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Pattern Recognition And Computational Intelligence Techniques Using Matlab
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Book Synopsis Pattern Recognition and Computational Intelligence Techniques Using Matlab by : E. S. Gopi
Download or read book Pattern Recognition and Computational Intelligence Techniques Using Matlab written by E. S. Gopi and published by Springer Nature. This book was released on 2019-10-17 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques. Presents pattern recognition and the computational intelligence using Matlab; Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly; Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.
Book Synopsis Introduction to Pattern Recognition by : Sergios Theodoridis
Download or read book Introduction to Pattern Recognition written by Sergios Theodoridis and published by Academic Press. This book was released on 2010-03-03 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition Solved examples in Matlab, including real-life data sets in imaging and audio recognition Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)
Book Synopsis Pattern Recognition by : Konstantinos Koutroumbas
Download or read book Pattern Recognition written by Konstantinos Koutroumbas and published by Academic Press. This book was released on 2008-11-26 with total page 981 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques · Many more diagrams included--now in two color--to provide greater insight through visual presentation · Matlab code of the most common methods are given at the end of each chapter. · More Matlab code is available, together with an accompanying manual, via this site · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869). Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor
Book Synopsis Computational Intelligence Paradigms by : S. Sumathi
Download or read book Computational Intelligence Paradigms written by S. Sumathi and published by CRC Press. This book was released on 2010-01-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a wide range of programming examples implemented in MATLAB®, Computational Intelligence Paradigms: Theory and Applications Using MATLAB® presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi–Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization. Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.
Book Synopsis Digital Speech Processing Using Matlab by : E. S. Gopi
Download or read book Digital Speech Processing Using Matlab written by E. S. Gopi and published by Springer Science & Business Media. This book was released on 2013-12-03 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Speech Processing Using Matlab deals with digital speech pattern recognition, speech production model, speech feature extraction, and speech compression. The book is written in a manner that is suitable for beginners pursuing basic research in digital speech processing. Matlab illustrations are provided for most topics to enable better understanding of concepts. This book also deals with the basic pattern recognition techniques (illustrated with speech signals using Matlab) such as PCA, LDA, ICA, SVM, HMM, GMM, BPN, and KSOM.
Book Synopsis Computational Intelligence Paradigms by : S.. PANEERSELVAM SUMATHI (SUREKHA.)
Download or read book Computational Intelligence Paradigms written by S.. PANEERSELVAM SUMATHI (SUREKHA.) and published by CRC Press. This book was released on 2019-08-30 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a wide range of programming examples implemented in MATLAB(R), Computational Intelligence Paradigms: Theory and Applications Using MATLAB(R) presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi-Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization. Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.
Book Synopsis Digital Signal Processing for Wireless Communication using Matlab by : E.S. Gopi
Download or read book Digital Signal Processing for Wireless Communication using Matlab written by E.S. Gopi and published by Springer. This book was released on 2015-08-10 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines signal processing techniques used in wireless communication illustrated by using the Matlab program. The author discusses these techniques as they relate to Doppler spread, Delay spread, Rayleigh and Rician channel modeling, rake receiver, diversity techniques, MIMO and OFDM based transmission techniques, and array signal processing. Related topics such as detection theory, Link budget, Multiple access techniques, spread spectrum, are also covered. • Illustrates signal processing techniques involved in wireless communication • Discusses multiple access techniques such as Frequency division multiple access, Time division multiple access, and Code division multiple access • Covers band pass modulation techniques such as Binary phase shift keying, Differential phase shift keying, Quadrature phase shift keying, Binary frequency shift keying, Minimum shift keying, and Gaussian minimum shift keying.
Book Synopsis Digital Signal Processing for Medical Imaging Using Matlab by : E.S. Gopi
Download or read book Digital Signal Processing for Medical Imaging Using Matlab written by E.S. Gopi and published by Springer Science & Business Media. This book was released on 2012-09-14 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes medical imaging systems, such as X-ray, Computed tomography, MRI, etc. from the point of view of digital signal processing. Readers will see techniques applied to medical imaging such as Radon transformation, image reconstruction, image rendering, image enhancement and restoration, and more. This book also outlines the physics behind medical imaging required to understand the techniques being described. The presentation is designed to be accessible to beginners who are doing research in DSP for medical imaging. Matlab programs and illustrations are used wherever possible to reinforce the concepts being discussed.
Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro
Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
Book Synopsis Computational Intelligence in Pattern Recognition by : Asit Kumar Das
Download or read book Computational Intelligence in Pattern Recognition written by Asit Kumar Das and published by Springer Nature. This book was released on 2021-09-04 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features high-quality research papers presented at the 3rd International Conference on Computational Intelligence in Pattern Recognition (CIPR 2021), held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 24 – 25 April 2021. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.
Book Synopsis Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/SIMULINK® by : S. Sumathi
Download or read book Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/SIMULINK® written by S. Sumathi and published by CRC Press. This book was released on 2018-09-03 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: Considered one of the most innovative research directions, computational intelligence (CI) embraces techniques that use global search optimization, machine learning, approximate reasoning, and connectionist systems to develop efficient, robust, and easy-to-use solutions amidst multiple decision variables, complex constraints, and tumultuous environments. CI techniques involve a combination of learning, adaptation, and evolution used for intelligent applications. Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® explores the performance of CI in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems. Focusing on the practical implementation of CI techniques, this book: Discusses the role of CI paradigms in engineering applications such as unit commitment and economic load dispatch, harmonic reduction, load frequency control and automatic voltage regulation, job shop scheduling, multidepot vehicle routing, and digital image watermarking Explains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applications Shows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® models Includes experimental analyses and results of test systems Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.
