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"Academic, industry, and government experts provide a global survey of the financial services sector's transformation by AI, data science, and blockchain"-- Provided by publisher.

• Table of Contents Introduction to Software Architecture API Design Data Modeling The Data Layer The Twelve-Factor App Methodology Web Server Structures Event-Driven Structures Advanced Event-Driven Structures Microservices vs Monolith Testing and TDD Package Management Logging Metrics Profiling Debugging Ongoing Architecture.
• (source: Nielsen Book Data)

Make the best of your test suites by using cutting-edge software architecture patterns in Python Key Features Learn how to create scalable and maintainable applications Build a web system for micro messaging using concepts in the book Use profiling to find bottlenecks and improve the speed of the system Book DescriptionDeveloping large-scale systems that continuously grow in scale and complexity requires a thorough understanding of how software projects should be implemented. Software developers, architects, and technical management teams rely on high-level software design patterns such as microservices architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD) to make their work easier. This book covers these proven architecture design patterns with a forward-looking approach to help Python developers manage application complexity-and get the most value out of their test suites. Starting with the initial stages of design, you will learn about the main blocks and mental flow to use at the start of a project. The book covers various architectural patterns like microservices, web services, and event-driven structures and how to choose the one best suited to your project. Establishing a foundation of required concepts, you will progress into development, debugging, and testing to produce high-quality code that is ready for deployment. You will learn about ongoing operations on how to continue the task after the system is deployed to end users, as the software development lifecycle is never finished. By the end of this Python book, you will have developed "architectural thinking": a different way of approaching software design, including making changes to ongoing systems. What you will learn Think like an architect, analyzing software architecture patterns Explore API design, data storage, and data representation methods Investigate the nuances of common architectural structures Utilize and interoperate elements of patterns such as microservices Implement test-driven development to perform quality code testing Recognize chunks of code that can be restructured as packages Maintain backward compatibility and deploy iterative changes Who this book is forThis book will help software developers and architects understand the structure of large complex systems and adopt architectural patterns that are scalable. Examples in the book are implemented in Python so a fair grasp of basic Python concepts is expected. Proficiency in any programming languages such as Java or JavaScript is sufficient.
(source: Nielsen Book Data)

• Table of Contents An Overview of Amazon EMR Exploring the Architecture and Deployment Options Common Use Cases and Architecture Patterns Big Data Applications and Notebooks Available in Amazon EMR Setting Up and Configuring EMR Clusters Monitoring, Scaling, and High Availability Understanding Security in Amazon EMR Understanding Data Governance in Amazon EMR Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Migrating On-Premises Hadoop Workloads to Amazon EMR Best Practices and Cost Optimization Techniques.
• (source: Nielsen Book Data)

Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key Features Build data pipelines that require distributed processing capabilities on a large volume of data Discover the security features of EMR such as data protection and granular permission management Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR Book DescriptionAmazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learn Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio Configure, deploy, and orchestrate Hadoop or Spark jobs in production Implement the security, data governance, and monitoring capabilities of EMR Build applications for batch and real-time streaming data analytics solutions Perform interactive development with a persistent EMR cluster and Notebook Orchestrate an EMR Spark job using AWS Step Functions and Apache Airflow Who this book is forThis book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
(source: Nielsen Book Data)

• Table of Contents Getting Started with TinyML Prototyping with Microcontrollers Building a Weather Station with TensorFlow Lite for Microcontrollers Voice Controlling LEDs with Edge Impulse Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano Building a Gesture-Based Interface for YouTube Playback Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS Toward the Next TinyML Generation with microNPU.
• (source: Nielsen Book Data)

Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learning Key Features Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi Pico Work with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge Impulse Explore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPU Book DescriptionThis book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you'll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase. What you will learn Understand the relevant microcontroller programming fundamentals Work with real-world sensors such as the microphone, camera, and accelerometer Run on-device machine learning with TensorFlow Lite for Microcontrollers Implement an app that responds to human voice with Edge Impulse Leverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE Sense Create a gesture-recognition app with Raspberry Pi Pico Design a CIFAR-10 model for memory-constrained microcontrollers Run an image classifier on a virtual Arm Ethos-U55 microNPU with microTVM Who this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.
(source: Nielsen Book Data)

This book aims to provide the latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to intelligent social networks and collaborative systems, intelligent networking systems, mobile collaborative systems, secure intelligent cloud systems, etc., as well as to reveal synergies among various paradigms in such a multi-disciplinary field intelligent collaborative systems. With the fast development of the Internet, we are experiencing a shift from the traditional sharing of information and applications as the main purpose of the Web to an emergent paradigm, which locates people at the very centre of networks and exploits the value of people's connections, relations and collaboration. Social networks are also playing a major role in the dynamics and structure of intelligent Web-based networking and collaborative systems. Virtual campuses, virtual communities and organizations strongly leverage intelligent networking and collaborative systems by a great variety of formal and informal electronic relations, such as business-to-business, peer-to-peer and many types of online collaborative learning interactions, including the emerging e-learning systems. This has resulted in entangled systems that need to be managed efficiently and in an autonomous way. In addition, latest and powerful technologies based on grid and wireless infrastructure as well as cloud computing are currently enhancing collaborative and networking applications as a great deal but also facing new issues and challenges. The principal purpose of the research and development community is to stimulate research that will lead to the creation of responsive environments for networking and, at longer-term, the development of adaptive, secure, mobile and intuitive intelligent systems for collaborative work and learning.
(source: Nielsen Book Data)

"This book examines artistic practices that use machine learning and computational technologies through historical perspectives surrounding adaptive systems from the 1950s onwards"-- Provided by publisher.

