WorldWideScience

Sample records for machine learning systems

  1. Machine learning systems

    Energy Technology Data Exchange (ETDEWEB)

    Forsyth, R

    1984-05-01

    With the dramatic rise of expert systems has come a renewed interest in the fuel that drives them-knowledge. For it is specialist knowledge which gives expert systems their power. But extracting knowledge from human experts in symbolic form has proved arduous and labour-intensive. So the idea of machine learning is enjoying a renaissance. Machine learning is any automatic improvement in the performance of a computer system over time, as a result of experience. Thus a learning algorithm seeks to do one or more of the following: cover a wider range of problems, deliver more accurate solutions, obtain answers more cheaply, and simplify codified knowledge. 6 references.

  2. Machine vision systems using machine learning for industrial product inspection

    Science.gov (United States)

    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.

  3. Building machine learning systems with Python

    CERN Document Server

    Richert, Willi

    2013-01-01

    This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro

  4. Status Checking System of Home Appliances using machine learning

    Directory of Open Access Journals (Sweden)

    Yoon Chi-Yurl

    2017-01-01

    Full Text Available This paper describes status checking system of home appliances based on machine learning, which can be applied to existing household appliances without networking function. Designed status checking system consists of sensor modules, a wireless communication module, cloud server, android application and a machine learning algorithm. The developed system applied to washing machine analyses and judges the four-kinds of appliance’s status such as staying, washing, rinsing and spin-drying. The measurements of sensor and transmission of sensing data are operated on an Arduino board and the data are transmitted to cloud server in real time. The collected data are parsed by an Android application and injected into the machine learning algorithm for learning the status of the appliances. The machine learning algorithm compares the stored learning data with collected real-time data from the appliances. Our results are expected to contribute as a base technology to design an automatic control system based on machine learning technology for household appliances in real-time.

  5. Reinforcement and Systemic Machine Learning for Decision Making

    CERN Document Server

    Kulkarni, Parag

    2012-01-01

    Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available-or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm-creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new an

  6. Quantum machine learning.

    Science.gov (United States)

    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.

  7. Machine learning paradigms applications in recommender systems

    CERN Document Server

    Lampropoulos, Aristomenis S

    2015-01-01

    This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and ...

  8. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    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 Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  9. Machine Learning.

    Science.gov (United States)

    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)

  10. Soft computing in machine learning

    CERN Document Server

    Park, Jooyoung; Inoue, Atsushi

    2014-01-01

    As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It...

  11. An Interactive Web-based Learning System for Assisting Machining Technology Education

    Directory of Open Access Journals (Sweden)

    Min Jou

    2008-05-01

    Full Text Available The key technique of manufacturing methods is machining. The degree of technique of machining directly affects the quality of the product. Therefore, the machining technique is of primary importance in promoting student practice ability during the training process. Currently, practical training is applied in shop floor to discipline student’s practice ability. Much time and cost are used to teach these techniques. Particularly, computerized machines are continuously increasing in use. The development of educating engineers on computerized machines becomes much more difficult than with traditional machines. This is because of the limitation of the extremely expensive cost of teaching. The quality and quantity of teaching cannot always be promoted in this respect. The traditional teaching methods can not respond well to the needs of the future. Therefore, this research aims to the following topics; (1.Propose the teaching strategies for the students to learning machining processing planning through web-based learning system. (2.Establish on-line teaching material for the computer-aided manufacturing courses including CNC coding method, CNC simulation. (3.Develop the virtual machining laboratory to bring the machining practical training to web-based learning system. (4.Integrate multi-media and virtual laboratory in the developed e-learning web-based system to enhance the effectiveness of machining education through web-based system.

  12. Human Machine Learning Symbiosis

    Science.gov (United States)

    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…

  13. Machine Learning and Radiology

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M.

    2012-01-01

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

  14. Machine Learning Method Applied in Readout System of Superheated Droplet Detector

    Science.gov (United States)

    Liu, Yi; Sullivan, Clair Julia; d'Errico, Francesco

    2017-07-01

    Direct readability is one advantage of superheated droplet detectors in neutron dosimetry. Utilizing such a distinct characteristic, an imaging readout system analyzes image of the detector for neutron dose readout. To improve the accuracy and precision of algorithms in the imaging readout system, machine learning algorithms were developed. Deep learning neural network and support vector machine algorithms are applied and compared with generally used Hough transform and curvature analysis methods. The machine learning methods showed a much higher accuracy and better precision in recognizing circular gas bubbles.

  15. Machine learning techniques for optical communication system optimization

    DEFF Research Database (Denmark)

    Zibar, Darko; Wass, Jesper; Thrane, Jakob

    In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction.......In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction....

  16. Machine learning and radiology.

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M

    2012-07-01

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

  17. Machine learning strategies for systems with invariance properties

    Science.gov (United States)

    Ling, Julia; Jones, Reese; Templeton, Jeremy

    2016-08-01

    In many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds Averaged Navier Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high performance computing has led to a growing availability of high fidelity simulation data. These data open up the possibility of using machine learning algorithms, such as random forests or neural networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these empirical models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first method, a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance at significantly reduced computational training costs.

  18. Which Management Control System principles and aspects are relevant when deploying a learning machine?

    OpenAIRE

    Martin, Johansson; Mikael, Göthager

    2017-01-01

    How shall a business adapt its management control systems when learning machines enter the arena? Will the control system continue to focus on humans aspects and continue to consider a learning machine to be an automation tool as any other historically programmed computer? Learning machines introduces productivity capabilities that achieve very high levels of efficiency and quality. A learning machine can sort through large amounts of data and make conclusions difficult by a human mind. Howev...

  19. Game-powered machine learning.

    Science.gov (United States)

    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.

  20. Machine Learning for Medical Imaging.

    Science.gov (United States)

    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.

  1. MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

    OpenAIRE

    Jerzy Balicki; Waldemar Korłub

    2017-01-01

    In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because ...

  2. Machine learning an artificial intelligence approach

    CERN Document Server

    Banerjee, R; Bradshaw, Gary; Carbonell, Jaime Guillermo; Mitchell, Tom Michael; Michalski, Ryszard Spencer

    1983-01-01

    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV a

  3. MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2017-03-01

    Full Text Available In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because some software applications can be automatically constructed by genetic programming. To obtain a competitive advantage, machine learning can be used for the management of self-organizing cloud computing systems performing calculations for business. Also the use of selected economic self-organizing distributed systems has been described, including some testing methods of predicting borrower reliability. Finally, some conclusions and directions for further research have been proposed.

  4. TensorFlow: A system for large-scale machine learning

    OpenAIRE

    Abadi, Martín; Barham, Paul; Chen, Jianmin; Chen, Zhifeng; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Irving, Geoffrey; Isard, Michael; Kudlur, Manjunath; Levenberg, Josh; Monga, Rajat; Moore, Sherry; Murray, Derek G.

    2016-01-01

    TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexib...

  5. Creativity in Machine Learning

    OpenAIRE

    Thoma, Martin

    2016-01-01

    Recent machine learning techniques can be modified to produce creative results. Those results did not exist before; it is not a trivial combination of the data which was fed into the machine learning system. The obtained results come in multiple forms: As images, as text and as audio. This paper gives a high level overview of how they are created and gives some examples. It is meant to be a summary of the current work and give people who are new to machine learning some starting points.

  6. 1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0

    CERN Document Server

    Beyerer, Jürgen

    2016-01-01

    The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

  7. Building machine learning systems with Python

    CERN Document Server

    Coelho, Luis Pedro

    2015-01-01

    This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.

  8. Emerging Paradigms in Machine Learning

    CERN Document Server

    Jain, Lakhmi; Howlett, Robert

    2013-01-01

    This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary ...

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

    Science.gov (United States)

    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.

  10. Adaptive Learning Systems: Beyond Teaching Machines

    Science.gov (United States)

    Kara, Nuri; Sevim, Nese

    2013-01-01

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

  11. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  12. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    OpenAIRE

    Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael

    2016-01-01

    TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algo...

  13. Extreme learning machines 2013 algorithms and applications

    CERN Document Server

    Toh, Kar-Ann; Romay, Manuel; Mao, Kezhi

    2014-01-01

    In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.   This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discu...

  14. Bypassing the Kohn-Sham equations with machine learning.

    Science.gov (United States)

    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.

  15. Rule based systems for big data a machine learning approach

    CERN Document Server

    Liu, Han; Cocea, Mihaela

    2016-01-01

    The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

  16. Teaching machine learning to design students

    NARCIS (Netherlands)

    Vlist, van der B.J.J.; van de Westelaken, H.F.M.; Bartneck, C.; Hu, J.; Ahn, R.M.C.; Barakova, E.I.; Delbressine, F.L.M.; Feijs, L.M.G.; Pan, Z.; Zhang, X.; El Rhalibi, A.

    2008-01-01

    Machine learning is a key technology to design and create intelligent systems, products, and related services. Like many other design departments, we are faced with the challenge to teach machine learning to design students, who often do not have an inherent affinity towards technology. We

  17. Machine Learning Control For Highly Reconfigurable High-Order Systems

    Science.gov (United States)

    2015-01-02

    calibration and applications,” Mechatronics and Embedded Systems and Applications (MESA), 2010 IEEE/ASME International Conference on, IEEE, 2010, pp. 38–43...AFRL-OSR-VA-TR-2015-0012 MACHINE LEARNING CONTROL FOR HIGHLY RECONFIGURABLE HIGH-ORDER SYSTEMS John Valasek TEXAS ENGINEERING EXPERIMENT STATION...DIMENSIONAL RECONFIGURABLE SYSTEMS FA9550-11-1-0302 Period of Performance 1 July 2011 – 29 September 2014 John Valasek Aerospace Engineering

  18. Considerations upon the Machine Learning Technologies

    OpenAIRE

    Alin Munteanu; Cristina Ofelia Sofran

    2006-01-01

    Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

  19. Considerations upon the Machine Learning Technologies

    Directory of Open Access Journals (Sweden)

    Alin Munteanu

    2006-01-01

    Full Text Available Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

  20. Can machine learning explain human learning?

    NARCIS (Netherlands)

    Vahdat, M.; Oneto, L.; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

    2016-01-01

    Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is

  1. Introduction to machine learning.

    Science.gov (United States)

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

  2. Intellectual Property and Machine Learning: An exploratory study

    OpenAIRE

    Øverlier, Lasse

    2017-01-01

    Our research makes a contribution by exemplifying what controls the freedom-to-operate for a company operating in the area of machine learning. Through interviews we demonstrate the industry’s alternating viewpoints to whether copyrighted data used as input to machine learning systems should be viewed differently than copying the data for storage or reproduction. In addition we show that unauthorized use of copyrighted data in machine learning systems is hard to detect with the burden of proo...

  3. Machine learning concepts in coherent optical communication systems

    DEFF Research Database (Denmark)

    Zibar, Darko; Schäffer, Christian G.

    2014-01-01

    Powerful statistical signal processing methods, used by the machine learning community, are addressed and linked to current problems in coherent optical communication. Bayesian filtering methods are presented and applied for nonlinear dynamic state tracking. © 2014 OSA.......Powerful statistical signal processing methods, used by the machine learning community, are addressed and linked to current problems in coherent optical communication. Bayesian filtering methods are presented and applied for nonlinear dynamic state tracking. © 2014 OSA....

  4. Active learning machine learns to create new quantum experiments.

    Science.gov (United States)

    Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

    2018-02-06

    How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

  5. An Android malware detection system based on machine learning

    Science.gov (United States)

    Wen, Long; Yu, Haiyang

    2017-08-01

    The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

  6. Simplify and Accelerate Earth Science Data Preparation to Systemize Machine Learning

    Science.gov (United States)

    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

  7. Research on the proficient machine system. Theoretical part; Jukutatsu machine system no chosa kenkyu. Rironhen

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-03-01

    The basic theory of the proficient machine system to be developed was studied. Important proficient techniques in manufacturing industries are becoming extinct because of insufficient succession to next generation. The proficient machine system was proposed to cope with such situation. This machine system includes the mechanism for progress and evolution of techniques and sensibilities to be adaptable to environmental changes by learning and recognizing various motions such as work and process. Consequently, the basic research fields are composed of thought, learning, perception and action. This machine requires not only deigned fixed functions but also introduction of the same proficient concept as human being to be adaptable to changes in situation, purpose, time and machine`s complexity. This report explains in detail the basic concept, system principle, approaching procedure and practical elemental technologies of the proficient machine system, and also describes the future prospect. 133 refs., 110 figs., 7 tabs.

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

    Science.gov (United States)

    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 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.

  9. A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

    Science.gov (United States)

    Frohlich, Holger; Claes, Kasper; De Wolf, Catherine; Van Damme, Xavier; Michel, Anne

    2018-05-01

    Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.

  10. Machine learning with R

    CERN Document Server

    Lantz, Brett

    2013-01-01

    Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

  11. Mastering machine learning with scikit-learn

    CERN Document Server

    Hackeling, Gavin

    2014-01-01

    If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.

  12. Machine learning algorithms for the creation of clinical healthcare enterprise systems

    Science.gov (United States)

    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.

  13. Man Machine Systems in Education.

    Science.gov (United States)

    Sall, Malkit S.

    This review of the research literature on the interaction between humans and computers discusses how man machine systems can be utilized effectively in the learning-teaching process, especially in secondary education. Beginning with a definition of man machine systems and comments on the poor quality of much of the computer-based learning material…

  14. Learning scikit-learn machine learning in Python

    CERN Document Server

    Garreta, Raúl

    2013-01-01

    The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

  15. Machine Learning and Conflict Prediction: A Use Case

    Directory of Open Access Journals (Sweden)

    Chris Perry

    2013-10-01

    Full Text Available For at least the last two decades, the international community in general and the United Nations specifically have attempted to develop robust, accurate and effective conflict early warning system for conflict prevention. One potential and promising component of integrated early warning systems lies in the field of machine learning. This paper aims at giving conflict analysis a basic understanding of machine learning methodology as well as to test the feasibility and added value of such an approach. The paper finds that the selection of appropriate machine learning methodologies can offer substantial improvements in accuracy and performance. It also finds that even at this early stage in testing machine learning on conflict prediction, full models offer more predictive power than simply using a prior outbreak of violence as the leading indicator of current violence. This suggests that a refined data selection methodology combined with strategic use of machine learning algorithms could indeed offer a significant addition to the early warning toolkit. Finally, the paper suggests a number of steps moving forward to improve upon this initial test methodology.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Qinghua Huang

    2018-01-01

    Full Text Available 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.

  18. Clinical quality needs complex adaptive systems and machine learning.

    Science.gov (United States)

    Marsland, Stephen; Buchan, Iain

    2004-01-01

    The vast increase in clinical data has the potential to bring about large improvements in clinical quality and other aspects of healthcare delivery. However, such benefits do not come without cost. The analysis of such large datasets, particularly where the data may have to be merged from several sources and may be noisy and incomplete, is a challenging task. Furthermore, the introduction of clinical changes is a cyclical task, meaning that the processes under examination operate in an environment that is not static. We suggest that traditional methods of analysis are unsuitable for the task, and identify complexity theory and machine learning as areas that have the potential to facilitate the examination of clinical quality. By its nature the field of complex adaptive systems deals with environments that change because of the interactions that have occurred in the past. We draw parallels between health informatics and bioinformatics, which has already started to successfully use machine learning methods.

  19. A Teaching System To Learn Programming: the Programmer's Learning Machine

    OpenAIRE

    Quinson , Martin; Oster , Gérald

    2015-01-01

    International audience; The Programmer's Learning Machine (PLM) is an interactive exerciser for learning programming and algorithms. Using an integrated and graphical environment that provides a short feedback loop, it allows students to learn in a (semi)-autonomous way. This generic platform also enables teachers to create specific programming microworlds that match their teaching goals. This paper discusses our design goals and motivations, introduces the existing material and the proposed ...

  20. Machine learning techniques applied to system characterization and equalization

    DEFF Research Database (Denmark)

    Zibar, Darko; Thrane, Jakob; Wass, Jesper

    2016-01-01

    Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.......Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals....

  1. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  2. Machine Learning-based Intelligent Formal Reasoning and Proving System

    Science.gov (United States)

    Chen, Shengqing; Huang, Xiaojian; Fang, Jiaze; Liang, Jia

    2018-03-01

    The reasoning system can be used in many fields. How to improve reasoning efficiency is the core of the design of system. Through the formal description of formal proof and the regular matching algorithm, after introducing the machine learning algorithm, the system of intelligent formal reasoning and verification has high efficiency. The experimental results show that the system can verify the correctness of propositional logic reasoning and reuse the propositional logical reasoning results, so as to obtain the implicit knowledge in the knowledge base and provide the basic reasoning model for the construction of intelligent system.

  3. BEBP: An Poisoning Method Against Machine Learning Based IDSs

    OpenAIRE

    Li, Pan; Liu, Qiang; Zhao, Wentao; Wang, Dongxu; Wang, Siqi

    2018-01-01

    In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning...

  4. Introduction to machine learning

    OpenAIRE

    Baştanlar, Yalın; Özuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning app...

  5. Deep learning: Using machine learning to study biological vision

    OpenAIRE

    Majaj, Najib; Pelli, Denis

    2017-01-01

    Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions.

  6. Machine Learning Phases of Strongly Correlated Fermions

    Directory of Open Access Journals (Sweden)

    Kelvin Ch’ng

    2017-08-01

    Full Text Available Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated fermions on cubic lattices. We show that a three-dimensional convolutional network trained on auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model can correctly predict the magnetic phase diagram of the model at the average density of one (half filling. We then use the network, trained at half filling, to explore the trend in the transition temperature as the system is doped away from half filling. This transfer learning approach predicts that the instability to the magnetic phase extends to at least 5% doping in this region. Our results pave the way for other machine learning applications in correlated quantum many-body systems.

