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Book Synopsis

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Book SynopsisCluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabilistic approaches are established. A combination of algorithms are proposed and evaluated on simulated and real data. Chapter 5 considers a co-clustering or bi-clustering as the search for coherent co-clusters in biological terms or the extraction of co-clusters under conditions. Classical algorithms will be described and evaluated on simulated and real data. Different indices to evaluate the quality of coclusters are noted and used in numerical experiments.Table of ContentsAcknowledgment xi Introduction xiii I.1. Types and representation of data xiii I.1.1. Binary data xiv I.1.2. Categorical data xiv I.1.3. Continuous data xv I.1.4. Contingency table xvii I.1.5. Data representations xix I.2. Simultaneous analysis xx I.2.1. Data analysis xx I.2.2. Co-clustering xxii I.2.3. Applications xxiii I.3. Notation xxvii I.4. Different approaches xxviii I.4.1. Two-mode partitioning xxviii I.4.2. Two-mode hierarchical clustering xxxvii I.4.3. Direct or block clustering xxxix I.4.4. Biclustering xxxix I.4.5. Other structures and other aims xliv I.5. Model-based co-clustering xlvi I.6. Outline xlix Chapter 1. Cluster Analysis 1 1.1. Introduction 1 1.2. Miscellaneous clustering methods 4 1.2.1. Hierarchical approach 4 1.2.2. The k-means algorithm 5 1.2.3. Other approaches 7 1.3. Model-based clustering and the mixture model 11 1.4. EM algorithm 15 1.4.1. Complete data and complete-data likelihood 16 1.4.2. Principle 17 1.4.3. Application to mixture models 18 1.4.4. Properties 19 1.4.5. EM: an alternating optimization algorithm 19 1.5. Clustering and the mixture model 20 1.5.1. The two approaches 20 1.5.2. Classification likelihood 21 1.5.3. The CEM algorithm 22 1.5.4. Comparison of the two approaches 22 1.5.5. Fuzzy clustering 24 1.6. Gaussian mixture model 26 1.6.1. The model 26 1.6.2. CEM algorithm 28 1.6.3. Spherical form, identical proportions and volumes 29 1.6.4. Spherical form, identical proportions but differing volumes 30 1.6.5. Identical covariance matrices and proportions 31 1.7. Binary data 32 1.7.1. Binary mixture model 32 1.7.2. Parsimonious model 33 1.7.3. Examples of application 35 1.8. Categorical variables 36 1.8.1. Multinomial mixture model 36 1.8.2. Parsimonious model 38 1.9. Contingency tables 41 1.9.1. MNDKI2 algorithm 41 1.9.2. Model-based approach 43 1.9.3. Illustration 47 1.10. Implementation 49 1.10.1. Choice of model and of the number of classes 51 1.10.2. Strategies for use 51 1.10.3. Extension to particular situations 52 1.11. Conclusion 53 Chapter 2. Model-Based Co-Clustering 55 2.1. Metric approach 55 2.2. Probabilistic models 57 2.3. Latent block model 59 2.3.1. Definition 59 2.3.2. Link with the mixture model 61 2.3.3. Log-likelihoods 62 2.3.4. A complex model 63 2.4. Maximum likelihood estimation and algorithms 67 2.4.1. Variational EM approach 69 2.4.2. Classification EM approach 72 2.4.3. Stochastic EM-Gibbs approach 73 2.5. Bayesian approach 75 2.6. Conclusion and miscellaneous developments 76 Chapter 3. Co-Clustering of Binary and Categorical Data 79 3.1. Example and notation 80 3.2. Metric approach 82 3.3. Bernoulli latent block model and algorithms 84 3.3.1. The model 84 3.3.2. Model identifiability 85 3.3.3. Binary LBVEM and LBCEM algorithms 86 3.4. Parsimonious Bernoulli LBMs 90 3.5. Categorical data 91 3.6. Bayesian inference 93 3.7. Model selection 96 3.7.1. The integrated completed log-likelihood (ICL) 96 3.7.2. Penalized information criteria 97 3.8. Illustrative experiments 98 3.8.1. Townships 98 3.8.2. Mero 101 3.9. Conclusion 105 Chapter 4. Co-Clustering of Contingency Tables 107 4.1. Measures of association 108 4.1.1. Phi-squared coefficient 109 4.1.2. Mutual information 111 4.2. Contingency table associated with a couple of partitions 113 4.2.1. Associated distributions 113 4.2.2. Associated measures of association 116 4.3. Co-clustering of contingency table 119 4.3.1. Two equivalent approaches 119 4.3.2. Parameter modification of criteria 121 4.3.3. Co-clustering with the phi-squared coefficient 124 4.3.4. Co-clustering with the mutual information 129 4.4. Model-based co-clustering 131 4.4.1. Block model for contingency tables 133 4.4.2. Poisson latent block model 137 4.4.3. Poisson LBVEM and LBCEM algorithms 138 4.5. Comparison of all algorithms 140 4.5.1. CROKI2 versus CROINFO 142 4.5.2. CROINFO versus Poisson LBCEM 142 4.5.3. Poisson LBVEM versus Poisson LBCEM 144 4.5.4. Behavior of CROKI2, CROINFO, LBCEM and LBVEM 147 4.6. Conclusion 149 Chapter 5. Co-Clustering of Continuous Data 151 5.1. Metric approach 152 5.1.1. Measure of information 153 5.1.2. Summarized data associated with partitions 153 5.1.3. Objective function 156 5.1.4. CROEUC algorithm 157 5.2. Gaussian latent block model 159 5.2.1. The model 159 5.2.2. Gaussian LBVEM and LBCEM algorithms 160 5.2.3. Parsimonious Gaussian latent block models 161 5.3. Illustrative example 163 5.4. Gaussian block mixture model 168 5.4.1. The model 169 5.4.2. GBEM algorithm 170 5.5. Numerical experiments 173 5.5.1. GBEM versus CROEUC and EM 174 5.5.2. Effect of the size of data 175 5.6. Conclusion 175 Bibliography 177 Index 199

