface for on-the-move mounted operations. The Vetronics Technology Integration (VTI) WMI currently provides capabilities such as mission planning and rehearsal, voice and data communications, and manned/unmanned vehicle payload and mobility control. A history of the crewstation and more importantly, the WMI software will be provided with an overview of requirements and criteria used for completing the design. Multiple phases of field and laboratory testing provide the opportunity to evaluate the design and hardware in stationary and motion environments. Lessons learned related to system usability and user performance are presented with mitigation strategies to be tested in the future.

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.

Application of Artificial Intelligence technology to the analysis and synthesis of reliable software systems

NASA Technical Reports Server (NTRS)

Wild, Christian; Eckhardt, Dave

1987-01-01

The development of a methodology for the production of highly reliable software is one of the greatest challenges facing the computer industry. Meeting this challenge will undoubtably involve the integration of many technologies. This paper describes the use of Artificial Intelligence technologies in the automated analysis of the formal algebraic specifications of abstract data types. These technologies include symbolic execution of specifications using techniques of automated deduction and machine learning through the use of examples. On-going research into the role of knowledge representation and problem solving in the process of developing software is also discussed.

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.

PubMed

Chen, Yang; Luo, Yan; Huang, Wei; Hu, Die; Zheng, Rong-Qin; Cong, Shu-Zhen; Meng, Fan-Kun; Yang, Hong; Lin, Hong-Jun; Sun, Yan; Wang, Xiu-Yan; Wu, Tao; Ren, Jie; Pei, Shu-Fang; Zheng, Ying; He, Yun; Hu, Yu; Yang, Na; Yan, Hongmei

2017-10-01

Hepatic fibrosis is a common middle stage of the pathological processes of chronic liver diseases. Clinical intervention during the early stages of hepatic fibrosis can slow the development of liver cirrhosis and reduce the risk of developing liver cancer. Performing a liver biopsy, the gold standard for viral liver disease management, has drawbacks such as invasiveness and a relatively high sampling error rate. Real-time tissue elastography (RTE), one of the most recently developed technologies, might be promising imaging technology because it is both noninvasive and provides accurate assessments of hepatic fibrosis. However, determining the stage of liver fibrosis from RTE images in a clinic is a challenging task. In this study, in contrast to the previous liver fibrosis index (LFI) method, which predicts the stage of diagnosis using RTE images and multiple regression analysis, we employed four classical classifiers (i.e., Support Vector Machine, Naïve Bayes, Random Forest and K-Nearest Neighbor) to build a decision-support system to improve the hepatitis B stage diagnosis performance. Eleven RTE image features were obtained from 513 subjects who underwent liver biopsies in this multicenter collaborative research. The experimental results showed that the adopted classifiers significantly outperformed the LFI method and that the Random Forest(RF) classifier provided the highest average accuracy among the four machine algorithms. This result suggests that sophisticated machine-learning methods can be powerful tools for evaluating the stage of hepatic fibrosis and show promise for clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Machine learning in smart home control systems - Algorithms and new opportunities

NASA Astrophysics Data System (ADS)

Berg, Ivan A.; Khorev, Oleg E.; Matvevnina, Arina I.; Prisjazhnyj, Alexey V.

2017-11-01

Worldwide, more and more attention is paid to issues related to a smart home. If in 2000 Scopus registered 25 publications with about "smart house", in 2016 their number increased up to 1600. The top three countries with interest in smart home technologies include the United States, China and India. Corporations begin to offer their package solutions for automation of the intellectual home, dozens of start-ups around the creation of technology are established. Where is such interest from? What can offer intelligent home technologies? What can an end user receive?

Corpus Linguistics for Korean Language Learning and Teaching. NFLRC Technical Report No. 26

ERIC Educational Resources Information Center

Bley-Vroman, Robert, Ed.; Ko, Hyunsook, Ed.

2006-01-01

Dramatic advances in personal computer technology have given language teachers access to vast quantities of machine-readable text, which can be analyzed with a view toward improving the basis of language instruction. Corpus linguistics provides analytic techniques and practical tools for studying language in use. This volume includes both an…

Ghosts in the Machine: Incarcerated Students and the Digital University

ERIC Educational Resources Information Center

Hopkins, Susan

2015-01-01

Providing higher education to offenders in custody has become an increasingly complex business in the age of digital learning. Most Australian prisoners still have no direct access to the internet and relatively unreliable access to information technology. As incarceration is now a business, prisons, like universities, are increasingly subject to…

A machine learning approach to improve contactless heart rate monitoring using a webcam.

PubMed

Monkaresi, Hamed; Calvo, Rafael A; Yan, Hong

2014-07-01

Unobtrusive, contactless recordings of physiological signals are very important for many health and human-computer interaction applications. Most current systems require sensors which intrusively touch the user's skin. Recent advances in contact-free physiological signals open the door to many new types of applications. This technology promises to measure heart rate (HR) and respiration using video only. The effectiveness of this technology, its limitations, and ways of overcoming them deserves particular attention. In this paper, we evaluate this technique for measuring HR in a controlled situation, in a naturalistic computer interaction session, and in an exercise situation. For comparison, HR was measured simultaneously using an electrocardiography device during all sessions. The results replicated the published results in controlled situations, but show that they cannot yet be considered as a valid measure of HR in naturalistic human-computer interaction. We propose a machine learning approach to improve the accuracy of HR detection in naturalistic measurements. The results demonstrate that the root mean squared error is reduced from 43.76 to 3.64 beats/min using the proposed method.

Deep learning for computational biology.

PubMed

Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold; Stegle, Oliver

2016-07-29

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

PubMed

Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

2016-01-01

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.

Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm

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

Patton, Robert M; Beckerman, Barbara G; Potok, Thomas E

2009-01-01

Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns in radiology reports that utilizes a genetic algorithm (GA) as the learning method. The approach described here successfully learned cue phrase patterns for two distinct classes of radiology reports. These patterns can then be used as a basis for automatically categorizing, clustering, ormore » retrieving relevant data for the user.« less

Aural mapping of STEM concepts using literature mining

NASA Astrophysics Data System (ADS)

Bharadwaj, Venkatesh

Recent technological applications have made the life of people too much dependent on Science, Technology, Engineering, and Mathematics (STEM) and its applications. Understanding basic level science is a must in order to use and contribute to this technological revolution. Science education in middle and high school levels however depends heavily on visual representations such as models, diagrams, figures, animations and presentations etc. This leaves visually impaired students with very few options to learn science and secure a career in STEM related areas. Recent experiments have shown that small aural clues called Audemes are helpful in understanding and memorization of science concepts among visually impaired students. Audemes are non-verbal sound translations of a science concept. In order to facilitate science concepts as Audemes, for visually impaired students, this thesis presents an automatic system for audeme generation from STEM textbooks. This thesis describes the systematic application of multiple Natural Language Processing tools and techniques, such as dependency parser, POS tagger, Information Retrieval algorithm, Semantic mapping of aural words, machine learning etc., to transform the science concept into a combination of atomic-sounds, thus forming an audeme. We present a rule based classification method for all STEM related concepts. This work also presents a novel way of mapping and extracting most related sounds for the words being used in textbook. Additionally, machine learning methods are used in the system to guarantee the customization of output according to a user's perception. The system being presented is robust, scalable, fully automatic and dynamically adaptable for audeme generation.

AF-TRUST, Air Force Team for Research in Ubiquitous Secure Technology

DTIC Science & Technology

2010-07-26

Charles Sutton, J. D. Tygar, and Kai Xia. Book chapter in Jeffrey J. P. Tsai and Philip S. Yu (eds.) Machine Learning in Cyber Trust: Security, Privacy...enterprise, tactical, embedded systems and command and control levels. From these studies, commissioned by Dr . Sekar Chandersekaran of the Secretary of the...Data centers avoid IP Multicast because of a series of problems with the technology. • Dr . Multicast (the MCMD), a system that maps traditional I PMC

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

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

NASA Astrophysics Data System (ADS)

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

PubMed Central

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics.

PubMed

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

PubMed

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study

PubMed Central

Cole, Casey A; Anshari, Dien; Lambert, Victoria; Thrasher, James F

2017-01-01

Background Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. Objective This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. Methods A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. Results In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. Conclusions Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events. PMID:29237580

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.

