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• Chapter 1. Revolutions in Infant fingerprint Recognition-A Survey.-
• Chapter 2. A Review of High Utility Itemset Mining for Transactional Database.-
• Chapter 3. A Cross-sectional study on distributed mutual exclusion algorithms for ad hoc networks.-
• Chapter 4. Electromagnetic Pollution Index Estimation of Green Mobile Communication of Macrocell.-
• Chapter 5. Prediction of Train delay System in Indian Railways using Machine Learning Techniques: Survey.-
• Chapter 6. Valence of emotion recognition using EEG.-
• Chapter 7. A Deep Learning-based Approach for Automated Brain Tumor Segmentation in MR images.-
• Chapter 8. MZI based Electro-Optic Reversible XNOR/XOR Derived from Modified Fredkin Gate.-
• Chapter 9. Secured Remote Access of Cloud Based Learning Management System (LMS) Using VPN.-
• Chapter 10. Surface EMG signal classification for hand gesture recognition.-
• Chapter 11. Improved Energy Efficiency in Street Lighting: A Coverage based Approach.-
• Chapter 12. Security and Challenges for Cognitive IOT Based Future City Architecture.-
• Chapter 13. A Heuristic Model for Friend Selection in Social Internet of Things.-
• Chapter 14. A Fuzzy string matching based reduplication with morphological attributes.-
• Chapter 15. Accelerating LOF Outlier Detection Approach. etc.
• (source: Nielsen Book Data)

This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing and their applications in real world. The topics covered in machine learning involves feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modelling from video, 3D object recognition, localization and tracking, medical image analysis and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG) and electromyogram (EMG). .
(source: Nielsen Book Data)

• Preface
• Notation
• Part I. Introduction and Overview: 1. Introduction
• 2. Mathematical background
• 3. Overview of a pattern recognition system
• 4. Evaluation
• Part II. Domain-Independent Feature Extraction: 5. Principal component analysis
• 6. Fisher's linear discriminant
• Part III. Classifiers and Tools: 7. Support vector machines
• 8. Probabilistic methods
• 9. Distance metrics and data transformations
• 10. Information theory and decision trees
• Part IV. Handling Diverse Data Formats: 11. Sparse and misaligned data
• 12. Hidden Markov model
• Part V. Advanced Topics: 13. The normal distribution
• 14. The basic idea behind expectation-maximization
• 15. Convolutional neural networks
• References
• Index.
• (source: Nielsen Book Data)

This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student's skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, and probability will be well prepared to master the concepts and mathematical analysis presented here.
(source: Nielsen Book Data)

• INTRODUCTORY ISSUES. Paradigmatic considerations on syntactic pattern recognition
• Methodology of syntactic pattern recognition ; STRING-BASED MODELS. Pattern recognition based on regular and CF grammars
• Enhanced string-based models for pattern recognition
• Inference (induction) of string languages
• Applications of string methods ; TREE-BASED MODELS. Pattern recognition based on tree languages
• Inference (induction) of tree languages
• Applications of tree methods ; GRAPH-BASED MODELS. Pattern analysis with graph grammars
• Inference (induction) of graph languages
• Applications of graph methods ; FUTURE OF SYNTACTIC PATTERN RECOGNITION. Summary of results and open problems
• Methodological issues.

This unique compendium presents the major methods of recognition and learning used in syntactic pattern recognition from the 1960s till 2018. Each method is introduced firstly in a formal way. Then, it is explained with the help of examples and its algorithms are described in a pseudocode. The survey of the applications contains more than 1,000 sources published since the 1960s. The open problems in the field, the challenges and the determinants of the future development of syntactic pattern recognition are discussed.This must-have volume provides a good read and serves as an excellent source of reference materials for researchers, academics, and postgraduate students in the fields of pattern recognition, machine perception, computer vision and artificial intelligence.
(source: Nielsen Book Data)

• Preface
• Acknowledgements
• Part I. Analysis: 1. Probabilistic models
• 2. Exact string matching
• 3. Constrained exact string matching
• 4. Generalized string matching
• 5. Subsequence pattern matching
• Part II. Applications: 6. Algorithms and data structures
• 7. Digital trees
• 8. Suffix trees and Lempel-Ziv'77
• 9. Lempel-Ziv'78 compression algorithm
• 10. String complexity
• Bibliography
• Index.
• (source: Nielsen Book Data)