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Book SynopsisA comprehensive reference for the design of high frequency communications systems and equipment. This revised edition is loaded with practical data, much of which cannot be found in other reference books. Its approach to the subject follows the needs of an engineer from system definition and performance requirements down to the individual circuit elements that make up radio transmitters and receivers.Table of Contents Chapter 1: Overview of Single Sideband Chapter 2: System Design Considerations Chapter 3: High-Frequency (HF) Link Establishment Chapter 4: Receiver Design Chapter 5: Exciter and Transceiver Design Chapter 6: IF Analog Filters Chapter 7: Speech Processing, Squelch, and Noise Blanking Chapter 8: Digital Signal Processing Chapter 9: Preselectors and Postselectors Chapter 10: Synthesizers for SSB Chapter 11: Frequency Standards for SSB Chapter 12: Solid-State Power Amplifiers Chapter 13: Ultralow-Distortion Power Amplifiers Chapter 14: High-Power Linear Amplifiers Chapter 15: Antenna Matching Techniques Chapter 16: Receiver Measurements and EMI Techniques Chapter 17: Software for SSB

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Book SynopsisThis book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection. Trade Review“The book discusses mathematical theories of machine learning. … The book is very technically written and it is addressed to professionals in the field.” (Smaranda Belciug, zbMATH 1422.68003, 2019)Table of ContentsChapter 1. Introduction.- Chapter 2. General Framework of Mathematics.- Chapter 3. Problem Formulation.- Chapter 4. Development of Novel Techniques of CoCoSSC Method.- Chapter 5. Further Discussions of the Proposed Method.- Chapter 6. Related Work on Geometry of Non-Convex Programs.- Chapter 7. Gradient Descent Converges to Minimizers.- Chapter 8. A Conservation Law Method Based on Optimization.- Chapter 9. Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations.- Chapter 10. Online Discovery for Stable and Grouping Causalities in Multi-Variate Time Series.- Chapter 11. Conclusion.