Computation of emotions in man and machines.

PubMed

Robinson, Peter; el Kaliouby, Rana

2009-12-12

The importance of emotional expression as part of human communication has been understood since Aristotle, and the subject has been explored scientifically since Charles Darwin and others in the nineteenth century. Advances in computer technology now allow machines to recognize and express emotions, paving the way for improved human-computer and human-human communications. Recent advances in psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory. At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication, decision-making and other applications. The computation of emotions includes both recognition and synthesis, using channels such as facial expressions, non-verbal aspects of speech, posture, gestures, physiology, brain imaging and general behaviour. The combination of new results in psychology with new techniques of computation is leading to new technologies with applications in commerce, education, entertainment, security, therapy and everyday life. However, there are important issues of privacy and personal expression that must also be considered.

Computation of emotions in man and machines

PubMed Central

Robinson, Peter; el Kaliouby, Rana

2009-01-01

The importance of emotional expression as part of human communication has been understood since Aristotle, and the subject has been explored scientifically since Charles Darwin and others in the nineteenth century. Advances in computer technology now allow machines to recognize and express emotions, paving the way for improved human–computer and human–human communications. Recent advances in psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory. At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication, decision-making and other applications. The computation of emotions includes both recognition and synthesis, using channels such as facial expressions, non-verbal aspects of speech, posture, gestures, physiology, brain imaging and general behaviour. The combination of new results in psychology with new techniques of computation is leading to new technologies with applications in commerce, education, entertainment, security, therapy and everyday life. However, there are important issues of privacy and personal expression that must also be considered. PMID:19884138

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

PubMed

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

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.

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

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

PubMed Central

Markides, Andreas; Skillman, Severin; Acton, Sahr Thomas; Elsaleh, Tarek; Hassanpour, Masoud; Ahrabian, Alireza; Kenny, Mark; Klein, Stuart; Rostill, Helen; Nilforooshan, Ramin; Barnaghi, Payam

2018-01-01

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%. PMID:29723236

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

PubMed

Enshaeifar, Shirin; Zoha, Ahmed; Markides, Andreas; Skillman, Severin; Acton, Sahr Thomas; Elsaleh, Tarek; Hassanpour, Masoud; Ahrabian, Alireza; Kenny, Mark; Klein, Stuart; Rostill, Helen; Nilforooshan, Ramin; Barnaghi, Payam

2018-01-01

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods †

PubMed Central

Gonzalez-Navarro, Felix F.; Stilianova-Stoytcheva, Margarita; Renteria-Gutierrez, Livier; Belanche-Muñoz, Lluís A.; Flores-Rios, Brenda L.; Ibarra-Esquer, Jorge E.

2016-01-01

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization. PMID:27792165

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

PubMed

van den Akker, Jeroen; Mishne, Gilad; Zimmer, Anjali D; Zhou, Alicia Y

2018-04-17

Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation. We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/- 0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing. This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to differentiate low from high confidence variants. Additionally, it reveals the importance of incorporating site-specific features as well as variant call features in such a model.

Big data analytics for early detection of breast cancer based on machine learning

NASA Astrophysics Data System (ADS)

Ivanova, Desislava

2017-12-01

This paper presents the concept and the modern advances in personalized medicine that rely on technology and review the existing tools for early detection of breast cancer. The breast cancer types and distribution worldwide is discussed. It is spent time to explain the importance of identifying the normality and to specify the main classes in breast cancer, benign or malignant. The main purpose of the paper is to propose a conceptual model for early detection of breast cancer based on machine learning for processing and analysis of medical big dataand further knowledge discovery for personalized treatment. The proposed conceptual model is realized by using Naive Bayes classifier. The software is written in python programming language and for the experiments the Wisconsin breast cancer database is used. Finally, the experimental results are presented and discussed.

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.

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.

Using Smart City Technology to Make Healthcare Smarter.

PubMed

Cook, Diane J; Duncan, Glen; Sprint, Gina; Fritz, Roschelle

2018-04-01

Smart cities use information and communication technologies (ICT) to scale services include utilities and transportation to a growing population. In this article we discuss how smart city ICT can also improve healthcare effectiveness and lower healthcare cost for smart city residents. We survey current literature and introduce original research to offer an overview of how smart city infrastructure supports strategic healthcare using both mobile and ambient sensors combined with machine learning. Finally, we consider challenges that will be faced as healthcare providers make use of these opportunities.

The ACODEA Framework: Developing Segmentation and Classification Schemes for Fully Automatic Analysis of Online Discussions

ERIC Educational Resources Information Center

Mu, Jin; Stegmann, Karsten; Mayfield, Elijah; Rose, Carolyn; Fischer, Frank

2012-01-01

Research related to online discussions frequently faces the problem of analyzing huge corpora. Natural Language Processing (NLP) technologies may allow automating this analysis. However, the state-of-the-art in machine learning and text mining approaches yields models that do not transfer well between corpora related to different topics. Also,…

Stimulating Students' Use of External Representations for a Distance Education Time Machine Design

ERIC Educational Resources Information Center

Baaki, John; Luo, Tian

2017-01-01

As faculty members in an instructional design and technology (IDT) program, we wanted to help our graduate students better understand and experience how designers design in the real world. We aimed to design a reflective and collaborative learning environment where we sparked students to engage in reflection, ideation, and the iterative process of…

Impact of an Engineering Design-Based Curriculum Compared to an Inquiry-Based Curriculum on Fifth Graders' Content Learning of Simple Machines

ERIC Educational Resources Information Center

Marulcu, Ismail; Barnett, Michael

2016-01-01

Background: Elementary Science Education is struggling with multiple challenges. National and State test results confirm the need for deeper understanding in elementary science education. Moreover, national policy statements and researchers call for increased exposure to engineering and technology in elementary science education. The basic…

Construction and Analysis of Educational Tests Using Abductive Machine Learning

ERIC Educational Resources Information Center

El-Alfy, El-Sayed M.; Abdel-Aal, Radwan E.

2008-01-01

Recent advances in educational technologies and the wide-spread use of computers in schools have fueled innovations in test construction and analysis. As the measurement accuracy of a test depends on the quality of the items it includes, item selection procedures play a central role in this process. Mathematical programming and the item response…

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture.

PubMed

Morota, Gota; Ventura, Ricardo V; Silva, Fabyano F; Koyama, Masanori; Fernando, Samodha C

2018-04-14

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.

Overlay improvements using a real time machine learning algorithm

NASA Astrophysics Data System (ADS)

Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

2014-04-01

While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

Autonomous unobtrusive detection of mild cognitive impairment in older adults.

PubMed

Akl, Ahmad; Taati, Babak; Mihailidis, Alex

2015-05-01

The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing approach equipped with a machine learning paradigm to process and analyze real-world data acquired using home-based unobtrusive sensing technologies. Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of three years, a number of measures associated with the subjects' walking speed and general activity in the home were calculated. Different time spans of these measures were used to generate feature vectors to train and test two machine learning algorithms namely support vector machines and random forests. We were able to autonomously detect MCI in older adults with an area under the ROC curve of 0.97 and an area under the precision-recall curve of 0.93 using a time window of 24 weeks. This study is of great significance since it can potentially assist in the early detection of cognitive impairment in older adults.

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)

Complex extreme learning machine applications in terahertz pulsed signals feature sets.

PubMed

Yin, X-X; Hadjiloucas, S; Zhang, Y

2014-11-01

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

PubMed Central

Hauschild, Anne-Christin; Kopczynski, Dominik; D’Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-01-01

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME). We manually generated a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors’ results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications. PMID:24957992

Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches.

PubMed

Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-04-16

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

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.

A Student Experiment Method for Learning the Basics of Embedded Software Technologies Including Hardware/Software Co-design

NASA Astrophysics Data System (ADS)

Kambe, Hidetoshi; Mitsui, Hiroyasu; Endo, Satoshi; Koizumi, Hisao

The applications of embedded system technologies have spread widely in various products, such as home appliances, cellular phones, automobiles, industrial machines and so on. Due to intensified competition, embedded software has expanded its role in realizing sophisticated functions, and new development methods like a hardware/software (HW/SW) co-design for uniting HW and SW development have been researched. The shortfall of embedded SW engineers was estimated to be approximately 99,000 in the year 2006, in Japan. Embedded SW engineers should understand HW technologies and system architecture design as well as SW technologies. However, a few universities offer this kind of education systematically. We propose a student experiment method for learning the basics of embedded system development, which includes a set of experiments for developing embedded SW, developing embedded HW and experiencing HW/SW co-design. The co-design experiment helps students learn about the basics of embedded system architecture design and the flow of designing actual HW and SW modules. We developed these experiments and evaluated them.