  7. Machine learning methods for planning

    CERN Document Server

    Minton, Steven

    1993-01-01

    Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

  8. Machine learning and medical imaging

    CERN Document Server

    Shen, Dinggang; Sabuncu, Mert

    2016-01-01

    Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, a...

  9. Machine learning in heart failure: ready for prime time.

    Science.gov (United States)

    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.

  10. Machine Learning for Hackers

    CERN Document Server

    Conway, Drew

    2012-01-01

    If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyz

  11. Machine Learning in Production Systems Design Using Genetic Algorithms

    OpenAIRE

    Abu Qudeiri Jaber; Yamamoto Hidehiko Rizauddin Ramli

    2008-01-01

    To create a solution for a specific problem in machine learning, the solution is constructed from the data or by use a search method. Genetic algorithms are a model of machine learning that can be used to find nearest optimal solution. While the great advantage of genetic algorithms is the fact that they find a solution through evolution, this is also the biggest disadvantage. Evolution is inductive, in nature life does not evolve towards a good solution but it evolves aw...

  12. Model-based machine learning.

    Science.gov (United States)

    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.

  13. Model-Agnostic Interpretability of Machine Learning

    OpenAIRE

    Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

    2016-01-01

    Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred f...

  14. Simulation-driven machine learning: Bearing fault classification

    Science.gov (United States)

    Sobie, Cameron; Freitas, Carina; Nicolai, Mike

    2018-01-01

    Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

  15. Student Modeling and Machine Learning

    OpenAIRE

    Sison , Raymund; Shimura , Masamichi

    1998-01-01

    After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

  16. Machine Learning for Security

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Applied statistics, aka ‘Machine Learning’, offers a wealth of techniques for answering security questions. It’s a much hyped topic in the big data world, with many companies now providing machine learning as a service. This talk will demystify these techniques, explain the math, and demonstrate their application to security problems. The presentation will include how-to’s on classifying malware, looking into encrypted tunnels, and finding botnets in DNS data. About the speaker Josiah is a security researcher with HP TippingPoint DVLabs Research Group. He has over 15 years of professional software development experience. Josiah used to do AI, with work focused on graph theory, search, and deductive inference on large knowledge bases. As rules only get you so far, he moved from AI to using machine learning techniques identifying failure modes in email traffic. There followed digressions into clustered data storage and later integrated control systems. Current ...

  17. Online transfer learning with extreme learning machine

    Science.gov (United States)

    Yin, Haibo; Yang, Yun-an

    2017-05-01

    In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

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

    Science.gov (United States)

    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.

  19. Microsoft Azure machine learning

    CERN Document Server

    Mund, Sumit

    2015-01-01

    The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.

  20. MLBCD: a machine learning tool for big clinical data.

    Science.gov (United States)

    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.

  1. IoT Security Techniques Based on Machine Learning

    OpenAIRE

    Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

    2018-01-01

    Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

  2. Machine learning with R cookbook

    CERN Document Server

    Chiu, Yu-Wei

    2015-01-01

    If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

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

    Science.gov (United States)

    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.

  4. Learning Machines Implemented on Non-Deterministic Hardware

    OpenAIRE

    Gupta, Suyog; Sindhwani, Vikas; Gopalakrishnan, Kailash

    2014-01-01

    This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementations. The hardware/software co-design methodology advocated here hinges on the deployment of compute-intensive machine learning kernels onto compute platforms that trade-off deter...

  5. Machine Learning Optimization of Evolvable Artificial Cells

    DEFF Research Database (Denmark)

    Caschera, F.; Rasmussen, S.; Hanczyc, M.

    2011-01-01

    can be explored. A machine learning approach (Evo-DoE) could be applied to explore this experimental space and define optimal interactions according to a specific fitness function. Herein an implementation of an evolutionary design of experiments to optimize chemical and biochemical systems based...... on a machine learning process is presented. The optimization proceeds over generations of experiments in iterative loop until optimal compositions are discovered. The fitness function is experimentally measured every time the loop is closed. Two examples of complex systems, namely a liposomal drug formulation...

  6. On the Use of Machine Learning for Identifying Botnet Network Traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus...... contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems....

  7. Scikit-learn: Machine Learning in Python

    OpenAIRE

    Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu

    2011-01-01

    International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic ...

  8. Scikit-learn: Machine Learning in Python

    OpenAIRE

    Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Louppe, Gilles; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu

    2012-01-01

    Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings....

  9. Interactive Algorithms for Unsupervised Machine Learning

    Science.gov (United States)

    2015-06-01

    in Neural Information Processing Systems, 2013. 14 [3] Louigi Addario-Berry, Nicolas Broutin, Luc Devroye, and Gábor Lugosi. On combinato- rial...Myung Jin Choi, Vincent Y F Tan , Animashree Anandkumar, and Alan S Willsky. Learn- ing Latent Tree Graphical Models. Journal of Machine Learning

  10. Machine Learning Based Localization and Classification with Atomic Magnetometers

    Science.gov (United States)

    Deans, Cameron; Griffin, Lewis D.; Marmugi, Luca; Renzoni, Ferruccio

    2018-01-01

    We demonstrate identification of position, material, orientation, and shape of objects imaged by a Rb 85 atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information extracted from the images created by the magnetometer, demonstrating the use of hidden data. Localization 2.6 times better than the spatial resolution of the imaging system and successful classification up to 97% are obtained. This circumvents the need of solving the inverse problem and demonstrates the extension of machine learning to diffusive systems, such as low-frequency electrodynamics in media. Automated collection of task-relevant information from quantum-based electromagnetic imaging will have a relevant impact from biomedicine to security.

  11. Quantum Machine Learning

    OpenAIRE

    Romero García, Cristian

    2017-01-01

    [EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

  12. Machine learning and complex-network for personalized and systems biomedicine

    KAUST Repository

    Cannistraci, Carlo Vittorio

    2016-01-27

    The talk will begin with an introduction on using machine learning to discover hidden information and unexpected patterns in large biomedical datasets. Then, recent results on the use of complex network theory in biomedicine and neuroscience will be discussed. In particular, metagenomics and metabolomics data, approaches for drug-target repositioning, functional/structural MR connectomes and gut-brain axis data will be presented. The conclusion will outline the novel and exciting perspectives offered by the translation of these methods from systems biology to systems medicine.

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

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-11

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

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

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-01

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

  15. An efficient flow-based botnet detection using supervised machine learning

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2014-01-01

    Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

  16. Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

    Science.gov (United States)

    Chen, Wei-Hsin; Hsieh, Sheau-Ling; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

    2013-05-23

    A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as

  17. Application of Machine Learning Techniques in Aquaculture

    OpenAIRE

    Rahman, Akhlaqur; Tasnim, Sumaira

    2014-01-01

    In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.

  18. An active role for machine learning in drug development

    Science.gov (United States)

    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

  19. Machine Learning and Applied Linguistics

    OpenAIRE

    Vajjala, Sowmya

    2018-01-01

    This entry introduces the topic of machine learning and provides an overview of its relevance for applied linguistics and language learning. The discussion will focus on giving an introduction to the methods and applications of machine learning in applied linguistics, and will provide references for further study.

  20. TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

    OpenAIRE

    Tang, Yuan

    2016-01-01

    TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning t...

  1. New Applications of Learning Machines

    DEFF Research Database (Denmark)

    Larsen, Jan

    * Machine learning framework for sound search * Genre classification * Music separation * MIMO channel estimation and symbol detection......* Machine learning framework for sound search * Genre classification * Music separation * MIMO channel estimation and symbol detection...

  2. Machine-Learning Research

    OpenAIRE

    Dietterich, Thomas G.

    1997-01-01

    Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.

  3. An introduction to machine learning with Scikit-Learn

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    This tutorial gives an introduction to the scientific ecosystem for data analysis and machine learning in Python. After a short introduction of machine learning concepts, we will demonstrate on High Energy Physics data how a basic supervised learning analysis can be carried out using the Scikit-Learn library. Topics covered include data loading facilities and data representation, supervised learning algorithms, pipelines, model selection and evaluation, and model introspection.

  4. Recent Advances in Predictive (Machine) Learning

    Energy Technology Data Exchange (ETDEWEB)

    Friedman, J

    2004-01-24

    Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

  5. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  6. Machine learning in healthcare informatics

    CERN Document Server

    Acharya, U; Dua, Prerna

    2014-01-01

    The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

  7. ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

    Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

  8. Classifying smoking urges via machine learning.

    Science.gov (United States)

    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

  9. Addressing uncertainty in atomistic machine learning

    DEFF Research Database (Denmark)

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

    2017-01-01

    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 predi......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...

  10. Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

    Science.gov (United States)

    Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

    2017-06-01

    Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

  11. Machine learning with R

    CERN Document Server

    Lantz, Brett

    2015-01-01

    Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

  12. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  13. A Machine Learning-based Rainfall System for GPM Dual-frequency Radar

    Science.gov (United States)

    Tan, H.; Chandrasekar, V.; Chen, H.

    2017-12-01

    Precipitation measurement produced by the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) plays an important role in researching the water circle and forecasting extreme weather event. Compare with its predecessor - Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), GRM DPR measures precipitation in two different frequencies (i.e., Ku and Ka band), which can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This paper presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train GPM DPR data in order to get space based rainfall product. Therein, data alignment between space DPR and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of GPM DPR observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar - 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the GPM standard products

  14. Machine Learning an algorithmic perspective

    CERN Document Server

    Marsland, Stephen

    2009-01-01

    Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le

  15. Reverse hypothesis machine learning a practitioner's perspective

    CERN Document Server

    Kulkarni, Parag

    2017-01-01

    This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as ...

  16. IDOCS: intelligent distributed ontology consensus system--the use of machine learning in retinal drusen phenotyping.

    Science.gov (United States)

    Thomas, George; Grassi, Michael A; Lee, John R; Edwards, Albert O; Gorin, Michael B; Klein, Ronald; Casavant, Thomas L; Scheetz, Todd E; Stone, Edwin M; Williams, Andrew B

    2007-05-01

    To use the power of knowledge acquisition and machine learning in the development of a collaborative computer classification system based on the features of age-related macular degeneration (AMD). A vocabulary was acquired from four AMD experts who examined 100 ophthalmoscopic images. The vocabulary was analyzed, hierarchically structured, and incorporated into a collaborative computer classification system called IDOCS. Using this system, three of the experts examined images from a second set of digital images compiled from more than 1000 patients with AMD. Images were annotated, and features were identified and defined. Decision trees, a machine learning method, were trained on the data collected and used to extract patterns. Interrelationships between the data from the different clinicians were investigated. Six drusen classes in the structured vocabulary were largely sufficient to describe all the identified features. The decision trees classified the data with 76.86% to 88.5% accuracy and distilled patterns in the form of hierarchical trees composed of 5 to 15 nodes. Experts were largely consistent in their characterization of soft, and to a lesser extent, hard drusen, but diverge in definition of other drusen. Size and crystalline morphology were the main determinants of drusen type across all experts. Machine learning is a powerful tool for the characterization of disease phenotypes. The creation of a defined feature set for AMD will facilitate the development of an IDOCS-based classification system.

  17. Machine learning for evolution strategies

    CERN Document Server

    Kramer, Oliver

    2016-01-01

    This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

  18. Machine Learning wins the Higgs Challenge

    CERN Multimedia

    Abha Eli Phoboo

    2014-01-01

    The winner of the four-month-long Higgs Machine Learning Challenge, launched on 12 May, is Gábor Melis from Hungary, followed closely by Tim Salimans from the Netherlands and Pierre Courtiol from France. The challenge explored the potential of advanced machine learning methods to improve the significance of the Higgs discovery.   Winners of the Higgs Machine Learning Challenge: Gábor Melis and Tim Salimans (top row), Tianqi Chen and Tong He (bottom row). Participants in the Higgs Machine Learning Challenge were tasked with developing an algorithm to improve the detection of Higgs boson signal events decaying into two tau particles in a sample of simulated ATLAS data* that contains few signal and a majority of non-Higgs boson “background” events. No knowledge of particle physics was required for the challenge but skills in machine learning - the training of computers to recognise patterns in data – were essential. The Challenge, hosted by Ka...

  19. Machine Learning for Robotic Vision

    OpenAIRE

    Drummond, Tom

    2018-01-01

    Machine learning is a crucial enabling technology for robotics, in particular for unlocking the capabilities afforded by visual sensing. This talk will present research within Prof Drummond’s lab that explores how machine learning can be developed and used within the context of Robotic Vision.

  20. Machine learning in virtual screening.

    Science.gov (United States)

    Melville, James L; Burke, Edmund K; Hirst, Jonathan D

    2009-05-01

    In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

  1. Quantum Machine Learning

    Science.gov (United States)

    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.

  2. Utvärdering av Amazon Machine Learning för taggsystem

    OpenAIRE

    Madosh, Farzana; Lundsten, Erik

    2017-01-01

    How companies deal with machine learning is currently a highly-discussed topic, as it can facilitate corporate manual work by training computers to recognize patterns and thus automate the working procedure. However, this requires resources and knowledge in the field. As a result, various companies like Amazon and Google provide machine learning services without requiring the user to have deep knowledge in the area. This study evaluates Amazon Machine Learning program for a tag system with da...

  3. Machine Learning for Neuroimaging with Scikit-Learn

    Directory of Open Access Journals (Sweden)

    Alexandre eAbraham

    2014-02-01

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

  4. Machine learning for neuroimaging with scikit-learn.

    Science.gov (United States)

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

    2014-01-01

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

  5. Machine learning in genetics and genomics

    Science.gov (United States)

    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

  6. Machine Learning in Medicine.

    Science.gov (United States)

    Deo, Rahul C

    2015-11-17

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

  7. Machine Learning in Medicine

    Science.gov (United States)

    Deo, Rahul C.

    2015-01-01

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

  8. Health Informatics via Machine Learning for the Clinical Management of Patients.

    Science.gov (United States)

    Clifton, D A; Niehaus, K E; Charlton, P; Colopy, G W

    2015-08-13

    To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice. We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case. Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups. Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

  9. Machine learning for micro-tomography

    Science.gov (United States)

    Parkinson, Dilworth Y.; Pelt, Daniël. M.; Perciano, Talita; Ushizima, Daniela; Krishnan, Harinarayan; Barnard, Harold S.; MacDowell, Alastair A.; Sethian, James

    2017-09-01

    Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab-from workstations to supercomputers-and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

  10. Probabilistic machine learning and artificial intelligence.

    Science.gov (United States)

    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.

  11. Probabilistic machine learning and artificial intelligence

    Science.gov (United States)

    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.

  12. MEDLINE MeSH Indexing: Lessons Learned from Machine Learning and Future Directions

    DEFF Research Database (Denmark)

    Jimeno-Yepes, Antonio; Mork, James G.; Wilkowski, Bartlomiej

    2012-01-01

    and analyzed the issues when using standard machine learning algorithms. We show that in some cases machine learning can improve the annotations already recommended by MTI, that machine learning based on low variance methods achieves better performance and that each MeSH heading presents a different behavior......Map and a k-NN approach called PubMed Related Citations (PRC). Our motivation is to improve the quality of MTI based on machine learning. Typical machine learning approaches fit this indexing task into text categorization. In this work, we have studied some Medical Subject Headings (MeSH) recommended by MTI...

  13. Learning About Climate and Atmospheric Models Through Machine Learning

    Science.gov (United States)

    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.

  14. Amplifying human ability through autonomics and machine learning in IMPACT

    Science.gov (United States)

    Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

    2017-05-01

    Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

  15. Introducing Machine Learning Concepts with WEKA.

    Science.gov (United States)

    Smith, Tony C; Frank, Eibe

    2016-01-01

    This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple data set, then proceeds to explain how one might approach a bioinformatics problem. Finally, it includes a brief summary of machine learning algorithms for other types of data mining problems, and provides suggestions about where to find additional information.

  16. Book review: A first course in Machine Learning

    DEFF Research Database (Denmark)

    Ortiz-Arroyo, Daniel

    2016-01-01

    "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background...... to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning....... This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark...

  17. Application of Machine Learning to Rotorcraft Health Monitoring

    Science.gov (United States)

    Cody, Tyler; Dempsey, Paula J.

    2017-01-01

    Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

  18. Machine learning in manufacturing: advantages, challenges, and applications

    Directory of Open Access Journals (Sweden)

    Thorsten Wuest

    2016-01-01

    Full Text Available The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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

    Science.gov (United States)

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

    2018-05-01

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

  20. Machine Learning Techniques in Clinical Vision Sciences.

    Science.gov (United States)

    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

  1. Investigating Mesoscale Convective Systems and their Predictability Using Machine Learning

    Science.gov (United States)

    Daher, H.; Duffy, D.; Bowen, M. K.

    2016-12-01

    A mesoscale convective system (MCS) is a thunderstorm region that lasts several hours long and forms near weather fronts and can often develop into tornadoes. Here we seek to answer the question of whether these tornadoes are "predictable" by looking for a defining characteristic(s) separating MCSs that evolve into tornadoes versus those that do not. Using NASA's Modern Era Retrospective-analysis for Research and Applications 2 reanalysis data (M2R12K), we apply several state of the art machine learning techniques to investigate this question. The spatial region examined in this experiment is Tornado Alley in the United States over the peak tornado months. A database containing select variables from M2R12K is created using PostgreSQL. This database is then analyzed using machine learning methods such as Symbolic Aggregate approXimation (SAX) and DBSCAN (an unsupervised density-based data clustering algorithm). The incentive behind using these methods is to mathematically define a MCS so that association rule mining techniques can be used to uncover some sort of signal or teleconnection that will help us forecast which MCSs will result in tornadoes and therefore give society more time to prepare and in turn reduce casualties and destruction.

  2. Building machines that learn and think like people.

    Science.gov (United States)

    Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

    2017-01-01

    Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

  3. Using Machine Learning to Advance Personality Assessment and Theory.

    Science.gov (United States)

    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.