• A Study of Energy Reduction Strategies in Renewable Hybrid Grid.- Classification and Watermarking of Brain Tumor using Artificial and Convolutional Neural Networks.- A Proposal for a Deep Learning Model to Enhance Student Guidance and Reduce Dropout.- EduBot: An Unsupervised Domain-Specific Chatbot for Educational Institutions.- SQL Generation from Natural Language using Supervised Learning and Recurrent Neural Networks.- Toward Intelligent Solution to Identify Learner Attitude from Source Code.
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This book gathers selected papers from Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by ENSAM-Meknes at Moulay Ismail University, Morocco. The 29 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as digital twin, multiagent systems, deep learning, image processing and analysis, control, prediction, modeling, optimization and design, as well as AI applications in industry, health, energy, agriculture, and education. The book is intended for AI experts, offering them a valuable overview and global outlook for the future, and highlights a wealth of innovative ideas and recent, important advances in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• A Fuzzy Ontology Based Approach To Support Product Eco-Design.-
• A Genetic-based SVM Approach for Quality Data Classification.- Towards a platform to implement an intelligent and predictive maintenance in the context of industry.- Towards a Prediction Analysis in an Industrial Context.- Methodology for Implementation of Industry . Technologies in Supply Chain for SMEs.- A Deep Reinforcement Learning DRL Decision Model For Heating Process Parameters Identification In Automotive Glass Manufacturing
• Analytic Hierarchy Process AHP for supply chain . risks management.- SmartDFRelevance: a Holonic Agent based System for Engineering Industrial Projects in Concurrent Engineering Context.- A Cyber-Physical Warehouse Management System Architecture in an.- Industry . context.- PLM and Smart technologies for product and supply chain design.- Production systems simulation considering Non-Productive Times and Human Factors.- Distributed and Embedded System to Control Traffic Collision Based on Artificial Intelligence.- The Emergence and Decision Support in Complex System with Fuzzy Logic Control.- Markov Decision Processes with Discounted Costs over a Finite Horizon: Action Elimination.- Robust adaptive fuzzy path tracking control of Differential Wheeled Mobile Robot.- Deep Learning approach for Automated Guided Vehicle System.- Path Planning Using Particle Swarm Optimization And Fuzzy Logic
• Prediction of Robot localization states using Hidden Markov Models.- A New Approach for Multi-Agent Reinforcement Learning.- Recommender System for Most Relevant k Pick-Up Points.- Feature Detection and Tracking for Visual Effects: Augmented Reality and Video Stabilization.- Spectral image recognition using artificial dynamic neural network in information resonance mode.- U-Net Based Model for Obfuscated Human Faces Reconstruction.- A Machine Learning Assistant for Choosing Operators and Tuning Their Parameters in Image Processing Tasks.- Convergence and parameters setting of continuous Hopfield neural networks applied to image restoration problem.- The Ibn Battouta Air Traffic Control Corpus with Real Life ADS-B and METAR data.- Weed Recognition System For Low-Land Rice Precision Farming Using Deep Learning Approach.- A Comparative Study Between Mixture and Stepwise Regression to Model The Parameters Of The Composting Process.- Deep Learning Based Sponge Gourd Diseases Recognition For Commercial Cultivation in Bangladesh.- Exploitation of vegetation indices and Random Forest for cartography of rosemary cover: application to Gourrama region, Morocco.
• (source: Nielsen Book Data)

This book gathers the refereed proceedings of the Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by the ENSAM-Meknes at Moulay Ismail University, Morocco. The 30 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as smart manufacturing, smart maintenance, smart supply chain management, supervised learning, unsupervised learning, reinforcement learning, graph-based and semi-supervised learning, neural networks, deep learning, planning and optimization, and other AI applications. The book is intended for AI experts, offering them a valuable overview of the status quo and a global outlook for the future, with many new and innovative ideas and recent important developments in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
• (source: Nielsen Book Data)

In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
(source: Nielsen Book Data)

• Chapter 1. A Novel Approach for Analysing and Detection of Obfuscated Malware Payloads in Android Platform Using Dexmonitor.- Chapter 2. A Study on the Prevention Mechanisms for Kernel Attacks.- Chapter 3. Enhancing Data Security Using DNA Algorithm in Cloud Storage.- Chapter 4. Stable Multi-Agent Clustering Method to Improve the Security in VANET.- Chapter 5. Anonymous Block Chain, Electronic Voting Using Linkable Ring Signatures.- Chapter 6. A Comprehensive Study on Ransomware Attacks in Online Pharmacy Community.- Chapter 7. A Comprehensive Survey on RF Module at 433 Mhzfor Different Applications.- Chapter 8. A Memory Efficient Tool for Bengali Parts Of Speech Tagging.- Chapter 9. Long-Term Wind Speed Forecasting - A Review.- Chapter 10. Methods for Epileptic Seizure Prediction Using EEG Signals: A Survey.- Chapter 11. Railway Wagon Health Monitoring System Using E-BMA Protocol in Wireless Sensor Networks.- Chapter 12. An Interactive Virtual E-Learning Framework Using Crowdsourced Analytics.- Chapter 13. Security for Data in IOT Using A Newaps Elliptic Curve Light Weight Cryptography Algorithm.- Chapter 14. Feature Selection Strategy for Academic Datasets Using Correlation Analysis.- Chapter 15. Exploration of Magnetic Resonance Imaging for Prognosis of Alzheimer Disease Using Convolutional Neural Network.
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This book features a collection of high-quality research papers presented at the International Conference on Advanced Computing Technology (ICACT 2020), held at the SRM Institute of Science and Technology, Chennai, India, on 23-24 January 2020. It covers the areas of computational intelligence, artificial intelligence, machine learning, deep learning, big data, and applications of artificial intelligence in networking, IoT and bioinformatics.
(source: Nielsen Book Data)

• Automatic Standardization of Data based on Machine Learning and Natural Language Processing.- Scoring Algorithm Identifying Anomalous Behavior in Enterprise Network.-APPLICATION OF BAYESIAN AUTOMATED HYPERPARAMETER TUNING ON CLASSIFIERS PREDICTING CUSTOMER RETENTION IN BANKING INDUSTRY.- Quantum Machine Learning: A Review and Current Status.- Survey of Transfer Learning and a Case Study of Emotion Recognition using Inductive Approach.- An Efficient Algorithm for Complete Linkage Clustering with a Merging Threshold.
• (source: Nielsen Book Data)

This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17-19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. .
(source: Nielsen Book Data)

• 1.
• Deep Learning for Innovative Medical Decision Support
• 2.
• Deep Learning and Image Analysis for Medical Decision Support
• 3.
• Deep Learning Oriented Systems for Medical Education
• 4.
• Hybrid Deep Systems for Medical Education and Decision Support
• 5.
• Deep Learning and Optimization for Medical Education and Decision Support 6.
• Deep Learning and Multimedia for Medical Education and Decision Support
• 7.
• Deep Learning and Traditional Methods for Medical Education and Decision Support.
• (source: Nielsen Book Data)

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today's problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation. .
(source: Nielsen Book Data)

• Chapter 1. Double hierarchy linguistic term set and its extensions.-
• Chapter 2. Measure methods for DHHFLTSs.-
• Chapter 3. Consistency theory framework of DHHFLPRs.-
• Chapter 4. Group consensus decision-making methods with DHHFLPRs, LPOs and self-confident DHLPRs.-
• Chapter 5. Large-scale group consensus decision-making methods with DHHFLPRs.
• (source: Nielsen Book Data)

