Artificial intelligence (AI) Books

Book Synopsis.- Adoption of Artificial Intelligence in Supply Chains: A Business Perspective.- Anomaly-Based Detection of Rogue Access Points in High-Risk Network Infrastructures.- AI Methods in Financial Risk Management Applications Using Cryptocurrencies.- AI Development Trajectory In Polish Bank, etc.

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Book SynopsisIntegrating HR Pratices with Business Analytics to Drive Organizational Performance at Varron Autokast LTD Nagpur.- The Impact of HR Analytics and Performance Management Systems in Wipro Limited Pune.- An Iterative Statistical Analytical Review of Blockchain-Based Federated Learning Consensus Mechanisms for Real Time Deployments.- ODDNet- Object Detection in Dark with Attention-driven RGB-Event Fusion.- Classification of Plant Species Based on Leaf Veins.- Student Performance Predictor.- Robust Online Action Detection: Advancing Multi-Object Tracking in Surveillance Scenarios.

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Book SynopsisBipolar Disorder Detection using CNN and LSTM: A Comparative Study of Twitter and Questionnaire Datasets.- Survey on Prediction of Bipolar Disorder using CNN and LSTM.- Breast Cancer Prediction Project using Machine Learning.- Enhancing the accuracy of heart disease through Hippopotamus Optimization Algorithm: An Evaluation of Machine Learning Algorithms.- The Role of Social Media Information Sharing on Generation Z's Green Purchase Intentions.

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Book SynopsisSentiment Analysis of Financial Tweets & News Using Machine Learning to Identify Trading Opportunities.- Tomato Leaf Disease Detection Using Fusion of Thepade’s SBTC and Haralick Moments (GLCM) Features with Machine Learning Algorithms.- Object Detection and Pursuit: Recent Advancements in Algorithmic Developments and Emerging Challenges.- FarmTech: Enhancing Agricultural Equipment.- AI-Powered Mobile Applications for Early.

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Book SynopsisThe Power of Technology in Saving Lives Enhancing Lung Cancer Detection with Image Processing.- TA DL Twin Adamoptimized Deep Convolutional Neural Network for Intrusion detection.- Exploring and Implementing AI based Voice Assistant with Python.- Heart Disease Detection and Classification By Deep Residual Network With Svm.- Advanced Predictive Analytics for Cryptocurrency Forecasting By DEEP LSTM RNN Network With Random Forest Regression.- Analysis of Early Detection and Prediction of Diabetic Retinopathy by Optimize deep learning with XG Boosting.- Optimizing Steganographic Techniques For Block Chain Integrating PSO with LSB for Improved Security and Image Quality.- Early Dementia Detection and Classification Of Stage By Efficient Segmentation And Artificial Neural Network.- Human Activity Recognition with Deep BiL STM Sequence Approach and Convolution Neural Network.- Automated KOA Detection in X ray Images.

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Book SynopsisOptimization of incomes from international cargo transportation on railway transport of Ukraine.- Development of a method for determining the rational number of trains on a railway corridor considering train speed forecasting and delay estimation.- Modern software trends in data processing and biblio-metric research mapping: some examples on the field of transport policy regulation.- Assessment of Urban Public Service Levels.- Directions of Passenger Air Transport Improvement in Poland: Opinion of West Pomeranian Voivodeship Citizens.- Satellite Image Classification for Monitoring and Distribution Analysis of Green Spaces in Kharkiv, Ukraine.

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Book Synopsis.- Disease Detection and Control in Field Crops with UAV and Image Processing Techniques..- Comprehensive Survey on Network Slicing in 5G14..- A Fine-tuning Hyperparameters for Performance Comparison of Fruit Ripeness Classification Using a Convolutional Neural Network..- SmartSync: Revolutionizing Remote Work with AIoT-Virtual Office Interactivity..- Combatting Online Hate: A Study on Detecting and Preventing Hate Speech Across Social Media Platforms..- Drone Detection and Recognition Using Visual Range Based on Deep Convolutional Neural Network..- Integrating Column Generation with Reinforcement Learning for Optimal Solutions in the One-Dimensional Cutting Stock Problem..- Developing an Intelligent Wheelchair for Individuals with Congenital Disabilities in Somalia..- Comparative Impact of Morphological Preprocessing on CNN-Based Plant Disease Detection..- Optimizing Heart Disease Prediction Using Fusion Feature Selection and Stacking Ensemble Approach..- Techno-Economic Assessment of Renewable Energy Integration in Higher Education Institutions in Somalia: Case Study of Jamhuriya University of Science and Technology..- IoT_LWC Secure Data Communication for Devices: A Review..- A Comprehensive Analysis of the Impact of Artificial Intelligence on Advertising Strategies.