  4. Learning Companion Systems, Social Learning Systems, and the Global Social Learning Club.

    Science.gov (United States)

    Chan, Tak-Wai

    1996-01-01

    Describes the development of learning companion systems and their contributions to the class of social learning systems that integrate artificial intelligence agents and use machine learning to tutor and interact with students. Outlines initial social learning projects, their programming languages, and weakness. Future improvements will include…

  5. An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

    Science.gov (United States)

    Lee, Unseok; Chang, Sungyul; Putra, Gian Anantrio; Kim, Hyoungseok; Kim, Dong Hwan

    2018-01-01

    A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

  6. ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

    Science.gov (United States)

    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.

  7. Gaussian processes for machine learning.

    Science.gov (United States)

    Seeger, Matthias

    2004-04-01

    Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.

  8. MLnet report: training in Europe on machine learning

    OpenAIRE

    Ellebrecht, Mario; Morik, Katharina

    1999-01-01

    Machine learning techniques offer opportunities for a variety of applications and the theory of machine learning investigates problems that are of interest for other fields of computer science (e.g., complexity theory, logic programming, pattern recognition). However, the impacts of machine learning can only be recognized by those who know the techniques and are able to apply them. Hence, teaching machine learning is necessary before this field can diversify computer science. In order ...

  9. An introduction to quantum machine learning

    Science.gov (United States)

    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.

  10. Learning with Support Vector Machines

    CERN Document Server

    Campbell, Colin

    2010-01-01

    Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a

  11. Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

    Science.gov (United States)

    Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.

    2011-01-01

    Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433

  12. Continual Learning through Evolvable Neural Turing Machines

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Risi, Sebastian

    2016-01-01

    Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENTM...

  13. Machine learning of molecular properties: Locality and active learning

    Science.gov (United States)

    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.

  14. Prototype-based models in machine learning.

    Science.gov (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. © 2016 Wiley Periodicals, Inc.

  15. Machine-learning-based real-bogus system for the HSC-SSP moving object detection pipeline

    Science.gov (United States)

    Lin, Hsing-Wen; Chen, Ying-Tung; Wang, Jen-Hung; Wang, Shiang-Yu; Yoshida, Fumi; Ip, Wing-Huen; Miyazaki, Satoshi; Terai, Tsuyoshi

    2018-01-01

    Machine-learning techniques are widely applied in many modern optical sky surveys, e.g., Pan-STARRS1, PTF/iPTF, and the Subaru/Hyper Suprime-Cam survey, to reduce human intervention in data verification. In this study, we have established a machine-learning-based real-bogus system to reject false detections in the Subaru/Hyper-Suprime-Cam Strategic Survey Program (HSC-SSP) source catalog. Therefore, the HSC-SSP moving object detection pipeline can operate more effectively due to the reduction of false positives. To train the real-bogus system, we use stationary sources as the real training set and "flagged" data as the bogus set. The training set contains 47 features, most of which are photometric measurements and shape moments generated from the HSC image reduction pipeline (hscPipe). Our system can reach a true positive rate (tpr) ˜96% with a false positive rate (fpr) ˜1% or tpr ˜99% at fpr ˜5%. Therefore, we conclude that stationary sources are decent real training samples, and using photometry measurements and shape moments can reject false positives effectively.

  16. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  17. Clojure for machine learning

    CERN Document Server

    Wali, Akhil

    2014-01-01

    A book that brings out the strengths of Clojure programming that have to facilitate machine learning. Each topic is described in substantial detail, and examples and libraries in Clojure are also demonstrated.This book is intended for Clojure developers who want to explore the area of machine learning. Basic understanding of the Clojure programming language is required, but thorough acquaintance with the standard Clojure library or any libraries are not required. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage.

  18. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  19. Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

    Science.gov (United States)

    2016-06-01

    monitoring. This analyzed payload is within the application layer of the OSI model . The analysis tries to establish whether or not the payload is...24 3.2.5 Model Drift Experiments...ADVERSARIAL ENVIRONMENTS (SPIE DSS 2014) .................................................. 58 APPENDIX C - EVALUATING MODEL DRIFT IN MACHINE LEARNING

  20. Using human brain activity to guide machine learning.

    Science.gov (United States)

    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.

  1. A Development of Automatic Audit System for Written Informed Consent using Machine Learning.

    Science.gov (United States)

    Yamada, Hitomi; Takemura, Tadamasa; Asai, Takahiro; Okamoto, Kazuya; Kuroda, Tomohiro; Kuwata, Shigeki

    2015-01-01

    In Japan, most of all the university and advanced hospitals have implemented both electronic order entry systems and electronic charting. In addition, all medical records are subjected to inspector audit for quality assurance. The record of informed consent (IC) is very important as this provides evidence of consent from the patient or patient's family and health care provider. Therefore, we developed an automatic audit system for a hospital information system (HIS) that is able to evaluate IC automatically using machine learning.

  2. Machine learning applications in genetics and genomics.

    Science.gov (United States)

    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.

  3. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

    Science.gov (United States)

    Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

    2017-07-18

    Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

  4. Machine learning molecular dynamics for the simulation of infrared spectra.

    Science.gov (United States)

    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.

  5. Machine learning topological states

    Science.gov (United States)

    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.

  6. Machine Learning and Inverse Problem in Geodynamics

    Science.gov (United States)

    Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

    2017-12-01

    During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques

  7. Adaptive Machine Aids to Learning.

    Science.gov (United States)

    Starkweather, John A.

    With emphasis on man-machine relationships and on machine evolution, computer-assisted instruction (CAI) is examined in this paper. The discussion includes the background of machine assistance to learning, the current status of CAI, directions of development, the development of criteria for successful instruction, meeting the needs of users,…

  8. Using Machine Learning to Predict Student Performance

    OpenAIRE

    Pojon, Murat

    2017-01-01

    This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

  9. Intelligent Vehicle Power Management Using Machine Learning and Fuzzy Logic

    National Research Council Canada - National Science Library

    Chen, ZhiHang; Masrur, M. A; Murphey, Yi L

    2008-01-01

    .... A machine learning algorithm, LOPPS, has been developed to learn about optimal power source combinations with respect to minimum power loss for all possible load requests and various system power states...

  10. The ATLAS Higgs Machine Learning Challenge

    CERN Document Server

    Cowan, Glen; The ATLAS collaboration; Bourdarios, Claire

    2015-01-01

    High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 1990s with Artificial Neural Net and more recently with Boosted Decision Trees, Random Forest etc. Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, data scientists are developing new Machine Learning algorithms to extract meaning from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, and at the same time data scientists have advanced algorithms: the goal of the HiggsML project was to bring the two together by a “challenge”: participants from all over the world and any scientific background could compete online to obtain the best Higgs to tau tau signal significance on a set of ATLAS fully simulated Monte Carlo signal and background. Instead of HEP physicists browsing through machine learning papers and trying to infer which new algorithms might be useful for HEP, then c...

  11. Machine learning analysis of binaural rowing sounds

    DEFF Research Database (Denmark)

    Johard, Leonard; Ruffaldi, Emanuele; Hoffmann, Pablo F.

    2011-01-01

    Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition metho...... methodology and the evaluation of different machine learning techniques for classifying rowing-sound data. We see that a combination of principal component analysis and shallow networks perform equally well as deep architectures, while being much faster to train.......Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition...

  12. Data Mining Practical Machine Learning Tools and Techniques

    CERN Document Server

    Witten, Ian H; Hall, Mark A

    2011-01-01

    Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

  13. Machine learning: Trends, perspectives, and prospects.

    Science.gov (United States)

    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.

  14. Python for probability, statistics, and machine learning

    CERN Document Server

    Unpingco, José

    2016-01-01

    This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowl...

  15. Research on machine learning framework based on random forest algorithm

    Science.gov (United States)

    Ren, Qiong; Cheng, Hui; Han, Hai

    2017-03-01

    With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

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

    Science.gov (United States)

    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.

  17. Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

    Science.gov (United States)

    Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin

    2017-01-01

    Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

  18. Amp: A modular approach to machine learning in atomistic simulations

    Science.gov (United States)

    Khorshidi, Alireza; Peterson, Andrew A.

    2016-10-01

    Electronic structure calculations, such as those employing Kohn-Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties-namely that they are noiseless and targeted training data can be produced on-demand-that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which

  19. A strategy for quantum algorithm design assisted by machine learning

    International Nuclear Information System (INIS)

    Bang, Jeongho; Lee, Jinhyoung; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin

    2014-01-01

    We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum–classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch–Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method. (paper)

  20. A strategy for quantum algorithm design assisted by machine learning

    Science.gov (United States)

    Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung

    2014-07-01

    We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

  1. BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

    Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

  2. Machine learning approaches in medical image analysis

    DEFF Research Database (Denmark)

    de Bruijne, Marleen

    2016-01-01

    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....

  3. Machine Learning Approaches in Cardiovascular Imaging.

    Science.gov (United States)

    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.

  4. Machine Learning of Musical Gestures

    OpenAIRE

    Caramiaux, Baptiste; Tanaka, Atau

    2013-01-01

    We present an overview of machine learning (ML) techniques and theirapplication in interactive music and new digital instruments design. We firstgive to the non-specialist reader an introduction to two ML tasks,classification and regression, that are particularly relevant for gesturalinteraction. We then present a review of the literature in current NIMEresearch that uses ML in musical gesture analysis and gestural sound control.We describe the ways in which machine learning is useful for cre...

  5. Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

    Directory of Open Access Journals (Sweden)

    Wei-Chien Wang

    2018-05-01

    Full Text Available Deep learning has become the most popular research subject in the fields of artificial intelligence (AI and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA, support vector machines (SVM, k-nearest neighbors algorithm (K-NN, and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW, with machine-learning classifiers, and histogram of oriented gradients (HOG/pyramid histogram of oriented gradients (PHOG with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50% and PHOG with SVM (53.0%.

  6. Machine learning enhanced optical distance sensor

    Science.gov (United States)

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

    2018-01-01

    Presented for the first time is a machine learning enhanced optical distance sensor. The distance sensor is based on our previously demonstrated distance measurement technique that uses an Electronically Controlled Variable Focus Lens (ECVFL) with a laser source to illuminate a target plane with a controlled optical beam spot. This spot with varying spot sizes is viewed by an off-axis camera and the spot size data is processed to compute the distance. In particular, proposed and demonstrated in this paper is the use of a regularized polynomial regression based supervised machine learning algorithm to enhance the accuracy of the operational sensor. The algorithm uses the acquired features and corresponding labels that are the actual target distance values to train a machine learning model. The optimized training model is trained over a 1000 mm (or 1 m) experimental target distance range. Using the machine learning algorithm produces a training set and testing set distance measurement errors of learning. Applications for the proposed sensor include industrial scenario distance sensing where target material specific training models can be generated to realize low <1% measurement error distance measurements.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2018-04-01

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

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

    Science.gov (United States)

    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 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.

  10. An introduction to quantum machine learning

    OpenAIRE

    Schuld, M.; Sinayskiy, I.; Petruccione, F.

    2014-01-01

    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 compute...

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

    Science.gov (United States)

    Stilgoe, Jack

    2018-02-01

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

  12. Trends in Machine Learning for Signal Processing

    DEFF Research Database (Denmark)

    Adali, Tulay; Miller, David J.; Diamantaras, Konstantinos I.

    2011-01-01

    By putting the accent on learning from the data and the environment, the Machine Learning for SP (MLSP) Technical Committee (TC) provides the essential bridge between the machine learning and SP communities. While the emphasis in MLSP is on learning and data-driven approaches, SP defines the main...... applications of interest, and thus the constraints and requirements on solutions, which include computational efficiency, online adaptation, and learning with limited supervision/reference data....

  13. Machine Learning Methods for Attack Detection in the Smart Grid.

    Science.gov (United States)

    Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

    2016-08-01

    Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

  14. Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain

    OpenAIRE

    Kurtulmus, A. Besir; Daniel, Kenny

    2018-01-01

    Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid fo...

  15. Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

    Science.gov (United States)

    Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

    2018-02-01

    Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Higgs Machine Learning Challenge 2014

    CERN Document Server

    Olivier, A-P; Bourdarios, C ; LAL / Orsay; Goldfarb, S ; University of Michigan

    2014-01-01

    High Energy Physics (HEP) has been using Machine Learning (ML) techniques such as boosted decision trees (paper) and neural nets since the 90s. These techniques are now routinely used for difficult tasks such as the Higgs boson search. Nevertheless, formal connections between the two research fields are rather scarce, with some exceptions such as the AppStat group at LAL, founded in 2006. In collaboration with INRIA, AppStat promotes interdisciplinary research on machine learning, computational statistics, and high-energy particle and astroparticle physics. We are now exploring new ways to improve the cross-fertilization of the two fields by setting up a data challenge, following the footsteps of, among others, the astrophysics community (dark matter and galaxy zoo challenges) and neurobiology (connectomics and decoding the human brain). The organization committee consists of ATLAS physicists and machine learning researchers. The Challenge will run from Monday 12th to September 2014.

  17. Machine learning in cardiovascular medicine: are we there yet?

    Science.gov (United States)

    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.

  18. Machine Learning: developing an image recognition program : with Python, Scikit Learn and OpenCV

    OpenAIRE

    Nguyen, Minh

    2016-01-01

    Machine Learning is one of the most debated topic in computer world these days, especially after the first Computer Go program has beaten human Go world champion. Among endless application of Machine Learning, image recognition, which problem is processing enormous amount of data from dynamic input. This thesis will present the basic concept of Machine Learning, Machine Learning algorithms, Python programming language and Scikit Learn – a simple and efficient tool for data analysis in P...

  19. In silico machine learning methods in drug development.

    Science.gov (United States)

    Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

    2014-01-01

    Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

  20. Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems

    Directory of Open Access Journals (Sweden)

    Zhiyuan Ma

    2018-01-01

    Full Text Available Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM_T to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM_T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.

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

    Science.gov (United States)

    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…

  2. Machine learning in radiation oncology theory and applications

    CERN Document Server

    El Naqa, Issam; Murphy, Martin J

    2015-01-01

    ​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided rad

  3. Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines

    DEFF Research Database (Denmark)

    van Tulder, Gijs; de Bruijne, Marleen

    2016-01-01

    The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may...... outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from...... unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both...

  4. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    Directory of Open Access Journals (Sweden)

    C. V. Subbulakshmi

    2015-01-01

    Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  5. Machine learning methods without tears: a primer for ecologists.

    Science.gov (United States)

    Olden, Julian D; Lawler, Joshua J; Poff, N LeRoy

    2008-06-01

    Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.

  6. Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

    Science.gov (United States)

    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.

  7. Prediction of druggable proteins using machine learning and systems biology: a mini-review

    Directory of Open Access Journals (Sweden)

    Gaurav eKandoi

    2015-12-01

    Full Text Available The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges.

  8. Mammogram retrieval through machine learning within BI-RADS standards.

    Science.gov (United States)

    Wei, Chia-Hung; Li, Yue; Huang, Pai Jung

    2011-08-01

    A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study

    Directory of Open Access Journals (Sweden)

    I. G. Damousis

    2012-01-01

    Full Text Available We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs, Artificial Neural Networks (ANNs, Fuzzy Expert Systems (FESs, and Support Vector Machines (SVMs. The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature.

  10. Machine learning and medicine: book review and commentary.

    Science.gov (United States)

    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.

  11. Deep learning versus traditional machine learning methods for aggregated energy demand prediction

    NARCIS (Netherlands)

    Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

    2018-01-01

    In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

  12. Learning Activity Packets for Milling Machines. Unit I--Introduction to Milling Machines.

    Science.gov (United States)

    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 identify parts and attachments of vertical and horizontal milling machines, identify work-holding devices, state safety rules, and…

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

    Science.gov (United States)

    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 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.

  14. Machine Learning Approaches for Clinical Psychology and Psychiatry.

    Science.gov (United States)

    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.

  15. Parsimonious Wavelet Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wang Qin

    2015-11-01

    Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

  16. On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

    Science.gov (United States)

    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.

  17. MACHINE LEARNING TECHNIQUES USED IN BIG DATA

    Directory of Open Access Journals (Sweden)

    STEFANIA LOREDANA NITA

    2016-07-01

    Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

  18. Acceleration of saddle-point searches with machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, Andrew A., E-mail: andrew-peterson@brown.edu [School of Engineering, Brown University, Providence, Rhode Island 02912 (United States)

    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.

  19. Acceleration of saddle-point searches with machine learning

    International Nuclear Information System (INIS)

    Peterson, Andrew A.

    2016-01-01

    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.

  20. Acceleration of saddle-point searches with machine learning.

    Science.gov (United States)

    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.

  1. Spatial extreme learning machines: An application on prediction of disease counts.

    Science.gov (United States)

    Prates, Marcos O

    2018-01-01

    Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

  2. Revisit of Machine Learning Supported Biological and Biomedical Studies.

    Science.gov (United States)

    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.

  3. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  4. Machine learning-based methods for prediction of linear B-cell epitopes.

    Science.gov (United States)

    Wang, Hsin-Wei; Pai, Tun-Wen

    2014-01-01

    B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

  5. Machine learning in the string landscape

    Science.gov (United States)

    Carifio, Jonathan; Halverson, James; Krioukov, Dmitri; Nelson, Brent D.