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Book SynopsisThis holistic book is an invaluable reference for addressing various practical challenges in architecting and engineering Intelligent IoT and eHealth solutions for industry practitioners, academic and researchers, as well as for engineers involved in product development. The first part provides a comprehensive guide to fundamentals, applications, challenges, technical and economic benefits, and promises of the Internet of Things using examples of real-world applications. It also addresses all important aspects of designing and engineering cutting-edge IoT solutions using a cross-layer approach from device to fog, and cloud covering standards, protocols, design principles, reference architectures, as well as all the underlying technologies, pillars, and components such as embedded systems, network, cloud computing, data storage, data processing, big data analytics, machine learning, distributed ledger technologies, and security. In addition, it discusses the effects of Intelligent IoT, which are reflected in new business models and digital transformation. The second part provides an insightful guide to the design and deployment of IoT solutions for smart healthcare as one of the most important applications of IoT. Therefore, the second part targets smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics that are aimed at providing connected health interventions to individuals for healthier lifestyles.Table of ContentsIntroduction.- Engineering an AI-driven IoT platform.- Smart and connected IoT devices.- Engineering IoT networks.- IoT cloud architecture and design.- End-to-end security.- Machine learning fundamentals.- Big Data and advanced analytics.- AI-driven IoT for smart health.- Biomedical engineering fundamentals.- Biosensors and connected wearable eHealth devices.- Applications of machine learning & IoT in healthcare.- AI-driven IoT eHealth prototyping lab.- Conclusion.

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Book SynopsisThe goal of this textbook is to provide enough background into the inner workings of the Internet to allow a novice to understand how the various protocols on the Internet work together to accomplish simple tasks, such as a search. By building an Internet with all the various services a person uses every day, one will gain an appreciation not only of the work that goes on unseen, but also of the choices made by designers to make life easier for the user.Each chapter consists of background information on a specific topic or Internet service, and where appropriate a final section on how to configure a Raspberry Pi to provide that service.While mainly meant as an undergraduate textbook for a course on networking or Internet protocols and services, it can also be used by anyone interested in the Internet as a step–by–step guide to building one's own Intranet, or as a reference guide as to how things work on the global Internet Trade Review“The detailed coverage of networking topics together with practical hands-on illustrations of the principles using the Raspberry Pi laboratory network make this an excellent candidate for a course textbook. In addition, the detailed table of contents, good index, and extensive list of references also make it useful for reference … . If you want just one book in your technical library to cover the Internet and networking technology, then this is an ideal candidate.” (David B. Henderson, Computing Reviews, August 17, 2021)Table of Contents1. Introduction.- 2. The OSI Seven Layer Model.- 3. The Physical Layer, L1.- 4. The Data Layer, L2.- 5. The Network Layer, L3.- 6. The OSI Upper Layers.- 7. Flow Control.- 8. The Laboratory Network.- 9. Raspberry Pi Operating System.- 10. Routing.- 11. The Router.- 12. Populating and Maintaining the Route Table.- 13. Shortest Path Through the Network.- 14. Dynamic Host Configuration.- 15. Routing Protocols.- 16. Route Interchange Protocol.- 17. Open Shortest Path First.- 18. Service Provider Protocols.- 19. Babel.- 20. Domain Name Service.- 21. Hyper Text Transfer Protocol: The Web.- 22. Simple Mail Transfer Protocol: Email.- 23. Other Services.- Glossary.- References.- Index.

