Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems
Author: Dr. Arun Kumar Sangaiah
Publsiher: Academic Press
Total Pages: 280
Release: 2019-07-26
ISBN 10: 0128172932
ISBN 13: 9780128172933
Language: EN, FR, DE, ES & NL

Deep Learning and Parallel Computing Environment for Bioengineering Systems Book Review:

Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

Soft Computing for Intelligent Systems

Soft Computing for Intelligent Systems
Author: Nikhil Marriwala
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 9811610487
ISBN 13: 9789811610486
Language: EN, FR, DE, ES & NL

Soft Computing for Intelligent Systems Book Review:

Deep Learning and Big Data for Intelligent Transportation

Deep Learning and Big Data for Intelligent Transportation
Author: Khaled R. Ahmed
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 3030656616
ISBN 13: 9783030656614
Language: EN, FR, DE, ES & NL

Deep Learning and Big Data for Intelligent Transportation Book Review:

Applications of Big Data in Large and Small Scale Systems

Applications of Big Data in Large  and Small Scale Systems
Author: Sam Goundar,Praveen Kumar Rayani
Publsiher: IGI Global
Total Pages: 377
Release: 2021
ISBN 10: 1799866750
ISBN 13: 9781799866756
Language: EN, FR, DE, ES & NL

Applications of Big Data in Large and Small Scale Systems Book Review:

"This book addresses the newest innovative and intelligent applications related to utilizing the large amounts of big data being generated that is increasingly driving decision making and changing the landscape of business intelligence, from governments to private organizations, from communities to individuals"--

Computational Intelligence in Healthcare

Computational Intelligence in Healthcare
Author: Amit Kumar Manocha
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 3030687236
ISBN 13: 9783030687236
Language: EN, FR, DE, ES & NL

Computational Intelligence in Healthcare Book Review:

Data Science

Data Science
Author: Jianchao Zeng,Pinle Qin,Weipeng Jing (Computer engineer),Xianhua Song,Zeguang Lu
Publsiher: Springer Nature
Total Pages: 545
Release: 2021
ISBN 10: 9811659400
ISBN 13: 9789811659409
Language: EN, FR, DE, ES & NL

Data Science Book Review:

This two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021. The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.

Understanding COVID 19

Understanding COVID 19
Author: Janmenjoy Nayak
Publsiher: Springer Nature
Total Pages: 569
Release: 2021
ISBN 10: 3030747611
ISBN 13: 9783030747619
Language: EN, FR, DE, ES & NL

Understanding COVID 19 Book Review:

This book provides a comprehensive description of the novel coronavirus infection, spread analysis, and related challenges for the effective combat and treatment. With a detailed discussion on the nature of transmission of COVID-19, few other important aspects such as disease symptoms, clinical application of radiomics, image analysis, antibody treatments, risk analysis, drug discovery, emotion and sentiment analysis, virus infection, and fatality prediction are highlighted. The main focus is laid on different issues and futuristic challenges of computational intelligence techniques in solving and identifying the solutions for COVID-19. The book drops radiance on the reasons for the growing profusion and complexity of data in this sector. Further, the book helps to focus on further research challenges and directions of COVID-19 for the practitioners as well as researchers. .

PATTERN RECOGNITION

PATTERN RECOGNITION
Author: Syed Thouheed Ahmed,Syed Muzamil Basha,Sajeev Ram Arumugam,Mallikarjun M Kodabagi
Publsiher: MileStone Research Publications
Total Pages: 156
Release: 2021-08-01
ISBN 10: 9354931375
ISBN 13: 9789354931376
Language: EN, FR, DE, ES & NL

PATTERN RECOGNITION Book Review:

This book covers the primary and supportive topics on pattern recognition with respect to beginners understand-ability. The aspects of pattern recognition is value added with an introductory of machine learning terminologies. This book covers the aspects of pattern validation, recognition, computation and processing. The initial aspects such as data representation and feature extraction is reported with supportive topics such as computational algorithms and decision trees. This text book covers the aspects as reported. Par t - I In this part, the initial foundation aspects of pattern recognition is discussed with reference to probabilities role in influencing a pattern occurrence, pattern extraction and properties. Introduction: Definition of Pattern Recognition, Applications, Datasets for Pattern Recognition, Different paradigms for Pattern Recognition, Introduction to probability, events, random variables, Joint distributions and densities, moments. Estimation minimum risk estimators, problems. Representation: Data structures for Pattern Recognition, Representation of clusters, proximity measures, size of patterns, Abstraction of Data set, Feature extraction, Feature selection, Evaluation. Par t - II In Part - II of the text, the mathematical representation and computation algorithms for extracting and evaluating patterns are discussed. The basic algorithms of machine learning classifiers with Nearest neighbor and Naive Bayes is reported with value added validation process using decision trees. Computational Algorithms: Nearest neighbor algorithm, variants of NN algorithms, use of NN for transaction databases, efficient algorithms, Data reduction, prototype selection, Bayes theorem, minimum error rate classifier, estimation of probabilities, estimation of probabilities, comparison with NNC, Naive Bayesclassifier, Bayesian belief network. Decision Trees: Introduction, Decision Tree for Pattern Recognition, Construction of Decision Tree, Splittingat the nodes, Over-fitting& Pruning, Examples.

Innovations in Smart Cities Applications Volume 4

Innovations in Smart Cities Applications Volume 4
Author: Mohamed Ben Ahmed,İsmail Rakıp Karaș,Domingos Santos,Olga Sergeyeva,Anouar Abdelhakim Boudhir
Publsiher: Springer Nature
Total Pages: 1522
Release: 2021-03-16
ISBN 10: 3030668401
ISBN 13: 9783030668402
Language: EN, FR, DE, ES & NL

Innovations in Smart Cities Applications Volume 4 Book Review:

This proceedings book is the fourth edition of a series of works which features emergent research trends and recent innovations related to smart city presented at the 5th International Conference on Smart City Applications SCA20 held in Safranbolu, Turkey. This book is composed of peer-reviewed chapters written by leading international scholars in the field of smart cities from around the world. This book covers all the smart city topics including Smart Citizenship, Smart Education, Smart Mobility, Smart Healthcare, Smart Mobility, Smart Security, Smart Earth Environment & Agriculture, Smart Economy, Smart Factory and Smart Recognition Systems. This book contains a special section intended for Covid-19 pandemic researches. This book edition is an invaluable resource for courses in computer science, electrical engineering and urban sciences for sustainable development.

Big Data Analytics and Cloud Computing

Big Data Analytics and Cloud Computing
Author: Syed Thouheed Ahmed,Syed Muzamil Basha,Sajeev Ram Arumugam,Kiran Kumari Patil
Publsiher: MileStone Research Publications
Total Pages: 101
Release: 2021-09-05
ISBN 10: 9354738281
ISBN 13: 9789354738289
Language: EN, FR, DE, ES & NL

Big Data Analytics and Cloud Computing Book Review:

Big data analytics and cloud computing is the fastest growing technologies in current era. This text book serves as a purpose in providing an understanding of big data principles and framework at the beginner?s level. The text book covers various essential concepts of big-data analytics and processing tools such as HADOOP and YARN. The Textbook covers an analogical understanding on bridging cloud computing with big-data technologies with essential cloud infrastructure protocol and ecosystem concepts. PART I: Hadoop Distributed File System Basics, Running Example Programs and Benchmarks, Hadoop MapReduce Framework Essential Hadoop Tools, Hadoop YARN Applications, Managing Hadoop with Apache Ambari, Basic Hadoop Administration Procedures PART II: Introduction to Cloud Computing: Origins and Influences, Basic Concepts and Terminology, Goals and Benefits, Risks and Challenges. Fundamental Concepts and Models: Roles and Boundaries, Cloud Characteristics, Cloud Delivery Models, Cloud Deployment Models. Cloud Computing Technologies:Broadband networks and internet architecture, data center technology, virtualization technology, web technology, multi-tenant technology, service Technology Cloud Infrastructure Mechanisms:Logical Network Perimeter, Virtual Server, Cloud Storage Device, Cloud Usage Monitor, Resource Replication, Ready-made environment