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

Biomimetic molecular design tools that learn, evolve, and adapt.

PubMed

Winkler, David A

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known "S curve", with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

Biomimetic molecular design tools that learn, evolve, and adapt

PubMed Central

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. PMID:28694872

Learning diagnostic models using speech and language measures.

PubMed

Peintner, Bart; Jarrold, William; Vergyriy, Dimitra; Richey, Colleen; Tempini, Maria Luisa Gorno; Ogar, Jennifer

2008-01-01

We describe results that show the effectiveness of machine learning in the automatic diagnosis of certain neurodegenerative diseases, several of which alter speech and language production. We analyzed audio from 9 control subjects and 30 patients diagnosed with one of three subtypes of Frontotemporal Lobar Degeneration. From this data, we extracted features of the audio signal and the words the patient used, which were obtained using our automated transcription technologies. We then automatically learned models that predict the diagnosis of the patient using these features. Our results show that learned models over these features predict diagnosis with accuracy significantly better than random. Future studies using higher quality recordings will likely improve these results.

The rise of deep learning in drug discovery.

PubMed

Chen, Hongming; Engkvist, Ola; Wang, Yinhai; Olivecrona, Marcus; Blaschke, Thomas

2018-06-01

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

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)

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

Dayman, Ken J; Ade, Brian J; Weber, Charles F

High-dimensional, nonlinear function estimation using large datasets is a current area of interest in the machine learning community, and applications may be found throughout the analytical sciences, where ever-growing datasets are making more information available to the analyst. In this paper, we leverage the existing relevance vector machine, a sparse Bayesian version of the well-studied support vector machine, and expand the method to include integrated feature selection and automatic function shaping. These innovations produce an algorithm that is able to distinguish variables that are useful for making predictions of a response from variables that are unrelated or confusing. We testmore » the technology using synthetic data, conduct initial performance studies, and develop a model capable of making position-independent predictions of the coreaveraged burnup using a single specimen drawn randomly from a nuclear reactor core.« less

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

Neural networks and applications tutorial

NASA Astrophysics Data System (ADS)

Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

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.

Development of SNS Stream Analysis Based on Forest Disaster Warning Information Service System

NASA Astrophysics Data System (ADS)

Oh, J.; KIM, D.; Kang, M.; Woo, C.; Kim, D.; Seo, J.; Lee, C.; Yoon, H.; Heon, S.

2017-12-01

Forest disasters, such as landslides and wildfires, cause huge economic losses and casualties, and the cost of recovery is increasing every year. While forest disaster mitigation technologies have been focused on the development of prevention and response technologies, they are now required to evolve into evacuation and border evacuation, and to develop technologies fused with ICT. In this study, we analyze the SNS (Social Network Service) stream and implement a system to detect the message that the forest disaster occurred or the forest disaster, and search the keyword related to the forest disaster in advance in real time. It is possible to detect more accurate forest disaster messages by repeatedly learning the retrieved results using machine learning techniques. To do this, we designed and implemented a system based on Hadoop and Spark, a distributed parallel processing platform, to handle Twitter stream messages that open SNS. In order to develop the technology to notify the information of forest disaster risk, a linkage of technology such as CBS (Cell Broadcasting System) based on mobile communication, internet-based civil defense siren, SNS and the legal and institutional issues for applying these technologies are examined. And the protocol of the forest disaster warning information service system that can deliver the SNS analysis result was developed. As a result, it was possible to grasp real-time forest disaster situation by real-time big data analysis of SNS that occurred during forest disasters. In addition, we confirmed that it is possible to rapidly propagate alarm or warning according to the disaster situation by using the function of the forest disaster warning information notification service. However, the limitation of system application due to the restriction of opening and sharing of SNS data currently in service and the disclosure of personal information remains a problem to be solved in the future. Keyword : SNS stream, Big data, Machine learning techniques, CBS, Forest disaster warning information service system Acknowledgement : This research was supported by the Forestry Technology 2015 Forestry Technology Research and Development Project (Planning project).

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.

Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

NASA Astrophysics Data System (ADS)

Zhou, Zu-De; Gui, Lin; Tan, Yue-Gang; Liu, Ming-Yao; Liu, Yi; Li, Rui-Ya

2017-09-01

Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC (computer numerical control) machine tools. Currently, there are many review literatures introducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature monitoring technology and thermal deformation monitoring technology. A new optical measurement technology called the "fiber Bragg grating (FBG) distributed sensing technology" for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articles to guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

A Cognitive Systems Engineering Approach to Developing Human Machine Interface Requirements for New Technologies

NASA Astrophysics Data System (ADS)

Fern, Lisa Carolynn

This dissertation examines the challenges inherent in designing and regulating to support human-automation interaction for new technologies that will be deployed into complex systems. A key question for new technologies with increasingly capable automation, is how work will be accomplished by human and machine agents. This question has traditionally been framed as how functions should be allocated between humans and machines. Such framing misses the coordination and synchronization that is needed for the different human and machine roles in the system to accomplish their goals. Coordination and synchronization demands are driven by the underlying human-automation architecture of the new technology, which are typically not specified explicitly by designers. The human machine interface (HMI), which is intended to facilitate human-machine interaction and cooperation, typically is defined explicitly and therefore serves as a proxy for human-automation cooperation requirements with respect to technical standards for technologies. Unfortunately, mismatches between the HMI and the coordination and synchronization demands of the underlying human-automation architecture can lead to system breakdowns. A methodology is needed that both designers and regulators can utilize to evaluate the predicted performance of a new technology given potential human-automation architectures. Three experiments were conducted to inform the minimum HMI requirements for a detect and avoid (DAA) system for unmanned aircraft systems (UAS). The results of the experiments provided empirical input to specific minimum operational performance standards that UAS manufacturers will have to meet in order to operate UAS in the National Airspace System (NAS). These studies represent a success story for how to objectively and systematically evaluate prototype technologies as part of the process for developing regulatory requirements. They also provide an opportunity to reflect on the lessons learned in order to improve the methodology for defining technology requirements for regulators in the future. The biggest shortcoming of the presented research program was the absence of the explicit definition, generation and analysis of potential human-automation architectures. Failure to execute this step in the research process resulted in less efficient evaluation of the candidate prototypes technologies in addition to a lack of exploration of different approaches to human-automation cooperation. Defining potential human-automation architectures a priori also allows regulators to develop scenarios that will stress the performance boundaries of the technology during the evaluation phase. The importance of adding this step of generating and evaluating candidate human-automation architectures prior to formal empirical evaluation is discussed. This document concludes with a look at both the importance of, and the challenges facing, the inclusion of examining human-automation coordination issues as part of the safety assurance activities of new technologies.

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

Machine learning and data science in soft materials engineering

NASA Astrophysics Data System (ADS)

Ferguson, Andrew L.

2018-01-01

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by ‘de-jargonizing’ data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Machine learning and data science in soft materials engineering.

PubMed

Ferguson, Andrew L

2018-01-31

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

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

The Integration of an API619 Screw Compressor Package into the Industrial Internet of Things

NASA Astrophysics Data System (ADS)

Milligan, W. J.; Poli, G.; Harrison, D. K.

2017-08-01

The Industrial Internet of Things (IIoT) is the industrial subset of the Internet of Things (IoT). IIoT incorporates big data technology, harnessing the instrumentation data, machine to machine communication and automation technologies that have existed in industrial settings for years. As industry in general trends towards the IIoT and as the screw compressor packages developed by Howden Compressors are designed with a minimum design life of 25 years, it is imperative this technology is embedded immediately. This paper provides the reader with a description on the Industrial Internet of Things before moving onto describing the scope of the problem for an organisation like Howden Compressors who deploy multiple compressor technologies across multiple locations and focuses on the critical measurements particular to high specification screw compressor packages. A brief analysis of how this differs from high volume package manufacturers deploying similar systems is offered. Then follows a description on how the measured information gets from the tip of the instrument in the process pipework or drive train through the different layers, with a description of each layer, into the final presentation layer. The functions available within the presentation layer are taken in turn and the benefits analysed with specific focus on efficiency and availability. The paper concludes with how packagers adopting the IIoT can not only optimise their package but by utilising the machine learning technology and pattern detection applications can adopt completely new business models.