    2017-09-01

    We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of 4/3× 2.96× {10}^{755} F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

  6. Machine Learning for ATLAS DDM Network Metrics

    CERN Document Server

    Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf

    2016-01-01

    The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  7. A quantum speedup in machine learning: finding an N-bit Boolean function for a classification

    International Nuclear Information System (INIS)

    Yoo, Seokwon; Lee, Jinhyoung; Bang, Jeongho; Lee, Changhyoup

    2014-01-01

    We compare quantum and classical machines designed for learning an N-bit Boolean function in order to address how a quantum system improves the machine learning behavior. The machines of the two types consist of the same number of operations and control parameters, but only the quantum machines utilize the quantum coherence naturally induced by unitary operators. We show that quantum superposition enables quantum learning that is faster than classical learning by expanding the approximate solution regions, i.e., the acceptable regions. This is also demonstrated by means of numerical simulations with a standard feedback model, namely random search, and a practical model, namely differential evolution. (paper)

  8. Machine learning of network metrics in ATLAS Distributed Data Management

    CERN Document Server

    AUTHOR|(INSPIRE)INSPIRE-00218873; The ATLAS collaboration; Toler, Wesley; Vamosi, Ralf; Bogado Garcia, Joaquin Ignacio

    2017-01-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our m...

  9. Machine learning of network metrics in ATLAS Distributed Data Management

    Science.gov (United States)

    Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

    2017-10-01

    The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

  10. Evaluation on knowledge extraction and machine learning in ...

    African Journals Online (AJOL)

    Evaluation on knowledge extraction and machine learning in resolving Malay word ambiguity. ... No 5S (2017) >. Log in or Register to get access to full text downloads. ... Keywords: ambiguity; lexical knowledge; machine learning; Malay word ...

  11. Machine learning \\& artificial intelligence in the quantum domain

    OpenAIRE

    Dunjko, Vedran; Briegel, Hans J.

    2017-01-01

    Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent th...

  12. Source localization in an ocean waveguide using supervised machine learning.

    Science.gov (United States)

    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.

  13. Machine-learned and codified synthesis parameters of oxide materials

    Science.gov (United States)

    Kim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa

    2017-09-01

    Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.

  14. Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research.

    Science.gov (United States)

    Installé, Arnaud Jf; Van den Bosch, Thierry; De Moor, Bart; Timmerman, Dirk

    2014-10-20

    Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models. The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring. The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability. The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase. To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries

  15. Machine Learning in Radiology: Applications Beyond Image Interpretation.

    Science.gov (United States)

    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.

  16. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    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.

  17. Applications of machine learning in cancer prediction and prognosis.

    Science.gov (United States)

    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.

  18. Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

    OpenAIRE

    Shan, Min

    2017-01-01

    With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

  19. Implementing Machine Learning in Radiology Practice and Research.

    Science.gov (United States)

    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.

  20. International Conference on Extreme Learning Machines 2014

    CERN Document Server

    Mao, Kezhi; Cambria, Erik; Man, Zhihong; Toh, Kar-Ann

    2015-01-01

    This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of “learning without iterative tuning”.  The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.  

  1. International Conference on Extreme Learning Machine 2015

    CERN Document Server

    Mao, Kezhi; Wu, Jonathan; Lendasse, Amaury; ELM 2015; Theory, Algorithms and Applications (I); Theory, Algorithms and Applications (II)

    2016-01-01

    This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. .

  2. Machine learning based Intelligent cognitive network using fog computing

    Science.gov (United States)

    Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

    2017-05-01

    In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

  3. Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

    Directory of Open Access Journals (Sweden)

    Yanjun Zhang

    2015-01-01

    Full Text Available A new optimized extreme learning machine- (ELM- based method for power system transient stability prediction (TSP using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

  4. Virtual Things for Machine Learning Applications

    OpenAIRE

    Bovet , Gérôme; Ridi , Antonio; Hennebert , Jean

    2014-01-01

    International audience; Internet-of-Things (IoT) devices, especially sensors are pro-ducing large quantities of data that can be used for gather-ing knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture able to ex-ecute machine learning algorithms within the sensor net-work, presenting advantages in terms of privacy and data transfer efficiency. We first argument that some classes of ...

  5. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

    OpenAIRE

    Ademujimi , Toyosi ,; Brundage , Michael ,; Prabhu , Vittaldas ,

    2017-01-01

    Part 6: Intelligent Diagnostics and Maintenance Solutions; International audience; Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learnin...

  6. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

    2014-08-15

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

  7. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

    2014-01-01

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

  8. Using Machine Learning for Risky Module Estimation of Safety-Critical Software

    International Nuclear Information System (INIS)

    Kim, Young Mi; Jeong, Choong Heui

    2009-01-01

    With the rapid development of digital computer and information processing technologies, nuclear I and C (Instrument and Control) system which needs safety critical function has adopted digital technologies. Software used in safety-critical system must have high dependability. Highly dependable software needs strict software testing and V and V activities. These days, regulatory demands for nuclear power plants are more and more increasing. But, human resources and time for regulation are limited. So, early software risky module prediction is very useful for software testing and regulation activities. Early estimation can be built from a collection of internal metrics during early development phase. Internal metrics are measures of a product derived from assessment of the product itself, and external metrics are measures of a product derived from assessment of the behavior of the systems. Internal metrics can be collected more easily and early than external metrics. In addition, internal metrics can be useful for estimating fault-prone software modules using machine learning. In this paper, we introduce current research status and techniques related to estimating risky software module using machine learning techniques. Section 2 describes the overview of the estimation model using machine learning and section 3 describes processes of the estimation model. Section 4 describes several estimation models using machine leanings. Section 5 concludes the paper

  9. Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

    International Nuclear Information System (INIS)

    Strušnik, Dušan; Marčič, Milan; Golob, Marjan; Hribernik, Aleš; Živić, Marija; Avsec, Jurij

    2016-01-01

    Highlights: • Steam ejector pump and electric liquid ring vacuum pump are analysed and modelled. • A supervised machine learning models by using real process data are applied. • The equation of ejector pumped mass flow from steam turbine condenser was solved. • The loss of specific energy capable of work in a SEPS or LRVP component was analysed. • The economic efficiency analysis per different coal heating values was made. - Abstract: This paper compares the vapour ejector and electric vacuum pump power consumptions with machine learning algorithms by using real process data and presents some novelty guideline for the selection of an appropriate condenser vacuum pump system of a steam turbine power plant. The machine learning algorithms are made by using the supervised machine learning methods such as artificial neural network model and local linear neuro-fuzzy models. The proposed non-linear models are designed by using a wide range of real process operation data sets from the CHP system in the thermal power plant. The novelty guideline for the selection of an appropriate condenser vacuum pumps system is expressed in the comparative analysis of the energy consumption and use of specific energy capable of work. Furthermore, the novelty is expressed in the economic efficiency analysis of the investment taking into consideration the operating costs of the vacuum pump systems and may serve as basic guidelines for the selection of an appropriate condenser vacuum pump system of a steam turbine.

  10. Automated mapping of building facades by machine learning

    DEFF Research Database (Denmark)

    Höhle, Joachim

    2014-01-01

    Facades of buildings contain various types of objects which have to be recorded for information systems. The article describes a solution for this task focussing on automated classification by means of machine learning techniques. Stereo pairs of oblique images are used to derive 3D point clouds...

  11. Machine learning with quantum relative entropy

    International Nuclear Information System (INIS)

    Tsuda, Koji

    2009-01-01

    Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

  12. Machine learning with quantum relative entropy

    Energy Technology Data Exchange (ETDEWEB)

    Tsuda, Koji [Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tuebingen, 72076 (Germany)], E-mail: koji.tsuda@tuebingen.mpg.de

    2009-12-01

    Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

  13. Machine learning control taming nonlinear dynamics and turbulence

    CERN Document Server

    Duriez, Thomas; Noack, Bernd R

    2017-01-01

    This is the first book on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading r...

  14. Machine Learning of Fault Friction

    Science.gov (United States)

    Johnson, P. A.; Rouet-Leduc, B.; Hulbert, C.; Marone, C.; Guyer, R. A.

    2017-12-01

    We are applying machine learning (ML) techniques to continuous acoustic emission (AE) data from laboratory earthquake experiments. Our goal is to apply explicit ML methods to this acoustic datathe AE in order to infer frictional properties of a laboratory fault. The experiment is a double direct shear apparatus comprised of fault blocks surrounding fault gouge comprised of glass beads or quartz powder. Fault characteristics are recorded, including shear stress, applied load (bulk friction = shear stress/normal load) and shear velocity. The raw acoustic signal is continuously recorded. We rely on explicit decision tree approaches (Random Forest and Gradient Boosted Trees) that allow us to identify important features linked to the fault friction. A training procedure that employs both the AE and the recorded shear stress from the experiment is first conducted. Then, testing takes place on data the algorithm has never seen before, using only the continuous AE signal. We find that these methods provide rich information regarding frictional processes during slip (Rouet-Leduc et al., 2017a; Hulbert et al., 2017). In addition, similar machine learning approaches predict failure times, as well as slip magnitudes in some cases. We find that these methods work for both stick slip and slow slip experiments, for periodic slip and for aperiodic slip. We also derive a fundamental relationship between the AE and the friction describing the frictional behavior of any earthquake slip cycle in a given experiment (Rouet-Leduc et al., 2017b). Our goal is to ultimately scale these approaches to Earth geophysical data to probe fault friction. References Rouet-Leduc, B., C. Hulbert, N. Lubbers, K. Barros, C. Humphreys and P. A. Johnson, Machine learning predicts laboratory earthquakes, in review (2017). https://arxiv.org/abs/1702.05774Rouet-LeDuc, B. et al., Friction Laws Derived From the Acoustic Emissions of a Laboratory Fault by Machine Learning (2017), AGU Fall Meeting Session S025

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

    Science.gov (United States)

    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.

  16. Learning as a Machine: Crossovers between Humans and Machines

    Science.gov (United States)

    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…

  17. Machine learning a Bayesian and optimization perspective

    CERN Document Server

    Theodoridis, Sergios

    2015-01-01

    This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...

  18. Studying depression using imaging and machine learning methods.

    Science.gov (United States)

    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.

  19. An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

    Science.gov (United States)

    Mansourvar, Marjan; Shamshirband, Shahaboddin; Raj, Ram Gopal; Gunalan, Roshan; Mazinani, Iman

    2015-01-01

    Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

  20. An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

    Science.gov (United States)

    Mansourvar, Marjan; Shamshirband, Shahaboddin; Raj, Ram Gopal; Gunalan, Roshan; Mazinani, Iman

    2015-01-01

    Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age. PMID:26402795

  1. MoleculeNet: a benchmark for molecular machine learning.

    Science.gov (United States)

    Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S; Leswing, Karl; Pande, Vijay

    2018-01-14

    Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

  2. The ATLAS Higgs machine learning challenge

    CERN Document Server

    Davey, W; The ATLAS collaboration; Rousseau, D; Cowan, G; Kegl, B; Germain-Renaud, C; Guyon, I

    2014-01-01

    High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 90's with Artificial Neural Net for example, more recently with Boosted Decision Trees, Random Forest etc... Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, Data Scientists are developing new Machine Learning algorithms to extract sense from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, data scientists have advanced algorithms: the goal of the HiggsML project is to bring the two together by a “challenge”: participants from all over the world and any scientific background can compete online ( https://www.kaggle.com/c/higgs-boson ) to obtain the best Higgs to tau tau signal significance on a set of ATLAS full simulated Monte Carlo signal and background. Winners with the best scores will receive money prizes ; authors of the best method (most usable) will be invited t...

  3. Inverse Problems in Geodynamics Using Machine Learning Algorithms

    Science.gov (United States)

    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.

  4. Attacking Machine Learning models as part of a cyber kill chain

    OpenAIRE

    Nguyen, Tam N.

    2017-01-01

    Machine learning is gaining popularity in the network security domain as many more network-enabled devices get connected, as malicious activities become stealthier, and as new technologies like Software Defined Networking emerge. Compromising machine learning model is a desirable goal. In fact, spammers have been quite successful getting through machine learning enabled spam filters for years. While previous works have been done on adversarial machine learning, none has been considered within...

  5. Kernel methods for interpretable machine learning of order parameters

    Science.gov (United States)

    Ponte, Pedro; Melko, Roger G.

    2017-11-01

    Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.

  6. Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity

    International Nuclear Information System (INIS)

    Stern, R.E.; Song, J.; Work, D.B.

    2017-01-01

    The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check. - Highlights: • Surrogate models of network connectivity are developed by machine-learning algorithms. • Developed surrogate models can reduce the runtime required for Monte Carlo simulations. • Support vector machine and logistic regressions are employed to develop surrogate models. • Numerical example of California gas distribution network demonstrate the proposed approach. • The developed models have accuracies 99%, and are 1–2 orders of magnitude faster than MCS.

  7. Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

    Directory of Open Access Journals (Sweden)

    Michael Veale

    2017-11-01

    Full Text Available Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM and fairness, accountability and transparency machine learning (FATML, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm.

  8. Predicting Solar Activity Using Machine-Learning Methods

    Science.gov (United States)

    Bobra, M.

    2017-12-01

    Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

  9. A comparative study of machine learning models for ethnicity classification

    Science.gov (United States)

    Trivedi, Advait; Bessie Amali, D. Geraldine

    2017-11-01

    This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

  10. Extracting meaning from audio signals - a machine learning approach

    DEFF Research Database (Denmark)

    Larsen, Jan

    2007-01-01

    * Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression......* Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression...

  11. Modern machine learning techniques and their applications in cartoon animation research

    CERN Document Server

    Yu, Jun

    2013-01-01

    The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

  12. Implementing Machine Learning in the PCWG Tool

    Energy Technology Data Exchange (ETDEWEB)

    Clifton, Andrew; Ding, Yu; Stuart, Peter

    2016-12-13

    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.

  13. Machine learning and data science in soft materials engineering

    Science.gov (United States)

    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.

  14. Machine learning and data science in soft materials engineering.

    Science.gov (United States)

    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.

  15. Using Machine Learning to Search for MSSM Higgs Bosons

    CERN Document Server

    Diesing, Rebecca

    2016-01-01

    This paper examines the performance of machine learning in the identification of Minimally Su- persymmetric Standard Model (MSSM) Higgs Bosons, and compares this performance to that of traditional cut strategies. Two boosted decision tree algorithms were tested, scikit-learn and XGBoost. These tests indicated that machine learning can perform significantly better than traditional cuts. However, since machine learning in this form cannot be directly implemented in a real MSSM Higgs analysis, this performance information was instead used to better understand the relationships between training variables. Further studies might use this information to construct an improved cut strategy.

  16. Applications of Machine Learning in Cancer Prediction and Prognosis

    Directory of Open Access Journals (Sweden)

    Joseph A. Cruz

    2006-01-01

    Full Text Available 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.

  17. A review of machine learning in obesity.

    Science.gov (United States)

    DeGregory, K W; Kuiper, P; DeSilvio, T; Pleuss, J D; Miller, R; Roginski, J W; Fisher, C B; Harness, D; Viswanath, S; Heymsfield, S B; Dungan, I; Thomas, D M

    2018-05-01

    Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity. © 2018 World Obesity Federation.

  18. Proceedings of the IEEE Machine Learning for Signal Processing XVII

    DEFF Research Database (Denmark)

    The seventeenth of a series of workshops sponsored by the IEEE Signal Processing Society and organized by the Machine Learning for Signal Processing Technical Committee (MLSP-TC). The field of machine learning has matured considerably in both methodology and real-world application domains and has...... become particularly important for solution of problems in signal processing. As reflected in this collection, machine learning for signal processing combines many ideas from adaptive signal/image processing, learning theory and models, and statistics in order to solve complex real-world signal processing......, and two papers from the winners of the Data Analysis Competition. The program included papers in the following areas: genomic signal processing, pattern recognition and classification, image and video processing, blind signal processing, models, learning algorithms, and applications of machine learning...

  19. Studying depression using imaging and machine learning methods

    Directory of Open Access Journals (Sweden)

    Meenal J. Patel

    2016-01-01

    Full Text Available 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.

  20. Machine Learning Algorithms For Predicting the Instability Timescales of Compact Planetary Systems

    Science.gov (United States)

    Tamayo, Daniel; Ali-Dib, Mohamad; Cloutier, Ryan; Huang, Chelsea; Van Laerhoven, Christa L.; Leblanc, Rejean; Menou, Kristen; Murray, Norman; Obertas, Alysa; Paradise, Adiv; Petrovich, Cristobal; Rachkov, Aleksandar; Rein, Hanno; Silburt, Ari; Tacik, Nick; Valencia, Diana

    2016-10-01

    The Kepler mission has uncovered hundreds of compact multi-planet systems. The dynamical pathways to instability in these compact systems and their associated timescales are not well understood theoretically. However, long-term stability is often used as a constraint to narrow down the space of orbital solutions from the transit data. This requires a large suite of N-body integrations that can each take several weeks to complete. This computational bottleneck is therefore an important limitation in our ability to characterize compact multi-planet systems.From suites of numerical simulations, previous studies have fit simple scaling relations between the instability timescale and various system parameters. However, the numerically simulated systems can deviate strongly from these empirical fits.We present a new approach to the problem using machine learning algorithms that have enjoyed success across a broad range of high-dimensional industry applications. In particular, we have generated large training sets of direct N-body integrations of synthetic compact planetary systems to train several regression models (support vector machine, gradient boost) that predict the instability timescale. We find that ensembling these models predicts the instability timescale of planetary systems better than previous approaches using the simple scaling relations mentioned above.Finally, we will discuss how these models provide a powerful tool for not only understanding the current Kepler multi-planet sample, but also for characterizing and shaping the radial-velocity follow-up strategies of multi-planet systems from the upcoming Transiting Exoplanet Survey Satellite (TESS) mission, given its shorter observation baselines.