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Book SynopsisShadow Management: Leveraging Responsible Agentic AI to Revolutionize Safe Decision-Making in Complex Organizations.- Investigating the Applicability of Long Short-Term Memory (LSTM) Algorithm in Project Decision-Making: A Case Study in ML-Driven Forecasting.- iSmart Guide: Transforming Adult Learning with AI-Powered Personalization.- A Case Study for Immersive Technology-Based Evidence Representation.- An AutoML Design Pattern Focusing on Transparency, Extendibility and Efficiency.- Adaptive Historical Education Through Generative AI and Immersive Game Design.- The Use of Artificial Intelligence in a Myo-to-Gesture Device.- Practical Decisions for Development of Retrieval-Augmented Generation Systems.- Legendre Chaotic Neural Network with Negative Self-Feedback Memory for Continuous Function Optimisation.- Price Models for Diagnostic Decision Support: Market Structure Analysis and Future Trends.

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Book Synopsis.- Deep Learning and Representation Learning..- Knowledge Distillation of Class Activation Maps from Two Teachers for Continual Learning..- RAPID: Robust Adaptive Probabilistic Inference with DINO Features..- MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning..- Revisiting Automatic Essay Assessment: A Relative Approach..- Evaluating the Usefulness of Large Language Models for Human Activity Recognition Data Augmentation via Few-shot Samples..- Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)..- Hybrid Vision System for Minor Pre-Assembly Identification in a Robotic Welding Cell..- An energy efficient model based on the feature pseudo-embedding..- HAIS Energy Applications..- Forecasting Thermal Demand in Citizen Energy Communities Using Machine Learning: Application to the Urberoa Case Study..- A Hybrid Modelling Approach for Forecasting the Acquired Electrical Power from Photovoltaic Panels..- KARMA: KAN meets ARIMA..- IIoT-Driven Time Series Imputation for Sustainable Metalworking Fluid Monitoring..- Evolutionary Computation and Optimization..- An Adaptive Memetic Algorithm for Solving the Multiple Knapsack Assignment Problem..- Advanced Decomposition-based Bioinspired Algorithms for Multiobjective Phylogenetics..- An Ant Colony Optimization Approach for Safest Path Pair Computation under Correlated Failures..- DRL-Driven Batch-Oriented SDN Migration with Weighted Traffic Matrix Clustering for Dynamic Networks..- Reinforcement Learning and AI Planning..- Emotional Value-aware Agents: a Viewpoint paper..- Neuro-Symbolic Reasoning with Multiple Large Language Models Combined by First-Order Logic..- SimplyQRL: A Modular Benchmarking Library for Hybrid Quantum Reinforcement Learning..- Enhancing World Models with Specialized Prediction Networks for Reinforcement Learning..- Smart Mobility and Transportation Optimization..- Clustering-Based Route Optimization for Mixed Ride-Sharing under Time Constraints..- Origin-Destination Frequency Tensors and Their Application in Machine Learning Modelling..- Fast-TRACLUS: An Optimized Trajectory Clustering Algorithm for Large-Scale Datasets..- Action Space Size Effects in Reinforcement Learning for the Vehicle Routing Problem..- Time Series and Forecasting Methods..- Perplexity, Uncertainty, and the Limits of Active Learning..- Channel selection and creation algorithms for Electroencephalography classification with HIVE-COTE..- Multivariate Regime Identification and Prediction in Financial Markets via Gaussian Mixture and Gradient Boosting Methods..- On-Edge Task Planning with Large Language Models for Service Robotics.