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.

Survey on Ontology Mapping

NASA Astrophysics Data System (ADS)

Zhu, Junwu

To create a sharable semantic space in which the terms from different domain ontology or knowledge system, Ontology mapping become a hot research point in Semantic Web Community. In this paper, motivated factors of ontology mapping research are given firstly, and then 5 dominating theories and methods, such as information accessing technology, machine learning, linguistics, structure graph and similarity, are illustrated according their technology class. Before we analyses the new requirements and takes a long view, the contributions of these theories and methods are summarized in details. At last, this paper suggest to design a group of semantic connector with the ability of migration learning for OWL-2 extended with constrains and the ontology mapping theory of axiom, so as to provide a new methodology for ontology mapping.

Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen

2014-01-01

One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine learning-based method for assessing activity quality in smart homes. To validate our approach we quantify activity quality for 179 volunteer participants who performed a complex, interweaved set of activities in our smart home apartment. We observed a statistically significant correlation (r=0.79) between automated assessment of task quality and direct observation scores. Using machine learning techniques to predict the cognitive health of the participants based on task quality is accomplished with an AUC value of 0.64. We believe that this capability is an important step in understanding everyday functional health of individuals in their home environments. PMID:25530925

Verification of directed self-assembly (DSA) guide patterns through machine learning

NASA Astrophysics Data System (ADS)

Shim, Seongbo; Cai, Sibo; Yang, Jaewon; Yang, Seunghune; Choi, Byungil; Shin, Youngsoo

2015-03-01

Verification of full-chip DSA guide patterns (GPs) through simulations is not practical due to long runtime. We develop a decision function (or functions), which receives n geometry parameters of a GP as inputs and predicts whether the GP faithfully produces desired contacts (good) or not (bad). We take a few sample GPs to construct the function; DSA simulations are performed for each GP to decide whether it is good or bad, and the decision is marked in n-dimensional space. The hyper-plane that separates good marks and bad marks in that space is determined through machine learning process, and corresponds to our decision function. We try a single global function that can be applied to any GP types, and a series of functions in which each function is customized for different GP type; they are then compared and assessed in 10nm technology.

Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen

2013-11-01

One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine learning-based method for assessing activity quality in smart homes. To validate our approach we quantify activity quality for 179 volunteer participants who performed a complex, interweaved set of activities in our smart home apartment. We observed a statistically significant correlation (r=0.79) between automated assessment of task quality and direct observation scores. Using machine learning techniques to predict the cognitive health of the participants based on task quality is accomplished with an AUC value of 0.64. We believe that this capability is an important step in understanding everyday functional health of individuals in their home environments.

A wearable computing platform for developing cloud-based machine learning models for health monitoring applications.

PubMed

Patel, Shyamal; McGinnis, Ryan S; Silva, Ikaro; DiCristofaro, Steve; Mahadevan, Nikhil; Jortberg, Elise; Franco, Jaime; Martin, Albert; Lust, Joseph; Raj, Milan; McGrane, Bryan; DePetrillo, Paolo; Aranyosi, A J; Ceruolo, Melissa; Pindado, Jesus; Ghaffari, Roozbeh

2016-08-01

Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge. In this paper, we present a novel wearable computing platform for unobtrusive collection of labeled datasets and a new paradigm for continuous development, deployment and evaluation of machine learning models to ensure robust model performance as we transition from the lab to home. Using this system, we train activity classification models across two studies and track changes in model performance as we go from constrained to unconstrained settings.

Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes.

PubMed

Ko, Jina; Bhagwat, Neha; Yee, Stephanie S; Ortiz, Natalia; Sahmoud, Amine; Black, Taylor; Aiello, Nicole M; McKenzie, Lydie; O'Hara, Mark; Redlinger, Colleen; Romeo, Janae; Carpenter, Erica L; Stanger, Ben Z; Issadore, David

2017-11-28

Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

PubMed Central

Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim W.

2016-01-01

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors. PMID:27285611

Computer-Aided TRIZ Ideality and Level of Invention Estimation Using Natural Language Processing and Machine Learning

NASA Astrophysics Data System (ADS)

Adams, Christopher; Tate, Derrick

Patent textual descriptions provide a wealth of information that can be used to understand the underlying design approaches that result in the generation of novel and innovative technology. This article will discuss a new approach for estimating Degree of Ideality and Level of Invention metrics from the theory of inventive problem solving (TRIZ) using patent textual information. Patent text includes information that can be used to model both the functions performed by a design and the associated costs and problems that affect a design’s value. The motivation of this research is to use patent data with calculation of TRIZ metrics to help designers understand which combinations of system components and functions result in creative and innovative design solutions. This article will discuss in detail methods to estimate these TRIZ metrics using natural language processing and machine learning with the use of neural networks.

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.

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

NASA Astrophysics Data System (ADS)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

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.

Machine learning topological states

NASA Astrophysics Data System (ADS)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-03-01

With the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves highly comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

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.

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.

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.

Possible applications of soaring technology to drag reduction in powered general aviation aircraft

NASA Technical Reports Server (NTRS)

Mcmasters, J. H.; Palmer, G. M.

1975-01-01

A brief examination of the performance figures achieved by modern soaring machines and a little reflection on the often huge disparity in L/D values between sailplanes and GA aircraft indicates that careful attention to lessons learned in sailplane design and manufacture hold realistic promise for substantial gains in the aerodynamic efficiency of several GA types.

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

Deep Learning in Medical Imaging: General Overview

PubMed Central

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae

2017-01-01

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. PMID:28670152

Deep Learning in Medical Imaging: General Overview.

PubMed

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

2017-01-01

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

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.

The Deflector Selector: A Machine Learning Framework for Prioritizing Deflection Technology Development

NASA Astrophysics Data System (ADS)

Van Heerden, Elmarie; Erasmus, Nicolas; Greenberg, Adam; Nesvold, Erika; Galache, Jose Luis; Dahlstrom, Eric; Marchis, Franck

2016-10-01

On 15 February, 2013, a ~15 m diameter asteroid entered the Earth's atmosphere over Russia. The resulting shockwave injured nearly 1500 people, and incurred ~33 million (USD) in infrastructure damages. The Chelyabinsk meteor served as a forceful demonstration of the threat posed to Earth by the hundreds of potentially hazardous objects (PHOs) that pass near the Earth every year. Although no objects have yet been discovered on an impact course for Earth, an impact is virtually statistically guaranteed at some point in the future. While many impactor deflection technologies have been proposed, humanity has yet to demonstrate the ability to divert an impactor when one is found. Developing and testing any single proposed technology will require significant research time and funding. This leaves open an obvious question - towards which technologies should funding and research be directed, in order to maximize our preparedness for when an impactor is eventually found?To help answer this question, we have created a detailed framework for analyzing various deflection technologies and their effectiveness. Using an n-body integrator (REBOUND), we have simulated the attempted deflections of a population of Earth-impacting objects with a variety of velocity perturbations (∂Vs), and measured the effects that these perturbations had on impact probability. We then mapped the ∂Vs applied in the orbital simulations to the technologies capable of achieving those perturbations, and analyzed which set of technologies would be most effective at preventing a PHO from impacting the earth. As a final step, we used the results of these simulations to train a machine learning algorithm. This algorithm, combined with a simulated PHO population, can predict which technologies are most likely to be needed. The algorithm can also reveal which impactor observables (mass, spin, orbit, etc.) have the greatest effect on the choice of deflection technology. These results can be used as a tool to inform funding decisions for both deflection technology development and PHO characterization missions.

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.

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Ethoscopes: An open platform for high-throughput ethomics.