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Book SynopsisThis book provides practical knowledge and skills on high-speed networks, emphasizing on Science Demilitarized Zones (Science DMZs). The Science DMZ is a high-speed network designed to facilitate the transfer of big science data which is presented in this book. These networks are increasingly important, as large data sets are now often transferred across sites. This book starts by describing the limitations of general-purpose networks which are designed for transferring basic data but face numerous challenges when transferring terabyte- and petabyte-scale data. This book follows a bottom-up approach by presenting an overview of Science DMZs and how they overcome the limitations of general-purpose networks. It also covers topics that have considerable impact on the performance of large data transfers at all layers: link layer (layer-2) and network layer (layer-3) topics such as maximum transmission unit (MTU), switch architectures, and router’s buffer size; transport layer (layer-4) topics including TCP features, congestion control algorithms for high-throughput high-latency networks, flow control, and pacing; applications (layer-5) used for large data transfers and for maintenance and operation of Science DMZs; and security considerations. Most chapters incorporate virtual laboratory experiments, which are conducted using network appliances running real protocol stacks. Students in computer science, information technology and similar programs, who are interested in learning fundamental concepts related to high-speed networks and corresponding implementations will find this book useful as a textbook. This book assumes minimal familiarity with networking, typically covered in an introductory networking course. It is appropriate for an upper-level undergraduate course and for a first-year graduate course. Industry professionals working in this field will also want to purchase this book.Table of ContentsIntroduction to high-speed networks and science DMZ.- Network cyberinfrastructure aspects for big data transfers.- Data link and network layer considerations for large data transfers.- Impact of TCP on high-speed networks and advances in congestion control algorithms.- Application and security aspects for large flows.- Security aspects.- Challenges and open research issues.

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Book SynopsisThis revised textbook presents updated material on its core content: an end-to-end IoT architecture that is comprised of devices, network, compute, storage, platform, applications along with management and security components. As with the second edition, it is organized into six main parts: an IoT reference model; fog computing and the drivers; IoT management and applications; smart services in IoT; IoT standards; and case studies. This edition’s features include overhaul of the IoT Protocols (Chapter 5) to include an expanded treatment of low-power wide area networks including narrow band IoT (NB-IoT) protocol, updated IoT platforms and capabilities (Chapter 7) to include comparison of commercially available platforms (e.g. AWS IoT Platform, Google Cloud IoT Platform, Microsoft Azure IoT Platform, and PTC ThinkWorx), updated security (Chapter 8) to include approaches for securing IoT devices with examples of IoT devices used in security attacks and associated solutions including MUD and DICE, and finally new Appendix B to include six IoT project detailed for students.Table of ContentsIntroduction.- Part I: IoT Overview and Architecture.- IoT Timeline.- IoT Reference Model.- Evolution of IoT Protocol Stack.- State of the Industry.- Part II: Fog Computing.- Why Fog.- Defining the Fog.- Data In Motion.- Part III: IoT Management and Applications.- IoT Management.- IoT Security.- IoT Traffic Engineering.- IoT Applications.- Part IV: IoT-Based Smart Services.- Smart Service Framework.- Creating Smart Services.- Service Creation, development and delivery.- Ecosystems Partners.- Services reference architecture.- Part V: IoT Standards.- OneM2M.- ATSI.- ETSI.- IEEE.- Part VI: Invited Chapters.- IoT Business Models.- Open Source Initiatives.- Data in Motion. The Blockchain in IoT.- Conclusion.

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Book SynopsisThis updated textbook covers digital design, fundamentals of computer architecture, and ARM assembly language. The book starts by introducing computer abstraction, basic number systems, character coding, basic knowledge in digital design, and components of a computer. The book goes on to discuss information representation in computing, Boolean algebra and logic gates, and sequential logic. The book also presents introduction to computer architecture, Cache mapping methods, and virtual memory. The author also covers ARM architecture, ARM instructions, ARM assembly language using Keil development tools, and bitwise control structure using C and ARM assembly language. The book includes a set of laboratory experiments related to digital design using Logisim software and ARM assembly language programming using Keil development tools. In addition, each chapter features objectives, summaries, key terms, review questions, and problems.Table of ContentsChapter1: Signal and number systems.- Chapter2: Boolean Logics and Logic Gates.- Chapter3: Minterms, Maxterms, Karnaugh Map (K-Map), and Universal Gates.- Chapter4: Combinational Logic.- Chapter5: Synchronous Sequential Logic.- Chapter6: Introduction to Computer Architecture.- Chapter7: Memory.- Chapter8: Assembly Language and ARM Instructions Part I.- Chapter9: ARM Assembly Language Programming Using Keil Development Tools.- Chapter10: ARM Instructions Part II and Instraction Formats.- Chapter11: Bitwise and Control Structures Used for Programming with C and ARM Assembly Language.