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Book Synopsis.- Agricultural and Environmental Monitoring..- Comparative Analysis of Cattle Behavior Across Intensive Dairy Farms through Dimensional Reduction Techniques..- Environmental Sound Recognition for Human-Robot Interaction in Social Robots..- A new Transformer-based hybrid model to forecast olive fruit fly using multimodal data..- Infrared Driver Monitoring Systems - A Review, New Opportunities and Trends..- Ambient Intelligence Integration in Vocational Education: Evaluating Smart Learning Environments for Digital Skills Development..- Zero-shot and Few-shot Learning with Vision-Language Models for post-disaster Structural Damage Assessment..- Biomedical Applications..- Analyzing the Impact of Data Augmentation on Tumor Detection and Classification in Mammograms..- An Enhanced Hybrid Machine Learning Model for Plant Disease Detection and Classification..- Interpretable ML for Stress Detection from Vital Signs Using SHAP..- Learning from Normal Brain Activity for Automatic Detection of Photoparoxysmal Responses as Electroencephalographic Anomalies..- Subsymbolic and Symbolic Pipeline for an Explainable EEG Authentication System..- Cybersecurity and Network Protection..- Analyzing DoS Attacks on CoAP Networks Using Low-Dimensional Latent Representations..- An Approach to Anomaly Detection with Dynamic Threshold Definition for Real-World Environments..- Loss Functions for Time Series Forecasting in Network Security Situation Awareness..- A Hybrid Feature Selection Approach Using Filter-Wrapped Evaluation (FWE) for Attack Detection in SDN..- RAG embeddings storage optimization through quantization and dimensionality reduction..- Exploratory Visualization of IoT Attacks on the NF-CSE-CIC-IDS2018 dataset..- Data Mining and Decision Support Systems..- A Microservice System Architecture for Receiving ETL System..- A Novel General Hybrid System for Data Feature Selection..- Profiling Public Instagram Accounts with a Multimodal Vector for Hate Exposure Analysis..- Symbolic Regressor: a interpretability tool for Non-Intrusive Load Monitoring..- IoT Device Fingerprinting: Optimized with Data Diversity and Feature Selection for Computational Efficiency..- Improvement of Multi-Label Self-Adjusting Memory kNN Classifier for Sparse and Class-Imbalanced Data Streams..- HYBPARSIMONY-IDT: Hybrid Parsimonious Search for Interpretable Decision Trees..- Analysis of Kernel Thinning for Scalable Support Vector Machines.

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Book Synopsis.- Modeling for safeguarding..- Interactive Repair of Inconsistencies in Conceptual Models..- Rethinking Learning: The Role of Unlearning in Generative AI-Based Conceptual Modeling..- ModelFed: a Federated Protocol for Collaborative Cross-Platform Modeling..- Ontological Modeling..- Group modelling as discourse: games, play, ontologies and the emergence of shared understandings..- Creon: A Capability Reference Ontology for the Analysis and Design of System Capabilities..- Enriching Ontology-based Data Management with Multidimensional Modeling..- LLM-enabled Modeling..- Towards Human-in-the-Loop LLM-Enabled Domain Modeling..- Towards Threat Modeling with Large Language Models - Automating Domain-Specific Language Creation in Meta Attack Language (MAL)..- Text-to-MDX: LLM-Assisted Generation of MDX Queries from User Questions..- Data and Process Modeling..- Tree-based Compliance Verification: Bridging the Gap between Compliance Requirements and Process Execution..- Detecting Conflicting Object Views in Object-Centric Process Choreographies..- To bind or not to bind? Discovering Stable Relationships in Object-centric Processes..- Discovering Inclusion Dependencies in a Multi-Model Scenario..- Possibilistic SQL Constraints..- Knowledge Graphs and Graph Database..- Shaping Your World – Conceptualizing Viewpoint Dynamics in Event-Centric Knowledge Graphs..- Comparative Experiments on Natural Language Querying of Knowledge Graphs Using a Graph RAG Approach..- Model-driven Schema Transformation for Graph Databases..- Applications of Conceptual Modeling..- Ontology-informed Design of Legal Visualizations..- Conceptual Modeling of disinformation campaigns..- Ontology for Personalized Recipe Recommendations Supporting Sustainability, Personal Preferences, and Health Restrictions..- OntoInsight - A Metric-Guided Tool for Ontology Quality Evaluation with LLM-Powered Recommendations..- Towards Automating the Life Cycle Management of Digital Twins.