PubMed

Geissmann, Quentin; Garcia Rodriguez, Luis; Beckwith, Esteban J; French, Alice S; Jamasb, Arian R; Gilestro, Giorgio F

2017-10-01

Here, we present the use of ethoscopes, which are machines for high-throughput analysis of behavior in Drosophila and other animals. Ethoscopes provide a software and hardware solution that is reproducible and easily scalable. They perform, in real-time, tracking and profiling of behavior by using a supervised machine learning algorithm, are able to deliver behaviorally triggered stimuli to flies in a feedback-loop mode, and are highly customizable and open source. Ethoscopes can be built easily by using 3D printing technology and rely on Raspberry Pi microcomputers and Arduino boards to provide affordable and flexible hardware. All software and construction specifications are available at http://lab.gilest.ro/ethoscope.

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.

Quantum-assisted learning of graphical models with arbitrary pairwise connectivity

NASA Astrophysics Data System (ADS)

Realpe-Gómez, John; Benedetti, Marcello; Biswas, Rupak; Perdomo-Ortiz, Alejandro

Mainstream machine learning techniques rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speedup these tasks. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful machine learning models. Here we show how to surpass this `curse of limited connectivity' bottleneck and illustrate our findings by training probabilistic generative models with arbitrary pairwise connectivity on a real dataset of handwritten digits and two synthetic datasets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding Boltzmann-like distribution. Therefore, the need to infer the effective temperature at each iteration is avoided, speeding up learning, and the effect of noise in the control parameters is mitigated, improving accuracy. This work was supported in part by NASA, AFRL, ODNI, and IARPA.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

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.

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.

Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

PubMed Central

Xu, Junyi; Yao, Li; Li, Le

2015-01-01

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification. PMID:25966359

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

PubMed

Jeong, Doo Seok; Hwang, Cheol Seong

2018-04-18

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant.

PubMed

Mastinu, Enzo; Doguet, Pascal; Botquin, Yohan; Hakansson, Bo; Ortiz-Catalan, Max

2017-08-01

Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

The role of thermoacoustics in the world of commercial cooling

NASA Astrophysics Data System (ADS)

Corey, John A.

2005-09-01

The science of thermoacoustics has been with us for nearly 30 years, but as yet few applications have made their way to the marketplace. Acoustic Stirling cryocoolers (also called pulse-tube Stirling or high-frequency pulse-tube coolers) have been the most successful commercial thermoacoustic devices, because they address a region of the cooling market in terms of temperature and cooling power that is not well served by existing technology. This talk will explore how thermoacoustics might fare in attempting to compete with existing technologies in refrigeration and air conditioning, what niche markets make the most sense as entry points, and how thermoacoustics compares to conventional (kinematic or free-piston) Stirling machines. In particular, why there are relatively few commercial Stirling devices in the marketplace (although Stirling cycle machines have been around for over 150 years) will be discussed, and what lessons learned with Stirlings are applicable to thermoacoustics.

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.

The Human Touch: Practical and Ethical Implications of Putting AI and Robotics to Work for Patients.

PubMed

Banks, Jim

2018-01-01

We live in a time when science fiction can quickly become science fact. Within a generation, the Internet has matured from a technological marvel to a utility, and mobile telephones have redefined how we communicate. Health care, as an industry, is quick to embrace technology, so it is no surprise that the application of programmable robotic systems that can carry out actions automatically and artificial intelligence (AI), e.g., machines that learn, solve problems, and respond to their environment, is being keenly explored.

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.

In vivo quantification of plant starch reserves at micrometer resolution using X-ray microCT imaging and machine learning.

PubMed

Earles, J Mason; Knipfer, Thorsten; Tixier, Aude; Orozco, Jessica; Reyes, Clarissa; Zwieniecki, Maciej A; Brodersen, Craig R; McElrone, Andrew J

2018-03-08

Starch is the primary energy storage molecule used by most terrestrial plants to fuel respiration and growth during periods of limited to no photosynthesis, and its depletion can drive plant mortality. Destructive techniques at coarse spatial scales exist to quantify starch, but these techniques face methodological challenges that can lead to uncertainty about the lability of tissue-specific starch pools and their role in plant survival. Here, we demonstrate how X-ray microcomputed tomography (microCT) and a machine learning algorithm can be coupled to quantify plant starch content in vivo, repeatedly and nondestructively over time in grapevine stems (Vitis spp.). Starch content estimated for xylem axial and ray parenchyma cells from microCT images was correlated strongly with enzymatically measured bulk-tissue starch concentration on the same stems. After validating our machine learning algorithm, we then characterized the spatial distribution of starch concentration in living stems at micrometer resolution, and identified starch depletion in live plants under experimental conditions designed to halt photosynthesis and starch production, initiating the drawdown of stored starch pools. Using X-ray microCT technology for in vivo starch monitoring should enable novel research directed at resolving the spatial and temporal patterns of starch accumulation and depletion in woody plant species. No claim to original US Government works New Phytologist © 2018 New Phytologist Trust.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

PubMed Central

Melo, Carlos Fernando Odir Rodrigues; Navarro, Luiz Claudio; de Oliveira, Diogo Noin; Guerreiro, Tatiane Melina; Lima, Estela de Oliveira; Delafiori, Jeany; Dabaja, Mohamed Ziad; Ribeiro, Marta da Silva; de Menezes, Maico; Rodrigues, Rafael Gustavo Martins; Morishita, Karen Noda; Esteves, Cibele Zanardi; de Amorim, Aline Lopes Lucas; Aoyagui, Caroline Tiemi; Parise, Pierina Lorencini; Milanez, Guilherme Paier; do Nascimento, Gabriela Mansano; Ribas Freitas, André Ricardo; Angerami, Rodrigo; Costa, Fábio Trindade Maranhão; Arns, Clarice Weis; Resende, Mariangela Ribeiro; Amaral, Eliana; Junior, Renato Passini; Ribeiro-do-Valle, Carolina C.; Milanez, Helaine; Moretti, Maria Luiza; Proenca-Modena, Jose Luiz; Avila, Sandra; Rocha, Anderson; Catharino, Rodrigo Ramos

2018-01-01

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a “fingerprint” for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening—faster and more accurate—with improved cost-effectiveness when compared to existing technologies. PMID:29696139

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus.

PubMed

Melo, Carlos Fernando Odir Rodrigues; Navarro, Luiz Claudio; de Oliveira, Diogo Noin; Guerreiro, Tatiane Melina; Lima, Estela de Oliveira; Delafiori, Jeany; Dabaja, Mohamed Ziad; Ribeiro, Marta da Silva; de Menezes, Maico; Rodrigues, Rafael Gustavo Martins; Morishita, Karen Noda; Esteves, Cibele Zanardi; de Amorim, Aline Lopes Lucas; Aoyagui, Caroline Tiemi; Parise, Pierina Lorencini; Milanez, Guilherme Paier; do Nascimento, Gabriela Mansano; Ribas Freitas, André Ricardo; Angerami, Rodrigo; Costa, Fábio Trindade Maranhão; Arns, Clarice Weis; Resende, Mariangela Ribeiro; Amaral, Eliana; Junior, Renato Passini; Ribeiro-do-Valle, Carolina C; Milanez, Helaine; Moretti, Maria Luiza; Proenca-Modena, Jose Luiz; Avila, Sandra; Rocha, Anderson; Catharino, Rodrigo Ramos

2018-01-01

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.

Additive Manufacturing Design Considerations for Liquid Engine Components

NASA Technical Reports Server (NTRS)

Whitten, Dave; Hissam, Andy; Baker, Kevin; Rice, Darron

2014-01-01

The Marshall Space Flight Center's Propulsion Systems Department has gained significant experience in the last year designing, building, and testing liquid engine components using additive manufacturing. The department has developed valve, duct, turbo-machinery, and combustion device components using this technology. Many valuable lessons were learned during this process. These lessons will be the focus of this presentation. We will present criteria for selecting part candidates for additive manufacturing. Some part characteristics are 'tailor made' for this process. Selecting the right parts for the process is the first step to maximizing productivity gains. We will also present specific lessons we learned about feature geometry that can and cannot be produced using additive manufacturing machines. Most liquid engine components were made using a two-step process. The base part was made using additive manufacturing and then traditional machining processes were used to produce the final part. The presentation will describe design accommodations needed to make the base part and lessons we learned about which features could be built directly and which require the final machine process. Tolerance capabilities, surface finish, and material thickness allowances will also be covered. Additive Manufacturing can produce internal passages that cannot be made using traditional approaches. It can also eliminate a significant amount of manpower by reducing part count and leveraging model-based design and analysis techniques. Information will be shared about performance enhancements and design efficiencies we experienced for certain categories of engine parts.