Smart Sensors for Industrial Internet of Things

Smart Sensors for Industrial Internet of Things
Author: Deepak Gupta,Victor Hugo C. de Albuquerque,Ashish Khanna,Purnima Lala Mehta
Publsiher: Springer Nature
Total Pages: 306
Release: 2021-02-01
ISBN 10: 3030526240
ISBN 13: 9783030526245
Language: EN, FR, DE, ES & NL

Smart Sensors for Industrial Internet of Things Book Review:

This book brings together the latest research in smart sensors technology and exposes the reader to myriad industrial applications that this technology has enabled. The book emphasizes several topics in the area of smart sensors in industrial real-world applications. The contributions in this book give a broader view on the usage of smart sensor devices covering a wide range of interdisciplinary areas like Intelligent Transport Systems, Healthcare, Agriculture, Drone communications and Security. By presenting an insight into Smart Sensors for Industrial IoT, this book directs the readers to explore the utility and advancement in smart sensors and their applications into numerous research fields. Lastly, the book aims to reach through a mass number of industry experts, researchers, scientists, engineers, and practitioners and help them guide and evolve to advance research practices.

Proceedings of International Conference on Computational Intelligence Data Science and Cloud Computing

Proceedings of International Conference on Computational Intelligence  Data Science and Cloud Computing
Author: Valentina Emilia Balas,Aboul Ella Hassanien,Satyajit Chakrabarti,Lopa Mandal
Publsiher: Springer Nature
Total Pages: 795
Release: 2021
ISBN 10: 9813349689
ISBN 13: 9789813349681
Language: EN, FR, DE, ES & NL

Proceedings of International Conference on Computational Intelligence Data Science and Cloud Computing Book Review:

This book includes selected papers presented at International Conference on Computational Intelligence, Data Science and Cloud Computing (IEM-ICDC) 2020, organized by the Department of Information Technology, Institute of Engineering & Management, Kolkata, India, during 25-27 September 2020. It presents substantial new research findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry.

Educating Engineers for Future Industrial Revolutions

Educating Engineers for Future Industrial Revolutions
Author: Michael E. Auer,Tiia Rüütmann
Publsiher: Springer Nature
Total Pages: 894
Release: 2021
ISBN 10: 3030682013
ISBN 13: 9783030682019
Language: EN, FR, DE, ES & NL

Educating Engineers for Future Industrial Revolutions Book Review:

This book contains papers in the fields of engineering pedagogy education, public-private partnership and entrepreneurship education, research in engineering pedagogy, evaluation and outcomes assessment, Internet of Things & online laboratories, IT & knowledge management in education and real-world experiences. We are currently witnessing a significant transformation in the development of education and especially post-secondary education. To face these challenges, higher education has to find innovative ways to quickly respond to these new needs. There is also pressure by the new situation in regard to the Covid pandemic. These were the aims connected with the 23rd International Conference on Interactive Collaborative Learning (ICL2020), which was held online by University of Technology Tallinn, Estonia from 23 to 25 September 2020. Since its beginning in 1998, this conference is devoted to new approaches in learning with a focus on collaborative learning. Nowadays the ICL conferences are a forum of the exchange of relevant trends and research results as well as the presentation of practical experiences in Learning and Engineering Pedagogy. In this way, we try to bridge the gap between 'pure' scientific research and the everyday work of educators. Interested readership includes policymakers, academics, educators, researchers in pedagogy and learning theory, school teachers, learning industry, further and continuing education lecturers, etc. .

Introduction to Internet of Things in Management Science and Operations Research

Introduction to Internet of Things in Management Science and Operations Research
Author: Fausto Pedro García Márquez,Benjamin Lev
Publsiher: Springer Nature
Total Pages: 306
Release: 2021-10-30
ISBN 10: 3030746445
ISBN 13: 9783030746445
Language: EN, FR, DE, ES & NL

Introduction to Internet of Things in Management Science and Operations Research Book Review:

This book aims to provide relevant theoretical frameworks and the latest empirical research findings in Internet of Things (IoT) in Management Science and Operations Research. It starts with basic concept and present cases, applications, theory, and potential future. The contributed chapters to the book cover wide array of topics as space permits. Examples are from smart industry; city; transportation; home and smart devices. They present future applications, trends, and potential future of this new discipline. Specifically, this book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning capabilities of managing IoT. This book deals with the implementation of latest IoT research findings in practice at the global economy level, at networks and organizations, at teams and work groups and, finally, IoT at the level of players in the networked environments. This book is intended for professionals in the field of engineering, information science, mathematics, economics, and researchers who wish to develop new skills in IoT, or who employ the IoT discipline as part of their work. It will improve their understanding of the strategic role of IoT at various levels of the information and knowledge organization. The book is complemented by a second volume of the same editors with practical cases.

