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. 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

  3. 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.

  4. 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 ...

  5. 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)

  6. 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.

  7. 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…

  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. 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

  10. 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

  11. 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.

  12. 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.

  13. 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....

  14. 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.

  15. 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 ...

  16. 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…

  17. 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.

  18. Building Artificial Vision Systems with Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    LeCun, Yann [New York University

    2011-02-23

    Three questions pose the next challenge for Artificial Intelligence (AI), robotics, and neuroscience. How do we learn perception (e.g. vision)? How do we learn representations of the perceptual world? How do we learn visual categories from just a few examples?

  19. 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

  20. 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.

  1. Automatic Anthropometric System Development Using Machine Learning

    Directory of Open Access Journals (Sweden)

    Long The Nguyen

    2016-08-01

    Full Text Available The contactless automatic anthropometric system is proposed for the reconstruction of the 3D-model of the human body using the conventional smartphone. Our approach involves three main steps. The first step is the extraction of 12 anthropological features. Then we determine the most important features. Finally, we employ these features to build the 3D model of the human body and classify them according to gender and the commonly used sizes. 

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  6. 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.

  7. 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....

  8. 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....

  9. 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...

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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...

  17. 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 ...

  18. 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.

  19. Error modeling for surrogates of dynamical systems using machine learning

    Science.gov (United States)

    Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

    2017-12-01

    A machine-learning-based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed `error indicators' (i.e., features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering), and subsequently constructs a `local' regression model to predict the time-instantaneous error within each identified region of feature space. We consider two uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance, and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (e.g., time-integrated errors). We apply the proposed framework to model errors in reduced-order models of nonlinear oil--water subsurface flow simulations. The reduced-order models used in this work entail application of trajectory piecewise linearization with proper orthogonal decomposition. When the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.

  20. 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.

  1. 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 ...

  2. 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...

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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 ...

  15. 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...

  16. 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.

  17. 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

  18. 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...

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Improved self-management of datacenter systems applying machine learning

    OpenAIRE

    Berral García, Josep Lluís

    2013-01-01

    Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the...

  5. 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.

  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. 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.

  9. The Industrial Immune System: Using Machine Learning for Next Generation

    Science.gov (United States)

    risk mitigation and management. And you hit it right on the head: The only way to do that today is by threat actor and I get into a health management system or a hospital system and I begin to change values there just like they do in the private sector because what is missing is a holistic approach to security

  10. 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.

  11. Applying machine learning to build a website interface adaptation system

    OpenAIRE

    MATESHUK EGOR; CHERNYSHEV ALEXANDER

    2015-01-01

    In this article we present the architecture and model of a website interface optimization system. We describe how we use clustering and genetic algorithms to automatically select a website interface with the highest conversion from website visitor to website user. In particular, we describe an algorithm for streamed clustering, which allows for real-time analysis of high traffic website users.

  12. 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.

  13. Machine learning for recommendation systems in job postings selection

    OpenAIRE

    Marcos Santamarta, Victor

    2016-01-01

    Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user?s preferences. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender sy...

  14. 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

  15. Facial Emotion Recognition System – A Machine Learning Approach

    Science.gov (United States)

    Ramalingam, V. V.; Pandian, A.; Jayakumar, Lavanya

    2018-04-01

    Frown is a medium for people correlation and it could be exercised in multiple real systems. Single crucial stage for frown realizing is to exactly select hysterical aspects. This journal proposed a frown realization scheme applying transformative Particle Swarm Optimization (PSO) based aspect accumulation. This entity initially employs changed LVP, handles crisscross adjacent picture element contrast, for achieving the selective first frown portrayal. Then the PSO entity inserted with a concept of micro Genetic Algorithm (mGA) called mGA-embedded PSO designed for achieving aspect accumulation. This study, the technique subsumes no disposable memory, a little-populace insignificant flock, a latest acceleration that amends with the approach and a sub dimension-based in-depth local frown aspect examines. Assistance of provincial utilization and comprehensive inspection examine structure of alleviating of an immature concurrence complication of conventional PSO. Numerous identifiers are used to diagnose different frown expositions. Stationed on extensive study within and other-sphere pictures from the continued Cohn Kanade and MMI benchmark directory appropriately. Determination of the application exceeds most advanced level PSO variants, conventional PSO, classical GA and alternate relevant frown realization structures is described with powerful limit. Extending our accession to a motion based FER application for connecting patch-based Gabor aspects with continuous data in multi-frames.

  16. 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...

  17. 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…

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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...

  5. Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

    Science.gov (United States)

    Gifford, Christopher M.

    2009-01-01

    This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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...

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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%.

  17. 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

  18. A New Profile Learning Model for Recommendation System based on Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Shereen H. Ali

    2016-03-01

    Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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...

  4. 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.

  5. 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.

  6. 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.

  7. 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...

  8. 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,…

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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

  20. Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

    Energy Technology Data Exchange (ETDEWEB)

    Jha, Sumit Kumar [University of Central Florida, Orlando; Pullum, Laura L [ORNL; Ramanathan, Arvind [ORNL

    2016-01-01

    Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

  1. 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...

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  6. 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.

  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. MACHINE LEARNING IMPLEMENTATION FOR THE CLASSIFICATION OF ATTACKS ON WEB SYSTEMS. PART 1

    Directory of Open Access Journals (Sweden)

    K. Smirnova

    2017-08-01

    Full Text Available The possibility of applying machine learning is considered for the classification of malicious requests to a Web application. This approach excludes the use of deterministic analysis systems (for example, expert systems, and based on the application of a cascade of neural networks or perceptrons on an approximate model to the real human brain. The main idea of the work is to enable to describe complex attack vectors consisting of feature sets, abstract terms for compiling a training sample, controlling the quality of recognition and classifying each of the layers (networks participating in the work, with the ability to adjust not the entire network, But only a small part of it, in the training of which a mistake or inaccuracy crept in.  The design of the developed network can be described as a cascaded, scalable neural network.  The developed system of intrusion detection uses a three-layer neural network. Layers can be built independently of each other by cascades. In the first layer, for each class of attack recognition, there is a corresponding network and correctness is checked on this network. To learn this layer, we have chosen classes of things that can be classified uniquely as yes or no, that is, they are linearly separable. Thus, a layer is obtained not just of neurons, but of their microsets, which can best determine whether is there some data class in the query or not. The following layers are not trained to recognize the attacks themselves, they are trained that a set of attacks creates certain threats. This allows you to more accurately recognize the attacker's attempts to bypass the defense system, as well as classify the target of the attack, and not just its fact. Simple layering allows you to minimize the percentage of false positives.

  10. 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...

  11. 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.

  12. 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

  13. MACHINE LEARNING IMPLEMENTATION FOR THE CLASSIFICATION OF ATTACKS ON WEB SYSTEMS. PART 2

    Directory of Open Access Journals (Sweden)

    K. Smirnova

    2017-11-01

    Full Text Available The possibility of applying machine learning for the classification of malicious requests to aWeb application is considered. This approach excludes the use of deterministic analysis systems (for example, expert systems,and is based on the application of a cascade of neural networks or perceptrons on an approximate model to the real humanbrain. The main idea of the work is to enable to describe complex attack vectors consisting of feature sets, abstract terms forcompiling a training sample, controlling the quality of recognition and classifying each of the layers (networks participatingin the work, with the ability to adjust not the entire network, but only a small part of it, in the training of which a mistake orinaccuracy crept in. The design of the developed network can be described as a cascaded, scalable neural network.When using neural networks to detect attacks on web systems, the issue of vectorization and normalization of features isacute. The most commonly used methods for solving these problems are not designed for the case of deliberate distortion ofthe signs of an attack.The proposed approach makes it possible to obtain a neural network that has been studied in more detail by small features,and also to eliminate the normalization issues in order to avoid deliberately bypassing the intrusion detection system. Byisolating one more group of neurons in the network and teaching it to samples containing various variants of circumvention ofthe attack classification, the developed intrusion detection system remains able to classify any types of attacks as well as theiraggregates, putting forward more stringent measures to counteract attacks. This allows you to follow the life cycle of theattack in more detail: from the starting trial attack to deliberate sophisticated attempts to bypass the system and introducemore decisive measures to actively counteract the attack, eliminating the chances of a false alarm system.

  14. An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning.

    Science.gov (United States)

    Yang, Geng; Deng, Jia; Pang, Gaoyang; Zhang, Hao; Li, Jiayi; Deng, Bin; Pang, Zhibo; Xu, Juan; Jiang, Mingzhe; Liljeberg, Pasi; Xie, Haibo; Yang, Huayong

    2018-01-01

    Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

  15. An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

    Science.gov (United States)

    Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

    2018-04-01

    Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic

  16. 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

  17. Promoting Probabilistic Programming System (PPS) Development in Probabilistic Programming for Advancing Machine Learning (PPAML)

    Science.gov (United States)

    2018-03-01

    invested in the future developments of PPSs. 3.0 METHODS , ASSUMPTIONS, AND PROCEDURES Section 3 describes the methods for each of the primary areas of...approaches for solving machine learning problems of interest to defense, science , and the economy. Within DoD, there are different needs for ...Datasets include social network data and vaccination statistics . Those data have different characteristics (e.g., percentages for CDC regional

  18. 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.

  19. An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Abdu Gumaei

    2018-01-01

    Full Text Available Multispectral palmprint recognition system (MPRS is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE and regularized extreme learning machine (RELM is proposed in this paper. The proposed approach is intended to make the recognition faster by reducing the number of palmprint features without degrading the accuracy of classifier. To achieve this objective, first, the region of interest (ROI from palmprint images is extracted by David Zhang’s method. Second, an efficient normalized Gist (NGist descriptor is used for palmprint feature extraction. Then, the dimensionality of extracted features is reduced using optimized AE. Finally, the reduced features are fed to the RELM for classification. A comprehensive set of experiments are conducted on the benchmark MS-PolyU dataset. The results were significantly high compared to the state-of-the-art approaches, and the robustness and efficiency of the proposed approach are revealed.