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Book SynopsisA Comprehensive Study on Hybrid Injection Attacks in Al- Driven Applications.- A Review of Ciphertext-Policy Attribute-Based Encryption Models:Comparative Analysis and Hybrid Integration for Secure Cloud Access Control.- Predicting Stroke Outcomes Using Machine Learning: Analysis of Clinical and Imaging Data.- Optimized Deep Learning Framework for Rheumatoid Arthritis Diagnosis Using Medical Imaging and Blood Biomarkers.- A Time-Aware Random Forest Regression Model for High- Accuracy AC Power Forecasting in Indian Solar Farms.- A High Speed Energy efficient GAN-Tuned Trunc-O-SRAM for Robust Flexible Image Intelligence Systems.- Explainable AI for Urban Air Quality Forecasting: Integrating Satellite, Traffic, and IoT Sensor Data.- AI-Powered Precision Agriculture: Integrating Computer Vision and IoT for Sustainable Crop Management.- Hybrid Deep Learning Framework for Automated Lung Cancer Detection and Stage Classification from CT scans using Deep Learning.- Hate Speech Detection in Social Media Using Deep Ensemble Algorithm.

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Book SynopsisNational cyber space.- Elements of the non kinetic warfare.- From human aspects of cybersecurity to human resources.- Cyber and information influence in a digitalized society ai tools for power and influence.- Pre emptive cyber security strategies for a small state.- Things get cybernetic how artificial intelligence may impact cybersecurity.- Information security policies and their context driven development.

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Book SynopsisResearch skills and integration with university teaching and community engagement.- Pedagogical Innovation: Use of Business Information Technologies in the Training Process of Public Accountant and Auditor at Universidad Santo Tomás.- School Theater in the Oral Expression of the English Language in High School Students.- Mentoring in Higher Education: a strategy for academic development.- Creation of seedbeds as an inter-institutional educational space between University-Schools to develop research skills.

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Book SynopsisDistributed Weak Independent Sets in Hypergraphs: Upper and Lower Bounds.- Approximating Temporal Modularity on Graphs of Small Underlying Treewidth.- Round-asynchronous Amnesiac Flooding.- Optimizing the Number of Drones for Aerial Power-Line Maintenance.- Almost Tight Oracles for Fastest-Path Queries on Temporal Trees.- Temporal Orienteering with Changing Fuel Costs.- Parameterised Algorithms for Temporally Satisfying Reconfiguration Problems.- Temporal Cycle Detection and Acyclic Temporalizations.- Graph Traversal via Connected Mobile Agents.- Time-optimal Asynchronous Minimal Vertex Covering by Myopic Robots on Graph.- Linear Search for Capturing an Oblivious Mobile target in the Sender/Receiver Model.- A Logarithmic Approximation Algorithm for the Activation Edge-multicover Problem.- Capturing an Invisible Robber using Separators.

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Book Synopsis.-Collective Intelligence and Collective Decision-Making:Towards Trustworthy Legal AI: a Multi-Agent Approach to Integrating Legislative Knowledge.-Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems.-A Multi-Agent System Based on Learning Automata for Solving the Coverage Problem in Self-Organizing Wireless Sensor Networks.-Automatic Assessment of Verbal Communication Components in Group Processes.-Hypersphere-Based Multimodal Knowledge Graph Completion with DURA Regularization.-On the Semantic Complexity of Association Relationships in Conceptual Modeling Languages.-Co-operative Strategies for Decision-Making & Optimisation:Minimax regret scheduling problem on unrelated machines with two uncertain parameters.-A Two-Step Approach to Modeling and Solving the University Course Timetabling Problem.-A Lightweight Drift Aware Aggregation Method for Time Series Forecasting.-Secure Cooperative Semantic Communication with Swin Transformer and Jamming.-A Causal Inference Approach to Assess the Effects of Atomic Concentration Changes on the Hardness of High-Entropy Alloys.-Privacy-Aware Knowledge Transfer for Cross-domain Recommender System.-Natural Language Processing:DESS: DeBERTa Enhanced Syntactic-Semantic Aspect Sentiment Triplet Extraction.-Text-JEPA: A Joint Embedding Predictive Architecture for the Conversion of Natural Language into First-Order Logic.-RAG-ViVerse: Enhancing Poetry Generation With Retrieval Augmented Generation and Large Language Models.-ViPhoVQA: Toward a Phonemic-Based Method for Mitigating Rare and Out-of-Vocab Words in Vietnamese Text-based Visual Question Answering.-Evaluating the Strategy to Deploy Large Language Models to Label Training Data in the Process of Fake News Detection.-Towards Complex Question Answering in Polish Language.-Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining.-A Hybrid Ensemble Framework for Topic Extraction in Vietnamese Legal Documents.-Knowledge Engineering & Industry 4.0 Applications:Code Similarity Detection Using Complexity-Based Birthmarks.-Balanced Assessment of Programmers’ Contribution in IT Projects.-A Systematic Approach to Measuring Developer Productivity: The Prismetrix Method.-LLMs as Code Review Agents: A Rapid Review and Experimental Evaluation with Human Expert Judges.-Enhancing Hotspot Detection with Behavioral Analysis and AI-Assisted Code Review.-Data Mining & Machine Learning:Efficient Algorithms for Mining Top-rank-k Frequent Closed Inter-transaction Patterns.-Mining Top-rank-k frequent inter-transaction patterns.-Predicting Students Academic Performance by Processing the Imbalanced Education Dataset.-Leveraging a Machine Learning Method for Continuous Segmentation of Airways in Videobronchoscopy.-Application of Whitebox Machine Learning Models for Optimizing Electrochemical Assays of Drug Permeability.-Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction.-A Light and Efficient Framework for e-Commerce Fraud Detection.-Convenient time series features extraction package FExtract.