Intelligent IoT Systems in Personalized Health Care

Intelligent IoT Systems in Personalized Health Care
Author: Arun Kumar Sangaiah,Subhas Chandra Mukhopadhyay
Publsiher: Academic Press
Total Pages: 360
Release: 2020-12-01
ISBN 10: 0128232048
ISBN 13: 9780128232040
Language: EN, FR, DE, ES & NL

Intelligent IoT Systems in Personalized Health Care Book Review:

Intelligent IoT Systems in Personalized Health Care delivers a significant forum for the technical advancement of IoMT learning in parallel computing environments across biomedical engineering diversified domains and its applications. Pursuing an interdisciplinary approach, the book focuses on methods used to identify and acquire valid, potentially useful knowledge sources. The book presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT and its capabilities in solving a diverse range of problems for biomedical engineering and its real-world personalized health care applications. The book is well suited for researchers exploring the significance of IoT based architecture to perform predictive analytics of user activities in sustainable health. Presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT Illustrates state-of-the-art developments in new theories and applications of IoMT techniques as applied to parallel computing environments in biomedical engineering systems Presents concepts and technologies successfully used in the implementation of today's intelligent data-centric IoT systems and Edge-Cloud-Big data

Deep Learning with PyTorch

Deep Learning with PyTorch
Author: Luca Pietro Giovanni Antiga,Eli Stevens,Thomas Viehmann
Publsiher: Simon and Schuster
Total Pages: 520
Release: 2020-07-01
ISBN 10: 1638354073
ISBN 13: 9781638354079
Language: EN, FR, DE, ES & NL

Deep Learning with PyTorch Book Review:

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Software Engineering Perspectives in Intelligent Systems

Software Engineering Perspectives in Intelligent Systems
Author: Radek Silhavy
Publsiher: Springer Nature
Total Pages: 135
Release: 2021
ISBN 10: 3030633195
ISBN 13: 9783030633196
Language: EN, FR, DE, ES & NL

Software Engineering Perspectives in Intelligent Systems Book Review:

Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
Author: Dr. Basant Agarwal,Valentina E. Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
Publsiher: Academic Press
Total Pages: 367
Release: 2020-01-14
ISBN 10: 0128190620
ISBN 13: 9780128190623
Language: EN, FR, DE, ES & NL

Deep Learning Techniques for Biomedical and Health Informatics Book Review:

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
Author: Ervin Sejdic,Tiago H. Falk
Publsiher: CRC Press
Total Pages: 606
Release: 2018-07-04
ISBN 10: 149877346X
ISBN 13: 9781498773461
Language: EN, FR, DE, ES & NL

Signal Processing and Machine Learning for Biomedical Big Data Book Review:

Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Nature Inspired Computation and Swarm Intelligence

Nature Inspired Computation and Swarm Intelligence
Author: Xin-She Yang
Publsiher: Academic Press
Total Pages: 442
Release: 2020-04-24
ISBN 10: 0128197145
ISBN 13: 9780128197141
Language: EN, FR, DE, ES & NL

Nature Inspired Computation and Swarm Intelligence Book Review:

Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence. Introduces nature-inspired algorithms and their fundamentals, including: particle swarm optimization, bat algorithm, cuckoo search, firefly algorithm, flower pollination algorithm, differential evolution and genetic algorithms as well as multi-objective optimization algorithms and others Provides a theoretical foundation and analyses of algorithms, including: statistical theory and Markov chain theory on the convergence and stability of algorithms, dynamical system theory, benchmarking of optimization, no-free-lunch theorems, and a generalized mathematical framework Includes a diversity of case studies of real-world applications: feature selection, clustering and classification, tuning of restricted Boltzmann machines, travelling salesman problem, classification of white blood cells, music generation by artificial intelligence, swarm robots, neural networks, engineering designs and others