  20. Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

    Science.gov (United States)

    Stoica, C; Camejo, J; Banciu, A; Nita-Lazar, M; Paun, I; Cristofor, S; Pacheco, O R; Guevara, M

    2016-01-01

    Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

  1. 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.

  2. 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.

  3. 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.

  4. A Framework for Intelligent Instructional Systems: An Artificial Intelligence Machine Learning Approach.

    Science.gov (United States)

    Becker, Lee A.

    1987-01-01

    Presents and develops a general model of the nature of a learning system and a classification for learning systems. Highlights include the relationship between artificial intelligence and cognitive psychology; computer-based instructional systems; intelligent instructional systems; and the role of the learner's knowledge base in an intelligent…

  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. Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

    Science.gov (United States)

    Beltrame, Thomas; Amelard, Robert; Wong, Alexander; Hughson, Richard L

    2018-02-01

    sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

  7. 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.

  8. 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.

  9. Neuromorphic Deep Learning Machines

    OpenAIRE

    Neftci, E; Augustine, C; Paul, S; Detorakis, G

    2017-01-01

    An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

  10. 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.

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

    Science.gov (United States)

    2014-05-16

    Huawei , Intel, Microsoft, NetApp, Pivotal, Splunk, Virdata, VMware, WANdisco and Yahoo!. ML-o-scope: a diagnostic visualization system for deep machine...Facebook, GameOnTalis, Guavus, HP, Huawei , Intel, Microsoft, NetApp, Pivotal, Splunk, Virdata, VMware, WANdisco and Yahoo!. References [1] Bruna, J., and

  12. 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...

  13. 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.

  14. Developing an Automated Machine Learning Marine Oil Spill Detection System with Synthetic Aperture Radar

    Science.gov (United States)

    Pinales, J. C.; Graber, H. C.; Hargrove, J. T.; Caruso, M. J.

    2016-02-01

    Previous studies have demonstrated the ability to detect and classify marine hydrocarbon films with spaceborne synthetic aperture radar (SAR) imagery. The dampening effects of hydrocarbon discharges on small surface capillary-gravity waves renders the ocean surface "radar dark" compared with the standard wind-borne ocean surfaces. Given the scope and impact of events like the Deepwater Horizon oil spill, the need for improved, automated and expedient monitoring of hydrocarbon-related marine anomalies has become a pressing and complex issue for governments and the extraction industry. The research presented here describes the development, training, and utilization of an algorithm that detects marine oil spills in an automated, semi-supervised manner, utilizing X-, C-, or L-band SAR data as the primary input. Ancillary datasets include related radar-borne variables (incidence angle, etc.), environmental data (wind speed, etc.) and textural descriptors. Shapefiles produced by an experienced human-analyst served as targets (validation) during the training portion of the investigation. Training and testing datasets were chosen for development and assessment of algorithm effectiveness as well as optimal conditions for oil detection in SAR data. The algorithm detects oil spills by following a 3-step methodology: object detection, feature extraction, and classification. Previous oil spill detection and classification methodologies such as machine learning algorithms, artificial neural networks (ANN), and multivariate classification methods like partial least squares-discriminant analysis (PLS-DA) are evaluated and compared. Statistical, transform, and model-based image texture techniques, commonly used for object mapping directly or as inputs for more complex methodologies, are explored to determine optimal textures for an oil spill detection system. The influence of the ancillary variables is explored, with a particular focus on the role of strong vs. weak wind forcing.

  15. 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

  16. 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...

  17. Analyzing the evolutionary mechanisms of the Air Transportation System-of-Systems using network theory and machine learning algorithms

    Science.gov (United States)

    Kotegawa, Tatsuya

    Complexity in the Air Transportation System (ATS) arises from the intermingling of many independent physical resources, operational paradigms, and stakeholder interests, as well as the dynamic variation of these interactions over time. Currently, trade-offs and cost benefit analyses of new ATS concepts are carried out on system-wide evaluation simulations driven by air traffic forecasts that assume fixed airline routes. However, this does not well reflect reality as airlines regularly add and remove routes. A airline service route network evolution model that projects route addition and removal was created and combined with state-of-the-art air traffic forecast methods to better reflect the dynamic properties of the ATS in system-wide simulations. Guided by a system-of-systems framework, network theory metrics and machine learning algorithms were applied to develop the route network evolution models based on patterns extracted from historical data. Constructing the route addition section of the model posed the greatest challenge due to the large pool of new link candidates compared to the actual number of routes historically added to the network. Of the models explored, algorithms based on logistic regression, random forests, and support vector machines showed best route addition and removal forecast accuracies at approximately 20% and 40%, respectively, when validated with historical data. The combination of network evolution models and a system-wide evaluation tool quantified the impact of airline route network evolution on air traffic delay. The expected delay minutes when considering network evolution increased approximately 5% for a forecasted schedule on 3/19/2020. Performance trade-off studies between several airline route network topologies from the perspectives of passenger travel efficiency, fuel burn, and robustness were also conducted to provide bounds that could serve as targets for ATS transformation efforts. The series of analysis revealed that high

  18. 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.

  19. 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...

  20. 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.

  1. 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...

  2. Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

    International Nuclear Information System (INIS)

    Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

    2017-01-01

    A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well

  3. 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...

  4. 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.

  5. 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.

  6. 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.

  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. System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants

    OpenAIRE

    Kroll, Björn; Schaffranek, David; Schriegel, Sebastian; Niggemann, Oliver

    2014-01-01

    Electricity, water or air are some Industrial energy carriers which are struggling under the prices of primary energy carriers. The European Union for example used more 20.000.000 GWh electricity in 2011 based on the IEA Report [1]. Cyber Physical Production Systems (CPPS) are able to reduce this amount, but they also help to increase the efficiency of machines above expectations which results in a more cost efficient production. Especially in the field of improving industrial plants, one of ...

  9. 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

  10. 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.

  11. Statistical and Machine-Learning Classifier Framework to Improve Pulse Shape Discrimination System Design

    Energy Technology Data Exchange (ETDEWEB)

    Wurtz, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kaplan, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-28

    Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-­realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-­building elements and their functions in a fully-­designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.

  12. 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…

  13. 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.

  14. Galaxy Classification using Machine Learning

    Science.gov (United States)

    Fowler, Lucas; Schawinski, Kevin; Brandt, Ben-Elias; widmer, Nicole

    2017-01-01

    We present our current research into the use of machine learning to classify galaxy imaging data with various convolutional neural network configurations in TensorFlow. We are investigating how five-band Sloan Digital Sky Survey imaging data can be used to train on physical properties such as redshift, star formation rate, mass and morphology. We also investigate the performance of artificially redshifted images in recovering physical properties as image quality degrades.

  15. 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...

  16. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

    Science.gov (United States)

    Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

    2018-01-01

    Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

  17. 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...

  18. 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

  19. A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

    Science.gov (United States)

    Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad

    2013-06-01

    This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

  20. A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

    International Nuclear Information System (INIS)

    Aziz, Nur Liyana Afiqah Abdul; Yap, Keem Siah; Bunyamin, Muhammad Afif

    2013-01-01

    This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of c omputing the word . The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

  1. 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

  2. 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.

  3. Machine learning in jet physics

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    High energy collider experiments produce several petabytes of data every year. Given the magnitude and complexity of the raw data, machine learning algorithms provide the best available platform to transform and analyse these data to obtain valuable insights to understand Standard Model and Beyond Standard Model theories. These collider experiments produce both quark and gluon initiated hadronic jets as the core components. Deep learning techniques enable us to classify quark/gluon jets through image recognition and help us to differentiate signals and backgrounds in Beyond Standard Model searches at LHC. We are currently working on quark/gluon jet classification and progressing in our studies to find the bias between event generators using domain adversarial neural networks (DANN). We also plan to investigate top tagging, weak supervision on mixed samples in high energy physics, utilizing transfer learning from simulated data to real experimental data.

  4. 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 ...

  5. A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.

    Science.gov (United States)

    Lu, Siyuan; Qiu, Xin; Shi, Jianping; Li, Na; Lu, Zhi-Hai; Chen, Peng; Yang, Meng-Meng; Liu, Fang-Yuan; Jia, Wen-Juan; Zhang, Yudong

    2017-01-01

    It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods; however they suffer from low accuracy. Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. The experimental results suggest that the proposed approach is accurate and robust in pathological brain detection. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  6. A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

    Directory of Open Access Journals (Sweden)

    Batoul Haidar

    2017-12-01

    Full Text Available With the abundance of Internet and electronic devices bullying has moved its place from schools and backyards into cyberspace; to be now known as Cyberbullying. Cyberbullying is affecting a lot of children around the world, especially Arab countries. Thus concerns from cyberbullying are rising. A lot of research is ongoing with the purpose of diminishing cyberbullying. The current research efforts are focused around detection and mitigation of cyberbullying. Previously, researches dealt with the psychological effects of cyberbullying on the victim and the predator. A lot of research work proposed solutions for detecting cyberbullying in English language and a few more languages, but none till now covered cyberbullying in Arabic language. Several techniques contribute in cyberbullying detection, mainly Machine Learning (ML and Natural Language Processing (NLP. This journal extends on a previous paper to elaborate on a solution for detecting and stopping cyberbullying. It first presents a thorough survey for the previous work done in cyberbullying detection. Then a solution that focuses on detecting cyberbullying in Arabic content is displayed and assessed.