Using a million cell simulation of the cerebellum: network scaling and task generality.

PubMed

Li, Wen-Ke; Hausknecht, Matthew J; Stone, Peter; Mauk, Michael D

2013-11-01

Several factors combine to make it feasible to build computer simulations of the cerebellum and to test them in biologically realistic ways. These simulations can be used to help understand the computational contributions of various cerebellar components, including the relevance of the enormous number of neurons in the granule cell layer. In previous work we have used a simulation containing 12000 granule cells to develop new predictions and to account for various aspects of eyelid conditioning, a form of motor learning mediated by the cerebellum. Here we demonstrate the feasibility of scaling up this simulation to over one million granule cells using parallel graphics processing unit (GPU) technology. We observe that this increase in number of granule cells requires only twice the execution time of the smaller simulation on the GPU. We demonstrate that this simulation, like its smaller predecessor, can emulate certain basic features of conditioned eyelid responses, with a slight improvement in performance in one measure. We also use this simulation to examine the generality of the computation properties that we have derived from studying eyelid conditioning. We demonstrate that this scaled up simulation can learn a high level of performance in a classic machine learning task, the cart-pole balancing task. These results suggest that this parallel GPU technology can be used to build very large-scale simulations whose connectivity ratios match those of the real cerebellum and that these simulations can be used guide future studies on cerebellar mediated tasks and on machine learning problems. Copyright © 2012 Elsevier Ltd. All rights reserved.

Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.

PubMed

Pedretti, G; Milo, V; Ambrogio, S; Carboni, R; Bianchi, S; Calderoni, A; Ramaswamy, N; Spinelli, A S; Ielmini, D

2017-07-13

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10 4 ) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.

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.

Assessing the quality of activities in a smart environment.

PubMed

Cook, Diane J; Schmitter-Edgecombe, M

2009-01-01

Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes. We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity. We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task. Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.

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.

ClearTK 2.0: Design Patterns for Machine Learning in UIMA

PubMed Central

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-01-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework. PMID:29104966

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

PubMed

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

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.

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

An Initial Look at Alternative Computing Technologies for the Intelligence Community

DTIC Science & Technology

2014-01-01

Recommendation (N-1): Guide hardware development with lessons from machine learning and neuroscience . Neuro-inspired computing suffers from a lack...not new to either the government or industry. We have described Google’s approach. The government—most notably The National Security Agency ( NSA ) and...increasing accumulation of knowledge in neuroscience and bio-molecular methods, new computational techniques may become available in the near future

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

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study.

PubMed

Cole, Casey A; Anshari, Dien; Lambert, Victoria; Thrasher, James F; Valafar, Homayoun

2017-12-13

Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events. ©Casey A Cole, Dien Anshari, Victoria Lambert, James F Thrasher, Homayoun Valafar. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 13.12.2017.

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 perspective on intelligent devices and environments in medical rehabilitation.

PubMed

Cooper, Rory A; Dicianno, Brad E; Brewer, Bambi; LoPresti, Edmund; Ding, Dan; Simpson, Richard; Grindle, Garrett; Wang, Hongwu

2008-12-01

Globally, the number of people older than 65 years is anticipated to double between 1997 and 2025, while at the same time the number of people with disabilities is growing at a similar rate, which makes technical advances and social policies critical to attain, prolong, and preserve quality of life. Recent advancements in technology, including computation, robotics, machine learning, communication, and miniaturization of sensors have been used primarily in manufacturing, military, space exploration, and entertainment. However, few efforts have been made to utilize these technologies to enhance the quality of life of people with disabilities. This article offers a perspective of future development in seven emerging areas: translation of research into clinical practice, pervasive assistive technology, cognitive assistive technologies, rehabilitation monitoring and coaching technologies, robotic assisted therapy, and personal mobility and manipulation technology.

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…

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.

Technology-enhanced human interaction in psychotherapy.

PubMed

Imel, Zac E; Caperton, Derek D; Tanana, Michael; Atkins, David C

2017-07-01

Psychotherapy is on the verge of a technology-inspired revolution. The concurrent maturation of communication, signal processing, and machine learning technologies begs an earnest look at how these technologies may be used to improve the quality of psychotherapy. Here, we discuss 3 research domains where technology is likely to have a significant impact: (1) mechanism and process, (2) training and feedback, and (3) technology-mediated treatment modalities. For each domain, we describe current and forthcoming examples of how new technologies may change established applications. Moreover, for each domain we present research questions that touch on theoretical, systemic, and implementation issues. Ultimately, psychotherapy is a decidedly human endeavor, and thus the application of modern technology to therapy must capitalize on-and enhance-our human capacities as counselors, students, and supervisors. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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.

Semantic knowledge for histopathological image analysis: from ontologies to processing portals and deep learning

NASA Astrophysics Data System (ADS)

Kergosien, Yannick L.; Racoceanu, Daniel

2017-11-01

This article presents our vision about the next generation of challenges in computational/digital pathology. The key role of the domain ontology, developed in a sustainable manner (i.e. using reference checklists and protocols, as the living semantic repositories), opens the way to effective/sustainable traceability and relevance feedback concerning the use of existing machine learning algorithms, proven to be very performant in the latest digital pathology challenges (i.e. convolutional neural networks). Being able to work in an accessible web-service environment, with strictly controlled issues regarding intellectual property (image and data processing/analysis algorithms) and medical data/image confidentiality is essential for the future. Among the web-services involved in the proposed approach, the living yellow pages in the area of computational pathology seems to be very important in order to reach an operational awareness, validation, and feasibility. This represents a very promising way to go to the next generation of tools, able to bring more guidance to the computer scientists and confidence to the pathologists, towards an effective/efficient daily use. Besides, a consistent feedback and insights will be more likely to emerge in the near future - from these sophisticated machine learning tools - back to the pathologists-, strengthening, therefore, the interaction between the different actors of a sustainable biomedical ecosystem (patients, clinicians, biologists, engineers, scientists etc.). Beside going digital/computational - with virtual slide technology demanding new workflows-, Pathology must prepare for another coming revolution: semantic web technologies now enable the knowledge of experts to be stored in databases, shared through the Internet, and accessible by machines. Traceability, disambiguation of reports, quality monitoring, interoperability between health centers are some of the associated benefits that pathologists were seeking. However, major changes are also to be expected for the relation of human diagnosis to machine based procedures. Improving on a former imaging platform which used a local knowledge base and a reasoning engine to combine image processing modules into higher level tasks, we propose a framework where different actors of the histopathology imaging world can cooperate using web services - exchanging knowledge as well as imaging services - and where the results of such collaborations on diagnostic related tasks can be evaluated in international challenges such as those recently organized for mitosis detection, nuclear atypia, or tissue architecture in the context of cancer grading. This framework is likely to offer an effective context-guidance and traceability to Deep Learning approaches, with an interesting promising perspective given by the multi-task learning (MTL) paradigm, distinguished by its applicability to several different learning algorithms, its non- reliance on specialized architectures and the promising results demonstrated, in particular towards the problem of weak supervision-, an issue found when direct links from pathology terms in reports to corresponding regions within images are missing.

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.

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.

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.

Correct machine learning on protein sequences: a peer-reviewing perspective.

PubMed

Walsh, Ian; Pollastri, Gianluca; Tosatto, Silvio C E

2016-09-01

Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

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.

Technology of machine tools. Volume 4. Machine tool controls

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

Not Available

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 3. Machine tool mechanics

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

Tlusty, J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 5. Machine tool accuracy

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

Hocken, R.J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

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.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-07-01

With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

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.

Analogy Mapping Development for Learning Programming

NASA Astrophysics Data System (ADS)

Sukamto, R. A.; Prabawa, H. W.; Kurniawati, S.