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Book SynopsisIntroduction.- Hardware faults.- Fault tolerance.- Generalized algorithm of fault tolerance.- GAFT generalization: a principle and model of active system safety.- System software support for hardware deficiency.- Testing, Checking and Hardware Syndrome.- Recovery preparation.- Searching and monitoring of correct software states.- Recovery algorithms.

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Book SynopsisIntroduction.- Visualizing in Sports Analytics.- IoT and Block Chain in Sports.- Sports Revenue Analytics.- Sports Marketing Analytics.- Forecasting in Sports.- Machine learning & AI in sports.- Classification and Regression techniques for sports.- Sports Analytics in different sports.- Sports analytics data'-Who uses it?.- Case Studies.- Conclusion.

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Book SynopsisPart I- AI, Machine Learning and its Applications. .- Chapter 1. Cervical cancer histopathological image classification using imbalanced domain learning.- Chapter 2. Prediction of Survival Status of Heart Failure Patients Using Machine Learning and Hyper-Parameter Optimi-zation Techniques.- Chapter 3. Breast Cancer Stage Identification Using Machine Learning.- Chapter 4. Deep Learning-Based Emotion Classification for Amharic Texts.- Chapter 5. Bilingual Word-Level Language Identification for Omotic Languages.- Chapter 6. ChatGPT in Education: Opportunities and Concerns for Functional English Sentence Structure Analysis for Automatic Question Generation.- Chapter 7. Artificial Intelligence Based Secured Smart Mirror Control Systems with Enhanced Vehicle and Sign Board Detection.- Chapter 8. Music Genre Classification Using Deep Neural Network with Feature Selection and Optimization Via Evolutionary Algorithm .- Chapter 9. Collaborative Filtering-Based Recommender System for Ethiopian Tourism Sites.- Chapter 10. Prediction of Compaction Parameters Based on Atterberg Limit by Using a Machine Learning Approach.- Chapter 11. Prediction of Fatigue Life of Materials Using Machine Learning Approach: A Review.- Part II- Industrial automation and control systems. .- Chapter 12. Adaptive Particle Swarm Optimization-Based Tuning of Fractional Order PID for Moisture Control in an Indus-trial Rotary Dryer System.- Chapter 13. TRAJECTORY TRACKING CONTROL OF THREE-DEGREE-OF-FREEDOM ARTICULATED ROBOT MANIPULATOR USING NEURAL NETWORK CONTROLLER.- Chapter 14. MODELING AND TRAJECTORY TRACKING CONTROLLER DESIGN FOR NONHOLONOMIC WHEELED MOBILE ROBOTS.- Part III-  Networking and communication systems . .- Chapter 15. ABF and PBF Optimization for IRS based WCS with practical phase shifts.- Chapter 16. Bi LSTM Based on Indoor Localization by using Smartphones and Wi-Fi with RSSI Values.- Chapter 17. Ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cyber Security Attack.- Chapter 18. Dual Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM Networks.- Part IV- Electrical power systems and automation. .- Chapter 19. Voltage Profile Improvement and Loss Minimization in a Transmission Line Using FLC Based UPFC at Bahir Dar Substation-II 230 kV Line.- Chapter 20. Simulation of a 100 MW grid-connected solar power plant and MPPT control using the PSO-P&O technique.- Chapter 21. Development of a Household Synthetic Load Profile for Rural Electrification.- Chapter 22. Optimal Sizing and Feasibility Study of Micro grid System for Rural Area in Gashamo Village, Somali Region of Ethiopia.- Chapter 23. Design and Simulation of SCADA System to Improve Reliability of a Distribution Feeder (Case Study on Bahir Dar, Feeder R6-G2).

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Book SynopsisAn Introduction.- Types Diagnosis and Treatment of Cervical Cancer.- Artificial Intelligence and Its Roles in Early Detection of Cervical Cancer.- Early Screening and Detection of Cervical Cancer using AI.