  7. Crowdsourced validation of a machine-learning classification system for autism and ADHD.

    Science.gov (United States)

    Duda, M; Haber, N; Daniels, J; Wall, D P

    2017-05-16

    Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) together affect >10% of the children in the United States, but considerable behavioral overlaps between the two disorders can often complicate differential diagnosis. Currently, there is no screening test designed to differentiate between the two disorders, and with waiting times from initial suspicion to diagnosis upwards of a year, methods to quickly and accurately assess risk for these and other developmental disorders are desperately needed. In a previous study, we found that four machine-learning algorithms were able to accurately (area under the curve (AUC)>0.96) distinguish ASD from ADHD using only a small subset of items from the Social Responsiveness Scale (SRS). Here, we expand upon our prior work by including a novel crowdsourced data set of responses to our predefined top 15 SRS-derived questions from parents of children with ASD (n=248) or ADHD (n=174) to improve our model's capability to generalize to new, 'real-world' data. By mixing these novel survey data with our initial archival sample (n=3417) and performing repeated cross-validation with subsampling, we created a classification algorithm that performs with AUC=0.89±0.01 using only 15 questions.

  8. 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...

  9. 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.

  10. 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

  11. 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...

  12. Archetypal Analysis for Machine Learning

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    2010-01-01

    Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - ...... for K-means [2]. We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering....

  13. 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....

  14. 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.

  15. Smart Machine Protection System

    International Nuclear Information System (INIS)

    Clark, S.; Nelson, D.; Grillo, A.; Spencer, N.; Hutchinson, D.; Olsen, J.; Millsom, D.; White, G.; Gromme, T.; Allison, S.; Underwood, K.; Zelazny, M.; Kang, H.

    1991-11-01

    A Machine Protection System implemented on the SLC automatically controls the beam repetition rates in the accelerator so that radiation or temperature faults slow the repetition rate to bring the fault within tolerance without shutting down the machine. This process allows the accelerator to aid in the fault diagnostic process, and the protection system automatically restores the beams back to normal rates when the fault is diagnosed and corrected. The user interface includes facilities to monitor the performance of the system, and track rate limits, faults, and recoveries. There is an edit facility to define the devices to be included in the protection system, along with their set points, limits, and trip points. This set point and limit data is downloaded into the CAMAC modules, and the configuration data is compiled into a logical decision tree for the 68030 processor. 3 figs

  16. Smart machine protection system

    International Nuclear Information System (INIS)

    Clark, S.; Nelson, D.; Grillo, A.

    1992-01-01

    A Machine Protection System implemented on the SLC automatically controls the beam repetition rates in the accelerator so that radiation or temperature faults slow the repetition rate to bring the fault within tolerance without shutting down the machine. This process allows the accelerators to aid in the fault diagnostic process, and the protection system automatically restores the beams back to normal rates when the fault is diagnosed and corrected. The user interface includes facilities to monitor the performance of the system, and track rate limits, faults, and recoveries. There is an edit facility to define the devices to be included in the protection system, along with their set points, limits, and trip points. This set point and limit data is downloaded into the CAMAC modules, and the configuration data is complied into a logical decision tree for the 68030 processor. (author)

  17. Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

    International Nuclear Information System (INIS)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

    2014-01-01

    Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

  18. Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

    Science.gov (United States)

    Hearn, Tristan A.

    2015-01-01

    This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

  19. 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

  20. 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 ...

  1. 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.

  2. 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.

  3. 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....

  4. Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

    Science.gov (United States)

    Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

    2017-10-01

    Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. A machine learning system for automated whole-brain seizure detection

    Directory of Open Access Journals (Sweden)

    P. Fergus

    2016-01-01

    Full Text Available Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures. Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier.

  6. Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales

    Directory of Open Access Journals (Sweden)

    Jihoon Oh

    2017-09-01

    Full Text Available Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders (N = 573 were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC was the highest for 1-month suicide attempts detection (0.93, followed by lifetime (0.89, and 1-year detection (0.87. Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87. Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings.

  7. Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales.

    Science.gov (United States)

    Oh, Jihoon; Yun, Kyongsik; Hwang, Ji-Hyun; Chae, Jeong-Ho

    2017-01-01

    Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders ( N  = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings.

  8. 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.

  9. 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 ...

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. Machine Learning in Medical Imaging.

    Science.gov (United States)

    Giger, Maryellen L

    2018-03-01

    Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine. Copyright © 2018. Published by Elsevier Inc.

  17. Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system.

    Science.gov (United States)

    Yeh, Shih-Ching; Huang, Ming-Chun; Wang, Pa-Chun; Fang, Te-Yung; Su, Mu-Chun; Tsai, Po-Yi; Rizzo, Albert

    2014-10-01

    Dizziness is a major consequence of imbalance and vestibular dysfunction. Compared to surgery and drug treatments, balance training is non-invasive and more desired. However, training exercises are usually tedious and the assessment tool is insufficient to diagnose patient's severity rapidly. An interactive virtual reality (VR) game-based rehabilitation program that adopted Cawthorne-Cooksey exercises, and a sensor-based measuring system were introduced. To verify the therapeutic effect, a clinical experiment with 48 patients and 36 normal subjects was conducted. Quantified balance indices were measured and analyzed by statistical tools and a Support Vector Machine (SVM) classifier. In terms of balance indices, patients who completed the training process are progressed and the difference between normal subjects and patients is obvious. Further analysis by SVM classifier show that the accuracy of recognizing the differences between patients and normal subject is feasible, and these results can be used to evaluate patients' severity and make rapid assessment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  18. 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).

  19. Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

    Science.gov (United States)

    Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

    2014-01-01

    The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

  20. Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System

    Directory of Open Access Journals (Sweden)

    Mert Bal

    2014-01-01

    Full Text Available The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

  1. Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

    International Nuclear Information System (INIS)

    Chou, Jui-Sheng; Ngo, Ngoc-Tri

    2016-01-01

    Highlights: • This study develops a novel time-series sliding window forecast system. • The system integrates metaheuristics, machine learning and time-series models. • Site experiment of smart grid infrastructure is installed to retrieve real-time data. • The proposed system accurately predicts energy consumption in residential buildings. • The forecasting system can help users minimize their electricity usage. - Abstract: Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kW h. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially

  2. 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…

  3. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM in advanced metering infrastructure of smart grid.

    Directory of Open Access Journals (Sweden)

    Yuancheng Li

    Full Text Available Advanced Metering Infrastructure (AMI realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

  4. What is the machine learning.

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. In this talk, I explore a 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 non-linear nature of the boundaries between signal and background. I will demonstrate these features in both an easy to understand toy model and an idealized LHC resonance scenario.

  5. An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

    Science.gov (United States)

    Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

    2017-12-07

    Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

  6. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Science.gov (United States)

    Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

    2018-01-01

    Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

  7. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiyuan Ma

    2018-03-01

    Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

  8. 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).

  9. 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...

  10. 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.

  11. 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.

  12. 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.

  13. Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning

    Science.gov (United States)

    Wang, Danshi; Zhang, Min; Cai, Zhongle; Cui, Yue; Li, Ze; Han, Huanhuan; Fu, Meixia; Luo, Bin

    2016-06-01

    An effective machine learning algorithm, the support vector machine (SVM), is presented in the context of a coherent optical transmission system. As a classifier, the SVM can create nonlinear decision boundaries to mitigate the distortions caused by nonlinear phase noise (NLPN). Without any prior information or heuristic assumptions, the SVM can learn and capture the link properties from only a few training data. Compared with the maximum likelihood estimation (MLE) algorithm, a lower bit-error rate (BER) is achieved by the SVM for a given launch power; moreover, the launch power dynamic range (LPDR) is increased by 3.3 dBm for 8 phase-shift keying (8 PSK), 1.2 dBm for QPSK, and 0.3 dBm for BPSK. The maximum transmission distance corresponding to a BER of 1 ×10-3 is increased by 480 km for the case of 8 PSK. The larger launch power range and longer transmission distance improve the tolerance to amplitude and phase noise, which demonstrates the feasibility of the SVM in digital signal processing for M-PSK formats. Meanwhile, in order to apply the SVM method to 16 quadratic amplitude modulation (16 QAM) detection, we propose a parameter optimization scheme. By utilizing a cross-validation and grid-search techniques, the optimal parameters of SVM can be selected, thus leading to the LPDR improvement by 2.8 dBm. Additionally, we demonstrate that the SVM is also effective in combating the laser phase noise combined with the inphase and quadrature (I/Q) modulator imperfections, but the improvement is insignificant for the linear noise and separate I/Q imbalance. The computational complexity of SVM is also discussed. The relatively low complexity makes it possible for SVM to implement the real-time processing.

  14. 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.

  15. Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.

    Science.gov (United States)

    Onay, Aytun; Onay, Melih; Abul, Osman

    2017-04-01

    Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N,N-dimethylethylamine, crotylamine, 5-methyl-2,4-heptadiene, octatriene and carbonyl group. This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang; Zhang, Yingchen

    2016-11-14

    This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

  17. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

    Science.gov (United States)

    Jo, ByungWan; Khan, Rana Muhammad Asad

    2018-03-21

    The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH₄, CO, SO₂, and H₂S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R ² and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

  18. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning

    Directory of Open Access Journals (Sweden)

    ByungWan Jo

    2018-03-01

    Full Text Available The implementation of wireless sensor networks (WSNs for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI. Principal component analysis (PCA identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

  19. Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning.

    Science.gov (United States)

    Takada, Kenji

    2016-09-01

    New approach for the diagnosis of extractions with neural network machine learning. Seok-Ki Jung and Tae-Woo Kim. Am J Orthod Dentofacial Orthop 2016;149:127-33. Not reported. Mathematical modeling. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. 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

  1. 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.  