  1. A Machine Learning Framework for Plan Payment Risk Adjustment.

    Science.gov (United States)

    Rose, Sherri

    2016-12-01

    To introduce cross-validation and a nonparametric machine learning framework for plan payment risk adjustment and then assess whether they have the potential to improve risk adjustment. 2011-2012 Truven MarketScan database. We compare the performance of multiple statistical approaches within a broad machine learning framework for estimation of risk adjustment formulas. Total annual expenditure was predicted using age, sex, geography, inpatient diagnoses, and hierarchical condition category variables. The methods included regression, penalized regression, decision trees, neural networks, and an ensemble super learner, all in concert with screening algorithms that reduce the set of variables considered. The performance of these methods was compared based on cross-validated R 2 . Our results indicate that a simplified risk adjustment formula selected via this nonparametric framework maintains much of the efficiency of a traditional larger formula. The ensemble approach also outperformed classical regression and all other algorithms studied. The implementation of cross-validated machine learning techniques provides novel insight into risk adjustment estimation, possibly allowing for a simplified formula, thereby reducing incentives for increased coding intensity as well as the ability of insurers to "game" the system with aggressive diagnostic upcoding. © Health Research and Educational Trust.

  2. Exploiting the Dynamics of Soft Materials for Machine Learning.

    Science.gov (United States)

    Nakajima, Kohei; Hauser, Helmut; Li, Tao; Pfeifer, Rolf

    2018-06-01

    Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions. We then demonstrate that this system can be used for the sensory time series prediction problem for the soft arm itself, which suggests its immediate applicability to a real-world machine learning problem. Our approach, on the one hand, represents a radical departure from traditional computational methods, whereas on the other hand, it fits nicely into a more general perspective of computation by way of exploiting the properties of physical materials in the real world.

  3. Assessing Implicit Knowledge in BIM Models with Machine Learning

    DEFF Research Database (Denmark)

    Krijnen, Thomas; Tamke, Martin

    2015-01-01

    architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models....

  4. Using Machine Learning in Adversarial Environments.

    Energy Technology Data Exchange (ETDEWEB)

    Davis, Warren Leon [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2016-02-01

    Intrusion/anomaly detection systems are among the first lines of cyber defense. Commonly, they either use signatures or machine learning (ML) to identify threats, but fail to account for sophisticated attackers trying to circumvent them. We propose to embed machine learning within a game theoretic framework that performs adversarial modeling, develops methods for optimizing operational response based on ML, and integrates the resulting optimization codebase into the existing ML infrastructure developed by the Hybrid LDRD. Our approach addresses three key shortcomings of ML in adversarial settings: 1) resulting classifiers are typically deterministic and, therefore, easy to reverse engineer; 2) ML approaches only address the prediction problem, but do not prescribe how one should operationalize predictions, nor account for operational costs and constraints; and 3) ML approaches do not model attackers’ response and can be circumvented by sophisticated adversaries. The principal novelty of our approach is to construct an optimization framework that blends ML, operational considerations, and a model predicting attackers reaction, with the goal of computing optimal moving target defense. One important challenge is to construct a realistic model of an adversary that is tractable, yet realistic. We aim to advance the science of attacker modeling by considering game-theoretic methods, and by engaging experimental subjects with red teaming experience in trying to actively circumvent an intrusion detection system, and learning a predictive model of such circumvention activities. In addition, we will generate metrics to test that a particular model of an adversary is consistent with available data.

  5. A framework for detection of malicious software in Android handheld systems using machine learning techniques

    OpenAIRE

    Torregrosa García, Blas

    2015-01-01

    The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning-based Detector for Android malware), a framework for detection of Android malware based on machine learning techniques, is introduced here. It is devised to identify malicious applications. Este trabajo tiene como objetivo el diseño y el desarrollo de nuevas formas de detección de aplicaciones maliciosas en los dispositivos...

  6. Using machine learning algorithms to guide rehabilitation planning for home care clients.

    Science.gov (United States)

    Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul

    2007-12-20

    Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

  7. Machine learning a probabilistic perspective

    CERN Document Server

    Murphy, Kevin P

    2012-01-01

    Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic method...

  8. Transductive and matched-pair machine learning for difficult target detection problems

    Science.gov (United States)

    Theiler, James

    2014-06-01

    This paper will describe the application of two non-traditional kinds of machine learning (transductive machine learning and the more recently proposed matched-pair machine learning) to the target detection problem. The approach combines explicit domain knowledge to model the target signal with a more agnostic machine-learning approach to characterize the background. The concept is illustrated with simulated data from an elliptically-contoured background distribution, on which a subpixel target of known spectral signature but unknown spatial extent has been implanted.

  9. Large-Scale Machine Learning for Classification and Search

    Science.gov (United States)

    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…

  10. A review of supervised machine learning applied to ageing research.

    Science.gov (United States)

    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.

  11. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

    Directory of Open Access Journals (Sweden)

    Hao Li

    2017-01-01

    Full Text Available Predicting the performance of solar water heater (SWH is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

  12. Quantum machine learning what quantum computing means to data mining

    CERN Document Server

    Wittek, Peter

    2014-01-01

    Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine L

  13. Machine learning modelling for predicting soil liquefaction susceptibility

    Directory of Open Access Journals (Sweden)

    P. Samui

    2011-01-01

    Full Text Available 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 [(N160] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N160 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.

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

    Science.gov (United States)

    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.

  15. A review on machine learning principles for multi-view biological data integration.

    Science.gov (United States)

    Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

    2018-03-01

    Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

  16. Learning Machine Learning: A Case Study

    Science.gov (United States)

    Lavesson, N.

    2010-01-01

    This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…

  17. Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain.

    Science.gov (United States)

    Tan, W Katherine; Hassanpour, Saeed; Heagerty, Patrick J; Rundell, Sean D; Suri, Pradeep; Huhdanpaa, Hannu T; James, Kathryn; Carrell, David S; Langlotz, Curtis P; Organ, Nancy L; Meier, Eric N; Sherman, Karen J; Kallmes, David F; Luetmer, Patrick H; Griffith, Brent; Nerenz, David R; Jarvik, Jeffrey G

    2018-03-28

    To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC. Copyright © 2018 The Association of University Radiologists. All rights reserved.

  18. Empirical Studies On Machine Learning Based Text Classification Algorithms

    OpenAIRE

    Shweta C. Dharmadhikari; Maya Ingle; Parag Kulkarni

    2011-01-01

    Automatic classification of text documents has become an important research issue now days. Properclassification of text documents requires information retrieval, machine learning and Natural languageprocessing (NLP) techniques. Our aim is to focus on important approaches to automatic textclassification based on machine learning techniques viz. supervised, unsupervised and semi supervised.In this paper we present a review of various text classification approaches under machine learningparadig...

  19. The immune system, adaptation, and machine learning

    Science.gov (United States)

    Farmer, J. Doyne; Packard, Norman H.; Perelson, Alan S.

    1986-10-01

    The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.

  20. Auto-Suggest Capability via Machine Learning in SMART NAS, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — We build machine learning capabilities that enables the Shadow Mode Assessment using Realistic Technologies for the NAS (SMART NAS) system to synthesize, optimize,...

  1. Teraflop-scale Incremental Machine Learning

    OpenAIRE

    Özkural, Eray

    2011-01-01

    We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on Stochastic Context Free Grammar together with four synergistic update algorithms that use the same grammar as a guiding probability distribution of programs. The update algorithms include adjusting production probabilities, re-u...

  2. The Simulation Computer Based Learning (SCBL) for Short Circuit Multi Machine Power System Analysis

    Science.gov (United States)

    Rahmaniar; Putri, Maharani

    2018-03-01

    Strengthening Competitiveness of human resources become the reply of college as a conductor of high fomal education. Electrical Engineering Program UNPAB (Prodi TE UNPAB) as one of the department of electrical engineering that manages the field of electrical engineering expertise has a very important part in preparing human resources (HR), Which is required by where graduates are produced by DE UNPAB, Is expected to be able to compete globally, especially related to the implementation of Asean Economic Community (AEC) which requires the active participation of graduates with competence and quality of human resource competitiveness. Preparation of HR formation Competitive is done with the various strategies contained in the Seven (7) Higher Education Standard, one part of which is the implementation of teaching and learning process in Electrical system analysis with short circuit analysis (SCA) This course is a course The core of which is the basis for the competencies of other subjects in the advanced semester at Development of Computer Based Learning model (CBL) is done in the learning of interference analysis of multi-machine short circuit which includes: (a) Short-circuit One phase, (B) Two-phase Short Circuit Disruption, (c) Ground Short Circuit Disruption, (d) Short Circuit Disruption One Ground Floor Development of CBL learning model for Electrical System Analysis course provides space for students to be more active In learning in solving complex (complicated) problems, so it is thrilling Ilkan flexibility of student learning how to actively solve the problem of short-circuit analysis and to form the active participation of students in learning (Student Center Learning, in the course of electrical power system analysis.

  3. Machine learning in geosciences and remote sensing

    Directory of Open Access Journals (Sweden)

    David J. Lary

    2016-01-01

    Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

  4. Massively collaborative machine learning

    NARCIS (Netherlands)

    Rijn, van J.N.

    2016-01-01

    Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop

  5. Contemporary machine learning: techniques for practitioners in the physical sciences

    Science.gov (United States)

    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.

  6. Predicting genome-wide redundancy using machine learning

    Directory of Open Access Journals (Sweden)

    Shasha Dennis E

    2010-11-01

    Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

  7. Machine Learning in Computer-Aided Synthesis Planning.

    Science.gov (United States)

    Coley, Connor W; Green, William H; Jensen, Klavs F

    2018-05-15

    Computer-aided synthesis planning (CASP) is focused on the goal of accelerating the process by which chemists decide how to synthesize small molecule compounds. The ideal CASP program would take a molecular structure as input and output a sorted list of detailed reaction schemes that each connect that target to purchasable starting materials via a series of chemically feasible reaction steps. Early work in this field relied on expert-crafted reaction rules and heuristics to describe possible retrosynthetic disconnections and selectivity rules but suffered from incompleteness, infeasible suggestions, and human bias. With the relatively recent availability of large reaction corpora (such as the United States Patent and Trademark Office (USPTO), Reaxys, and SciFinder databases), consisting of millions of tabulated reaction examples, it is now possible to construct and validate purely data-driven approaches to synthesis planning. As a result, synthesis planning has been opened to machine learning techniques, and the field is advancing rapidly. In this Account, we focus on two critical aspects of CASP and recent machine learning approaches to both challenges. First, we discuss the problem of retrosynthetic planning, which requires a recommender system to propose synthetic disconnections starting from a target molecule. We describe how the search strategy, necessary to overcome the exponential growth of the search space with increasing number of reaction steps, can be assisted through a learned synthetic complexity metric. We also describe how the recursive expansion can be performed by a straightforward nearest neighbor model that makes clever use of reaction data to generate high quality retrosynthetic disconnections. Second, we discuss the problem of anticipating the products of chemical reactions, which can be used to validate proposed reactions in a computer-generated synthesis plan (i.e., reduce false positives) to increase the likelihood of experimental success

  8. Bionic machines and systems

    Energy Technology Data Exchange (ETDEWEB)

    Halme, A.; Paanajaervi, J. (eds.)

    2004-07-01

    Introduction Biological systems form a versatile and complex entirety on our planet. One evolutionary branch of primates, called humans, has created an extraordinary skill, called technology, by the aid of which it nowadays dominate life on the planet. Humans use technology for producing and harvesting food, healthcare and reproduction, increasing their capability to commute and communicate, defending their territory etc., and to develop more technology. As a result of this, humans have become much technology dependent, so that they have been forced to form a specialized class of humans, called engineers, who take care of the knowledge of technology developing it further and transferring it to later generations. Until now, technology has been relatively independent from biology, although some of its branches, e.g. biotechnology and biomedical engineering, have traditionally been in close contact with it. There exist, however, an increasing interest to expand the interface between technology and biology either by directly utilizing biological processes or materials by combining them with 'dead' technology, or by mimicking in technological solutions the biological innovations created by evolution. The latter theme is in focus of this report, which has been written as the proceeding of the post-graduate seminar 'Bionic Machines and Systems' held at HUT Automation Technology Laboratory in autumn 2003. The underlaying idea of the seminar was to analyze biological species by considering them as 'robotic machines' having various functional subsystems, such as for energy, motion and motion control, perception, navigation, mapping and localization. We were also interested about intelligent capabilities, such as learning and communication, and social structures like swarming behavior and its mechanisms. The word 'bionic machine' comes from the book which was among the initial material when starting our mission to the fascinating world

  9. Study of Environmental Data Complexity using Extreme Learning Machine

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2017-04-01

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

  10. Efficient tuning in supervised machine learning

    NARCIS (Netherlands)

    Koch, Patrick

    2013-01-01

    The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the

  11. Combining Formal Logic and Machine Learning for Sentiment Analysis

    DEFF Research Database (Denmark)

    Petersen, Niklas Christoffer; Villadsen, Jørgen

    2014-01-01

    This paper presents a formal logical method for deep structural analysis of the syntactical properties of texts using machine learning techniques for efficient syntactical tagging. To evaluate the method it is used for entity level sentiment analysis as an alternative to pure machine learning...

  12. Machine Learning Principles Can Improve Hip Fracture Prediction

    DEFF Research Database (Denmark)

    Kruse, Christian; Eiken, Pia; Vestergaard, Peter

    2017-01-01

    Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

  13. Machine learning for Big Data analytics in plants.

    Science.gov (United States)

    Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

    2014-12-01

    Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Advances in Machine Learning and Data Mining for Astronomy

    Science.gov (United States)

    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.

  15. Machine Learning via Mathematical Programming

    National Research Council Canada - National Science Library

    Mamgasarian, Olivi

    1999-01-01

    Mathematical programming approaches were applied to a variety of problems in machine learning in order to gain deeper understanding of the problems and to come up with new and more efficient computational algorithms...

  16. Machine Learning examples on Invenio

    CERN Document Server

    CERN. Geneva

    2017-01-01

    This talk will present the different Machine Learning tools that the INSPIRE is developing and integrating in order to automatize as much as possible content selection and curation in a subject based repository.

  17. Development of E-Learning Materials for Machining Safety Education

    Science.gov (United States)

    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.

  18. Modeling Geomagnetic Variations using a Machine Learning Framework

    Science.gov (United States)

    Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

    2017-12-01

    We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

  19. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...... system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here...

  20. Classification of older adults with/without a fall history using machine learning methods.

    Science.gov (United States)

    Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

    2015-01-01

    Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

  1. Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

    Science.gov (United States)

    de Greeff, Joachim; Belpaeme, Tony

    2015-01-01

    Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance. PMID:26422143

  2. Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

    Science.gov (United States)

    de Greeff, Joachim; Belpaeme, Tony

    2015-01-01

    Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

  3. Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

    Directory of Open Access Journals (Sweden)

    Joachim de Greeff

    Full Text Available Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference; the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

  4. Classification and authentication of unknown water samples using machine learning algorithms.

    Science.gov (United States)

    Kundu, Palash K; Panchariya, P C; Kundu, Madhusree

    2011-07-01

    This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Introduction to Machine Learning: Class Notes 67577

    OpenAIRE

    Shashua, Amnon

    2009-01-01

    Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

  6. An experimental result of estimating an application volume by machine learning techniques.

    Science.gov (United States)

    Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

    2015-01-01

    In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

  7. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    Science.gov (United States)

    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…

  8. Mining the Kepler Data using Machine Learning

    Science.gov (United States)

    Walkowicz, Lucianne; Howe, A. R.; Nayar, R.; Turner, E. L.; Scargle, J.; Meadows, V.; Zee, A.

    2014-01-01

    Kepler's high cadence and incredible precision has provided an unprecedented view into stars and their planetary companions, revealing both expected and novel phenomena and systems. Due to the large number of Kepler lightcurves, the discovery of novel phenomena in particular has often been serendipitous in the course of searching for known forms of variability (for example, the discovery of the doubly pulsating elliptical binary KOI-54, originally identified by the transiting planet search pipeline). In this talk, we discuss progress on mining the Kepler data through both supervised and unsupervised machine learning, intended to both systematically search the Kepler lightcurves for rare or anomalous variability, and to create a variability catalog for community use. Mining the dataset in this way also allows for a quantitative identification of anomalous variability, and so may also be used as a signal-agnostic form of optical SETI. As the Kepler data are exceptionally rich, they provide an interesting counterpoint to machine learning efforts typically performed on sparser and/or noisier survey data, and will inform similar characterization carried out on future survey datasets.

  9. Introduction to machine learning for brain imaging.

    Science.gov (United States)

    Lemm, Steven; Blankertz, Benjamin; Dickhaus, Thorsten; Müller, Klaus-Robert

    2011-05-15

    Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. Copyright © 2010 Elsevier Inc. All rights reserved.

  10. Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

    Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. In this paper, a comparative performance analysis of different machine learning techniques is explored f or software bug prediction on public available data sets. Results showed most of the mac ...

  11. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks

    OpenAIRE

    Airola, Rasmus; Hager, Kristoffer

    2017-01-01

    The use of machine learning and specifically neural networks is a growing trend in software development, and has grown immensely in the last couple of years in the light of an increasing need to handle big data and large information flows. Machine learning has a broad area of application, such as human-computer interaction, predicting stock prices, real-time translation, and self driving vehicles. Large companies such as Microsoft and Google have already implemented machine learning in some o...

  12. Machine learning for healthcare technologies

    CERN Document Server

    Clifton, David A

    2016-01-01

    This book brings together chapters on the state-of-the-art in machine learning (ML) as it applies to the development of patient-centred technologies, with a special emphasis on 'big data' and mobile data.

  13. Component Pin Recognition Using Algorithms Based on Machine Learning

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2017-07-01

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

  15. A Machine LearningFramework to Forecast Wave Conditions

    Science.gov (United States)

    Zhang, Y.; James, S. C.; O'Donncha, F.

    2017-12-01

    Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in

  16. Machine learning spatial geometry from entanglement features

    Science.gov (United States)

    You, Yi-Zhuang; Yang, Zhao; Qi, Xiao-Liang

    2018-02-01

    Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on a 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT2 point).