This book presents the concept of the double hierarchy linguistic term set and its extensions, which can deal with dynamic and complex decision-making problems. With the rapid development of science and technology and the acceleration of information updating, the complexity of decision-making problems has become increasingly obvious. This book provides a comprehensive and systematic introduction to the latest research in the field, including measurement methods, consistency methods, group consensus and large-scale group consensus decision-making methods, as well as their practical applications. Intended for engineers, technicians, and researchers in the fields of computer linguistics, operations research, information science, management science and engineering, it also serves as a textbook for postgraduate and senior undergraduate university students.
(source: Nielsen Book Data)

• Part I: Artificial Intelligence and Optimization.- Stochastic Proximal Gradient Algorithm with Minibatches Application to Large Scale Learning Models.- The Human Mental Search Algorithm for Solving Optimisation Problems.- Reducing Redundant Association Rules Using Type-2 Fuzzy Logic.- Identifiability of Discrete Concentration Graphical Models with a Latent Variable.- An Automatic Classification of Genetic Mutations by Exploring Different Classifiers.- Towards Artificial Intelligence: Concepts, Applications, and Innovations.- Part II: Big Data and Artificial Intelligence Applications.- In Depth Analysis, Applications and Future Issues of Artificial Neural Network.- Big Data and Deep Learning in Plant Leaf Diseases Classification for Agriculture.- Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities.
• (source: Nielsen Book Data)

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.
(source: Nielsen Book Data)

• Faster Result Retrieval from Health Care Product Sales Data Warehouse using Materialized Queries.- Feature Selection using Ant Colony Optimization and Weighted Visibility Graph.- From Generic to Custom: A Survey on Role of Machine Learning in Pharmacogenomics, Its Applications and Challenges.- Personalized Structure Balance Theory Based Movie Recommendation System.- Machine Learning Techniques for the Investigation of Phishing Websites.
• (source: Nielsen Book Data)

This book presents the proceedings of 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), which aims to bring together researchers, scientists, engineers and practitioners to share new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. The book is divided into two volumes: Evolution in Computational Intelligence (Volume 1) and Intelligent Data Engineering and Analytics (Volume 2). Covering a broad range of topics in computational intelligence, the book features papers on theoretical as well as practical aspects of areas such as ANN and genetic algorithms, computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, and multi-agent systems. As such, it is a valuable reference resource for postgraduate students in various engineering disciplines.
(source: Nielsen Book Data)

• Table of Contents An Introduction to Generative AI: "Drawing" Data from Models Setting Up a TensorFlow Lab Building Blocks of Deep Neural Networks Teaching Networks to Generate Digits Painting Pictures with Neural Networks Using VAEs Image Generation with GANs Style Transfer with GANs Deepfakes with GANs The Rise of Methods for Text Generation NLP 2
• .0: Using Transformers to Generate Text Composing Music with Generative Models Play Video Games with Generative AI: GAIL Emerging Applications in Generative AI.
• (source: Nielsen Book Data)

Understand the theory behind deep generative models and experiment with practical examples Key Features Build a solid understanding of the inner workings of generative models Experiment with practical TensorFlow 2.x implementations of state-of-the-art models Explore a wide range of current and emerging use cases for deep generative AI Book DescriptionDeep generative models are powerful tools that rival human creative capabilities. In this book, you'll discover how these models emerged, from restricted Boltzmann machines and deep belief networks to VAEs, GANs, and beyond. You'll develop a foundational understanding of generative AI and learn how to implement models yourself in TensorFlow, supported by references to seminal and current research. After getting to grips with the fundamentals of deep neural networks, you'll set up a scalable code lab in the cloud and begin to explore the huge breadth of potential use cases for generative models. You'll look at Open AI's news generator, networks for style transfer and deepfakes, synergy with reinforcement learning, and more. As you progress, you'll recreate the code that makes these possible, piecing together TensorFlow layers, utility functions, and training loops to uncover links between the different modes of generation. By the end of this book, you will have acquired the knowledge to create and implement your own generative AI models. What you will learn Implement paired and unpaired style transfer with networks like StyleGAN Use facial landmarks, autoencoders, and pix2pix GAN to create deepfakes Build several text generation pipelines based on LSTMs, BERT, and GPT-2, learning how attention and transformers changed the NLP landscape Compose music using hands-on LSTM models, simple GANs, and the intricate MuseGAN Train a deep learning agent to move through a simulated physical environment Discover emerging applications of generative AI, such as folding proteins and creating videos from images Who this book is forThis book will appeal to Python programmers, seasoned modelers, and machine learning engineers who are keen to learn about the creation and implementation of generative models. To make the most out of this book, you should have a basic familiarity with probability theory, linear algebra, and deep learning.
(source: Nielsen Book Data)

• Stochastic Investigation of Two-unit Redundant System with Provision of Different Repair Schemes.- A Workflow Allocation Strategy under Precedence Constraints for IaaS Cloud Environment.- Artificial Neural Network Analysis for Predicting Spatial Patterns of Urbanization in India.- Satellite Radar Interferometry for DEM Generation using Sentinel-1A Imagery.
• (source: Nielsen Book Data)

This book presents high-quality, peer-reviewed papers from the International Conference on "Innovations in Computational Intelligence and Computer Vision (ICICV 2020), " hosted by Manipal University Jaipur, Rajasthan, India, on January 17-19, 2020. Offering a collection of innovative ideas from researchers, scientists, academics, industry professionals and students, the book covers a variety of topics, such as artificial intelligence and computer vision, image processing and video analysis, applications and services of artificial intelligence and computer vision, interdisciplinary areas combining artificial intelligence and computer vision, and other innovative practices.
(source: Nielsen Book Data)

• Frontmatter
• Remerciements
• Sommaire
• Préface
• 1. Qu'est-ce au juste que l'intelligence artificielle?
• 2. Avec l'intelligence artificielle générale pour Graal
• 3. Langage, créativité et émotions
• 4. Les réseaux de neurones artificiels
• 5. Les robots et la vie artificielle (A-life)
• 6. Mais, est-ce réellement de l'intelligence?
• 7. La Singularité
• Références
• Lectures complémentaires
• Index

L'intelligence artificielle, qui consiste à faire faire aux ordinateurs ce que peut faire l'esprit humain et ses applications touchent tous les aspects de notre vie et se trouvent tout autour de nous. Ce petit livre nous rappelle que les résultats de l'intelligence artificielle ont été inestimables pour les biologistes, les psychologues et les linguistes en les aidant à comprendre les processus de la mémoire, de l'apprentissage et du langage sous un angle nouveau. L'intelligence artificielle a aussi alimenté et aiguisé les débats philosophiques concernant la nature de l'esprit, l'intelligence et le caractère unique des êtres humains. Ainsi, Margaret Boden, leader dans le domaine de la recherche en intelligence artificielle, passe en revue ici tous les défis philosophiques et technologiques soulevés par l'intelligence artificielle, et tente de répondre à la question si complexe : les programmes pourront-ils être un jour réellement intelligents, créatifs, ou même conscients ?