  2. 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...

  3. 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. .

  4. [Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].

    Science.gov (United States)

    Wang, Yue; Yu, Lei; Fu, Jianming; Fang, Qiang

    2014-04-01

    In order to realize an individualized and specialized rehabilitation assessment of remoteness and intelligence, we set up a remote intelligent assessment system of upper limb movement function of post-stroke patients during rehabilitation. By using the remote rehabilitation training sensors and client data sampling software, we collected and uploaded the gesture data from a patient's forearm and upper arm during rehabilitation training to database of the server. Then a remote intelligent assessment system, which had been developed based on the extreme learning machine (ELM) algorithm and Brunnstrom stage assessment standard, was used to evaluate the gesture data. To evaluate the reliability of the proposed method, a group of 23 stroke patients, whose upper limb movement functions were in different recovery stages, and 4 healthy people, whose upper limb movement functions were normal, were recruited to finish the same training task. The results showed that, compared to that of the experienced rehabilitation expert who used the Brunnstrom stage standard table, the accuracy of the proposed remote Brunnstrom intelligent assessment system can reach a higher level, as 92.1%. The practical effects of surgery have proved that the proposed system could realize the intelligent assessment of upper limb movement function of post-stroke patients remotely, and it could also make the rehabilitation of the post-stroke patients at home or in a community care center possible.

  5. 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.

  6. 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.

  7. 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.

  8. 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...

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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...

  14. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study

    Directory of Open Access Journals (Sweden)

    Fisnik Dalipi

    2016-01-01

    Full Text Available We present our data-driven supervised machine-learning (ML model to predict heat load for buildings in a district heating system (DHS. Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR, Partial Least Square (PLS, and random forest (RF. We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE, mean absolute percentage error (MAPE, and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.

  15. 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...

  16. 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...

  17. 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.

  18. Computer vision and machine learning for archaeology

    NARCIS (Netherlands)

    van der Maaten, L.J.P.; Boon, P.; Lange, G.; Paijmans, J.J.; Postma, E.

    2006-01-01

    Until now, computer vision and machine learning techniques barely contributed to the archaeological domain. The use of these techniques can support archaeologists in their assessment and classification of archaeological finds. The paper illustrates the use of computer vision techniques for

  19. 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.

  20. 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....

  1. 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.

  2. 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.

  3. 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.

  4. Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

    Science.gov (United States)

    Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

    2016-03-01

    The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care

  5. Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

    Science.gov (United States)

    Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

    2017-12-01

    Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was

  6. 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...

  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. Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning

    OpenAIRE

    Akkaya, Ilge

    2016-01-01

    Emerging distributed cyber-physical systems (CPSs) integrate a wide range of heterogeneous components that need to be orchestrated in a dynamic environment. While model-based techniques are commonly used in CPS design, they be- come inadequate in capturing the complexity as systems become larger and extremely dynamic. The adaptive nature of the systems makes data-driven approaches highly desirable, if not necessary.Traditionally, data-driven systems utilize large volumes of static data sets t...

  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. A Machine Learning Recommender System to Tailor Preference Assessments to Enhance Person-Centered Care Among Nursing Home Residents.

    Science.gov (United States)

    Gannod, Gerald C; Abbott, Katherine M; Van Haitsma, Kimberly; Martindale, Nathan; Heppner, Alexandra

    2018-05-21

    Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items (72) to be a barrier to using the assessment. Using a sample of n = 255 NH resident responses to the PELI-NH, we used the 16 preference items from the MDS 3.0 Section F to develop a machine learning recommender system to identify additional PELI-NH items that may be important to specific residents. Much like the Netflix recommender system, our system is based on the concept of collaborative filtering whereby insights and predictions (e.g., filters) are created using the interests and preferences of many users. The algorithm identifies multiple sets of "you might also like" patterns called association rules, based upon responses to the 16 MDS preferences that recommends an additional set of preferences with a high likelihood of being important to a specific resident. In the evaluation of the combined apriori and logistic regression approach, we obtained a high recall performance (i.e., the ratio of correctly predicted preferences compared with all predicted preferences and nonpreferences) and high precision (i.e., the ratio of correctly predicted rules with respect to the rules predicted to be true) of 80.2% and 79.2%, respectively. The recommender system successfully provides guidance on how to best tailor the preference items asked of residents and can support preference capture in busy clinical environments, contributing to the feasibility of delivering person-centered care.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing

    Directory of Open Access Journals (Sweden)

    Derck Koolen

    2017-11-01

    Full Text Available Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.

  16. Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing

    NARCIS (Netherlands)

    Koolen, D. (Derck); Sadat-Razavi, N. (Navid); W. Ketter (Wolfgang)

    2017-01-01

    textabstractEnergy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer.

  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. 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.

  19. Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems

    Directory of Open Access Journals (Sweden)

    Yuwono Mitchell

    2012-02-01

    Full Text Available Abstract Background Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. Method We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF neural networks. Results Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems.

  20. 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...

  1. 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

  2. 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.

  3. 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.

  4. 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.

  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. CRDM motion analysis using machine learning technique

    International Nuclear Information System (INIS)

    Nishimura, Takuya; Nakayama, Hiroyuki; Saitoh, Mayumi; Yaguchi, Seiji

    2017-01-01

    Magnetic jack type Control Rod Drive Mechanism (CRDM) for pressurized water reactor (PWR) plant operates control rods in response to electrical signals from a reactor control system. CRDM operability is evaluated by quantifying armature's response of closed/opened time which means interval time between coil energizing/de-energizing points and armature closed/opened points. MHI has already developed an automatic CRDM motion analysis and applied it to actual plants so far. However, CRDM operational data has wide variation depending on their characteristics such as plant condition, plant, etc. In the existing motion analysis, there is an issue of analysis accuracy for applying a single analysis technique to all plant conditions, plants, etc. In this study, MHI investigated motion analysis using machine learning (Random Forests) which is flexibly accommodated to CRDM operational data with wide variation, and is improved analysis accuracy. (author)

  7. Trends in extreme learning machines: a review.

    Science.gov (United States)

    Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou

    2015-01-01

    Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

  8. Quantum learning and universal quantum matching machine

    International Nuclear Information System (INIS)

    Sasaki, Masahide; Carlini, Alberto

    2002-01-01

    Suppose that three kinds of quantum systems are given in some unknown states vertical bar f> xN , vertical bar g 1 > xK , and vertical bar g 2 > xK , and we want to decide which template state vertical bar g 1 > or vertical bar g 2 >, each representing the feature of the pattern class C 1 or C 2 , respectively, is closest to the input feature state vertical bar f>. This is an extension of the pattern matching problem into the quantum domain. Assuming that these states are known a priori to belong to a certain parametric family of pure qubit systems, we derive two kinds of matching strategies. The first one is a semiclassical strategy that is obtained by the natural extension of conventional matching strategies and consists of a two-stage procedure: identification (estimation) of the unknown template states to design the classifier (learning process to train the classifier) and classification of the input system into the appropriate pattern class based on the estimated results. The other is a fully quantum strategy without any intermediate measurement, which we might call as the universal quantum matching machine. We present the Bayes optimal solutions for both strategies in the case of K=1, showing that there certainly exists a fully quantum matching procedure that is strictly superior to the straightforward semiclassical extension of the conventional matching strategy based on the learning process

  9. 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 ...

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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)

  16. 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.

  17. Tracking by Machine Learning Methods

    CERN Document Server

    Jofrehei, Arash

    2015-01-01

    Current track reconstructing methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast Simulation might not be as realistic as real data but tacking has been done for that with 100 percent efficiency while by using real data we would probably be limited to current efficiency.

  18. 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.

  19. 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.

  20. Slide system for machine tools

    Science.gov (United States)

    Douglass, Spivey S.; Green, Walter L.

    1982-01-01

    The present invention relates to a machine tool which permits the machining of nonaxisymmetric surfaces on a workpiece while rotating the workpiece about a central axis of rotation. The machine tool comprises a conventional two-slide system (X-Y) with one of these slides being provided with a relatively short travel high-speed auxiliary slide which carries the material-removing tool. The auxiliary slide is synchronized with the spindle speed and the position of the other two slides and provides a high-speed reciprocating motion required for the displacement of the cutting tool for generating a nonaxisymmetric surface at a selected location on the workpiece.

  1. 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...

  2. 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.

  3. 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...

  4. ML Confidential : machine learning on encrypted data

    NARCIS (Netherlands)

    Graepel, T.; Lauter, K.; Naehrig, M.; Kwon, T.; Lee, M.-K.; Kwon, D.

    2013-01-01

    We demonstrate that, by using a recently proposed leveled homomorphic encryption scheme, it is possible to delegate the execution of a machine learning algorithm to a computing service while retaining con¿dentiality of the training and test data. Since the computational complexity of the homomorphic

  5. Machine Learning for Flapping Wing Flight Control

    NARCIS (Netherlands)

    Goedhart, Menno; van Kampen, E.; Armanini, S.F.; de Visser, C.C.; Chu, Q.

    2018-01-01

    Flight control of Flapping Wing Micro Air Vehicles is challenging, because of their complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a

  6. ML Confidential : machine learning on encrypted data

    NARCIS (Netherlands)

    Graepel, T.; Lauter, K.; Naehrig, M.

    2012-01-01

    We demonstrate that by using a recently proposed somewhat homomorphic encryption (SHE) scheme it is possible to delegate the execution of a machine learning (ML) algorithm to a compute service while retaining confidentiality of the training and test data. Since the computational complexity of the

  7. Document Classification Using Distributed Machine Learning

    OpenAIRE

    Aydin, Galip; Hallac, Ibrahim Riza

    2018-01-01

    In this paper, we investigate the performance and success rates of Na\\"ive Bayes Classification Algorithm for automatic classification of Turkish news into predetermined categories like economy, life, health etc. We use Apache Big Data technologies such as Hadoop, HDFS, Spark and Mahout, and apply these distributed technologies to Machine Learning.