  17. Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

    Directory of Open Access Journals (Sweden)

    Muneki Yasuda

    2018-04-01

    Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

  18. Proceedings of IEEE Machine Learning for Signal Processing Workshop XVI

    DEFF Research Database (Denmark)

    Larsen, Jan

    These proceedings contains refereed papers presented at the sixteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP'2006), held in Maynooth, Co. Kildare, Ireland, September 6-8, 2006. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP......). The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized by the Machine Learning for Signal Processing Technical Committee...... the same standard as the printed version and facilitates the reading and searching of the papers. The field of machine learning has matured considerably in both methodology and real-world application domains and has become particularly important for solution of problems in signal processing. As reflected...

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

    Science.gov (United States)

    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.

  20. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

    Science.gov (United States)

    Grisafi, Andrea; Wilkins, David M; Csányi, Gábor; Ceriotti, Michele

    2018-01-19

    Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

  1. Financial signal processing and machine learning

    CERN Document Server

    Kulkarni,Sanjeev R; Dmitry M. Malioutov

    2016-01-01

    The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analy...

  2. Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

    Science.gov (United States)

    Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

    2018-01-11

    By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

  3. Large-scale Machine Learning in High-dimensional Datasets

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen

    Over the last few decades computers have gotten to play an essential role in our daily life, and data is now being collected in various domains at a faster pace than ever before. This dissertation presents research advances in four machine learning fields that all relate to the challenges imposed...... are better at modeling local heterogeneities. In the field of machine learning for neuroimaging, we introduce learning protocols for real-time functional Magnetic Resonance Imaging (fMRI) that allow for dynamic intervention in the human decision process. Specifically, the model exploits the structure of f...

  4. Improved Extreme Learning Machine and Its Application in Image Quality Assessment

    Directory of Open Access Journals (Sweden)

    Li Mao

    2014-01-01

    Full Text Available Extreme learning machine (ELM is a new class of single-hidden layer feedforward neural network (SLFN, which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted ELM, and propose a modified (weighted extreme learning machine (M-ELM and M-WELM. Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.

  5. Newton Methods for Large Scale Problems in Machine Learning

    Science.gov (United States)

    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…

  6. Machine learning-based dual-energy CT parametric mapping.

    Science.gov (United States)

    Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

    2018-05-22

    The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρe), mean excitation energy (Ix), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 seconds. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency. . © 2018 Institute of Physics and Engineering in

  7. Mutual learning in a tree parity machine and its application to cryptography

    International Nuclear Information System (INIS)

    Rosen-Zvi, Michal; Klein, Einat; Kanter, Ido; Kinzel, Wolfgang

    2002-01-01

    Mutual learning of a pair of tree parity machines with continuous and discrete weight vectors is studied analytically. The analysis is based on a mapping procedure that maps the mutual learning in tree parity machines onto mutual learning in noisy perceptrons. The stationary solution of the mutual learning in the case of continuous tree parity machines depends on the learning rate where a phase transition from partial to full synchronization is observed. In the discrete case the learning process is based on a finite increment and a full synchronized state is achieved in a finite number of steps. The synchronization of discrete parity machines is introduced in order to construct an ephemeral key-exchange protocol. The dynamic learning of a third tree parity machine (an attacker) that tries to imitate one of the two machines while the two still update their weight vectors is also analyzed. In particular, the synchronization times of the naive attacker and the flipping attacker recently introduced in Ref. 9 are analyzed. All analytical results are found to be in good agreement with simulation results

  8. BELM: Bayesian extreme learning machine.

    Science.gov (United States)

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

    2011-03-01

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

  9. C4.5 programs for machine learning

    CERN Document Server

    Quinlan, J Ross

    1992-01-01

    Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available for download (see below). C4.5 starts with large sets of cases belonging to known classes. The cases,

  10. Bidirectional extreme learning machine for regression problem and its learning effectiveness.

    Science.gov (United States)

    Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

    2012-09-01

    It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

  11. Corporate Disruption in the Science of Machine Learning

    OpenAIRE

    Work, Sam

    2016-01-01

    This MSc dissertation considers the effects of the current corporate interest on researchers in the field of machine learning. Situated within the field's cyclical history of academic, public and corporate interest, this dissertation investigates how current researchers view recent developments and negotiate their own research practices within an environment of increased commercial interest and funding. The original research consists of in-depth interviews with 12 machine learning researchers...

  12. Online learning control using adaptive critic designs with sparse kernel machines.

    Science.gov (United States)

    Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

    2013-05-01

    In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

  13. Application of machine learning methods in bioinformatics

    Science.gov (United States)

    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.

  14. What is the machine learning?

    Science.gov (United States)

    Chang, Spencer; Cohen, Timothy; Ostdiek, Bryan

    2018-03-01

    Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.

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

    Science.gov (United States)

    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.

  16. ClearTK 2.0: Design Patterns for Machine Learning in UIMA

    OpenAIRE

    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, r...

  17. Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    There is a deep connection between machine learning and jet physics - after all, jets are defined by unsupervised learning algorithms. Jet physics has been a driving force for studying modern machine learning in high energy physics. Domain specific challenges require new techniques to make full use of the algorithms. A key focus is on understanding how and what the algorithms learn. Modern machine learning techniques for jet physics are demonstrated for classification, regression, and generation. In addition to providing powerful baseline performance, we show how to train complex models directly on data and to generate sparse stacked images with non-uniform granularity.

  18. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

    2017-01-01

    With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

  19. Informatics and machine learning to define the phenotype.

    Science.gov (United States)

    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.

  20. Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

    Science.gov (United States)

    Jaspers, Arne; Beéck, Tim Op De; Brink, Michel S; Frencken, Wouter G P; Staes, Filip; Davis, Jesse J; Helsen, Werner F

    2017-12-28

    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 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 two 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 two machine learning techniques, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (LASSO), and one naive baseline method. The predictions were based on a large set of external load indicators. Using each technique, one group model involving all players and one individual model for each player was constructed. These models' performance on predicting the reported RPE values for future training sessions was compared to the naive baseline's performance. Both the ANN and LASSO models outperformed the baseline. Additionally, the LASSO model made more accurate predictions for the RPE than the ANN model. Furthermore, decelerations were identified as important external load indicators. 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 the RPE for future sessions to optimize training design and evaluation. Additionally, these techniques may be used in conjunction with expert knowledge to select key external load indicators for load monitoring.

  1. Machine protection systems

    CERN Document Server

    Macpherson, A L

    2010-01-01

    A summary of the Machine Protection System of the LHC is given, with particular attention given to the outstanding issues to be addressed, rather than the successes of the machine protection system from the 2009 run. In particular, the issues of Safe Machine Parameter system, collimation and beam cleaning, the beam dump system and abort gap cleaning, injection and dump protection, and the overall machine protection program for the upcoming run are summarised.

  2. Nonlinear machine learning in soft materials engineering and design

    Science.gov (United States)

    Ferguson, Andrew

    The inherently many-body nature of molecular folding and colloidal self-assembly makes it challenging to identify the underlying collective mechanisms and pathways governing system behavior, and has hindered rational design of soft materials with desired structure and function. Fundamentally, there exists a predictive gulf between the architecture and chemistry of individual molecules or colloids and the collective many-body thermodynamics and kinetics. Integrating machine learning techniques with statistical thermodynamics provides a means to bridge this divide and identify emergent folding pathways and self-assembly mechanisms from computer simulations or experimental particle tracking data. We will survey a few of our applications of this framework that illustrate the value of nonlinear machine learning in understanding and engineering soft materials: the non-equilibrium self-assembly of Janus colloids into pinwheels, clusters, and archipelagos; engineering reconfigurable ''digital colloids'' as a novel high-density information storage substrate; probing hierarchically self-assembling onjugated asphaltenes in crude oil; and determining macromolecular folding funnels from measurements of single experimental observables. We close with an outlook on the future of machine learning in soft materials engineering, and share some personal perspectives on working at this disciplinary intersection. We acknowledge support for this work from a National Science Foundation CAREER Award (Grant No. DMR-1350008) and the Donors of the American Chemical Society Petroleum Research Fund (ACS PRF #54240-DNI6).

  3. A distributed algorithm for machine learning

    Science.gov (United States)

    Chen, Shihong

    2018-04-01

    This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.

  4. Strategies and Principles of Distributed Machine Learning on Big Data

    Directory of Open Access Journals (Sweden)

    Eric P. Xing

    2016-06-01

    Full Text Available The rise of big data has led to new demands for machine learning (ML systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area

  5. Machine learning and next-generation asteroid surveys

    Science.gov (United States)

    Nugent, Carrie R.; Dailey, John; Cutri, Roc M.; Masci, Frank J.; Mainzer, Amy K.

    2017-10-01

    Next-generation surveys such as NEOCam (Mainzer et al., 2016) will sift through tens of millions of point source detections daily to detect and discover asteroids. This requires new, more efficient techniques to distinguish between solar system objects, background stars and galaxies, and artifacts such as cosmic rays, scattered light and diffraction spikes.Supervised machine learning is a set of algorithms that allows computers to classify data on a training set, and then apply that classification to make predictions on new datasets. It has been employed by a broad range of fields, including computer vision, medical diagnoses, economics, and natural language processing. It has also been applied to astronomical datasets, including transient identification in the Palomar Transient Factory pipeline (Masci et al., 2016), and in the Pan-STARRS1 difference imaging (D. E. Wright et al., 2015).As part of the NEOCam extended phase A work we apply machine learning techniques to the problem of asteroid detection. Asteroid detection is an ideal application of supervised learning, as there is a wealth of metrics associated with each extracted source, and suitable training sets are easily created. Using the vetted NEOWISE dataset (E. L. Wright et al., 2010, Mainzer et al., 2011) as a proof-of-concept of this technique, we applied the python package sklearn. We report on reliability, feature set selection, and the suitability of various algorithms.

  6. VIRTUAL MACHINES IN EDUCATION – CNC MILLING MACHINE WITH SINUMERIK 840D CONTROL SYSTEM

    Directory of Open Access Journals (Sweden)

    Ireneusz Zagórski

    2014-11-01

    Full Text Available Machining process nowadays could not be conducted without its inseparable element: cutting edge and frequently numerically controlled milling machines. Milling and lathe machining centres comprise standard equipment in many companies of the machinery industry, e.g. automotive or aircraft. It is for that reason that tertiary education should account for this rising demand. This entails the introduction into the curricula the forms which enable visualisation of machining, milling process and virtual production as well as virtual machining centres simulation. Siemens Virtual Machine (Virtual Workshop sets an example of such software, whose high functionality offers a range of learning experience, such as: learning the design of machine tools, their configuration, basic operation functions as well as basics of CNC.

  7. IRB Process Improvements: A Machine Learning Analysis.

    Science.gov (United States)

    Shoenbill, Kimberly; Song, Yiqiang; Cobb, Nichelle L; Drezner, Marc K; Mendonca, Eneida A

    2017-06-01

    Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

  8. Learning Extended Finite State Machines

    Science.gov (United States)

    Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard

    2014-01-01

    We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

  9. Machine learning in updating predictive models of planning and scheduling transportation projects

    Science.gov (United States)

    1997-01-01

    A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

  10. Combining extreme learning machines using support vector machines for breast tissue classification.

    Science.gov (United States)

    Daliri, Mohammad Reza

    2015-01-01

    In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

  11. Application of machine learning and expert systems to Statistical Process Control (SPC) chart interpretation

    Science.gov (United States)

    Shewhart, Mark

    1991-01-01

    Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.

  12. Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Liu, Xueyi; Gao, Chuanhou; Li, Ping

    2012-09-01

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

  14. Machine Learning Methods for Production Cases Analysis

    Science.gov (United States)

    Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

    2018-03-01

    Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

  15. 2015 International Conference on Machine Learning and Signal Processing

    CERN Document Server

    Woo, Wai; Sulaiman, Hamzah; Othman, Mohd; Saat, Mohd

    2016-01-01

    This book presents important research findings and recent innovations in the field of machine learning and signal processing. A wide range of topics relating to machine learning and signal processing techniques and their applications are addressed in order to provide both researchers and practitioners with a valuable resource documenting the latest advances and trends. The book comprises a careful selection of the papers submitted to the 2015 International Conference on Machine Learning and Signal Processing (MALSIP 2015), which was held on 15–17 December 2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers, academicians, and practitioners an ideal opportunity to disseminate their findings and achievements. All of the included contributions were chosen by expert peer reviewers from across the world on the basis of their interest to the community. In addition to presenting the latest in design, development, and research, the book provides access to numerous new algorithms for machine learni...

  16. Machine Learning applications in CMS

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine Learning is used in many aspects of CMS data taking, monitoring, processing and analysis. We review a few of these use cases and the most recent developments, with an outlook to future applications in the LHC Run III and for the High-Luminosity phase.

  17. IEEE International Workshop on Machine Learning for Signal Processing: Preface

    DEFF Research Database (Denmark)

    Tao, Jianhua

    The 21st IEEE International Workshop on Machine Learning for Signal Processing will be held in Beijing, China, on September 18–21, 2011. The workshop series is the major annual technical event of the IEEE Signal Processing Society's Technical Committee on Machine Learning for Signal Processing...

  18. RG-inspired machine learning for lattice field theory

    Directory of Open Access Journals (Sweden)

    Foreman Sam

    2018-01-01

    Full Text Available Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use renormalization group (RG ideas in the context of machine learning. We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data. We discuss the correspondence between principal components analysis (PCA and RG flows across the transition for worm configurations of the 2D Ising model. Preliminary results regarding the logarithmic divergence of the leading PCA eigenvalue were presented at the conference. More generally, we discuss the relationship between PCA and observables in Monte Carlo simulations and the possibility of reducing the number of learning parameters in supervised learning based on RG inspired hierarchical ansatzes.

  19. RG-inspired machine learning for lattice field theory

    Science.gov (United States)

    Foreman, Sam; Giedt, Joel; Meurice, Yannick; Unmuth-Yockey, Judah

    2018-03-01

    Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use renormalization group (RG) ideas in the context of machine learning. We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data. We discuss the correspondence between principal components analysis (PCA) and RG flows across the transition for worm configurations of the 2D Ising model. Preliminary results regarding the logarithmic divergence of the leading PCA eigenvalue were presented at the conference. More generally, we discuss the relationship between PCA and observables in Monte Carlo simulations and the possibility of reducing the number of learning parameters in supervised learning based on RG inspired hierarchical ansatzes.

  20. Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS

    Science.gov (United States)

    Armstrong, David J.; Günther, Maximilian N.; McCormac, James; Smith, Alexis M. S.; Bayliss, Daniel; Bouchy, François; Burleigh, Matthew R.; Casewell, Sarah; Eigmüller, Philipp; Gillen, Edward; Goad, Michael R.; Hodgkin, Simon T.; Jenkins, James S.; Louden, Tom; Metrailler, Lionel; Pollacco, Don; Poppenhaeger, Katja; Queloz, Didier; Raynard, Liam; Rauer, Heike; Udry, Stéphane; Walker, Simon R.; Watson, Christopher A.; West, Richard G.; Wheatley, Peter J.

    2018-05-01

    State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.

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

    Science.gov (United States)

    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.

  2. Convergence Of Cloud Computing Internet Of Things And Machine Learning The Future Of Decision Support Systems

    Directory of Open Access Journals (Sweden)

    Gilberto Crespo-Perez

    2017-07-01

    Full Text Available The objective of this research was to develop a framework for understanding the Convergence of Cloud Computing Machine Learning and Internet of Things as the future of Decision Support Systems. To develop this framework the researchers analyzed and synthesized 35 research articles from 2006 to 2017. The results indicated that when the data is massive it is necessary to use computational algorithms and complex analytical techniques. The Internet of Things in combination with the large accumulation of data and data mining improves the learning of automatic intelligence for business. This is due to the fact that the technology has the intelligence to infer and provide solutions based on past experiences and past events.

  3. PREZICEREA PERFORMANŢELOR STUDENŢILOR FOLOSIND ÎNVĂŢAREA AUTOMATĂ (Machine Learning

    Directory of Open Access Journals (Sweden)

    Maria CRISTEI

    2017-08-01

    Full Text Available În prezent, învăţarea automată (machine learning ocupă un loc important în inteligenţa artificială, preocupându-se de dezvoltarea algoritmilor ce permit unui sistem informatic să înveţe date, reguli şi algoritmi. Învăţarea automată pre­supune în primul rând identificarea şi implementarea unei modalităţi cât mai eficiente de reprezentare a informaţiilor, în sensul facilitării căutării, reorganizării şi modificării acestora. În acest sens, în prezentul articol se descrie utilitatea şi aplicabilitatea tehnicilor de învăţare automată supervizată la problemele de predicţie şi implementarea acestora în dez­vol­tarea aplicaţiilor informatice. Aplicaţia elaborată este unică prin felul ei de executare a modelului machine learning de predicţie. Metodologia folosită în aplicaţia elaborată este mixtă, cuprinzând tehnologii complexe de ultimă oră: mediul de dezvoltare Jupyter Notebook, limbajul de programare Python împreună cu cele mai populare librării ale acestuia utilizate în machine learning, instrumente de dezvoltare a aplicaţiei web Flask.PREDICTING STUDENT PERFORMANCE USING MACHINE LEARNINGAt present, machine learning occupies an important place in artificial intelligence, and is concerned with the development of algorithms that allow an information system to learn data, rules, and algorithms. Automatic learning involves first and foremost the identification and implementation of a more efficient way of representing information in order to facilitate search, reorganization and change. In this respect, this article describes the utility and applicability of supervised auto­mated learning techniques to prediction problems and their implementation in the development of computer applications. The elaborate application is unique in its way of executing the Machine learning prediction model. The methodology used in the developed application is mixed, including state-of-the-art complex

  4. Advanced methods in NDE using machine learning approaches

    Science.gov (United States)

    Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

    2018-04-01

    Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

  5. Machine-Learning Algorithms to Code Public Health Spending Accounts.

    Science.gov (United States)

    Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

    Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

  6. Statistical and Machine Learning Models to Predict Programming Performance

    OpenAIRE

    Bergin, Susan

    2006-01-01

    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  7. Machine learning in medicine cookbook

    CERN Document Server

    Cleophas, Ton J

    2014-01-01

    The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks. General purposes ...