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Book SynopsisA Chatbot for Specialized Domain.- Some Bibliometric Considerations for Computer Science Conferences.- Time Series Analysis and Modeling with Federated Leaning Techniques in Cloud Edge Scenario: A Case Study on Environmental Air Quality in Homes.- Cloud Framework for Data Practitioners for Research and Higher Education Community.- P2FL: Privacy-Preserving Federated Learning Approach for Healthcare Informatics at the Edge.- Enhancing Customer-Perceived Value Through Personal Data Utilization in CRM Platforms: A Data Science Perspective.- Connecting AI and Blockchain to Improve Security of Financial Services.- A Comprehensive State-of-the-Art Review for Digital Twin: Cybersecurity Perspectives and Open Challenges.- Towards Quantum Machine Learning in Ransomware Detection.- A Detour Route Selection Method Based on Node Density in Skip Graph.- EDoViT-Alz: Alzheimer's Disease Identification with Vision Transformer Using Extremely Downscaled MRI Data.- A Comparison Study Between Cuckoo Search and Particle Swarm Optimization Based Intelligent Systems for Optimization of Mesh Routers in a Small-Scale WMN.- A Fuzzy-based System for Assessment of Tie Strength in Online Social Networks.- An Efficient Algorithm to Prevent Procrastination in Spatial Crowdsourcing.- A Learning Web System for Website Development.

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Book SynopsisIntroduction.- Introduction to Clustering.- Clustering Algorithms.- Nonsmooth Optimization Models in Cluster Analysis.- Nonsmooth Optimization.- Optimization based Clustering Algorithms.- Implementation and Numerical Results.- Conclusion.

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Book SynopsisIntroduction.- Image Processing and Medical Imaging.- Pattern Recognition.- Statistical Analysis.- Accurate Diagnosis.- Virtual Health Support.- Targeted Treatment.- Automated Image Diagnosis.- Drug Discovery.- Machine Assisted Surgery.- Data Analysis and Decision Making.- Conclusion.

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Book SynopsisChapter 1. Implicit Test Case Identification/Selection for Test Case Prioritisation using Natural Language Processing .- Chapter 2. Feasibility of Adversarial Attacks Against Machine Learning Models.- Chapter 3. Enhanced Public Safety: Real-Time Crime Detection with CNN-LSTM in Video Surveillance.- Chapter 4. Deep Reinforcement Learning-Based Open and Hybrid Switching-Driven Software-Defined Networking:  Adaptability and Comparison.- Chapter 5. Machine Learning Algorithms for Energy Consumption Prediction in Smart Homes: A Comparative Study.- Chapter 6. IoT Device Fingerprinting for Anomaly Detection.- Chapter 7. Survey-Based Machine Learning Models for Early Detection of Diabetes .- Chapter 8. OptimizingWearable Sensors with Multi-Feature Approximate Computing.- Chapter 9. Breast Cancer Histology Images Segmentation Using Connected Component Analysis.- Chapter 10. Optimized Smart Insole Design for Temporal Gait Parameters Measurement.- Chapter 11. Transforming Agriculture: Disease and Pest Management through CNN-Based Image Classification in Computer Vision.- Chapter 12. Anomaly detection in PD-NOMA Cognitive Radio IoT Networks.

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Book SynopsisIntroduction.- Authoring Tools and Content Development.- Web Based Learning, Wikis and Blogs.- E-learning Platforms, Portals.- Digital Libraries for E-learning.- Multimedia and E-learning.- Mobile Learning.- Massive Online Open Courses.- Ontologies and Meta-Data Standards.- Emotional State in Education Process Identificated using Internet of Things.- Blended and Collaborative Learning.- Modeling of Educational Activities.- Personalized and Adaptive E-learning.- Assessment Methods in Blended Learning Environments.- Education Supported Smart Technologies.- Pedagogy Enhancement with E-learning.- Ubiquitous Learning Environments and Applications.- Technology Enhanced Learning.- Virtual Labs and Virtual Classrooms.- E-learning Success Cases.- Web Information Filtering and Retrieval.- Web Data Mining.- Knowledge Discovery and Data Analysis.- Meta-Knowledge Discovery and Representation.- Personalized Web Sites and Services.- Natural Language Processing.- Information Retrieval and Extraction.- Educational Data Mining.- Learning Analytics.- Text Mining.- Internet of Things and Intelligent Systems.- Artificial Intelligence and Internet of Things.- Machine Learning for Internet of Things.- Conclusion.