  8. 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...

  9. Parallelization of TMVA Machine Learning Algorithms

    CERN Document Server

    Hajili, Mammad

    2017-01-01

    This report reflects my work on Parallelization of TMVA Machine Learning Algorithms integrated to ROOT Data Analysis Framework during summer internship at CERN. The report consists of 4 impor- tant part - data set used in training and validation, algorithms that multiprocessing applied on them, parallelization techniques and re- sults of execution time changes due to number of workers.

  10. Supporting visual quality assessment with machine learning

    NARCIS (Netherlands)

    Gastaldo, P.; Zunino, R.; Redi, J.

    2013-01-01

    Objective metrics for visual quality assessment often base their reliability on the explicit modeling of the highly non-linear behavior of human perception; as a result, they may be complex and computationally expensive. Conversely, machine learning (ML) paradigms allow to tackle the quality

  11. 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.

  12. Machine learning a theoretical approach

    CERN Document Server

    Natarajan, Balas K

    2014-01-01

    This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation

  13. Machine Learning and Quantum Mechanics

    Science.gov (United States)

    Chapline, George

    The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

  14. 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...

  15. 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 ...

  16. 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...

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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...

  3. 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 ...

  4. A Machine Learning Concept for DTN Routing

    Science.gov (United States)

    Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos

    2017-01-01

    This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

  5. 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...

  6. 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....

  7. Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

    Directory of Open Access Journals (Sweden)

    Fernando Castaño

    2017-09-01

    Full Text Available Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.. The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

  8. Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System.

    Science.gov (United States)

    Castaño, Fernando; Beruvides, Gerardo; Haber, Rodolfo E; Artuñedo, Antonio

    2017-09-14

    Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

  9. 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.

  10. 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...

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Controls and Machine Protection Systems

    CERN Document Server

    Carrone, E.

    2016-01-01

    Machine protection, as part of accelerator control systems, can be managed with a 'functional safety' approach, which takes into account product life cycle, processes, quality, industrial standards and cybersafety. This paper will discuss strategies to manage such complexity and the related risks, with particular attention to fail-safe design and safety integrity levels, software and hardware standards, testing, and verification philosophy. It will also discuss an implementation of a machine protection system at the SLAC National Accelerator Laboratory's Linac Coherent Light Source (LCLS).

  16. 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.

  17. 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.

  18. 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...

  19. 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.

  20. Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

    Science.gov (United States)

    Howard, Rebecca; Rattray, Magnus; Prosperi, Mattia; Custovic, Adnan

    2015-07-01

    Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.

  1. Designing anticancer peptides by constructive machine learning.

    Science.gov (United States)

    Grisoni, Francesca; Neuhaus, Claudia; Gabernet, Gisela; Müller, Alex; Hiss, Jan; Schneider, Gisbert

    2018-04-21

    Constructive machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a generative model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on alpha-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. 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.

  3. Randomized Algorithms for Scalable Machine Learning

    OpenAIRE

    Kleiner, Ariel Jacob

    2012-01-01

    Many existing procedures in machine learning and statistics are computationally intractable in the setting of large-scale data. As a result, the advent of rapidly increasing dataset sizes, which should be a boon yielding improved statistical performance, instead severely blunts the usefulness of a variety of existing inferential methods. In this work, we use randomness to ameliorate this lack of scalability by reducing complex, computationally difficult inferential problems to larger sets o...

  4. Two-Dimensional Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Bo Jia

    2015-01-01

    (BP networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.

  5. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

    With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

  6. 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.

  7. 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...

  8. Automated Essay Grading using Machine Learning Algorithm

    Science.gov (United States)

    Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu

    2018-04-01

    Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

  9. 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.

  10. 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.

  11. Quantum machine learning: a classical perspective.

    Science.gov (United States)

    Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

    2018-01-01

    Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

  12. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian

    2012-01-01

    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  13. Quantum machine learning: a classical perspective

    Science.gov (United States)

    Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

    2018-01-01

    Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

  14. 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.

  15. DIANA: A Machine Learning Mechanism for Adjusting the TDD Uplink-Downlink Configuration in XG-PON-LTE Systems

    Directory of Open Access Journals (Sweden)

    Panagiotis Sarigiannidis

    2017-01-01

    Full Text Available Modern broadband hybrid optical-wireless access networks have gained the attention of academia and industry due to their strategic advantages (cost-efficiency, huge bandwidth, flexibility, and mobility. At the same time, the proliferation of Software Defined Networking (SDN enables the efficient reconfiguration of the underlying network components dynamically using SDN controllers. Hence, effective traffic-aware schemes are feasible in dynamically determining suitable configuration parameters for advancing the network performance. To this end, a novel machine learning mechanism is proposed for an SDN-enabled hybrid optical-wireless network. The proposed architecture consists of a 10-gigabit-capable passive optical network (XG-PON in the network backhaul and multiple Long Term Evolution (LTE radio access networks in the fronthaul. The proposed mechanism receives traffic-aware knowledge from the SDN controllers and applies an adjustment on the uplink-downlink configuration in the LTE radio communication. This traffic-aware mechanism is capable of determining the most suitable configuration based on the traffic dynamics in the whole hybrid network. The introduced scheme is evaluated in a realistic environment using real traffic traces such as Voice over IP (VoIP, real-time video, and streaming video. According to the obtained numerical results, the proposed mechanism offers significant improvements in the network performance in terms of latency and jitter.

  16. 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)

  17. 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.

  18. 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.

  19. Finding New Perovskite Halides via Machine learning

    Directory of Open Access Journals (Sweden)

    Ghanshyam ePilania

    2016-04-01

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

  20. Finding New Perovskite Halides via Machine learning

    Science.gov (United States)

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

    2016-04-01

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

  1. Diagnostic information system dynamics in the evaluation of machine learning algorithms for the supervision of energy efficiency of district heating-supplied buildings

    International Nuclear Information System (INIS)

    Kiluk, Sebastian

    2017-01-01

    Highlights: • Energy efficiency classification sustainability benefits from knowledge prediction. • Diagnostic classification can be validated with its dynamics and current data. • Diagnostic classification dynamics provides novelty extraction for knowledge update. • Data mining comparison can be performed with knowledge dynamics and uncertainty. • Diagnostic information refinement benefits form comparing classifiers dynamics. - Abstract: Modern ways of exploring the diagnostic knowledge provided by data mining and machine learning raise some concern about the ways of evaluating the quality of output knowledge, usually represented by information systems. Especially in district heating, the stationarity of efficiency models, and thus the relevance of diagnostic classification system, cannot be ensured due to the impact of social, economic or technological changes, which are hard to identify or predict. Therefore, data mining and machine learning have become an attractive strategy for automatically and continuously absorbing such dynamics. This paper presents a new method of evaluation and comparison of diagnostic information systems gathered algorithmically in district heating efficiency supervision based on exploring the evolution of information system and analyzing its dynamic features. The process of data mining and knowledge discovery was applied to the data acquired from district heating substations’ energy meters to provide the automated discovery of diagnostic knowledge base necessary for the efficiency supervision of district heating-supplied buildings. The implemented algorithm consists of several steps of processing the billing data, including preparation, segmentation, aggregation and knowledge discovery stage, where classes of abstract models representing energy efficiency constitute an information system representing diagnostic knowledge about the energy efficiency of buildings favorably operating under similar climate conditions and

  2. 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...

  3. Data Mining and Machine Learning in Astronomy

    Science.gov (United States)

    Ball, Nicholas M.; Brunner, Robert J.

    We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

  4. 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.

  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 search for variable stars

    Science.gov (United States)

    Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

    2018-04-01

    Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

  7. A Controller Design with ANFIS Architecture Attendant Learning Ability for SSSC-Based Damping Controller Applied in Single Machine Infinite Bus System

    Directory of Open Access Journals (Sweden)

    A. Khoshsaadat

    2014-09-01

    Full Text Available Static Synchronous Series Compensator (SSSC is a series compensating Flexible AC Transmission System (FACTS controller for maintaining to the power flow control on a transmission line by injecting a voltage in quadrature with the line current and in series mode with the line. In this work, an Adaptive Network-based Fuzzy Inference System controller (ANFISC has been proposed for controlling of the SSSC-based damping system and applied to a Single Machine Infinite Bus (SMIB power system. For implementation of the learning process in this controller, we use of the one approach of the learning ability that named as Forward Signal and Backward Error Back-Propagation (FSBEBP method for improving of the system efficiency. This artificial intelligence-based control model leads to a controller with adaptive structure, improved correctness, high damping ability and dynamic performance. System implementation is easy and it requires 49 fuzzy rules for inference engine of the system. As compared with the other complex neuro-fuzzy systems, this controller has medium number of the fuzzy rules and low number of layers, but it has high accuracy. In order to demonstrate of the proposed controller ability, it is simulated and its output compared with that of classic Lead-Lag-based Controller (LLC and PI controller.

  8. 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...

  9. 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.

  10. 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.

  11. Machine Learning Methods to Predict Diabetes Complications.

    Science.gov (United States)

    Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

    2018-03-01

    One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

  12. Machine Learning Interface for Medical Image Analysis.

    Science.gov (United States)

    Zhang, Yi C; Kagen, Alexander C

    2017-10-01

    TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. A simple neural network was constructed in TensorFlow to classify images into normal or Parkinson's disease groups. Training was executed over 1000 iterations for each cross-validation set. The gradient descent optimization and Adagrad optimization algorithms were used to minimize cross-entropy between the predicted and ground-truth labels. Cross-validation was performed ten times to produce a mean accuracy of 0.938 ± 0.047 (95 % CI 0.908-0.967). The mean sensitivity was 0.974 ± 0.043 (95 % CI 0.947-1.00) and mean specificity was 0.822 ± 0.207 (95 % CI 0.694-0.950). We extended the TensorFlow API to enable DICOM compatibility in the context of DaTscan image analysis. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models. These results indicate the potential role of TensorFlow as a useful adjunct diagnostic tool in the clinical setting.