• Data Jackets as Communicable Metadata for Potential Innovators - Toward Opening to Social Contexts.- A Proposal Based on Instance Typicality for Dealing with Nominal Attribute Values in Instance-Based Learning Environments.- Dataset for Intrusion Detection in Mobile Ad-Hoc Networks.- Visual Password Scheme Using Bag Context Shape Grammars.- Peak detection enhancement in autonomous wearable Fall Detection.- Automated detection of tuberculosis from sputum smear microscopic images using transfer learning techniques.- Comparative Performance Analysis of Neural Network Base Training Algorithm and Neuro-Fuzzy System with SOM for the Purpose of Prediction of the Features of Superconductors.
• (source: Nielsen Book Data)

This book highlights recent research on intelligent systems and nature-inspired computing. It presents 62 selected papers from the 19th International Conference on Intelligent Systems Design and Applications (ISDA 2019), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 33 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
(source: Nielsen Book Data)

• Introduction.- IoT Overview.- Emerging Trends in IoT.- IoT Use Cases.- Energy and Utilities.- Context Management in IoT.- Context and Context Management.- Context-aware Architecture.- Augmented Reality and IoT.- Augmented Reality and IoT design Issues.- Augmented Reality and IoT enabled Architecture.- Conclusions.
• (source: Nielsen Book Data)

This book discusses the use of converged technology, a rapidly growing area that enhancements smart devices, communication, Internet of things (IoT), and augmented reality (AR). The book also explores the need for convergence of IoT and AR for various purposes, like personalized services, context awareness, and bridging the gap between the physical and digital world. Furthermore, it examines the implementation of IoT and AR in use cases to define pathways that allow application developers to design modern solutions to satisfy requirements like scalability, abstraction and security. Featuring an introduction, and covering sensing techniques, and effective architecture in AR-based IoT real-time use cases, the book also addresses the issues and challenges in designing standard architecture and middleware to support diverse applications. Given its scope, it is a valuable resource for teachers and students in engineering, as well as researchers, developers, and users working in multi-disciplinary areas.
(source: Nielsen Book Data)

• PART I - INTELLIGENT TRANSPORT SYSTEMS AND INFRASTRUCTURE
• CHAPTER 1: An Overview on Intelligent Transportation Systems in the Context of Internet of Vehicles
• CHAPTER 2: Intelligent Transportation Systems and its Necessity in Various Traffic Conditions in Indian Scenarios PART II - CONNECTED VEHICLES
• CHAPTER 3: Autonomous Driving Cars: Decision-Making
• CHAPTER 4: IEEE 802.11ah for Internet of Vehicles: Design Issues and Challenges
• CHAPTER 5: Medium access control in connected vehicles: Advances and Limitations PART III - ADVANCE DRIVER ASSISTED SYSTEM
• CHAPTER 6: An Overview of ADAS in Internet of Vehicles
• CHAPTER 7: iDriveAR: in Vehicle Driver Awareness and Drowsiness Framework based on Facial Tracking and Augmented reality
• CHAPTER 8: The Concept of Fusion for Clear Vision of Hazy Roads in ADAS PART VI - EVALUATION METHODS AND SECURITY ISSUES
• CHAPTER 9: Blockchain-enabled Security and Privacy for Internet of Vehicles
• CHAPTER 10: Approximation Algorithm and Linear Congruence: A state-of-art Approach in Information Security issues towards Internet of Vehicles
• CHAPTER 11: E-powertrain error detection using AI Techniques.
• (source: Nielsen Book Data)

This book provides an insight on the importance that Internet of Vehicles (IoV) solutions can have in taking care of vehicular safety through internetworking and automation. Key features of the book are the inclusion and elaboration of recent and emerging developments in various specializations of intelligent transportation systems and their solutions by incorporating IoT (Internet of Things) and IoV. This book presents to its readers useful IoV applications and architectures that cater to their improved driving requirements and lead towards autonomous driving. The application domains have a large range in which vehicular networking, communication technology, sensor devices, computing materials and devices, IoT communication, vehicular and on-road safety, data security and other topics are included. .
(source: Nielsen Book Data)

• Introduction
• Monitoring of Weld Pool Surface with Active Vision
• Visual Sensing of 3D Weld Pool Geometry with Passive Vision Image
• Penetration prediction with machine learning models
• Penetration Control for Bead-on plate welding
• Penetration Detection of narrow U-groove welding
• Lack of fusion detection inside narrow U-groove
• Conclustions and recommendations.

This book describes the application of vision-sensing technologies in welding processes, one of the key technologies in intelligent welding manufacturing. Gas tungsten arc welding (GTAW) is one of the main welding techniques and has a wide range of applications in the manufacturing industry. As such, the book also explores the application of AI technologies, such as vision sensing and machine learning, in GTAW process sensing and feature extraction and monitoring, and presents the state-of-the-art in computer vision, image processing and machine learning to detect welding defects using non-destructive methods in order to improve welding productivity. Featuring the latest research from ORNL (Oak Ridge National Laboratory) using digital image correlation technology, this book will appeal to researchers, scientists and engineers in the field of advanced manufacturing.
(source: Nielsen Book Data)

"The first book that provides a systematic and rigorous logical theory of causality"-- Provided by publisher.

• Part I: Natural Language Processing.- A Hybrid Feature Selection Approach Based on LSI for Classification of Urdu Text.- Automated Document Categorization Model.- Automated Categorization & Mining Tweets for Disaster Management.- Sentiment Analysis in Airline Data: Customer Rating Based Recommendation Prediction using WEKA.- Part II: Computer Vision.- Image Inpainting for Irregular Holes using Extreme Learning Machine.- OCR using Computer Vision and Machine Learning.- Few Shot Learning for Medical Imaging.- Hyperspectral Remote Sensing Image Classification using Active Learning.- A Smart Document Converter: Conversion of Handwritten Text Document to Computerized Text Document.- GRNN Based An Intelligent Technique for Image Inpainting.- Part III: Data Analysis and Prediction.- Content-Based Airline Recommendation Prediction using Machine Learning Techniques.- Meta-heuristic Based Approach for Slope Stability Analysis to Design an Optimal Soil Slope.- An Application of Operational Analytics: for Predicting Sales Revenue of Restaurant.- Application of Machine Learning Algorithm for Anomaly Detection for Industrial Pumps.- Part IV: Decision Making System.- Fast Accessing Non-volatile, High Performance-High Density, Optimized Array for Machine Learning Processor.- Long Term Evolution for Secured Smart Railway Communications using Internet of Things.- Application of the Flower Pollination Algorithm to Locate Critical Failure Surface for Slope Stability Analysis.
• (source: Nielsen Book Data)