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Book Synopsis What does humankind expect from AI? What kind of relationship between man and intelligent machine are we aiming for? Does an AI need to be able to recognize human unconscious dynamics to act for the "best" of humans—that "best" that not even humans can clearly define? Humanizing AI analyses AI and its numerous applications from a psychoanalytical point of view to answer these questions. This important, interdisciplinary contribution to the social sciences, as applied to AI, shows that reflecting on AI means reflecting on the human psyche and personality; therefore conceiving AI as a process of deconstruction and reconstruction of human identity. AI gives rise to processes of identification and de-identification that are not simply extensions of human identities—as post-humanist or trans-humanist approaches believe—but completely new forms of identification. Humanizing AI will benefit a broad audience: undergraduates, postgraduates and teachers in sociology, social theory, science and technology studies, cultural studies, philosophy, social psychology, and international relations. It will also appeal to programmers, software designers, students, and professionals in the sciences.

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Book Synopsis

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Book Synopsis

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Book Synopsis

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Book SynopsisThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.Trade Review“Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web … . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. … Without a doubt, this is an excellent addition to my bookshelf!” (Fernando Berzal, Computing Reviews, February, 2017)Table of ContentsAn Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.

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Book SynopsisThe second edition of this handbook provides a state-of-the-art overview on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people and their lives. The credible prospect of practical robots among humans is the result of the scientific endeavour of a half a century of robotic developments that established robotics as a modern scientific discipline. The ongoing vibrant expansion and strong growth of the field during the last decade has fueled this second edition of the Springer Handbook of Robotics. The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organization’s Award for Engineering & Technology. The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook’s team. A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app. Springer Handbook of Robotics Multimedia Extension Portal: http://handbookofrobotics.org/Trade Review"This handbook describes a very large number of topics on robotics. ... Each chapter has a summary and references (books, papers, and videos). ... The book can be used by students of many disciplines (robotics, computer science, mechanical engineering, physics, civil engineering, sociology, management, and so on) and at many levels (undergraduate, graduate, and research)." (Maulik A. Dave, Computing Reviews, February, 2017)Table of ContentsIntroductionPart A ― Robotics FoundationsKinematics.- Dynamics.- Mechanisms and Actuation.- Sensing and Estimation.- Model Identification.- Motion Planning.- Motion Control.- Force Control.- Redundant Manipulators.- Robots with Flexible Elements.- Robotic Systems Architectures and Programming.- Behavior-Based Systems.- AI Reasoning Methods for Robotics.- Machine LearningPart B ― DesignDesign and Performance Evaluation.- Limbed Structures.- Parallel Mechanisms.- Robot Hands.- Snake-Like and Continuum Robots.- Soft Robots.- Modular Robots.- Biomimetic Robots.- Wheeled Robots.- Underwater Robots.- Flying Robots.- Micro/NanorobotsPart C ― Sensing and PerceptionForce and Tactile Sensing.- Inertial Sensing.- GPS and Odometry.- Sonar Sensing.- Range Sensing.- 3-D Vision.- Object Recognition.- Visual Servoing.- Multisensor Data FusionPart D ― Manipulation and InterfacesMotion for Manipulation Tasks.- Contact Modeling and Manipulation.- Grasping.- Cooperative Manipulation.- Mobility and Manipulation.- Haptics.- Active Manipulation for Perception.- Telerobotics.- Networked RobotsPart E ― Moving in the EnvironmentWorld Modeling.- Simultaneous Localization and Mapping.- Motion Planning and Obstacle Avoidance.- Modeling and Control of Legged Robots.- Modeling and Control of Wheeled Mobile Robots.- Modeling and Control of Robots on Rough Terrain.- Modeling and Control of Underwater Robots.- Modeling and Control of Aerial Robots.- Multiple Mobile Robot SystemsPart F ― Robots at WorkIndustrial Robotics.- Space Robotics.- Robotics in Agriculture and Forestry.- Robotics in Construction.- Robotics in Hazardous Applications.- Robotics in Mining.- Search and Rescue Robotics.- Robot Surveillance and Security.- Intelligent Vehicles.- Medical Robotics and Computer-Integrated Surgery.- Rehabilitation and Health Care Robotics.- Domestic Robotics.- Robotics Competitions and ChallengesPart G ― Robots and HumansHumanoids.- Human Motion Reconstruction.- Physical Human-Robot Interaction.- Human-Robot Augmentation.- Cognitive Human-Robot Interaction.- Social Robotics.- Socially Assistive Robotics.- Learning from Humans.- Biologically-Inspired Robotics.- Evolutionary Robotics.- Neurorobotics: From Vision to Action.- Perceptual Robotics.- Robotics for Education.- Roboethics: Social and Ethical ImplicationsAcknowledgements.- About the Authors.- Subject Index