  8. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  9. Machine Learning Techniques for Stellar Light Curve Classification

    Science.gov (United States)

    Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

    2018-07-01

    We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

  10. Modelling tick abundance using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Kjær, Lene Jung; Korslund, L.; Kjelland, V.

    satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

  11. Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

    Science.gov (United States)

    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…

  12. Indirect Tire Monitoring System - Machine Learning Approach

    Science.gov (United States)

    Svensson, O.; Thelin, S.; Byttner, S.; Fan, Y.

    2017-10-01

    The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an

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

    Science.gov (United States)

    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

  14. Machine Learning for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Wass, J.; Thrane, Jakob; Piels, Molly

    2016-01-01

    Supervised machine learning methods are applied and demonstrated experimentally for inband OSNR estimation and modulation format classification in optical communication systems. The proposed methods accurately evaluate coherent signals up to 64QAM using only intensity information....

  15. Machine learning in Python essential techniques for predictive analysis

    CERN Document Server

    Bowles, Michael

    2015-01-01

    Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

  16. Machine learning methods in predicting the student academic motivation

    Directory of Open Access Journals (Sweden)

    Ivana Đurđević Babić

    2017-01-01

    Full Text Available Academic motivation is closely related to academic performance. For educators, it is equally important to detect early students with a lack of academic motivation as it is to detect those with a high level of academic motivation. In endeavouring to develop a classification model for predicting student academic motivation based on their behaviour in learning management system (LMS courses, this paper intends to establish links between the predicted student academic motivation and their behaviour in the LMS course. Students from all years at the Faculty of Education in Osijek participated in this research. Three machine learning classifiers (neural networks, decision trees, and support vector machines were used. To establish whether a significant difference in the performance of models exists, a t-test of the difference in proportions was used. Although, all classifiers were successful, the neural network model was shown to be the most successful in detecting the student academic motivation based on their behaviour in LMS course.

  17. Summary of vulnerability related technologies based on machine learning

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Nguyen, Dung H M; Patrick, Jon D

    2014-01-01

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

  19. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

    Science.gov (United States)

    Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

    2016-04-01

    This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

  20. Research progress in machine learning methods for gene-gene interaction detection.

    Science.gov (United States)

    Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

    2018-03-20

    Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

  1. Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data

    DEFF Research Database (Denmark)

    Madsen, Kristoffer Hougaard; Krohne, Laerke G; Cai, Xin-Lu

    2018-01-01

    Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While...... the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning....... We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives....

  2. Multivariate Mapping of Environmental Data Using Extreme Learning Machines

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2014-05-01

    In most real cases environmental data are multivariate, highly variable at several spatio-temporal scales, and are generated by nonlinear and complex phenomena. Mapping - spatial predictions of such data, is a challenging problem. Machine learning algorithms, being universal nonlinear tools, have demonstrated their efficiency in modelling of environmental spatial and space-time data (Kanevski et al. 2009). Recently, a new approach in machine learning - Extreme Learning Machine (ELM), has gained a great popularity. ELM is a fast and powerful approach being a part of the machine learning algorithm category. Developed by G.-B. Huang et al. (2006), it follows the structure of a multilayer perceptron (MLP) with one single-hidden layer feedforward neural networks (SLFNs). The learning step of classical artificial neural networks, like MLP, deals with the optimization of weights and biases by using gradient-based learning algorithm (e.g. back-propagation algorithm). Opposed to this optimization phase, which can fall into local minima, ELM generates randomly the weights between the input layer and the hidden layer and also the biases in the hidden layer. By this initialization, it optimizes just the weight vector between the hidden layer and the output layer in a single way. The main advantage of this algorithm is the speed of the learning step. In a theoretical context and by growing the number of hidden nodes, the algorithm can learn any set of training data with zero error. To avoid overfitting, cross-validation method or "true validation" (by randomly splitting data into training, validation and testing subsets) are recommended in order to find an optimal number of neurons. With its universal property and solid theoretical basis, ELM is a good machine learning algorithm which can push the field forward. The present research deals with an extension of ELM to multivariate output modelling and application of ELM to the real data case study - pollution of the sediments in

  3. Machine learning applied to the prediction of citrus production

    OpenAIRE

    Díaz Rodríguez, Susana Irene; Mazza, Silvia M.; Fernández-Combarro Álvarez, Elías; Giménez, Laura I.; Gaiad, José E.

    2017-01-01

    An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analyse...

  4. Identifying student stuck states in programmingassignments using machine learning

    OpenAIRE

    Lindell, Johan

    2014-01-01

    Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of...

  5. Machine learning application in the life time of materials

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Materials design and development typically takes several decades from the initial discovery to commercialization with the traditional trial and error development approach. With the accumulation of data from both experimental and computational results, data based machine learning becomes an emerging field in materials discovery, design and property prediction. This manuscript reviews the history of materials science as a disciplinary the most common machine learning method used in materials sc...

  6. Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

    Science.gov (United States)

    Negro Maggio, Valentina; Iocchi, Luca

    2015-02-01

    Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

  7. Machine Learning Techniques for Prediction of Early Childhood Obesity.

    Science.gov (United States)

    Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

    2015-01-01

    This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

  8. Machine learning for adaptive many-core machines a practical approach

    CERN Document Server

    Lopes, Noel

    2015-01-01

    The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.

  9. Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

    Science.gov (United States)

    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

  10. Predicting the concentration of residual methanol in industrial formalin using machine learning

    OpenAIRE

    Heidkamp, William

    2016-01-01

    In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to pr...

  11. A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

    OpenAIRE

    Hayden Wimmer; Loreen Powell

    2014-01-01

    While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Baye...

  12. The use of machine learning and nonlinear statistical tools for ADME prediction.

    Science.gov (United States)

    Sakiyama, Yojiro

    2009-02-01

    Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

  13. Machine learning techniques to examine large patient databases.

    Science.gov (United States)

    Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

    2009-03-01

    Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

  14. Advances in independent component analysis and learning machines

    CERN Document Server

    Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko

    2015-01-01

    In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

  15. Machine learning Z2 quantum spin liquids with quasiparticle statistics

    Science.gov (United States)

    Zhang, Yi; Melko, Roger G.; Kim, Eun-Ah

    2017-12-01

    After decades of progress and effort, obtaining a phase diagram for a strongly correlated topological system still remains a challenge. Although in principle one could turn to Wilson loops and long-range entanglement, evaluating these nonlocal observables at many points in phase space can be prohibitively costly. With growing excitement over topological quantum computation comes the need for an efficient approach for obtaining topological phase diagrams. Here we turn to machine learning using quantum loop topography (QLT), a notion we have recently introduced. Specifically, we propose a construction of QLT that is sensitive to quasiparticle statistics. We then use mutual statistics between the spinons and visons to detect a Z2 quantum spin liquid in a multiparameter phase space. We successfully obtain the quantum phase boundary between the topological and trivial phases using a simple feed-forward neural network. Furthermore, we demonstrate advantages of our approach for the evaluation of phase diagrams relating to speed and storage. Such statistics-based machine learning of topological phases opens new efficient routes to studying topological phase diagrams in strongly correlated systems.

  16. Weighing the Dark and Light in Cosmology with Machine Learning

    Science.gov (United States)

    Trac, Hy

    2017-09-01

    Galaxy clusters contain large amounts of cold dark matter, hot ionized gas, and tens to hundreds of visible galaxies. They are the largest gravitationally bound systems in the Universe and make excellent laboratories for studying cosmology and astrophysics. Historically, Fritz Zwicky postulated the existence of dark matter when he inferred the total mass of the nearby Coma Cluster from the motions of its galaxies and found it to be much larger than the visible mass. Nowadays, the abundance of clusters as a function of mass and time can be used to study structure formation and constrain cosmological parameters. Dynamical measurements of the motions of galaxies can be used to probe the entire mass distribution, but standard analyses yield unwanted high mass errors. First, we show that modern machine learning algorithms can improve mass measurements by more than a factor of two compared to using standard scaling relations. Support Distribution Machines are used to train and test on the entire distribution of galaxy velocities to maximally use available information. Second, we discuss how Deep Learning can be used to train on multi-wavelength images of galaxies and clusters and to predict the underlying total matter distribution. By applying machine learning to observations and simulations, we can map out the dark and light in the Universe. DOE DE-SC0011114, NSF RI-1563887.

  17. SU-F-T-462: Lessons Learned From a Machine Incident Reporting System

    International Nuclear Information System (INIS)

    Sutlief, S; Hoisak, J

    2016-01-01

    Purpose: Linear accelerators must operate with minimal downtime. Machine incident logs are a crucial tool to meet this requirement. They providing a history of service and demonstrate whether a fix is working. This study investigates the information content of a large department linear accelerator incident log. Methods: Our department uses an electronic reporting system to provide immediate information to both key department staff and the field service department. This study examines reports for five linac logs during 2015. The report attributes for analysis include frequency, level of documentation, who solved the problem, and type of fix used. Results: Of the reports, 36% were documented as resolved. In another 25% the resolution allowed treatment to proceed although the reported problem recurred within days. In 5% only intermediate troubleshooting was documented. The remainder lacked documentation. In 60% of the reports, radiation therapists resolved the problem, often by clearing the appropriate faults or reinitializing a software or hardware service. 22% were resolved by physics and 10% by field service engineers. The remaining 8% were resolved by IT, Facilities, or resolved spontaneously. Typical fixes, in order of scope, included clearing the fault and moving on, closing and re-opening the patient session or software, cycling power to a sub-unit, recalibrating a device (e.g., optical surface imaging), and calling in Field Service (usually resolving the problem through maintenance or component replacement). Conclusion: The reports with undocumented resolution represent a missed opportunity for learning. Frequency of who resolves a problem scales with the proximity of the person’s role (therapist, physicist, or service engineer), which is inversely related to the permanence of the resolution. Review of lessons learned from machine incident logs can form the basis for guidance to radiation therapists and medical physicists to minimize equipment downtime and

  18. SU-F-T-462: Lessons Learned From a Machine Incident Reporting System

    Energy Technology Data Exchange (ETDEWEB)

    Sutlief, S; Hoisak, J [University of California, San Diego, La Jolla, CA (United States)

    2016-06-15

    Purpose: Linear accelerators must operate with minimal downtime. Machine incident logs are a crucial tool to meet this requirement. They providing a history of service and demonstrate whether a fix is working. This study investigates the information content of a large department linear accelerator incident log. Methods: Our department uses an electronic reporting system to provide immediate information to both key department staff and the field service department. This study examines reports for five linac logs during 2015. The report attributes for analysis include frequency, level of documentation, who solved the problem, and type of fix used. Results: Of the reports, 36% were documented as resolved. In another 25% the resolution allowed treatment to proceed although the reported problem recurred within days. In 5% only intermediate troubleshooting was documented. The remainder lacked documentation. In 60% of the reports, radiation therapists resolved the problem, often by clearing the appropriate faults or reinitializing a software or hardware service. 22% were resolved by physics and 10% by field service engineers. The remaining 8% were resolved by IT, Facilities, or resolved spontaneously. Typical fixes, in order of scope, included clearing the fault and moving on, closing and re-opening the patient session or software, cycling power to a sub-unit, recalibrating a device (e.g., optical surface imaging), and calling in Field Service (usually resolving the problem through maintenance or component replacement). Conclusion: The reports with undocumented resolution represent a missed opportunity for learning. Frequency of who resolves a problem scales with the proximity of the person’s role (therapist, physicist, or service engineer), which is inversely related to the permanence of the resolution. Review of lessons learned from machine incident logs can form the basis for guidance to radiation therapists and medical physicists to minimize equipment downtime and

  19. Quantum machine learning for quantum anomaly detection

    Science.gov (United States)

    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.

  20. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2015-01-01

    Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

  1. Accuracy comparison among different machine learning techniques for detecting malicious codes

    Science.gov (United States)

    Narang, Komal

    2016-03-01

    In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

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

    Science.gov (United States)

    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%.

  3. Predicting the dissolution kinetics of silicate glasses using machine learning

    Science.gov (United States)

    Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

    2018-05-01

    Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

  4. Different protein-protein interface patterns predicted by different machine learning methods.

    Science.gov (United States)

    Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

    2017-11-22

    Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

  5. Novel jet observables from machine learning

    Science.gov (United States)

    Datta, Kaustuv; Larkoski, Andrew J.

    2018-03-01

    Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H\\to b\\overline{b} decays from massive g\\to b\\overline{b} splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b\\overline{b} pair.

  6. Machine Learning-based discovery of closures for reduced models of dynamical systems

    Science.gov (United States)

    Pan, Shaowu; Duraisamy, Karthik

    2017-11-01

    Despite the successful application of machine learning (ML) in fields such as image processing and speech recognition, only a few attempts has been made toward employing ML to represent the dynamics of complex physical systems. Previous attempts mostly focus on parameter calibration or data-driven augmentation of existing models. In this work we present a ML framework to discover closure terms in reduced models of dynamical systems and provide insights into potential problems associated with data-driven modeling. Based on exact closure models for linear system, we propose a general linear closure framework from viewpoint of optimization. The framework is based on trapezoidal approximation of convolution term. Hyperparameters that need to be determined include temporal length of memory effect, number of sampling points, and dimensions of hidden states. To circumvent the explicit specification of memory effect, a general framework inspired from neural networks is also proposed. We conduct both a priori and posteriori evaluations of the resulting model on a number of non-linear dynamical systems. This work was supported in part by AFOSR under the project ``LES Modeling of Non-local effects using Statistical Coarse-graining'' with Dr. Jean-Luc Cambier as the technical monitor.

  7. Using machine learning to parse breast pathology reports.

    Science.gov (United States)

    Yala, Adam; Barzilay, Regina; Salama, Laura; Griffin, Molly; Sollender, Grace; Bardia, Aditya; Lehman, Constance; Buckley, Julliette M; Coopey, Suzanne B; Polubriaginof, Fernanda; Garber, Judy E; Smith, Barbara L; Gadd, Michele A; Specht, Michelle C; Gudewicz, Thomas M; Guidi, Anthony J; Taghian, Alphonse; Hughes, Kevin S

    2017-01-01

    Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women's Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.

  8. Attention: A Machine Learning Perspective

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2012-01-01

    We review a statistical machine learning model of top-down task driven attention based on the notion of ‘gist’. In this framework we consider the task to be represented as a classification problem with two sets of features — a gist of coarse grained global features and a larger set of low...

  9. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2016-01-01

    Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

  10. Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

    Directory of Open Access Journals (Sweden)

    Jongryun Roh

    2018-01-01

    Full Text Available Sitting posture monitoring systems (SPMSs help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced.

  11. Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Thrane, Jakob; Wass, Jesper; Piels, Molly

    2017-01-01

    Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

  12. Solving a Higgs optimization problem with quantum annealing for machine learning.

    Science.gov (United States)

    Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

    2017-10-18

    The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

  13. Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

    Science.gov (United States)

    Shamir, Reuben R; Dolber, Trygve; Noecker, Angela M; Walter, Benjamin L; McIntyre, Cameron C

    2015-01-01

    Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  15. Machine Shop I. Learning Activity Packets (LAPs). Section D--Power Saws and Drilling Machines.

    Science.gov (United States)

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

    This document contains two learning activity packets (LAPs) for the "power saws and drilling machines" instructional area of a Machine Shop I course. The two LAPs cover the following topics: power saws and drill press. Each LAP contains a cover sheet that describes its purpose, an introduction, and the tasks included in the LAP; learning…

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

    Science.gov (United States)

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

    2017-01-01

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

  17. Conformal prediction for reliable machine learning theory, adaptations and applications

    CERN Document Server

    Balasubramanian, Vineeth; Vovk, Vladimir

    2014-01-01

    The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

  18. Splendidly blended: a machine learning set up for CDU control

    Science.gov (United States)

    Utzny, Clemens

    2017-06-01

    As the concepts of machine learning and artificial intelligence continue to grow in importance in the context of internet related applications it is still in its infancy when it comes to process control within the semiconductor industry. Especially the branch of mask manufacturing presents a challenge to the concepts of machine learning since the business process intrinsically induces pronounced product variability on the background of small plate numbers. In this paper we present the architectural set up of a machine learning algorithm which successfully deals with the demands and pitfalls of mask manufacturing. A detailed motivation of this basic set up followed by an analysis of its statistical properties is given. The machine learning set up for mask manufacturing involves two learning steps: an initial step which identifies and classifies the basic global CD patterns of a process. These results form the basis for the extraction of an optimized training set via balanced sampling. A second learning step uses this training set to obtain the local as well as global CD relationships induced by the manufacturing process. Using two production motivated examples we show how this approach is flexible and powerful enough to deal with the exacting demands of mask manufacturing. In one example we show how dedicated covariates can be used in conjunction with increased spatial resolution of the CD map model in order to deal with pathological CD effects at the mask boundary. The other example shows how the model set up enables strategies for dealing tool specific CD signature differences. In this case the balanced sampling enables a process control scheme which allows usage of the full tool park within the specified tight tolerance budget. Overall, this paper shows that the current rapid developments off the machine learning algorithms can be successfully used within the context of semiconductor manufacturing.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2015-04-01

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

  1. What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?

    Science.gov (United States)

    Lee, Ernest Y; Lee, Michelle W; Fulan, Benjamin M; Ferguson, Andrew L; Wong, Gerard C L

    2017-12-06

    Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections. We show that machine learning on AMPs can be used to identify essential physico-chemical determinants of AMP functionality, and identify and design peptide sequences to generate membrane curvature. In a broader scope, we discuss the implications of our findings for the discovery of membrane-active peptides in general, and uncovering membrane activity in new and existing peptide taxonomies.