  13. Machine learning methods for metabolic pathway prediction

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2010-01-01

    Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

  14. Machine learning methods for metabolic pathway prediction

    Science.gov (United States)

    2010-01-01

    Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

  15. From Curve Fitting to Machine Learning

    CERN Document Server

    Zielesny, Achim

    2011-01-01

    The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clus

  16. Unintended consequences of machine learning in medicine?

    Science.gov (United States)

    McDonald, Laura; Ramagopalan, Sreeram V; Cox, Andrew P; Oguz, Mustafa

    2017-01-01

    Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not specific to ML, and thus the article may lead to an adverse perception about this technique in particular. Whilst ML is not without its limitations like any methodology, a balanced view is needed in order to not hamper its use in potentially enabling better patient care.

  17. The cerebellum: a neuronal learning machine?

    Science.gov (United States)

    Raymond, J. L.; Lisberger, S. G.; Mauk, M. D.

    1996-01-01

    Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

  18. 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…

  19. 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

  20. 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...

  1. 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.

  2. 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.

  3. 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.

  4. Robust Matching Pursuit Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Zejian Yuan

    2018-01-01

    Full Text Available Extreme learning machine (ELM is a popular learning algorithm for single hidden layer feedforward networks (SLFNs. It was originally proposed with the inspiration from biological learning and has attracted massive attentions due to its adaptability to various tasks with a fast learning ability and efficient computation cost. As an effective sparse representation method, orthogonal matching pursuit (OMP method can be embedded into ELM to overcome the singularity problem and improve the stability. Usually OMP recovers a sparse vector by minimizing a least squares (LS loss, which is efficient for Gaussian distributed data, but may suffer performance deterioration in presence of non-Gaussian data. To address this problem, a robust matching pursuit method based on a novel kernel risk-sensitive loss (in short KRSLMP is first proposed in this paper. The KRSLMP is then applied to ELM to solve the sparse output weight vector, and the new method named the KRSLMP-ELM is developed for SLFN learning. Experimental results on synthetic and real-world data sets confirm the effectiveness and superiority of the proposed method.

  5. Towards Machine Learning of Motor Skills

    Science.gov (United States)

    Peters, Jan; Schaal, Stefan; Schölkopf, Bernhard

    Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks that a robot should fulfill. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. Machine-learning the string landscape

    Directory of Open Access Journals (Sweden)

    Yang-Hui He

    2017-11-01

    Full Text Available We propose a paradigm to apply machine learning various databases which have emerged in the study of the string landscape. In particular, we establish neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi–Yau manifolds and vector bundles, to quiver representations for gauge theories, using a novel framework of recasting geometrical and physical data as pixelated images. We find that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minutes and can also predict hithertofore unencountered results, whereby rendering the paradigm a valuable tool in physics as well as pure mathematics.

  12. Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

    OpenAIRE

    Rohit Punnoose; Pankaj Ajit

    2016-01-01

    Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared t...

  13. 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

  14. 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...

  15. PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

    International Nuclear Information System (INIS)

    Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.; McEwen, Jason D.

    2016-01-01

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

  16. PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

    Energy Technology Data Exchange (ETDEWEB)

    Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); McEwen, Jason D., E-mail: dr.michelle.lochner@gmail.com [Mullard Space Science Laboratory, University College London, Surrey RH5 6NT (United Kingdom)

    2016-08-01

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

  17. Quantum machine learning: a classical perspective

    Science.gov (United States)

    Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Severini, Simone; Wossnig, Leonard

    2018-01-01

    Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed. PMID:29434508

  18. Global Bathymetry: Machine Learning for Data Editing

    Science.gov (United States)

    Sandwell, D. T.; Tea, B.; Freund, Y.

    2017-12-01

    The accuracy of global bathymetry depends primarily on the coverage and accuracy of the sounding data and secondarily on the depth predicted from gravity. A main focus of our research is to add newly-available data to the global compilation. Most data sources have 1-12% of erroneous soundings caused by a wide array of blunders and measurement errors. Over the years we have hand-edited this data using undergraduate employees at UCSD (440 million soundings at 500 m resolution). We are developing a machine learning approach to refine the flagging of the older soundings and provide automated editing of newly-acquired soundings. The approach has three main steps: 1) Combine the sounding data with additional information that may inform the machine learning algorithm. The additional parameters include: depth predicted from gravity; distance to the nearest sounding from other cruises; seafloor age; spreading rate; sediment thickness; and vertical gravity gradient. 2) Use available edit decisions as training data sets for a boosted tree algorithm with a binary logistic objective function and L2 regularization. Initial results with poor quality single beam soundings show that the automated algorithm matches the hand-edited data 89% of the time. The results show that most of the information for detecting outliers comes from predicted depth with secondary contributions from distance to the nearest sounding and longitude. A similar analysis using very high quality multibeam data shows that the automated algorithm matches the hand-edited data 93% of the time. Again, most of the information for detecting outliers comes from predicted depth secondary contributions from distance to the nearest sounding and longitude. 3) The third step in the process is to use the machine learning parameters, derived from the training data, to edit 12 million newly acquired single beam sounding data provided by the National Geospatial-Intelligence Agency. The output of the learning algorithm will be

  19. Inverse analysis of turbidites by machine learning

    Science.gov (United States)

    Naruse, H.; Nakao, K.

    2017-12-01

    This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small

  20. 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…

  1. Space Weather in the Machine Learning Era: A Multidisciplinary Approach

    Science.gov (United States)

    Camporeale, E.; Wing, S.; Johnson, J.; Jackman, C. M.; McGranaghan, R.

    2018-01-01

    The workshop entitled Space Weather: A Multidisciplinary Approach took place at the Lorentz Center, University of Leiden, Netherlands, on 25-29 September 2017. The aim of this workshop was to bring together members of the Space Weather, Mathematics, Statistics, and Computer Science communities to address the use of advanced techniques such as Machine Learning, Information Theory, and Deep Learning, to better understand the Sun-Earth system and to improve space weather forecasting. Although individual efforts have been made toward this goal, the community consensus is that establishing interdisciplinary collaborations is the most promising strategy for fully utilizing the potential of these advanced techniques in solving Space Weather-related problems.

  2. 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...

  3. 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.

  4. 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,

  5. 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

  6. Machine Learning Based Diagnosis of Lithium Batteries

    Science.gov (United States)

    Ibe-Ekeocha, Chinemerem Christopher

    The depletion of the world's current petroleum reserve, coupled with the negative effects of carbon monoxide and other harmful petrochemical by-products on the environment, is the driving force behind the movement towards renewable and sustainable energy sources. Furthermore, the growing transportation sector consumes a significant portion of the total energy used in the United States. A complete electrification of this sector would require a significant development in electric vehicles (EVs) and hybrid electric vehicles (HEVs), thus translating to a reduction in the carbon footprint. As the market for EVs and HEVs grows, their battery management systems (BMS) need to be improved accordingly. The BMS is not only responsible for optimally charging and discharging the battery, but also monitoring battery's state of charge (SOC) and state of health (SOH). SOC, similar to an energy gauge, is a representation of a battery's remaining charge level as a percentage of its total possible charge at full capacity. Similarly, SOH is a measure of deterioration of a battery; thus it is a representation of the battery's age. Both SOC and SOH are not measurable, so it is important that these quantities are estimated accurately. An inaccurate estimation could not only be inconvenient for EV consumers, but also potentially detrimental to battery's performance and life. Such estimations could be implemented either online, while battery is in use, or offline when battery is at rest. This thesis presents intelligent online SOC and SOH estimation methods using machine learning tools such as artificial neural network (ANN). ANNs are a powerful generalization tool if programmed and trained effectively. Unlike other estimation strategies, the techniques used require no battery modeling or knowledge of battery internal parameters but rather uses battery's voltage, charge/discharge current, and ambient temperature measurements to accurately estimate battery's SOC and SOH. The developed

  7. Nuclear reactor machine refuelling system

    International Nuclear Information System (INIS)

    Cashen, W.S.; Erwin, D.

    1977-01-01

    Part of an on-line fuelling machine for a CANDU pressure-tube reactor is described. The present invention provides a refuelling machine wherein the fuelling components, including the fuel carrier and the closure adapter, are positively positioned and retained within the machine magazine or positively secured to the machine charge tube head, and cannot be accidentally disengaged as in former practice. The positive positioning devices include an arcuate keeper plate. Simplified hooked fingers are used. (NDH)

  8. 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

  9. 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.

  10. 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.

  11. Superconducting magnetic systems and electrical machines

    International Nuclear Information System (INIS)

    Glebov, I.A.

    1975-01-01

    The use of superconductors for magnets and electrical machines attracts close attention of designers and scientists. A description is given of an ongoing research program to create superconductive magnetic systems, commutator motors, homopolar machines, topological generators and turbogenerators with superconductive field windings. All the machines are tentative experimental models and serve as a basis for further developments

  12. Pileup Mitigation with Machine Learning (PUMML)

    Science.gov (United States)

    Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin; Schwartz, Matthew D.

    2017-12-01

    Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.

  13. Using Machine Learning to Predict MCNP Bias

    Energy Technology Data Exchange (ETDEWEB)

    Grechanuk, Pavel Aleksandrovi [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-01-09

    For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental keff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.