This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics. Accordingly, there is a pressing need for novel and innovative algorithms to help us find effective solutions in industrial application areas such as media, healthcare, travel, finance, and retail. In all of these areas, data is the crucial parameter, and the main key to unlocking the value of industry. The book presents a range of intelligent algorithms that can be used to filter useful information in the above-mentioned application areas and efficiently solve particular problems. Its main objective is to raise awareness for this important field among students, researchers, and industrial practitioners. .
(source: Nielsen Book Data)

• Table of Contents Getting Started with TensorFlow 2.x The TensorFlow Way Keras Linear Regression Boosted Trees Neural Networks Predicting with Tabular Data Convolutional Neural Networks Recurrent Neural Networks Transformers Reinforcement Learning with TensorFlow and TF-Agents Taking TensorFlow to Production.
• (source: Nielsen Book Data)

Master TensorFlow to create powerful machine learning algorithms, with valuable insights on Keras, Boosted Trees, Tabular Data, Transformers, Reinforcement Learning and more Key Features Work with the latest code and examples for TensorFlow 2 Get to grips with the fundamentals including variables, matrices, and data sources Learn advanced deep learning techniques to make your algorithms faster and more accurate Book DescriptionThe independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. You will work through recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google's machine learning library, TensorFlow. This cookbook begins by introducing you to the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You'll then take a deep dive into some real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and for regression to provide a baseline for tabular data problems. As you progress, you'll explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be applied to computer vision and natural language processing (NLP) problems. Once you are familiar with the TensorFlow ecosystem, the final chapter will teach you how to take a project to production. By the end of this machine learning book, you will be proficient in using TensorFlow 2. You'll also understand deep learning from the fundamentals and be able to implement machine learning algorithms in real-world scenarios. What you will learn Grasp linear regression techniques with TensorFlow Use Estimators to train linear models and boosted trees for classification or regression Execute neural networks and improve predictions on tabular data Master convolutional neural networks and recurrent neural networks through practical recipes Apply reinforcement learning algorithms using the TF-Agents framework Implement and fine-tune Transformer models for various NLP tasks Take TensorFlow into production Who this book is forIf you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.
(source: Nielsen Book Data)

"A collection of critical essays dealing with the social and ethical impacts of AI including issues of trust, reliability, and bias"-- Provided by publisher.

This book intends to bring together researchers and developers from industry, the education field, and the academic world to report on the latest scientific research, technical advances, and methodologies. The 10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning is hosted by the University of L'Aquila and is going to be held in L'Aquila (Italy). Initially planned on the 17th to the 19th of June 2020, it was postponed to the 7th to the 9th of October 2020, due to the COVID-19 outbreak. The 10th edition of this conference and its related workshops expand the topics of the evidence-based TEL workshops series in order to provide an open forum for discussing intelligent systems for TEL, their roots in novel learning theories, empirical methodologies for their design or evaluation, stand-alone solutions, or web-based ones. This bridge has been realized also thanks to the sponsor of this edition of MIS4TEL: the Armundia Group https://www.armundia.com, the support from national associations (AEPIA, APPIA, CINI, and EurAI), and organizers (UNIVAQ, UNIROMA1, UNIBZ, UCV, UFSC, USAL, AIR institute, UNC, and UNIBA).
(source: Nielsen Book Data)
This book features papers from workshops at the 10th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, which was hosted by the University of L'Aquila (Italy) from 17th to 19th June 2020. The workshops provided participants with the opportunity to present and discuss novel research ideas on emerging topics complementing the main conference. They particularly focused on multi-disciplinary and transversal aspects such as TEL in nursing education programs, social and personal computing for web-supported learning communities, interactive environments and emerging technologies for eLearning, and TEL for future citizens.
(source: Nielsen Book Data)

• Introduction
• Kinematic Geometry of the PnP Robots
• Differential Kinematics of the Robots
• Kinematic Performance Criteria
• Elastostatics and Stiffness
• Rigid Multibody Dynamics
• Robot Elastodynamics
• Design optimization of parallel PnP Robots.

This book discusses the parametric modeling, performance evaluation, design optimization and comparative study of the high-speed, parallel pick-and-place robots. It collects the modeling methodology, evaluation criteria and design guidelines for parallel PnP robots to provide a systematic analysis method for robotic developers. Furthermore, it gathers the research results previously scattered in many prestigious international journals and conference proceedings and methodically edits them and presents them in a unified form. The book is of interest to researchers, R&D engineers and graduate students in industrial parallel robotics who wish to learn the core principles, methods, algorithms, and applications.
(source: Nielsen Book Data)

• Ability Study of Proximity Measure for Big Data Mining Context on Clustering.- Not Yet Decided.- An Approach to Implement MapReduce and aggregation pipeline utilizing NoSql Technologies.- Energy Hole Alleviation in Wireless sensor.
• (source: Nielsen Book Data)

This book offers a collection of high-quality peer-reviewed research papers presented at the Second International Conference on Communication and Computational Technologies (ICCCT 2019), held at Rajasthan Institute of Engineering and Technology, Jaipur, Rajasthan, India, on 30-31 August 2019. In contributions prepared by researchers from academia and industry alike, the book discusses a wide variety of industrial, engineering and scientific applications of emerging techniques.
(source: Nielsen Book Data)

• Development of an Industry 4.0 demonstrator using Sequence Planner and ROS2.- ROS2 for ROS1 users.- Multi-Robot SLAM framework for ROS with Efficient Information Sharing.- Agile experimentation of robot swarms in large scale.- Lessons learned building a self-driving car on top of ROS.- Landing a UAV on a moving platform using a front facing camera.- Integrating the Functional Mock-up Interface with ROS and Gazebo.- An ARVA sensor simulator.- ROS Implementation for Untethered Microrobot Manipulation.- ClegS: A package to develop C-legged robots.- Video frames selection method for 3D Reconstruction depending on ROS-based monocular SLAM.- ROS Rescue: Fault Tolerance System for ROS.
• (source: Nielsen Book Data)

This book is the fifth volume in the successful book series Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage on the Robot Operating System (ROS), which is currently considered to be the primary development framework for robotics applications, and the latest trends and contributing systems. The content is divided into six parts. Pat I presents for the first time the emerging ROS 2.0 framework, while Part II focuses on multi-robot systems, namely on SLAM and Swarm coordination. Part III provides two chapters on autonomous systems, namely self-driving cars and unmanned aerial systems. In turn, Part IV addresses the contributions of simulation frameworks for ROS. In Part V, two chapters explore robotic manipulators and legged robots. Finally, Part VI presents emerging topics in monocular SLAM and a chapter on fault tolerance systems for ROS. Given its scope, the book will offer a valuable companion for ROS users and developers, helping them deepen their knowledge of ROS capabilities and features. .
(source: Nielsen Book Data)