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Book SynopsisIn this book the author argues that the basis of what we consider computer intelligence has algorithmic roots, and he presents this with a holistic view, showing examples and explaining approaches that encompass theoretical computer science and machine learning via engineered algorithmic solutions.Part I of the book introduces the basics. The author starts with a hands-on programming primer for solving combinatorial problems, with an emphasis on recursive solutions. The other chapters in the first part of the book explain shortest paths, sorting, deep learning, and Monte Carlo search. A key function of computational tools is processing Big Data efficiently, and the chapters in Part II of the book examine traditional graph problems such as finding cliques, colorings, independent sets, vertex covers, and hitting sets, and the subsequent chapters cover multimedia, network, image, and navigation data. The highly topical research areas detailed in Part III are machine learning, problem solving, action planning, general game playing, multiagent systems, and recommendation and configuration. Finally, in Part IV the author uses application areas such as model checking, computational biology, logistics, additive manufacturing, robot motion planning, and industrial production to explain how the techniques described may be exploited in modern settings.The book is supported with a comprehensive index and references, and it will be of value to researchers, practitioners, and students in the areas of artificial intelligence and computational intelligence.Table of ContentsPreface.- Towards a Characterization.- Part I, Basics.- 1. Programming Primer.- 2. Shortest Paths.- 3. Sorting.- 4. Deep Learning.- 5. Monte-Carlo Search.- Part II, Big Data.- 6. Graph data.- 7. Multimedia Data.- 8. Network Data.- 9. Image Data.- 10. Navigation Data.- Part III, Research Areas.- 11. Machine Learning.- 12. Problem Solving.- 13. Card Game Playing.- 14. Action Planning.- 15. General Game Playing.- 16. Multiagent Systems.- 17. Recommendation and Configuration Part IV, Applications.- 18. Adversarial Planning.- 19. Model Checking.- 20. Computational Biology.- 21. Logistics.- 22. Additive Manufacturing.- 23. Robot Motion Planning.- 24. Industrial Production.- 25. Further Application Areas. - Index and References

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Book SynopsisThis book constitutes the refereed proceedings of the 9th International Conference on Data-Driven Innovation, ICT Innovations 2017, held in Skopje, Macedonia, in September 2017. The 26 full papers presented were carefully reviewed and selected from 90 submissions. They cover the following topics: big data analytics, cloud computing, data mining, digital signal processing, e-health, embedded systems, emerging mobile technologies, multimedia, Internet of Things (IoT), machine learning, software engineering, security and cryptography, coding theory, wearable technologies, wireless communication, and sensor networks.Table of ContentsData-driven innovations, organized around topics such as increasing migration of socio-economic activities to the Internet.- The decline in the cost of data collection, storage and processing.- The generation and use of huge volumes of data.- Large datasets becoming a core asset in research and economy fostering new discoveries, new industries, new processes.