Book Synopsis Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques by : Kulkarni, Siddhivinayak
Download or read book Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques written by Kulkarni, Siddhivinayak and published by IGI Global. This book was released on 2012-06-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Book Synopsis Pattern Recognition by : Sergios Theodoridis
Download or read book Pattern Recognition written by Sergios Theodoridis and published by Elsevier. This book was released on 2003-05-15 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms. *Approaches pattern recognition from the designer's point of view *New edition highlights latest developments in this growing field, including independent components and support vector machines, not available elsewhere *Supplemented by computer examples selected from applications of interest
Book Synopsis Pattern Recognition & Matlab Intro by : Sergios Theodoridis
Download or read book Pattern Recognition & Matlab Intro written by Sergios Theodoridis and published by Academic Press. This book was released on 2010-04-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This specially priced set includes a copy of Theodoridis/Koutroumbas, Pattern Recognition 4e and Theodoridis/Pikrakis/Koutroumbas/Cavouras, Introduction to Pattern Recognition: A Matlab Approach. The main text provides breadth and depth of coverage of pattern recognition theory and application, including modern topics like non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, and combining clustering algorithms. Together with worked examples, exercises, and Matlab applications it provides the most comprehensive coverage currently available. The accompanying manual includes MATLAB code of the most common methods and algorithms in the book, together with a descriptive summary and solved problems, and including real-life data sets in imaging and audio recognition.
Book Synopsis Computational Intelligence Techniques and Their Applications to Software Engineering Problems by : Ankita Bansal
Download or read book Computational Intelligence Techniques and Their Applications to Software Engineering Problems written by Ankita Bansal and published by CRC Press. This book was released on 2020-09-27 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book: Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more Highlights swarm intelligence-based optimization solutions for software testing and reliability problems
Download or read book Medical Imaging written by K.C. Santosh and published by CRC Press. This book was released on 2019-08-20 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
Book Synopsis Computational Intelligence and Its Applications by : H K Lam
Download or read book Computational Intelligence and Its Applications written by H K Lam and published by World Scientific. This book was released on 2012-07-17 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on computational intelligence techniques and their applications — fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches. Sample Chapter(s) Chapter 1: Maximal Margin Algorithms for Pose Estimation (658 KB) Contents:Evolutionary Computation and Its Applications:Maximal Margin Algorithms for Pose Estimation (Ying Guo and Jiaming Li)Polynomial Modeling in a Dynamic Environment Based on a Particle Swarm Optimization (Kit Yan Chan and Tharam S Dillon)Restoration of Half-toned Color-quantized Images Using Particle Swarm Optimization with Multi-wavelet Mutation (Frank H F Leung, Benny C W Yeung and Y H Chan)Fuzzy Logics and Their Applications:Hypoglycemia Detection for Insulin-dependent Diabetes Mellitus: Evolved Fuzzy Inference System Approach (S H Ling, P P San and H T Nguyen)Neural Networks and Their Applications:Study of Limit Cycle Behavior of Weights of Perceptron (C Y F Ho and B W K Ling)Artificial Neural Network Modeling with Application to Nonlinear Dynamics (Yi Zhao)Solving Eigen-problems of Matrices by Neural Networks (Yiguang Liu, Zhisheng You, Bingbing Liu and Jiliu Zhou)Automated Screw Insertion Monitoring Using Neural Networks: A Computational Intelligence Approach to Assembly in Manufacturing (Bruno Lara, Lakmal D Seneviratne and Kaspar Althoefer)Support Vector Machines and Their Applications:On the Applications of Heart Disease Risk Classification and Hand-written Character Recognition Using Support Vector Machines (S R Alty, H K Lam and J Prada)Nonlinear Modeling Using Support Vector Machine for Heart Rate Response to Exercise (Weidong Chen, Steven W Su, Yi Zhang, Ying Guo, Nghir Nguyen, Branko G Celler and Hung T Nguyen)Machine Learning-based Nonlinear Model Predictive Control for Heart Rate Response to Exercise (Yi Zhang, Steven W Su, Branko G Celler and Hung T Nguyen)Intelligent Fault Detection and Isolation of HVAC System Based on Online Support Vector Machine (Davood Dehestani, Ying Guo, Sai Ho Ling, Steven W Su and Hung T Nguyen) Readership: Graduates and researchers in computer science, especially those specialising in artificial intelligence, neural networks, fuzzy logic and pattern recognition. Keywords:Evolutionary Computation;Fuzzy Logic;Neural Networks;Support Vector MachineKey Features:Covers wide-ranging applications from pattern recognition, control systems to biomedical applications. Various computational techniques are proposed and presented in detail for the treatment of various problemsMost of the applications in this book are real and high impact, such as hypoglycaemia, detection for diabetes patients, cardio respiratory response estimation, pattern recognition and pose estimationAddresses important related problems and difficulties using the collective experiences and knowledge from the contributors, who are each prominent in their own area of research