How do you distinguish a cat from a dog by their DNA? Did Shakespeare really write all of his plays? Pattern matching techniques can offer answers to these questions and to many others, from molecular biology, to telecommunications, to classifying Twitter content. This book for researchers and graduate students demonstrates the probabilistic approach to pattern matching, which predicts the performance of pattern matching algorithms with very high precision using analytic combinatorics and analytic information theory. Part I compiles known results of pattern matching problems via analytic methods. Part II focuses on applications to various data structures on words, such as digital trees, suffix trees, string complexity and string-based data compression. The authors use results and techniques from Part I and also introduce new methodology such as the Mellin transform and analytic depoissonization. More than 100 end-of-chapter problems help the reader to make the link between theory and practice.
(source: Nielsen Book Data)

• Introduction
• Classifiers Based on Bayes Decision Theory
• Linear Classifiers
• Nonlinear Classifiers
• Feature Selection
• Feature Generation I
• Feature Generation II
• Template Matching
• Context-Dependant Classification
• System Evaluation
• Clustering: Basic Concepts Clustering Algorithms I (Sequential)
• Clustering Algorithms II (Hierarchical)
• Clustering Algorithms III (Functional Optimization)
• Clustering Algorithms IV (Graph Theory)
• Cluster Validity.
• (source: Nielsen Book Data)

Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of interest both in academia and in applications-oriented communities, c) for the first time treats audio along with image applications since in today's world the most advanced applications are treated in a unified way and d) the subject of classifier combinations is treated, since this is a hot topic currently of interest in the pattern recognition community.
(source: Nielsen Book Data)

• Introduction. Classifiers Based on Bayes Decision Theory. Linear Classifiers. Non Linear Classifiers. Feature Selection. Feature Generation I: Linear Transforms.. Feature Generation II. Template Matching. Context Dependent Classification. System Evaluation. Clustering: Basic Concepts. Clustering Algorithms I: Sequential Algorithms. Clustering Algorithms II: Hierarchical Algorithms. Clustering Algorithms III: Schemes Based on Function Optimization. Clustering Algorithms IV. Cluster Validity. Appendices.
• (source: Nielsen Book Data)

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. This volume's treatment covers pattern recognition applications, from image analysis to speech recognition and communications. It includes discussion of the latest techniques in wavelets, wavelet packets, and fractals. This book presents material on neural networks, 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.
(source: Nielsen Book Data)

• Contents:
• 1. Introduction.
• 2. Data Projection.
• 3. Feature Extraction and Optimization.
• 4. Classifier Architecture.
• 5. Hidden Markov Models.
• 6. Hierarchical and Hybrid Classification.
• 7. Pattern Recognition Summary.
• 8. Bioreject.
• 9. Reduced Dimension Image Compression.
• 10. Full Spectrum Signal Processing.
• 11. LFA Echo Characterization.
• 12. Nonlinear System Modeling with HMM.
• 13. Prediction and Trend Analysis.
• 14. Knowledge Creation in Business.
• (source: Nielsen Book Data)

Market: Engineers and researchers in neural networks, image processing, audio/speech, and medical imaging. This book begins by focusing on the theoretical aspect of pattern recognition and introduces an integrated pattern recognition paradigm, which combines preprocessing, low dimensional signal characterization, feature optimization, and mapping classifier architecture to good features in a seamless fashion. Later, the authors reinforce the concepts of pattern recognition and prediction with challenging real- world examples, encompassing financial market prediction, image coding, active and passive sonar processing, chaos modeling, and intelligent product design.
(source: Nielsen Book Data)

• Segmentation
• Features
• Patterns and Textures: Characterization and Segmentation
• Statistical Decision Methods: Some Background
• Bayes' Optimal Decisions
• Beyond Bayes
• Formal Linguistic Methods
• Structural and Hybrid Methods
• Learning Techniques
• Epilogue: Where Do We Go from Here
• Appendix
• Selected Problems
• References
• Index.
• (source: Nielsen Book Data)

This introduction to pattern recognition features the parallel development of the statistical and structural approaches to the subject. A feature of the text is the emphasis placed upon pattern recognition techniques that model human perception.
(source: Nielsen Book Data)

• Probability theory for random vectors
• simple statistical decision procedures
• operations upon random vectors
• feature extraction and nonlinear mapping
• quadratic and linear classifiers
• parameter estimation
• nonparametric estimation and classification
• estimating and bounding the probability of error
• classification of stationary time series
• context-dependent methods
• other methods of classification.
• (source: Nielsen Book Data)

This book presents the fundamentals of decision-making systems in general, and pattern recognition systems in particular, in a concise, informative, and easy-to-read manner. It provides background mathematics, then shows how to use statistical pattern recognition in the design of algorithms for automated systems. Later chapters include recent research results. The volume will be of use to computer scientists, workers in artificial intelligence and expert systems and students.
(source: Nielsen Book Data)