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Book SynopsisThis book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership – while others concern more specific business situations (e.g., initial public offering, growth strategies, etc.). The important matter of selecting the right skills and people for an effective team will be extensively explained, and practical ways to recognize them and understanding their personalities will be provided. Finally, few relevant technological future trends will be acknowledged (i.e., IoT, Artificial intelligence, blockchain, etc.), especially for their close relation with the increasing amount of data and our ability to analyse them faster and more effectively.Trade Review“The book presents a new viewpoint to approach the big data concept that tries to distance itself from previous approaches. It introduces a consistent framework to handle and apply data strategies. … The book would be interesting reading for big data analysts or company managers because it discloses some important facets of this new technological challenge, although some subjects are quite hastily explained.” (Svetlana Segarceanu, Computing Reviews, April, 2017)Table of ContentsIntroduction.- What Data Science Means to the Business.- Key Data Challenges to Strategic Business Decisions.- A Chimera Called Data Scientist: Why they don’t Exist (but they will in the Future).- Future Data Trends.- Where are we Going? The Path Toward an Artificial Intelligence.- Conclusions.

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Book SynopsisThis book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.Trade Review“The book seems practical and interesting for newcomers to the feld and also experts. This book covers a range of introductory to advanced issues of AI and can respond well to the concerns of researchers. The presented examples … prepare the ground for familiarity with the research process and future research trends in this feld. Based on the reviews, we can recommend this book to researchers as a desirable book as a gateway to enter this feld.” (Shahabedin Nabavi and Mohammad Mohammadi, Physical and Engineering Sciences in Medicine, Vol. 44, 2021)Table of ContentsPART I: INTRODUCTION 1 Introduction: Game changers in radiology PART II: TECHNIQUES 2 The role of medical imaging computing, informatics and machine learning in healthcare 2 History and evolution of A.I. in medical imaging 3 Deep Learning and Neural Networks in imaging: basic principles PART III DEVELOPMENT of AI APPLICATIONS 4 Imaging biomarkers 5 How to develop A.I. applications 6 Validation of A.I. applications PART IV: BIG DATA IN RADIOLOGY 7 The value of enterprise imaging 8 Data mining in radiology 9 Image biobanks 10 The quest for medical images and data 11 Clearance of medical images and data 12 Legal and ethical issues in AI PART V: CLINICAL USE OF A.I. IN RADIOLOGY 13 Pulmonary diseases 14 Cardiac diseases 15 Breast cancer 16 Neurological diseases PART VI: IMPACT of A.I. on RADIOLOGY 17 Applications of A.I. beyond image analysis 18 Value of structured reporting for A.I. 19 The role of A.I. for clinical trials 20 Market and economy of A.I.: evolution 21 The role of an A.I. ecosystem for radiology 22 Advantages and risks of A.I. for radiologists 23 Re-thinking radiology

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Book Synopsis This book presents two new decomposition methods to decompose a time series in intrinsic components of low and high frequencies. The methods are based on Singular Value Decomposition (SVD) of a Hankel matrix (HSVD). The proposed decomposition is used to improve the accuracy of linear and nonlinear auto-regressive models. Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs) have been found insufficient because of the highly complicated nature of some time series. Hybrid models are a recent solution to deal with non-stationary processes which combine pre-processing techniques with conventional forecasters, some pre-processing techniques broadly implemented are Singular Spectrum Analysis (SSA) and Stationary Wavelet Transform (SWT). Although the flexibility of SSA and SWT allows their usage in a wide range of forecast problems, there is a lack of standard methods to select their parameters. The proposed decomposition HSVD and Multilevel SVD are described in detail through time series coming from the transport and fishery sectors. Further, for comparison purposes, it is evaluated the forecast accuracy reached by SSA and SWT, both jointly with AR-based models and ANNs. Table of ContentsPreface 1. Time Series and Forecasting 1.1. Introduction 1.2. Time series 1.3. Linear Autoregressive Models 1.4. Artificial Neural Networks 1.5. Hybrid models 1.5.1. Singular Spectrum Analysis 1.5.2. Wavelet Transform 1.6. Forecasting Accuracy Measures 1.7. Empirical Applications 1.7.1. Traffic Accidents Forecasting based on AR, ANNs and Hybrid models. 1.7.2. Anchovy Stock Forecasting based on AR, ANNs and Hybrid models. 1.7.3. Sardine Stock Forecasting based on AR, ANNs and Hybrid models. 2. Decomposition methods based on Singular Value Decomposition of a Hankel matrix 2.1. Introduction 2.2. Eigenvalues and Eigenvectors 2.3. Theorem of Singular Values Decomposition 2.4. One-level Singular Value Decomposition of a Hankel matrix 2.4.1. Embedding 2.4.2. Decomposition 2.4.3. Unembedding 2.4.4. Window Length Selection 2.5. Multi-level Singular Value Decomposition of a Hankel matrix 2.5.1. Embedding 2.5.2. Decomposition 2.5.3. Unembedding 2.5.4. Singular Spectrum Rate 2.6. Empirical Applications 2.6.1. Extraction of Components from traffic accidents time series based on HSVD and MSVD 2.6.2. Extraction of Components from fishery time series based on HSVD and MSVD 3. Forecasting based on components 3.1. Introduction 3.2. One-step ahead forecasting 3.3. Multi-step ahead forecasting 3.3.1. Direct Strategy 3.3.2. MIMO Strategy 3.4. Empirical Applications 3.4.1. Forecasting of traffic accidents based on HSVD and MSVD 3.4.2. Forecasting of anchovy stock based on HSVD and MSVD 3.4.3. Forecasting of sardine stock based on HSVD and MSVD List of Figures List of Tables List of Acronyms List of Symbols References