• Intro
• Preface
• Introduction
• Contents
• 1 Safety the Basis for Future Mobility
• 1.1 Safety is Much More Than Safety Engineering
• 1.2 Safety as a Social Right
• 1.3 Legal Basis for "Auto-Mobility"
• 1.3.1 The German Road Traffic Act (StVG)
• 1.3.2 Origin of the German Road Traffic Act
• 1.3.3 Adaptation of the Traffic Right for the Globalisation Trend
• 1.3.4 Activities in Germany for Future Mobility Solutions
• 1.3.5 Genevan and Viennese Convention
• 1.4 European Union (EU) and Legislation
• 1.4.1 EU Directives Related to Road Traffic
• 1.4.2 European Vehicle Class

• 1.4.3 EU Directive for Future Mobility
• 1.5 Licensing Act
• 1.6 US Road Traffic Regulations
• 1.7 UNECE
• 1.8 Legal View on Future Mobility
• 1.8.1 New Licensing Approach for Automated Vehicles
• 1.8.2 ITS Law
• 1.8.3 ETSI-European Telecommunications Standards Institute
• 1.8.4 OECD, Organisation for Economic Co-operation and Development
• 1.8.5 ETSC European Transport Safety Council
• 1.8.6 IT Security Act
• 1.9 Product Liability
• 1.10 Common Law and Civil Law
• 1.11 Legal Regulation in China
• 1.12 Insurance
• 2 Risk Management
• 2.1 Risk Management Cycle

• 2.1.1 Development of the Context
• 2.1.2 Identification of Risk
• 2.1.3 Assessment of the Risk in the Target Context
• 2.1.4 Development of a Strategy and Treatment of Potential Risk
• 2.1.5 Development and Defining of Activities and Their Objectives
• 2.1.6 Development of an Implementation and Realisation Strategy
• 2.1.7 Review and Evaluation of the Plan
• 2.2 Technical Risk
• 2.3 Risk in Road Traffic
• 2.3.1 Causes of Technical Risk
• 2.3.2 Control of Technical Risk
• 2.4 Standardisation of Safety for Road Vehicles
• 2.4.1 Risk and Integrity Definition from the IEC 61508

• 2.4.2 Risk According to ISO 26262
• 2.5 Critical Infrastructure
• 2.5.1 Organisation for Critical Infrastructure
• 2.5.2 Cloud Computing
• 2.5.3 Transport and Traffic and Critical Infrastructure
• 3 Automation in Mobility
• 3.1 Human Driving: a Closed-Loop Control System
• 3.2 Human Driving
• 3.2.1 Driver's HMI
• 3.2.2 Driver in the Loop
• 3.2.3 Human Driving Journey
• 3.3 Human Control Mechanism
• 3.4 Human Behaviour and Context
• 3.4.1 Observer of the Observer
• 3.4.2 Industry Automation
• 3.4.3 Multiple Contexts for Different Layers of Abstraction

• 3.5 Communication and Interfaces
• 3.6 Human Driver as a Control System
• 3.6.1 Human Communication
• 3.6.2 Human Perception
• 3.6.3 Technical Control System Comparison
• 3.6.4 Control System Architecture in Comparison with the Human Control System
• 3.7 Cyclic Data Processing Versus Distributed Event-Driven Data Processing
• 3.7.1 Distributed Control
• 3.7.2 Configuration of Human Control System
• 3.8 Cybernetics and Control
• 3.8.1 Digital Twin
• 3.8.2 Cybernetics About Behaviour and Control
• 3.8.3 Cybernetics and Sufficiently Perceiving the Environment

The book provides background information about technical solutions, processes and methodology to develop future automated mobility solutions. Beginning from the legal requirements as the minimum tolerable risk level of the society, the book provides state-of-the-art risk-management methodologies. The system engineering approach based on todays engineering best practices enhanced by principles derived from cybernetics. The approach derived from the typical behaviour of a human driver in public road traffic to a cybernetical based system engineering approach. Beyond the system engineering approach, a common behaviour model for the operational domain will show aspects how to extend the system engineering model with principles of cybernetics. The role and the human factors of road traffic participants and drivers of motor vehicles are identified and several viewpoints for different observers show how such mixed traffic scenarios could be assessed and optimised. The influence of the changing mobility demands of the society and the resulting changes to the origination of producer, owner, driver and supplier show aspects for future liability and risk share option for new supply chains. Examples from various industries provide some well-proven engineering principles how to adapt those for the future mobility for the benefit of the users. The aim of the book is to raise awareness that the safety provided by a product, a means of transport or a system up to an entire traffic system depends on the capabilities of the various actors. In addition to the driver and passengers, there are also other road users, maintenance personnel and service providers, who must have certain abilities to act safely in traffic. These are also the capabilities of the organisation, not only the organisation that develops or brings the product to market, but also the organisation that is responsible for the operation and the whole lifecycle of the products. The book is for people who want to get invo lved in the mobility of the future. People, that have ideas to become a player who want to help shape the future mobility of society and who want to bring responsible solutions for users into the market.

• Chapter 1. An Integrated Design of Fuzzy C-Means and NCA based Multi-Properties Features Reduction for Brain Tumor Recognition.-
• Chapter 2. Hybrid Image Processing based Examination of 2D Brain MRI Slices to Detect Brain Tumour/Stroke Section - A Study.-
• Chapter 3. Edge Enhancing Coherence Diffusion Filter for Level Set Segmentation and Asymmetry Analysis using Curvelets in Breast Thermograms.-
• Chapter 4. Lung Cancer Diagnosis Based on Image Fusion and prediction using CT and PET image.-
• Chapter 5. Segmentation and Validation of Infrared Breast Images using Weighted Level Set and Phase Congruency Edge Map Framework.-
• Chapter 6. Analysis of Material Profile for Polymer Based Mechanical Microgripper for Thin Plate Holding.-
• Chapter 7. Design and Testing of Elbow Actuated Wearable Robotic Arm for Muscular Disorders.-
• Chapter 8. A Comprehensive Study of Image Fusion Techniques and Their Applications.-
• Chapter 9. Multilevel Mammogram Image Analysis for Identifying Outliers, Misclassification using Machine Learning.-
• Chapter 10. A Review on Automatic Detection of Retinal Lesions in Fundus Images for Diabetic Retinopathy.-
• Chapter 11. Medical Image Watermarking: A Review on Wavelet Based Methods.-
• Chapter 12. EEG Signal Extraction Analysis Techniques.-
• Chapter 13. Classification of sEMG Signal based Arm Action using Convolutional Neural Network.-
• Chapter 14. An Automated Approach for the Identification of TB Images Enhanced by Non-uniform Illumination Correction.
• (source: Nielsen Book Data)