  14. 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...

  15. 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...

  16. 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...

  17. 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…

  18. 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…

  19. 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…

  20. 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.

  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. 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).

  4. 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...

  5. 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…

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. 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

  12. 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…

  13. Precision Parameter Estimation and Machine Learning

    Science.gov (United States)

    Wandelt, Benjamin D.

    2008-12-01

    I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

  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 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...

  16. 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,...

  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. Opportunities and Challenges of Multiplex Assays: A Machine Learning Perspective.

    Science.gov (United States)

    Chen, Junfang; Schwarz, Emanuel

    2017-01-01

    Multiplex assays that allow the simultaneous measurement of multiple analytes in small sample quantities have developed into a widely used technology. Their implementation spans across multiple assay systems and can provide readouts of similar quality as the respective single-plex measures, albeit at far higher throughput. Multiplex assay systems are therefore an important element for biomarker discovery and development strategies but analysis of the derived data can face substantial challenges that may limit the possibility of identifying meaningful biological markers. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, in particular from the perspective of machine learning aimed at identification of predictive biological signatures.

  19. 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.

  20. 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...

  1. 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...

  2. Performance Analysis of Machine-Learning Approaches for Modeling the Charging/Discharging Profiles of Stationary Battery Systems with Non-Uniform Cell Aging

    Directory of Open Access Journals (Sweden)

    Nandha Kumar Kandasamy

    2017-06-01

    Full Text Available The number of Stationary Battery Systems (SBS connected to various power distribution networks across the world has increased drastically. The increase in the integration of renewable energy sources is one of the major contributors to the increase in the number of SBS. SBS are also used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. Accurately modeling the charging/discharging characteristics of such SBS at various instances (charging/discharging profile is vital for many applications. Capacity loss due to the aging of the batteries is an important factor to be considered for estimating the charging/discharging profile of SBS more accurately. Empirical modeling is a common approach used in the literature for estimating capacity loss, which is further used for estimating the charging/discharging profiles of SBS. However, in the case of SBS used for renewable integration and other grid related applications, machine-learning (ML based models provide extreme flexibility and require minimal resources for implementation. The models can even leverage existing smart meter data to estimate the charging/discharging profile of SBS. In this paper, an analysis on the performance of different ML approaches that can be applied for lithium iron phosphate battery systems and vanadium redox flow battery systems used as SBS is presented for the scenarios where the aging of individual cells is non-uniform.

  3. 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...

  4. 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...

  5. The Diamond machine protection system

    International Nuclear Information System (INIS)

    Heron, M.T.; Lay, S.; Chernousko, Y.; Hamadyk, P.; Rotolo, N.

    2012-01-01

    The Diamond Light Source Machine Protection System (MPS) manages the hazards from high power photon beams and other hazards to ensure equipment protection on the booster synchrotron and storage ring. The system has a shutdown requirement, on a beam mis-steer of under 1 msec and has to manage in excess of a thousand interlocks. This is realised using a combination of bespoke hardware and programmable logic controllers. The MPS monitors a large number of interlock signals from diagnostics instrumentation, vacuum instrumentation, photon front ends and plant monitoring subsystems. Based on logic it can then remove the source of the energy to ensure protection of equipment. Depending on requirements, interlocks are managed on a Local or a Global basis. The Global system is structured as two layers, and supports fast- and slow-response-time interlock requirements. A Global MPS module takes the interlock permits for a given interlock circuit from each of the cells of the accelerator, and, subject to all interlocks being good, produces a permit to operate the source of energy: the RF amplifier for vessel protection and the PSU for magnet protection. The Local MPS module takes fast Interlock inputs from one cell of the Storage Ring or one quadrant of the Booster. Fast interlocks are those that must drop the beam in under 400 μsec (the maximum speed of the interlock) in the event of failure. EPIC provides the user interface to the MPS system

  6. 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.

  7. 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.

  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. 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.

  10. Machine learning methods for clinical forms analysis in mental health.

    Science.gov (United States)

    Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

    2013-01-01

    In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

  11. Fall detection using supervised machine learning algorithms: A comparative study

    KAUST Repository

    Zerrouki, Nabil; Harrou, Fouzi; Houacine, Amrane; Sun, Ying

    2017-01-01

    Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

  12. Fall detection using supervised machine learning algorithms: A comparative study

    KAUST Repository

    Zerrouki, Nabil

    2017-01-05

    Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

  13. Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.

    Science.gov (United States)

    Mathur, Neha; Glesk, Ivan; Buis, Arjan

    2016-10-01

    Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  14. 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…

  15. Finding protein sites using machine learning methods

    Directory of Open Access Journals (Sweden)

    Jaime Leonardo Bobadilla Molina

    2003-07-01

    Full Text Available The increasing amount of protein three-dimensional (3D structures determined by x-ray and NMR technologies as well as structures predicted by computational methods results in the need for automated methods to provide inital annotations. We have developed a new method for recognizing sites in three-dimensional protein structures. Our method is based on a previosly reported algorithm for creating descriptions of protein microenviroments using physical and chemical properties at multiple levels of detail. The recognition method takes three inputs: 1. A set of control nonsites that share some structural or functional role. 2. A set of control nonsites that lack this role. 3. A single query site. A support vector machine classifier is built using feature vectors where each component represents a property in a given volume. Validation against an independent test set shows that this recognition approach has high sensitivity and specificity. We also describe the results of scanning four calcium binding proteins (with the calcium removed using a three dimensional grid of probe points at 1.25 angstrom spacing. The system finds the sites in the proteins giving points at or near the blinding sites. Our results show that property based descriptions along with support vector machines can be used for recognizing protein sites in unannotated structures.

  16. 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.

  17. The RADAR Test Methodology: Evaluating a Multi-Task Machine Learning System with Humans in the Loop

    Science.gov (United States)

    2006-10-01

    details, static websites, and an ecommerce vendor portal. The “corpus” consists of the email and world state content. The latter consists of facts...learned fact variation, and the opportunity to induce a substantial crisis workload. The conference itself was a 4-day, multi-track technical conference...General 1. I am confident I completed the task well. 2. The task was difficult to complete. 3. I could have done as good of a job without the

  18. Learning to discover: machine learning in high-energy physics

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    In this talk we will survey some of the latest developments in machine learning research through the optics of potential applications in high-energy physics. We will then describe three ongoing projects in detail. The main subject of the talk is the data challenge we are organizing with ATLAS on optimizing the discovery significance for the Higgs to tau-tau channel. Second, we describe our collaboration with the LHCb experiment on designing and optimizing fast multi-variate techniques that can be implemented as online classifiers in triggers. Finally, we will sketch a relatively young project with the ILC (Calice) group in which we are attempting to apply deep learning techniques for inference on imaging calorimeter data.

  19. 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....

  20. 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.

  1. 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.

  2. 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.

  3. A machine learning approach for the classification of metallic glasses

    Science.gov (United States)

    Gossett, Eric; Perim, Eric; Toher, Cormac; Lee, Dongwoo; Zhang, Haitao; Liu, Jingbei; Zhao, Shaofan; Schroers, Jan; Vlassak, Joost; Curtarolo, Stefano

    Metallic glasses possess an extensive set of mechanical properties along with plastic-like processability. As a result, they are a promising material in many industrial applications. However, the successful synthesis of novel metallic glasses requires trial and error, costing both time and resources. Therefore, we propose a high-throughput approach that combines an extensive set of experimental measurements with advanced machine learning techniques. This allows us to classify metallic glasses and predict the full phase diagrams for a given alloy system. Thus this method provides a means to identify potential glass-formers and opens up the possibility for accelerating and reducing the cost of the design of new metallic glasses.

  4. Classifying BCI signals from novice users with extreme learning machine

    Directory of Open Access Journals (Sweden)

    Rodríguez-Bermúdez Germán

    2017-07-01

    Full Text Available Brain computer interface (BCI allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

  5. Machine learning for fab automated diagnostics

    Science.gov (United States)

    Giollo, Manuel; Lam, Auguste; Gkorou, Dimitra; Liu, Xing Lan; van Haren, Richard

    2017-06-01

    Process optimization depends largely on field engineer's knowledge and expertise. However, this practice turns out to be less sustainable due to the fab complexity which is continuously increasing in order to support the extreme miniaturization of Integrated Circuits. On the one hand, process optimization and root cause analysis of tools is necessary for a smooth fab operation. On the other hand, the growth in number of wafer processing steps is adding a considerable new source of noise which may have a significant impact at the nanometer scale. This paper explores the ability of historical process data and Machine Learning to support field engineers in production analysis and monitoring. We implement an automated workflow in order to analyze a large volume of information, and build a predictive model of overlay variation. The proposed workflow addresses significant problems that are typical in fab production, like missing measurements, small number of samples, confounding effects due to heterogeneity of data, and subpopulation effects. We evaluate the proposed workflow on a real usecase and we show that it is able to predict overlay excursions observed in Integrated Circuits manufacturing. The chosen design focuses on linear and interpretable models of the wafer history, which highlight the process steps that are causing defective products. This is a fundamental feature for diagnostics, as it supports process engineers in the continuous improvement of the production line.

  6. GAME: GAlaxy Machine learning for Emission lines

    Science.gov (United States)

    Ucci, G.; Ferrara, A.; Pallottini, A.; Gallerani, S.