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Book Synopsis

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Book SynopsisTable of Contents1 Einführung.- 1.1 Motivation.- 1.2 Literaturüberblick.- 1.3 Ziele und Gliederung der Arbeit.- 2 Grundlagen.- 2.1 Die Denavit-Hartenberg Notation.- 2.2 Duale (3×3)-Matrizen und Quaternionen.- 2.3 Der Paul’sche Ansatz zur Lösung des IKP.- 2.4 Erkennung global degenerierter Roboter.- 2.5 Vereinfachung der Gelenktabelle.- 2.6 Spiegelung von Robotergeometrien.- 2.7 Festlegung der Systemeingabe.- 3 Roboter mit einer ebenen Gelenkgruppe.- 3.1 Ebene Gelenkgruppen in Heiß’schen Klassen.- 3.2 Herleitung der Ansatzgleichungen.- 3.3 Berechnung der Lösung im allgemeinen Fall.- 3.4 Sonderfälle quadratischer Lösbarkeit.- 3.5 Lösung der verbleibenden Gelenkvariablen.- 3.6 Ergebnisse.- 4 Konzepte.- 4.1 Systemschale und Systemkern.- 4.2 Funktionsweise des Systemkerns.- 4.3 Konzepte anderer Invertierungssysteme.- 5 Prototypgleichungen.- 5.1 Neu entwickelte Prototypgleichungen.- 5.2 Prototypen für ein Invertierungssystem.- 6 Implementierung.- 6.1 Auswahl von Prolog.- 6.2 Interne Repräsentation von Gleichungen.- 6.3 Gleichungsmerkmale.- 6.4 Suche nach lösbaren Gleichungen.- 6.5 Extraktion der Gleichungsparameter.- 6.6 Ein- und Ausgaben des Programms SKIP.- 7 Leistungsbetrachtung.- 7.1 Implementierungsstand der Testversion.- 7.2 Geometrien aus Heiß’schen Klassen.- 7.3 Orthogonale Geometrien aus Klasse 3 und 9.- 7.4 Drei sich schneidende Rotationsachsen.- 7.5 Spezialfälle aus den Klassen 2 und 5.- 7.6 Geometrien mit einer ebenen Gelenkgruppe.- 7.7 Exemplarische Betrachtung der Lösungsgüte.- 7.8 Zusammenfassende Bewertung.- 8 Anwendungen.- 8.1 Invertierung redundanter Roboter.- 8.2 Integration in ein Robotersimulationssystem.- 9 Zusammenfassung und Ausblick.- A Ergänzung der Grundlagen.- A.1 Invertierung einer SCARA-Geometrie.- A.2 Determinante und globale Degeneration.- A.3 Spiegelung einer Robotergeometrie.- B Gleichungsmaterial Kapitel 3.- B.1 Fall 1.- B.2 Fall 2.1.- B.3 Fall 2.2.- C Prototypgleichungen.- C.1 Aus der Literatur bekannte Prototypen.- C.2 Ableitung der Prototypen 15, 16 und 17.- D Protokolle.- D.1 Gleichungssatz für die Stanford-Geometrie.- D.2 6-achsige SCARA-Geometrie.- D.3 8-achsige redundante Geometrie.- E Protokolle zur Leistungsbewertung.- E.1 Geometrien aus Heiß’schen Klassen.- E.2 Lösungen über Prototyp 18.- E.3 Geometrien mit einer ebenen Gelenkgruppe.

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