This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.
(source: Nielsen Book Data)

• Introduction.- A Brief Review of Socio-Inspired Metaheuristics.- Multi Cohort Intelligence Algorithm.- Optimization of Electric Discharge Machining (EDM).- Optimization of Abrasive Water Jet Machining (AWJM).- Optimization of Micro Milling Process.- Optimization of Micro Drilling Process.- Optimization of Cutting Forces in Micro Drilling of CFRP Composites for Aerospace Applications.- Optimization of Micro Turning Process.- Optimization of Machining Process Parameters of Titanium Alloy Under (MQL) Environment.
• (source: Nielsen Book Data)

This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods. .
(source: Nielsen Book Data)

• Intro
• Preface
• Invited Speakers
• DSS, Classifications, Trends and Enabling Modern Information and Communication Technologies
• Distorted Statistics based on Choquet Calculus
• Overview of QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
• Nature-Inspired Optimization of Type-2 Fuzzy Logic Controllers
• Seeing Is Believing
• Contents
• Short CVs of Guest Editors
• I Soft Computing and Conventional Techniques in Power Engineering Methods and Applications in Electrical Engineering I

• Daily Load Curve Forecasting. Comparative Analysis: Conventional vs. Unconventional Methods
• Abstract
• 1 Introduction
• 2 Developed Algorithms
• 2.1 Artificial Neural Networks (ANN)
• 2.2 Multiple Linear Regression (MLR)
• 2.3 Curve Fitting (CF)
• 2.4 Decision Trees (DT)
• 3 Numerical Results. Discussions
• 3.1 Forecast Performed for 2017 Year
• 3.2 Forecast Performed for 2018 Year
• 3.3 Comparative Analysis for Several Forecasts
• 4 Conclusion
• Acknowledgment
• References
• LoRaWAN Based Airport Runway Lights Monitoring System
• Abstract
• 1 Introduction
• 2 LoRa Technology

• 3 Case Study and Results
• 4 Conclusions
• Acknowledgment
• References
• The Enhanced Crow Search Algorithm for Fuel-Cost Function Parameters Assessment of the Cogeneration Units from Thermal Power Plants
• Abstract
• 1 Introduction
• 2 Estimating the Fuel-Cost Function Parameters
• 3 The Crow Search Algorithm (CS)
• 4 The Enhanced CS Algorithm (ECS)
• 5 Case Studies and Results
• 6 Conclusions
• References
• Modelling a Photovoltaic Power Station
• Abstract
• 1 Introduction
• 2 Simulation of the Photovoltaic Panels Using MatLAB Software

• 2.1 Simulink: Modelling of a PV Solar Cell
• 2.2 The Intensity of the Induced Electrical Current by the Sun
• 2.3 Dependence on the Temperature
• 3 The Simulation of a PV Power Station
• 4 Conclusions
• References
• Identification of Fault Locations on Tree Structure Systems
• 1 Introduction
• 2 Description of the Troubleshooting System Currently Used by the Power Suppliers
• 3 Mathematical Background of the Proposed Method
• 4 Fault Probability Data Bank
• 5 SGFI Algorithm
• 5.1 Localization of the Fault Zone (Procedure 1)

• 5.2 Fault Position Localization in the Fault Zone (Procedure 2)
• 5.3 Result of the Proposed Algorithm
• 5.4 Simulation Analysis of the SGFI Algorithm
• 6 Conclusions
• References
• Probability Distribution Functions for Short-Term Wind Power Forecasting
• 1 Introduction
• 2 Wind Probability Distribution Functions
• 2.1 Weibull Distribution
• 2.2 Lindley Distribution for Random Variables
• 2.3 Joint Probability Distribution Function for Wind Forecasting
• 3 Short-Term Wind Power Forecasting Using SVR and ANN
• 3.1 Wind Power Forecasting Using Support Vector Regressors
• 3.2 Wind Power Forecasting Using ANN.

This book presents the proceedings of the 8th International Workshop on Soft Computing Applications, SOFA 2018, held on 13-15 September 2018 in Arad, Romania. The workshop was organized by Aurel Vlaicu University of Arad, in conjunction with the Institute of Computer Science, Iasi Branch of the Romanian Academy, IEEE Romanian Section, Romanian Society of Control Engineering and Technical Informatics - Arad Section, General Association of Engineers in Romania - Arad Section and BTM Resources Arad. The papers included in these proceedings, published post-conference, cover the research including Knowledge-Based Technologies for Web Applications, Cloud Computing, Security Algorithms and Computer Networks, Business Process Management, Computational Intelligence in Education and Modelling and Applications in Textiles and many other areas related to the Soft Computing. The book is directed to professors, researchers, and graduate students in area of soft computing techniques and applications.
(source: Nielsen Book Data)

• Table of Contents Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let Your Data Do the Talking: Story, Questions, and Answers Detecting Customer Emotions to Make Predictions Analyzing Fake News with Transformers Appendix: Answers to the Questions.
• (source: Nielsen Book Data)

Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models Key Features Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine Learn training tips and alternative language understanding methods to illustrate important key concepts Book DescriptionThe transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learn Use the latest pretrained transformer models Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models Create language understanding Python programs using concepts that outperform classical deep learning models Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more Measure the productivity of key transformers to define their scope, potential, and limits in production Who this book is forSince the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.
(source: Nielsen Book Data)