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Book SynopsisPart 1 Thinking Swarm: Foundations.- Chapter 1 Thinking about Thinking Swarms.- Chapter 2 Understanding the Roots of Swarm Intelligence in Defence to Find the Path Forward: A Scientometric Study of Autonomous Systems.- Chapter 3 Definitions, Sources and Categorisations for Thinking Swarms.- Chapter 4 A Philosophical Analysis of “Thinking Swarms”.- Chapter 5 Robots as Research and Drones as War: Unpacking Metaphors from Media Coverage of Swarm Technology.- Part 2 Thinking Swarm: Behaviour.- Chapter 6 Swarm Hermeneutics: A Preliminary Review.- Chapter 7 Identifying and Predicting Hidden Coordinated Behaviour Using Synthetic Language Narrative Models.- Chapter 8 Intelligent Swarming Narratives: Situating ‘Noise’ within Interacting Information Fields.- Chapter 9 Regulating Maritime Autonomous Swarms.- Chapter 10 An Approach to the Legal Review of Autonomous Swarms: The legal implications for designing thinking swarms in armed conflict.- Part 3 Thinking Swarm: Topology and Architecture.- Chapter 11 Cognitive Architecture of Aware System of Systems.- Chapter 12 Trochoids: Spirograph, Multi-agent Formation and Beyond.- Chapter 13 The Barriers and Opportunities of Effective Underwater Autonomous Swarms.- Part 4 Thinking Swarm: Cognition.- Chapter 14 Introducing Lifelong Learning in Swarm Robotics.- Chapter 15 Learner-centred, Teacher-centred and Blended Curriculum Design in Swarm Systems.- Chapter 16 Hyper-teaming: Adaptive Teaming and Coordination of Multi-domain Autonomous Robotic Systems.- Part 5 Thinking Swarm: Way Forward.- Chapter 17 Future Directions.

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Book SynopsisChapter 1. 5G/5G-Advanced, Wi-Fi 6/7, and Bluetooth 5/6 Introduction.- Chapter 2. Data Communication Systems Protocol Stacks.- Chapter 3. The Wireless Path.- Chapter 4. Digital Modulation: The Basic Principles.- Chapter 5. Channel Coding and Link Adaptation.- Chapter 6. Channel Usage Techniques.- Chapter 7. Multiple Antenna Techniques.- Chapter 8. 5G/5G-Advanced Overview.- Chapter 9. Wi-Fi 6/6E and Wi-Fi 7 Overview.- Chapter 10. Bluetooth 5/6 Overview.- Chapter 72. Menstruation Effect on Well-being: Exploring the Mediating Role of Physical Pain and Psychological Anguish.

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Book SynopsisChapter 1 Introduction.- Chapter 2 Literature Review and Fundamentals.- Chapter 3 Experimental Setup and Comparison of Contextual Factors.- Chapter 4 Person-Related Privacy Influencing Factors.- Chapter 5 Person-Related General Influencing Factors.- Chapter 6 Empirical Investigation of the Economic Perspective of Privacy Taxonomy.- Chapter 7 Conclusion and Outlook.