2017-02-01

Programming skill is an important skill for computer science students, whereas nowadays, there many computer science students are lack of skills and information technology knowledges in Indonesia. This is contrary with the implementation of the ASEAN Economic Community (AEC) since the end of 2015 which is the qualified worker needed. This study provided an effort for nailing programming skills by mapping program code to visual analogies as learning media. The developed media was based on state machine and compiler principle and was implemented in C programming language. The state of every basic condition in programming were successful determined as analogy visualization.

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.

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.

Big Data Bioinformatics

PubMed Central

GREENE, CASEY S.; TAN, JIE; UNG, MATTHEW; MOORE, JASON H.; CHENG, CHAO

2017-01-01

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the “big data” era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both “machine learning” algorithms as well as “unsupervised” and “supervised” examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. PMID:27908398

Big Data Bioinformatics

PubMed Central

GREENE, CASEY S.; TAN, JIE; UNG, MATTHEW; MOORE, JASON H.; CHENG, CHAO

2017-01-01

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the “big data” era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both “machine learning” algorithms as well as “unsupervised” and “supervised” examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. PMID:24799088

Ethoscopes: An open platform for high-throughput ethomics

PubMed Central

Geissmann, Quentin; Garcia Rodriguez, Luis; Beckwith, Esteban J.; French, Alice S.; Jamasb, Arian R.

2017-01-01

Here, we present the use of ethoscopes, which are machines for high-throughput analysis of behavior in Drosophila and other animals. Ethoscopes provide a software and hardware solution that is reproducible and easily scalable. They perform, in real-time, tracking and profiling of behavior by using a supervised machine learning algorithm, are able to deliver behaviorally triggered stimuli to flies in a feedback-loop mode, and are highly customizable and open source. Ethoscopes can be built easily by using 3D printing technology and rely on Raspberry Pi microcomputers and Arduino boards to provide affordable and flexible hardware. All software and construction specifications are available at http://lab.gilest.ro/ethoscope. PMID:29049280

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.

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.

Single-machine group scheduling problems with deteriorating and learning effect

NASA Astrophysics Data System (ADS)

Xingong, Zhang; Yong, Wang; Shikun, Bai

2016-07-01

The concepts of deteriorating jobs and learning effects have been individually studied in many scheduling problems. However, most studies considering the deteriorating and learning effects ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as 'group technology' in the literature. In this paper, a new group scheduling model with deteriorating and learning effects is proposed, where learning effect depends not only on job position, but also on the position of the corresponding job group; deteriorating effect depends on its starting time of the job. This paper shows that the makespan and the total completion time problems remain polynomial optimal solvable under the proposed model. In addition, a polynomial optimal solution is also presented to minimise the maximum lateness problem under certain agreeable restriction.

Technology of machine tools. Volume 2. Machine tool systems management and utilization

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

Thomson, A.R.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Reviewing the connection between speech and obstructive sleep apnea.

PubMed

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

2016-02-20

Sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). The altered UA structure or function in OSA speakers has led to hypothesize the automatic analysis of speech for OSA assessment. In this paper we critically review several approaches using speech analysis and machine learning techniques for OSA detection, and discuss the limitations that can arise when using machine learning techniques for diagnostic applications. A large speech database including 426 male Spanish speakers suspected to suffer OSA and derived to a sleep disorders unit was used to study the clinical validity of several proposals using machine learning techniques to predict the apnea-hypopnea index (AHI) or classify individuals according to their OSA severity. AHI describes the severity of patients' condition. We first evaluate AHI prediction using state-of-the-art speaker recognition technologies: speech spectral information is modelled using supervectors or i-vectors techniques, and AHI is predicted through support vector regression (SVR). Using the same database we then critically review several OSA classification approaches previously proposed. The influence and possible interference of other clinical variables or characteristics available for our OSA population: age, height, weight, body mass index, and cervical perimeter, are also studied. The poor results obtained when estimating AHI using supervectors or i-vectors followed by SVR contrast with the positive results reported by previous research. This fact prompted us to a careful review of these approaches, also testing some reported results over our database. Several methodological limitations and deficiencies were detected that may have led to overoptimistic results. The methodological deficiencies observed after critically reviewing previous research can be relevant examples of potential pitfalls when using machine learning techniques for diagnostic applications. We have found two common limitations that can explain the likelihood of false discovery in previous research: (1) the use of prediction models derived from sources, such as speech, which are also correlated with other patient characteristics (age, height, sex,…) that act as confounding factors; and (2) overfitting of feature selection and validation methods when working with a high number of variables compared to the number of cases. We hope this study could not only be a useful example of relevant issues when using machine learning for medical diagnosis, but it will also help in guiding further research on the connection between speech and OSA.

Classification of Variable Objects in Massive Sky Monitoring Surveys

NASA Astrophysics Data System (ADS)

Woźniak, Przemek; Wyrzykowski, Łukasz; Belokurov, Vasily

2012-03-01

The era of great sky surveys is upon us. Over the past decade we have seen rapid progress toward a continuous photometric record of the optical sky. Numerous sky surveys are discovering and monitoring variable objects by hundreds of thousands. Advances in detector, computing, and networking technology are driving applications of all shapes and sizes ranging from small all sky monitors, through networks of robotic telescopes of modest size, to big glass facilities equipped with giga-pixel CCD mosaics. The Large Synoptic Survey Telescope will be the first peta-scale astronomical survey [18]. It will expand the volume of the parameter space available to us by three orders of magnitude and explore the mutable heavens down to an unprecedented level of sensitivity. Proliferation of large, multidimensional astronomical data sets is stimulating the work on new methods and tools to handle the identification and classification challenge [3]. Given exponentially growing data rates, automated classification of variability types is quickly becoming a necessity. Taking humans out of the loop not only eliminates the subjective nature of visual classification, but is also an enabling factor for time-critical applications. Full automation is especially important for studies of explosive phenomena such as γ-ray bursts that require rapid follow-up observations before the event is over. While there is a general consensus that machine learning will provide a viable solution, the available algorithmic toolbox remains underutilized in astronomy by comparison with other fields such as genomics or market research. Part of the problem is the nature of astronomical data sets that tend to be dominated by a variety of irregularities. Not all algorithms can handle gracefully uneven time sampling, missing features, or sparsely populated high-dimensional spaces. More sophisticated algorithms and better tools available in standard software packages are required to facilitate the adoption of machine learning in astronomy. The goal of this chapter is to show a number of successful applications of state-of-the-art machine learning methodology to time-resolved astronomical data, illustrate what is possible today, and help identify areas for further research and development. After a brief comparison of the utility of various machine learning classifiers, the discussion focuses on support vector machines (SVM), neural nets, and self-organizing maps. Traditionally, to detect and classify transient variability astronomers used ad hoc scan statistics. These methods will remain important as feature extractors for input into generic machine learning algorithms. Experience shows that the performance of machine learning tools on astronomical data critically depends on the definition and quality of the input features, and that a considerable amount of preprocessing is required before standard algorithms can be applied. However, with continued investments of effort by a growing number of astro-informatics savvy computer scientists and astronomers the much-needed expertise and infrastructure are growing faster than ever.

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.

Fuzzy support vector machines for adaptive Morse code recognition.

PubMed

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

Why Johnny can't reengineer health care processes with information technology.

PubMed

Webster, C; McLinden, S; Begler, K

1995-01-01

Many educational institutions are developing curricula that integrate computer and business knowledge and skills concerning a specific industry, such as banking or health care. We have developed a curriculum that emphasizes, equally, medical, computer, and business management concepts. Along the way we confronted a formidable obstacle, namely the domain specificity of the reference disciplines. Knowledge within each domain is sufficiently different from other domains that it reduces the leverage of building on preexisting knowledge and skills. We review this problem from the point of view of cognitive science (in particular, knowledge representation and machine learning) to suggest strategies for coping with incommensurate domain ontologies. These strategies include reflective judgment, implicit learning, abstraction, generalization, analogy, multiple inheritance, project-orientation, selectivity, goal- and failure-driven learning, and case- and story-based learning.

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.

Modeling and Analysis Compute Environments, Utilizing Virtualization Technology in the Climate and Earth Systems Science domain

NASA Astrophysics Data System (ADS)

Michaelis, A.; Nemani, R. R.; Wang, W.; Votava, P.; Hashimoto, H.