  2. Machine learning in laboratory medicine: waiting for the flood?

    Science.gov (United States)

    Cabitza, Federico; Banfi, Giuseppe

    2018-03-28

    This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to laboratory data for both diagnostic and prognostic purposes deserves more attention by the readership of this journal, as well as by physician-scientists who will want to take advantage of this new computer-based support in pathology and laboratory medicine.

  3. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  4. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. Machine learning for the automatic detection of anomalous events

    Science.gov (United States)

    Fisher, Wendy D.

    In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

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

    Science.gov (United States)

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

    2015-01-01

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

  7. Machine Learning Approaches to Increasing Value of Spaceflight Omics Databases

    Science.gov (United States)

    Gentry, Diana

    2017-01-01

    The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may

  8. Machine learning vortices at the Kosterlitz-Thouless transition

    Science.gov (United States)

    Beach, Matthew J. S.; Golubeva, Anna; Melko, Roger G.

    2018-01-01

    Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed-matter and statistical physics. Supervised algorithms trained on raw samples of microstates can successfully detect conventional phase transitions via learning a bulk feature such as an order parameter. In this paper, we investigate whether neural networks can learn to classify phases based on topological defects. We address this question on the two-dimensional classical XY model which exhibits a Kosterlitz-Thouless transition. We find significant feature engineering of the raw spin states is required to convincingly claim that features of the vortex configurations are responsible for learning the transition temperature. We further show a single-layer network does not correctly classify the phases of the XY model, while a convolutional network easily performs classification by learning the global magnetization. Finally, we design a deep network capable of learning vortices without feature engineering. We demonstrate the detection of vortices does not necessarily result in the best classification accuracy, especially for lattices of less than approximately 1000 spins. For larger systems, it remains a difficult task to learn vortices.

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

    Directory of Open Access Journals (Sweden)

    Hamed Asadi

    datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.

  10. Using Machine Learning to Match Assistive Technology to People with Disabilities.

    Science.gov (United States)

    Rafi, Abe

    2017-01-01

    This paper describes the initial results of work to create a recommender system to match technology products to people with I/DD by applying machine learning to a large volume of data about: people with I/DD; the technology products they use; and the outcomes they aim to achieve with technology.

  11. Visible Machine Learning for Biomedicine.

    Science.gov (United States)

    Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

    2018-06-14

    A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

  12. Outsmarting neural networks: an alternative paradigm for machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Protopopescu, V.; Rao, N.S.V.

    1996-10-01

    We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

  13. Machine learning derived risk prediction of anorexia nervosa.

    Science.gov (United States)

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  14. Intelligent Machine Learning Approaches for Aerospace Applications

    Science.gov (United States)

    Sathyan, Anoop

    Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire

  15. Overlay improvements using a real time machine learning algorithm

    Science.gov (United States)

    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.

  16. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

    Science.gov (United States)

    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.

  17. Hidden physics models: Machine learning of nonlinear partial differential equations

    Science.gov (United States)

    Raissi, Maziar; Karniadakis, George Em

    2018-03-01

    While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

  18. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  19. Introduction to machine learning: k-nearest neighbors.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-06-01

    Machine learning techniques have been widely used in many scientific fields, but its use in medical literature is limited partly because of technical difficulties. k-nearest neighbors (kNN) is a simple method of machine learning. The article introduces some basic ideas underlying the kNN algorithm, and then focuses on how to perform kNN modeling with R. The dataset should be prepared before running the knn() function in R. After prediction of outcome with kNN algorithm, the diagnostic performance of the model should be checked. Average accuracy is the mostly widely used statistic to reflect the kNN algorithm. Factors such as k value, distance calculation and choice of appropriate predictors all have significant impact on the model performance.

  20. Applications of learning based systems at AREVA group

    International Nuclear Information System (INIS)

    Jeanmart, F.; Leclerc, C.

    2006-01-01

    As part of its work on advanced information systems, AREVA is exploring the use of computerized tools based on 'machine learning' techniques. Some of these studies are being carried out by EURIWARE - continuing on from previous work done by AREVA NC - focused on the supervision of complex systems. Systems based on machine learning techniques are one of the possible solutions being investigated by AREVA: knowing that the stakes are high and involve better anticipation and control and high financial considerations. (authors)

  1. CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

    2017-07-14

    We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

  2. A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

    Science.gov (United States)

    S K, Somasundaram; P, Alli

    2017-11-09

    of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).

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

    Science.gov (United States)

    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.

  4. Machine Learning for Knowledge Extraction from PHR Big Data.

    Science.gov (United States)

    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.

  5. A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping

    Science.gov (United States)

    Yan, Wang; Jiajin, Le; Yun, Zhang

    2014-01-01

    The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results' evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer's obvious improvement of mapping error rate. PMID:25250372

  6. AHaH Computing–From Metastable Switches to Attractors to Machine Learning

    Science.gov (United States)

    Nugent, Michael Alexander; Molter, Timothy Wesley

    2014-01-01

    Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH) plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures–all key capabilities of biological nervous systems and modern machine learning algorithms with real world application. PMID:24520315

  7. AHaH computing-from metastable switches to attractors to machine learning.

    Directory of Open Access Journals (Sweden)

    Michael Alexander Nugent

    Full Text Available Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures-all key capabilities of biological nervous systems and modern machine learning algorithms with real world application.

  8. A survey on Barrett's esophagus analysis using machine learning.

    Science.gov (United States)

    de Souza, Luis A; Palm, Christoph; Mendel, Robert; Hook, Christian; Ebigbo, Alanna; Probst, Andreas; Messmann, Helmut; Weber, Silke; Papa, João P

    2018-05-01

    This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  10. Machine learning applied to crime prediction

    OpenAIRE

    Vaquero Barnadas, Miquel

    2016-01-01

    Machine Learning is a cornerstone when it comes to artificial intelligence and big data analysis. It provides powerful algorithms that are capable of recognizing patterns, classifying data, and, basically, learn by themselves to perform a specific task. This field has incredibly grown in popularity these days, however, it still remains unknown for the majority of people, and even for most professionals. This project intends to provide an understandable explanation of what is it, what types ar...

  11. Voice based gender classification using machine learning

    Science.gov (United States)

    Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

    2017-11-01

    Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

  12. Evaluation of Machine Learning Methods for LHC Optics Measurements and Corrections Software

    CERN Document Server

    AUTHOR|(CDS)2206853; Henning, Peter

    The field of artificial intelligence is driven by the goal to provide machines with human-like intelligence. However modern science is currently facing problems with high complexity that cannot be solved by humans in the same timescale as by machines. Therefore there is a demand on automation of complex tasks. To identify the category of tasks which can be performed by machines in the domain of optics measurements and correction on the Large Hadron Collider (LHC) is one of the central research subjects of this thesis. The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In High Energy Physics these concepts are mostly used in offline analysis of experiments data and to perform regression tasks. In Accelerator Physics the machine learning approach has not found a wide application yet. Therefore potential tasks for machine learning solutions can be specified in this domain. The appropriate methods and their suitability for...

  13. Learning Activity Packets for Grinding Machines. Unit I--Grinding Machines.

    Science.gov (United States)

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

    This learning activity packet (LAP) is one of three that accompany the curriculum guide on grinding machines. It outlines the study activities and performance tasks for the first unit of this curriculum guide. Its purpose is to aid the student in attaining a working knowledge of this area of training and in achieving a skilled or moderately…

  14. A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

    Science.gov (United States)

    Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

    2013-01-01

    Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

  15. A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

    Directory of Open Access Journals (Sweden)

    Ching Lee Koo

    2013-01-01

    Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

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

    Science.gov (United States)

    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.

  17. Machine learning of the reactor core loading pattern critical parameters

    International Nuclear Information System (INIS)

    Trontl, K.; Pevec, D.; Smuc, T.

    2007-01-01

    The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employed a recently introduced machine learning technique, Support Vector Regression (SVR), which has a strong theoretical background in statistical learning theory. Superior empirical performance of the method has been reported on difficult regression problems in different fields of science and technology. SVR is a data driven, kernel based, nonlinear modelling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modelling. The starting set of experimental data for training and testing of the machine learning algorithm was obtained using a two-dimensional diffusion theory reactor physics computer code. We illustrate the performance of the solution and discuss its applicability, i.e., complexity, speed and accuracy, with a projection to a more realistic scenario involving machine learning from the results of more accurate and time consuming three-dimensional core modelling code. (author)

  18. Social Intelligence in a Human-Machine Collaboration System

    Science.gov (United States)

    Nakajima, Hiroshi; Morishima, Yasunori; Yamada, Ryota; Brave, Scott; Maldonado, Heidy; Nass, Clifford; Kawaji, Shigeyasu

    In this information society of today, it is often argued that it is necessary to create a new way of human-machine interaction. In this paper, an agent with social response capabilities has been developed to achieve this goal. There are two kinds of information that is exchanged by two entities: objective and functional information (e.g., facts, requests, states of matters, etc.) and subjective information (e.g., feelings, sense of relationship, etc.). Traditional interactive systems have been designed to handle the former kind of information. In contrast, in this study social agents handling the latter type of information are presented. The current study focuses on sociality of the agent from the view point of Media Equation theory. This article discusses the definition, importance, and benefits of social intelligence as agent technology and argues that social intelligence has a potential to enhance the user's perception of the system, which in turn can lead to improvements of the system's performance. In order to implement social intelligence in the agent, a mind model has been developed to render affective expressions and personality of the agent. The mind model has been implemented in a human-machine collaborative learning system. One differentiating feature of the collaborative learning system is that it has an agent that performs as a co-learner with which the user interacts during the learning session. The mind model controls the social behaviors of the agent, thus making it possible for the user to have more social interactions with the agent. The experiment with the system suggested that a greater degree of learning was achieved when the students worked with the co-learner agent and that the co-learner agent with the mind model that expressed emotions resulted in a more positive attitude toward the system.

  19. Machine learning for epigenetics and future medical applications.

    Science.gov (United States)

    Holder, Lawrence B; Haque, M Muksitul; Skinner, Michael K

    2017-07-03

    Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.

  20. Improved Extreme Learning Machine and Its Application in Image Quality Assessment

    OpenAIRE

    Mao, Li; Zhang, Lidong; Liu, Xingyang; Li, Chaofeng; Yang, Hong

    2014-01-01

    Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting...

  1. Sample-Based Extreme Learning Machine with Missing Data

    Directory of Open Access Journals (Sweden)

    Hang Gao

    2015-01-01

    Full Text Available Extreme learning machine (ELM has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information. However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

  2. Advances in machine learning and data mining for astronomy

    CERN Document Server

    Way, Michael J

    2012-01-01

    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

  3. Interactive machine learning for health informatics: when do we need the human-in-the-loop?

    Science.gov (United States)

    Holzinger, Andreas

    2016-06-01

    Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

  4. Using Machine Learning for Land Suitability Classification

    African Journals Online (AJOL)

    User

    West African Journal of Applied Ecology, vol. ... evidence for the utility of machine learning methods in land suitability classification especially MCS methods. ... Artificial intelligence tools. ..... Numerical values of index for the various classes.

  5. Machine learning approaches to the social determinants of health in the health and retirement study.

    Science.gov (United States)

    Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

    2018-04-01

    Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

  6. Machine learning on geospatial big data

    CSIR Research Space (South Africa)

    Van Zyl, T

    2014-02-01

    Full Text Available When trying to understand the difference between machine learning and statistics, it is important to note that it is not so much the set of techniques and theory that are used but more importantly the intended use of the results. In fact, many...

  7. Machine learning for the New York City power grid.

    Science.gov (United States)

    Rudin, Cynthia; Waltz, David; Anderson, Roger N; Boulanger, Albert; Salleb-Aouissi, Ansaf; Chow, Maggie; Dutta, Haimonti; Gross, Philip N; Huang, Bert; Ierome, Steve; Isaac, Delfina F; Kressner, Arthur; Passonneau, Rebecca J; Radeva, Axinia; Wu, Leon

    2012-02-01

    Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce 1) feeder failure rankings, 2) cable, joint, terminator, and transformer rankings, 3) feeder Mean Time Between Failure (MTBF) estimates, and 4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or realtime, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City’s electrical grid.

  8. Machine Learning and Data Mining Methods in Diabetes Research.

    Science.gov (United States)

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

    2017-01-01

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

  9. Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

    Science.gov (United States)

    Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

    2018-01-01

    Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie

  10. Obtaining Global Picture From Single Point Observations by Combining Data Assimilation and Machine Learning Tools

    Science.gov (United States)

    Shprits, Y.; Zhelavskaya, I. S.; Kellerman, A. C.; Spasojevic, M.; Kondrashov, D. A.; Ghil, M.; Aseev, N.; Castillo Tibocha, A. M.; Cervantes Villa, J. S.; Kletzing, C.; Kurth, W. S.

    2017-12-01

    Increasing volume of satellite measurements requires deployment of new tools that can utilize such vast amount of data. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the space environment. In this study we show how measurements can be combined by means of data assimilation and how machine learning can help analyze large amounts of data and can help develop global models that are trained on single point measurement. Data Assimilation: Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude. We show results of the long term reanalysis of radiation belt measurements along with fully operational real-time predictions using data assimilative VERB code. Machine Learning: We present application of the machine learning tools for the analysis of NASA Van Allen Probes upper-hybrid frequency measurements. Using the obtained data set we train a new global predictive neural network. The results for the Van Allen Probes based neural network are compared with historical IMAGE satellite observations. We also show examples of predictions of geomagnetic indices using neural networks. Combination of machine learning and data assimilation: We discuss how data assimilation tools and machine learning tools can be combine so that physics-based insight into the dynamics of the particular system can be combined with empirical knowledge of it's non-linear behavior.

  11. Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

    Science.gov (United States)

    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.

  12. Potentials of Industrie 4.0 and Machine Learning for Mechanical Joining

    OpenAIRE

    Jäckel, Mathias

    2017-01-01

    -Sensitivity analysis of the influence of component properties and joining parameters on the joining result for self-pierce riveting -Possibilities to link mechanical joining technologies with the automotive process chain for quality and flexibility improvements -Potential of using machine learning to reduce automotive product development cycles in relation to mechanical joining -Datamining for machine learning at mechanical joining

  13. Parallelization of the ROOT Machine Learning Methods

    CERN Document Server

    Vakilipourtakalou, Pourya

    2016-01-01

    Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

  14. Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

    Science.gov (United States)

    Fujii, Keisuke; Nakajima, Kohei

    2017-08-01

    The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

  15. Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

    Directory of Open Access Journals (Sweden)

    Jiali Du

    2014-12-01

    Full Text Available This paper discusses the application of computational linguistics in the machine learning (ML system for the processing of garden path sentences. ML is closely related to artificial intelligence and linguistic cognition. The rapid and efficient processing of the complex structures is an effective method to test the system. By means of parsing the garden path sentence, we draw a conclusion that the integration of theoretical and statistical methods is helpful for the development of ML system.

  16. Proceedings of IEEE Machine Learning for Signal Processing Workshop XV

    DEFF Research Database (Denmark)

    Larsen, Jan

    These proceedings contains refereed papers presented at the Fifteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP’2005), held in Mystic, Connecticut, USA, September 28-30, 2005. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP) organized...... by the NNSP Technical Committee of the IEEE Signal Processing Society. The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized...... by the Machine Learning for Signal Processing Technical Committee with sponsorship of the IEEE Signal Processing Society. Following the practice started two years ago, the bound volume of the proceedings is going to be published by IEEE following the Workshop, and we are pleased to offer to conference attendees...

  17. Ship localization in Santa Barbara Channel using machine learning classifiers.

    Science.gov (United States)

    Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

    2017-11-01

    Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

  18. Applying machine learning techniques for forecasting flexibility of virtual power plants

    DEFF Research Database (Denmark)

    MacDougall, Pamela; Kosek, Anna Magdalena; Bindner, Henrik W.

    2016-01-01

    network as well as the multi-variant linear regression. It is found that it is possible to estimate the longevity of flexibility with machine learning. The linear regression algorithm is, on average, able to estimate the longevity with a 15% error. However, there was a significant improvement with the ANN...... approach to investigating the longevity of aggregated response of a virtual power plant using historic bidding and aggregated behaviour with machine learning techniques. The two supervised machine learning techniques investigated and compared in this paper are, multivariate linear regression and single...... algorithm achieving, on average, a 5.3% error. This is lowered 2.4% when learning for the same virtual power plant. With this information it would be possible to accurately offer residential VPP flexibility for market operations to safely avoid causing further imbalances and financial penalties....

  19. Machine learning and computer vision approaches for phenotypic profiling.

    Science.gov (United States)

    Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

    2017-01-02

    With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

  20. Manifold learning in machine vision and robotics

    Science.gov (United States)

    Bernstein, Alexander

    2017-02-01

    Smart algorithms are used in Machine vision and Robotics to organize or extract high-level information from the available data. Nowadays, Machine learning is an essential and ubiquitous tool to automate extraction patterns or regularities from data (images in Machine vision; camera, laser, and sonar sensors data in Robotics) in order to solve various subject-oriented tasks such as understanding and classification of images content, navigation of mobile autonomous robot in uncertain environments, robot manipulation in medical robotics and computer-assisted surgery, and other. Usually such data have high dimensionality, however, due to various dependencies between their components and constraints caused by physical reasons, all "feasible and usable data" occupy only a very small part in high dimensional "observation space" with smaller intrinsic dimensionality. Generally accepted model of such data is manifold model in accordance with which the data lie on or near an unknown manifold (surface) of lower dimensionality embedded in an ambient high dimensional observation space; real-world high-dimensional data obtained from "natural" sources meet, as a rule, this model. The use of Manifold learning technique in Machine vision and Robotics, which discovers a low-dimensional structure of high dimensional data and results in effective algorithms for solving of a large number of various subject-oriented tasks, is the content of the conference plenary speech some topics of which are in the paper.