    2018-06-01

    We present an updated, optimized version of GAME (GAlaxy Machine learning for Emission lines), a code designed to infer key interstellar medium physical properties from emission line intensities of ultraviolet /optical/far-infrared galaxy spectra. The improvements concern (a) an enlarged spectral library including Pop III stars, (b) the inclusion of spectral noise in the training procedure, and (c) an accurate evaluation of uncertainties. We extensively validate the optimized code and compare its performance against empirical methods and other available emission line codes (PYQZ and HII-CHI-MISTRY) on a sample of 62 SDSS stacked galaxy spectra and 75 observed HII regions. Very good agreement is found for metallicity. However, ionization parameters derived by GAME tend to be higher. We show that this is due to the use of too limited libraries in the other codes. The main advantages of GAME are the simultaneous use of all the measured spectral lines and the extremely short computational times. We finally discuss the code potential and limitations.

  7. Predicting Increased Blood Pressure Using Machine Learning

    Science.gov (United States)

    Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Soares, Telma de Jesus; dos Reis, Luciana Araujo

    2014-01-01

    The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R 2 (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R 2 (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power. PMID:24669313

  8. Predicting increased blood pressure using machine learning.

    Science.gov (United States)

    Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Gomes, Cristiano Mauro Assis; Soares, Telma de Jesus; Dos Reis, Luciana Araujo; Santos, Joselito

    2014-01-01

    The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

  9. GAME: GAlaxy Machine learning for Emission lines

    Science.gov (United States)

    Ucci, G.; Ferrara, A.; Pallottini, A.; Gallerani, S.

    2018-03-01

    We present an updated, optimized version of GAME (GAlaxy Machine learning for Emission lines), a code designed to infer key interstellar medium physical properties from emission line intensities of UV/optical/far infrared galaxy spectra. The improvements concern: (a) an enlarged spectral library including Pop III stars; (b) the inclusion of spectral noise in the training procedure, and (c) an accurate evaluation of uncertainties. We extensively validate the optimized code and compare its performance against empirical methods and other available emission line codes (pyqz and HII-CHI-mistry) on a sample of 62 SDSS stacked galaxy spectra and 75 observed HII regions. Very good agreement is found for metallicity. However, ionization parameters derived by GAME tend to be higher. We show that this is due to the use of too limited libraries in the other codes. The main advantages of GAME are the simultaneous use of all the measured spectral lines, and the extremely short computational times. We finally discuss the code potential and limitations.

  10. Predicting Increased Blood Pressure Using Machine Learning

    Directory of Open Access Journals (Sweden)

    Hudson Fernandes Golino

    2014-01-01

    Full Text Available The present study investigates the prediction of increased blood pressure by body mass index (BMI, waist (WC and hip circumference (HC, and waist hip ratio (WHR using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42, misclassification (.19, and the higher pseudo R2 (.43. This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25, misclassification (.16, and the higher pseudo R2 (.46. This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

  11. Optimal interference code based on machine learning

    Science.gov (United States)

    Qian, Ye; Chen, Qian; Hu, Xiaobo; Cao, Ercong; Qian, Weixian; Gu, Guohua

    2016-10-01

    In this paper, we analyze the characteristics of pseudo-random code, by the case of m sequence. Depending on the description of coding theory, we introduce the jamming methods. We simulate the interference effect or probability model by the means of MATLAB to consolidate. In accordance with the length of decoding time the adversary spends, we find out the optimal formula and optimal coefficients based on machine learning, then we get the new optimal interference code. First, when it comes to the phase of recognition, this study judges the effect of interference by the way of simulating the length of time over the decoding period of laser seeker. Then, we use laser active deception jamming simulate interference process in the tracking phase in the next block. In this study we choose the method of laser active deception jamming. In order to improve the performance of the interference, this paper simulates the model by MATLAB software. We find out the least number of pulse intervals which must be received, then we can make the conclusion that the precise interval number of the laser pointer for m sequence encoding. In order to find the shortest space, we make the choice of the greatest common divisor method. Then, combining with the coding regularity that has been found before, we restore pulse interval of pseudo-random code, which has been already received. Finally, we can control the time period of laser interference, get the optimal interference code, and also increase the probability of interference as well.

  12. (Machine) learning to do more with less

    Science.gov (United States)

    Cohen, Timothy; Freytsis, Marat; Ostdiek, Bryan

    2018-02-01

    Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (which relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail — both analytically and numerically — with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to a class of systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC. Example code is provided on GitHub.

  13. Machine Protection System response in 2011

    CERN Document Server

    Zerlauth, M; Wenninger, J

    2012-01-01

    The performance of the machine protection system during the 2011 run is summarized in this paper. Following an analysis of the beam dump causes in comparison to the previous 2010 run, special emphasis will be given to analyse events which risked to exposed parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems as well as in the change management will be evaluated along with their impact on the 2012 run. The role of the restricted Machine Protection Panel ( rMPP ) during the various operational phases such as commissioning, the intensity ramp up and Machine Developments is being discussed.

  14. 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...

  15. 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.

  16. Reduced multiple empirical kernel learning machine.

    Science.gov (United States)

    Wang, Zhe; Lu, MingZhe; Gao, Daqi

    2015-02-01

    Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3

  17. 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.

  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. Energy and carbon emissions analysis and prediction of complex petrochemical systems based on an improved extreme learning machine integrated interpretative structural model

    International Nuclear Information System (INIS)

    Han, Yongming; Zhu, Qunxiong; Geng, Zhiqiang; Xu, Yuan

    2017-01-01

    Highlights: • The ELM integrated ISM (ISM-ELM) method is proposed. • The proposed method is more efficient and accurate than the ELM through the UCI data set. • Energy and carbon emissions analysis and prediction of petrochemical industries based ISM-ELM is obtained. • The proposed method is valid in improving energy efficiency and reducing carbon emissions of ethylene plants. - Abstract: Energy saving and carbon emissions reduction of the petrochemical industry are affected by many factors. Thus, it is difficult to analyze and optimize the energy of complex petrochemical systems accurately. This paper proposes an energy and carbon emissions analysis and prediction approach based on an improved extreme learning machine (ELM) integrated interpretative structural model (ISM) (ISM-ELM). ISM based the partial correlation coefficient is utilized to analyze key parameters that affect the energy and carbon emissions of the complex petrochemical system, and can denoise and reduce dimensions of data to decrease the training time and errors of the ELM prediction model. Meanwhile, in terms of the model accuracy and the training time, the robustness and effectiveness of the ISM-ELM model are better than the ELM through standard data sets from the University of California Irvine (UCI) repository. Moreover, a multi-inputs and single-output (MISO) model of energy and carbon emissions of complex ethylene systems is established based on the ISM-ELM. Finally, detailed analyses and simulations using the real ethylene plant data demonstrate the effectiveness of the ISM-ELM and can guide the improvement direction of energy saving and carbon emissions reduction in complex petrochemical systems.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. AGE GROUP CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

    OpenAIRE

    Arshdeep Singh Syal*1 & Abhinav Gupta2

    2017-01-01

    A human face provides a lot of information that allows another person to identify characteristics such as age, sex, etc. Therefore, the challenge is to develop an age group prediction system using the automatic learning method. The task of estimating the age group of the human from their frontal facial images is very captivating, but also challenging because of the pattern of personalized and non-linear aging that differs from one person to another. This paper examines the problem of predicti...

  5. Probabilistic models and machine learning in structural bioinformatics

    DEFF Research Database (Denmark)

    Hamelryck, Thomas

    2009-01-01

    . Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis...

  6. 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...

  7. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  8. Sparse Machine Learning Methods for Understanding Large Text Corpora

    Data.gov (United States)

    National Aeronautics and Space Administration — Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational...

  9. 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.

  10. Exploration of Machine Learning Approaches to Predict Pavement Performance

    Science.gov (United States)

    2018-03-23

    Machine learning (ML) techniques were used to model and predict pavement condition index (PCI) for various pavement types using a variety of input variables. The primary objective of this research was to develop and assess PCI predictive models for t...

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

    OpenAIRE

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

    2017-01-01

    Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better ...

  13. 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...

  14. A Comparative Analysis of Machine Learning Techniques for Credit Scoring

    OpenAIRE

    Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

    2012-01-01

    Abstract Credit Scoring has become an oft researched topic in light of the increasing volatility of the global economy and the recent world financial crisis. Amidst the many methods used for credit scoring, machine learning techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. Furthermore machine learning techniques minimize the risk of human bias and error and maximize speed as they are able to perform computation...

  15. 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...

  16. 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...

  17. 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...

  18. 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.

  19. 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.

  20. Bone-suppressed radiography using machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jun Beom; Kim, Dae Cheon; Kim, Ho Kyung [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    The single-shot dual-energy imaging suffers from reduced contrast-to-noise ratio performance due to poor spectral separation. Tomosynthesis requires more complex motion equipment and may require higher patient dose. An alternative tissue-specific imaging technique was introduced. This alternative technique usually possesses a filter to generate bone-only images for given digital radiographs. Therefore, it provides soft-tissue-enhanced images from the subtraction of given radiographs and filtered bone-only images. Only bone-suppressed imaging capability is a limitation of the method. The filter can be obtained from a machine-learning algorithm, e.g. artificial neural network (ANN), with the dual-energy bone-only images (called 'teaching' images). We suspect the robustness of the filter may be dependent upon the number of teaching images and the number of patients from whose radiographs we obtain the teaching images. In this study, we design an ANN to obtain a bone-extracting filter from a radiograph, and investigate the filter properties with respect to various ANN parameters. Preliminary results are summarized in Fig. 3. We extracted 5,000 subregions in a 21x21 pixel format from the lung region in the bone-enhanced dual-energy image and we used them for teaching images during training the ANN. The resultant bone-enhanced image from the ANN nonlinear filter is shown in Fig. 3 (a). From the weighted logarithmic subtraction between Fig. 2 (a) and Fig. 3 (a), we could obtain the bone-suppressed image as shown in Fig. 3 (b). The quality of the bone-suppressed image is comparable to the ground truth Fig. 2 (c).