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Book SynopsisIntroduction.- Artificial Intelligence in Connected Vehicles.- A Hybrid Model Integrating Local and Global Spatial Correlation for Connected Vehicles Traffic Prediction.- Sdscnn: A Hybrid Model Integrating Static and Dynamic Spatial Correlation Neural Network For Connected Vehicles Traffic Prediction.- Spatial-Temporal Complex Graph Convolution Network for Connected Vehicles Traffic Prediction.- Prior Knowledge Enhanced Time-Varying Graph Convolution Network for Connected Vehicles Traffic Prediction.- Spatial-Temporal Heterogeneous and Synchronous Graph Convolution Network For Connected Vehicles Traffic Prediction.- Multi-Sequential Temporal Convolution Gated Graph Neural Network For Connected Vehicles Traffic Prediction.- Connected Vehicles Traffic Prediction Based On Multi-Temporal Graph Convolutional Networks.- Urban Road Network Connected Vehicles Traffic Speed Prediction Model Based On Global Spatio-Temporal Characteristics.- Future Challenges Of Connected Vehicles Traffic Prediction.- Conclusion.

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Book SynopsisIntroduction.- The future of cybersecurity at sea: Human vs. human, or AI vs. AI.- Human factors in maritime cybersecurity.- Human factor and culture in maritime cybersecurity.- Rethinking seafarer training for the digital age.- Ports of Tallinn and Koper comparative analysis including cybersecurity.- Simulating cyberattacks on autonomous sea surface vessel's rudder controller.- A scope review of secure broadcasting protocols for the automated identification system.- Using incremental inductive logic programming for learning spoofing attacks on maritime automated identification system data.- A concept of integrating an open-source intrusion detection system into marine vehicles.- Integrating artificial neural networks with inertial navigation systems for resilience against GPS cyber-attacks.- Machine learning cybersecurity risk valuation in autonomous surface vessels.- Automatic identification system intrusion detection using federated leering approach.- Conclusion.

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Book SynopsisIntroduction.- RF/Microwave/ Millimetre wave Communications Devices and Systems.- Satellite Systems, Applications, Missions and Operations.- Antennas and Propagation Engineering.- Wireless Sensors and Mobile Communications Networks.- Applied Computational Electromagnetic.- Adaptive Systems Development and Operation.- Real-time System of Systems Data Analytics.- Complex Infrastructure Systems Management.- Systems Design, Modelling and Engineering.- Smart Advanced Industrial Manufacturing Systems.- Smart IoT Sensors Design, Integration and Applications.- Cyber Security and Information Encryption.- Robotics and Human-Machine Systems.- AI/Machine Learning Algorithms and Applications.- Renewable Energy Harvesting Techniques.- Energy Grid Management and Sustainability.- On-demand Renewable Energy Networks.- Conclusion.

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Book SynopsisIntroduction.- Anomaly Detection in Smart Manufacturing Using an Intelligent Decision Support System with Applications in Supply Chain Management.- Lung and colon cancer detection system in histopathological images based on deep transfer Learning.- Design of an application to support the diagnosis of skin cancer using deep learning.- Enhancing Vascular Structure from Computed Tomography Angiography Images using Score-based Generative Models.- Remote medical assistance y wearable devices in health applications.- Product Demand Forecasting in the Industry 4.0 through machine learning and Intelligent Decision Support Systems.- Application of the Sugeno-Takagi-Kang considering fuzzy logic for demand forecasting in the supply chain: a Fuzzy temporary series forecast model (FTS) proposal.- Optimizing a Smart City's Urban Park Lighting using Monkey Algorithm.- Intelligent System for Multimedia Data Analysis in Industry 4.0 using a Smart color management approach.- Technologies for Vehicle Classification in Intelligent Traffic Systems: Advances, Challenges, and Perspectives.- Conclusion.

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Book SynopsisIntroduction.- Big Data Technologies for e-Health.- A Review of Computer-Assisted Techniques Performances in Malaria Diagnosis.- Research on Cloud Health Privacy Information Protection Algorithm Based on Data Mining.- Data Security Mining Method for Social Media Users' Mental Health Status Test Based on Machine Learning Algorithm.- Early Warning Method of College Students Mental Subhealth Based on Internet of Things.- Automatic Assessment Method of College Students Psychological Stress Based on Medical Big Data.- Research on Medical Sensitive Data Protection Algorithm Based on Differential Privacy.- Conclusion.

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