2010-12-01

Given the increasing complexity of climate modeling and analysis tools, it is often difficult and expensive to build or recreate an exact replica of the software compute environment used in past experiments. With the recent development of new technologies for hardware virtualization, an opportunity exists to create full modeling, analysis and compute environments that are “archiveable”, transferable and may be easily shared amongst a scientific community or presented to a bureaucratic body if the need arises. By encapsulating and entire modeling and analysis environment in a virtual machine image, others may quickly gain access to the fully built system used in past experiments, potentially easing the task and reducing the costs of reproducing and verify past results produced by other researchers. Moreover, these virtual machine images may be used as a pedagogical tool for others that are interested in performing an academic exercise but don't yet possess the broad expertise required. We built two virtual machine images, one with the Community Earth System Model (CESM) and one with Weather Research Forecast Model (WRF), then ran several small experiments to assess the feasibility, performance overheads costs, reusability, and transferability. We present a list of the pros and cons as well as lessoned learned from utilizing virtualization technology in the climate and earth systems modeling domain.

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.

The NASA SCI Files[TM]: The Case of the Powerful Pulleys. A Lesson Guide with Activities in Mathematics, Science, and Technology.

ERIC Educational Resources Information Center

Ricles, Shannon

This teacher's guide, with accompanying videotape, presents an episode of the NASA SCI Files. In this episode, one of the tree house detectives has had an accident and cannot get into the tree house. Using problem-based learning, the rest of the gang investigates the world of simple machines and physical science and "pull" together to…

Advanced Physiological Estimation of Cognitive Status

DTIC Science & Technology

2011-05-24

Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS fatigue mental workload cognitive status EEG machine learning algorithms Leonard J. Trejo...Information Transfer (NOIT)” • ARO Proposal No. 56469-LS • Three-year basic research with UCLA team 2. “ EEG -guided Input Lateralization and Hemispheric...Activation with Neurofeedback for Display Data Control and Apprehension.” • ARO Proposal No. 59502-LS • One-year Infrastructure technology transfer to

Naval Computer-Based Instruction: Cost, Implementation and Effectiveness Issues.

DTIC Science & Technology

1988-03-01

logical follow on to MITIPAC and are an attempt to use some artificial intelligence (AI) techniques with computer-based training. A good intelligent ...principles of steam plant operation and maintenance. Steamer was written in LISP on a LISP machine in an attempt to use artificial intelligence . "What... Artificial Intelligence and Speech Technology", Electronic Learning, September 1987. Montague, William. E., code 5, Navy Personnel Research and

Data Stream Mining

NASA Astrophysics Data System (ADS)

Gaber, Mohamed Medhat; Zaslavsky, Arkady; Krishnaswamy, Shonali

Data mining is concerned with the process of computationally extracting hidden knowledge structures represented in models and patterns from large data repositories. It is an interdisciplinary field of study that has its roots in databases, statistics, machine learning, and data visualization. Data mining has emerged as a direct outcome of the data explosion that resulted from the success in database and data warehousing technologies over the past two decades (Fayyad, 1997,Fayyad, 1998,Kantardzic, 2003).

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

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.

A fast and precise indoor localization algorithm based on an online sequential extreme learning machine.

PubMed

Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

2015-01-15

Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications

PubMed Central

Moreno-Roldán, José-Miguel; Luque-Nieto, Miguel-Ángel; Poncela, Javier; Otero, Pablo

2017-01-01

Video services are meant to be a fundamental tool in the development of oceanic research. The current technology for underwater networks (UWNs) imposes strong constraints in the transmission capacity since only a severely limited bitrate is available. However, previous studies have shown that the quality of experience (QoE) is enough for ocean scientists to consider the service useful, although the perceived quality can change significantly for small ranges of variation of video parameters. In this context, objective video quality assessment (VQA) methods become essential in network planning and real time quality adaptation fields. This paper presents two specialized models for objective VQA, designed to match the special requirements of UWNs. The models are built upon machine learning techniques and trained with actual user data gathered from subjective tests. Our performance analysis shows how both of them can successfully estimate quality as a mean opinion score (MOS) value and, for the second model, even compute a distribution function for user scores. PMID:28333123

Smart Point Cloud: Definition and Remaining Challenges

NASA Astrophysics Data System (ADS)

Poux, F.; Hallot, P.; Neuville, R.; Billen, R.

2016-10-01

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.

Exploring Deep Learning and Sparse Matrix Format Selection

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

Zhao, Y.; Liao, C.; Shen, X.

We proposed to explore the use of Deep Neural Networks (DNN) for addressing the longstanding barriers. The recent rapid progress of DNN technology has created a large impact in many fields, which has significantly improved the prediction accuracy over traditional machine learning techniques in image classifications, speech recognitions, machine translations, and so on. To some degree, these tasks resemble the decision makings in many HPC tasks, including the aforementioned format selection for SpMV and linear solver selection. For instance, sparse matrix format selection is akin to image classification—such as, to tell whether an image contains a dog or a cat;more » in both problems, the right decisions are primarily determined by the spatial patterns of the elements in an input. For image classification, the patterns are of pixels, and for sparse matrix format selection, they are of non-zero elements. DNN could be naturally applied if we regard a sparse matrix as an image and the format selection or solver selection as classification problems.« less

Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.

PubMed

Shouval, R; Bondi, O; Mishan, H; Shimoni, A; Unger, R; Nagler, A

2014-03-01

Data collected from hematopoietic SCT (HSCT) centers are becoming more abundant and complex owing to the formation of organized registries and incorporation of biological data. Typically, conventional statistical methods are used for the development of outcome prediction models and risk scores. However, these analyses carry inherent properties limiting their ability to cope with large data sets with multiple variables and samples. Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM). It enables prediction in complex data scenarios, familiar to practitioners and researchers. Technological and commercial applications are all around us, gradually entering clinical research. In the following review, we would like to expose hematologists and stem cell transplanters to the concepts, clinical applications, strengths and limitations of such methods and discuss current research in HSCT. The aim of this review is to encourage utilization of the ML and DM techniques in the field of HSCT, including prediction of transplantation outcome and donor selection.

Big Data in Public Health: Terminology, Machine Learning, and Privacy.

PubMed

Mooney, Stephen J; Pejaver, Vikas

2018-04-01

The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

Wind energy education projects. Final report

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

Ziegler, P.; Conlon, T.R.; Arcadi, T.

Two projects under DOE's Small-Scale Appropriate Energy Technology Grants Program have educated the public in a hands on way about wind energy systems. The first was awarded to Peter Ziegler of Berkeley, California, to design and build a walk-through exhibition structure powered by an adjoining wind-generator. This Wind Energy Pavilion was erected at Fort Funston in the Golden Gate National Recreation Area. It currently serves both as an enclosure for batteries and a variety of monitoring instruments, and as a graphic environment where the public can learn about wind energy. The second project, entitled Wind and Kid Power, involved anmore » educational program for a classroom of first through third grades in the Vallejo, Unified School District. The students studied weather, measured wind speeds and built small models of wind machines. They also built a weather station, and learned to use weather instruments. The grant funds enabled them to actually build and erect a Savonius wind machine at the Loma Vista Farm School.« less

Clustering Single-Cell Expression Data Using Random Forest Graphs.

PubMed

Pouyan, Maziyar Baran; Nourani, Mehrdad

2017-07-01

Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as single-cell cytometry provides researchers access to valuable biological data. Applying machine-learning techniques to these high-throughput datasets provides deep insights into the cellular landscape of the tissue where those cells are a part of. In this paper, we propose the use of random-forest-based single-cell profiling, a new machine-learning-based technique, to profile different cell types of intricate tissues using single-cell cytometry data. Our technique utilizes random forests to capture cell marker dependences and model the cellular populations using the cell network concept. This cellular network helps us discover what cell types are in the tissue. Our experimental results on public-domain datasets indicate promising performance and accuracy of our technique in extracting cell populations of complex tissues.

[Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

PubMed

Liu, Guo-hai; Jiang, Hui; Xiao, Xia-hong; Zhang, Dong-juan; Mei, Cong-li; Ding, Yu-han

2012-04-01

Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000-4 000 cm(-1), and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 1040-1 topology construction as follows: R(p) = 0.961 8 and RMSEP = 0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.

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.

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

2016-08-01

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an instance-dependent effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to k -step contrastive divergence (CD-k ) with k up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one-step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here.