• CHAPTER 1 Introduction
• CHAPTER 2 Accelerated Algorithms for Unconstrained Convex Optimization
• 1. Preliminaries
• 2. Accelerated Gradient Method for smooth optimization
• 3. Extension to the Composite Optimization
• 3.1. Nesterov's First Scheme
• 3.2. Nesterov's Second Scheme
• 3.2.1. A Primal Dual Perspective
• 3.3. Nesterov's Third Scheme
• 4. Inexact Proximal and Gradient Computing
• 4.1. Inexact Accelerated Gradient Descent
• 4.2. Inexact Accelerated Proximal Point Method
• 5. Restart
• 6. Smoothing for Nonsmooth Optimization
• 7. Higher Order Accelerated Method
• 8. Explanation: An Variational Perspective
• 8.1. Discretization
• CHAPTER 3 Accelerated Algorithms for Constrained Convex Optimization
• 1. Preliminaries
• 1.1. Case Study: Linear Equality Constraint
• 2. Accelerated Penalty Method
• 2.1. Non-strongly Convex Objectives
• 2.2. Strong Convex Objectives
• 3. Accelerated Lagrange Multiplier Method
• 3.1. Recovering the Primal Solution
• 3.2. Accelerated Augmented Lagrange Multiplier Method
• 4. Accelerated Alternating Direction Method of Multipliers
• 4.1. Non-strongly Convex and Non-smooth
• 4.2. Strongly Convex and Non-smooth
• 4.3. Non-strongly Convex and Smooth
• 4.4. Strongly Convex and Smooth
• 4.5. Non-ergodic Convergence Rate
• 4.5.1. Original ADMM
• 4.5.2. ADMM with Extrapolation and Increasing Penalty Parameter
• 5. Accelerated Primal Dual Method
• 5.1. Case 1
• 5.2. Case 2
• 5.3. Case 3
• 5.4. Case 4
• CHAPTER 4 Accelerated Algorithms for Nonconvex Optimization
• 1. Proximal Gradient with Momentum
• 1.1. Basic Assumptions
• 1.2. Convergence Theorem
• 1.3. Another Method: Monotone APG
• 2. AGD Achieves the Critical Points Quickly
• 2.1. AGD as a Convexity Monitor
• 2.2. Negative Curvature
• 2.3. Accelerating Nonconvex Optimization
• 3. AGD Escapes the Saddle Points Quickly
• 3.1. Almost Convex
• 3.2. Negative Curvature Descent
• 3.3. AGD for Non-Convex Problem
• 3.3.1. Locally Almost Convex! Globally Almost Convex
• 3.3.2. Outer Iterations
• 3.3.3. Inner Iterations
• CHAPTER 5 Accelerated Stochastic Algorithms
• 1. The Individual Convexity Case
• 1.1. Accelerated Stochastic Coordinate Descent
• 1.2. Background for Variance Reduction Methods
• 1.3. Accelerated Stochastic Variance Reduction Method
• 1.4. Black-Box Acceleration
• 2. The Individual Non-convexity Case
• 2.1. Individual Non-convex but Integrally Convex
• 3. The Non-Convexity Case
• 3.1. SPIDER
• 3.2. Momentum Acceleration
• 4. Constrained Problem
• 5. Infinity Case
• CHAPTER 6 Paralleling Algorithms
• 1. Accelerated Asynchronous Algorithms
• 1.1. Asynchronous Accelerated Gradient Descent
• 1.2. Asynchronous Accelerated Stochastic Coordinate Descent
• 2. Accelerated Distributed Algorithms
• 2.1. Centralized Topology
• 2.1.1. Large Mini-batch Algorithms
• 2.1.2. Dual Communication-Efficient Methods
• 2.2. Decentralized Topology
• CHAPTER 7 Conclusions
• APPENDIX Mathematical Preliminaries.
• (source: Nielsen Book Data)

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
(source: Nielsen Book Data)

• Part I: Basics and Theory
• Photometry
• Geometry
• Sensors
• Light Sources
• Part II: Algorithms Photometric stereo
• Structured light
• Other shape reconstruction techniques
• Photometric estimation Part III: Applications
• Visualization, Augmented Reality, Virtual Reality, and Mixed Reality Systems
• Biomedical Applications
• E-Heritage
• Robot Vision, Autonomous Vehicles, and Humans.
• (source: Nielsen Book Data)

This book describes active illumination techniques in computer vision. We can classify computer vision techniques into two classes: passive and active techniques. Passive techniques observe the scene statically and analyse it as is. Active techniques give the scene some actions and try to facilitate the analysis. In particular, active illumination techniques project specific light, for which the characteristics are known beforehand, to a target scene to enable stable and accurate analysis of the scene. Traditional passive techniques have a fundamental limitation. The external world surrounding us is three-dimensional; the image projected on a retina or an imaging device is two-dimensional. That is, reduction of one dimension has occurred. Active illumination techniques compensate for the dimensional reduction by actively controlling the illumination. The demand for reliable vision sensors is rapidly increasing in many application areas, such as robotics and medical image analysis. This book explains this new endeavour to explore the augmentation of reduced dimensions in computer vision. This book consists of three parts: basic concepts, techniques, and applications. The first part explains the basic concepts for understanding active illumination techniques. In particular, the basic concepts of optics are explained so that researchers and engineers outside the field can understand the later chapters. The second part explains currently available active illumination techniques, covering many techniques developed by the authors. The final part shows how such active illumination techniques can be applied to various domains, describing the issue to be overcome by active illumination techniques and the advantages of using these techniques. This book is primarily aimed at 4th year undergraduate and 1st year graduate students, and will also help engineers from fields beyond computer vision to use active illumination techniques. Additionally, the book is suitable as course material for technical seminars.
(source: Nielsen Book Data)

• Designing and Developing Adaptive Instructional Systems.- Sensor-based Adaptive Instructional Systems in Live Simulation Training.- An Ambient & Pervasive Personalized Learning Ecosystem: "Smart Learning" in the Age of the Internet of Things.- Bridging Conceptual Models and Architectural Interchange for Adaptive Instructional Systems.- Dewey's Ethics of Moral Principles and Deliberation: Extending IEEE's Ethics Initiative for Adaptive Instructional Systems.- Realistic and Relevant Role-Players for Experiential Learning.- Learning Traces, Measurement and Assessment Templates for AIS Interoperability.- Supporting different Roles and Responsibilities in Developing and Using Context-based Adaptive Personalized Collaboration Environments Compliant to the Law.- Experiential Instruction of Metacognitive Strategies.- Falling Forward: Lessons Learned from Real-Life Implementation of Adaptive Learning Solutions.- Usability Dimensions of Simulated Trainers for Buried Explosives.- Toward Zero Authoring: Considering How to Maximize Courseware Quality and Affordability Simultaneously.- Agent-Based Methods in Support of Adaptive Instructional Decisions.- Representing Functional Relationships of Adaptive Instructional Systems in a Conceptual Model.- Knowledge-to-Information Translation Training (KITT): An Adaptive Approach to Explainable Artificial Intelligence.- User Rights and Adaptive A/IS - From Passive Interaction to Real Empowerment.- Supporting Metacognitive Learning Strategies through an Adaptive Application.- Towards Iteration by Design: An Interaction Design Concept for Safety Critical Systems.- Learner Modelling and Methods of Adaptation.- Bayesian Student Modelling in the AC&NL Tutor.- Nature at Your Service - Nature Inspired Representations Combined with Eye-gaze Features to Infer User Attention and Provide Contextualized Support.- Adapting Instruction by Measuring Engagement with Machine Learning in Virtual Reality Training.- Realizing the Promise of AI-Powered, Adaptive, Automated, Instant Feedback on Writing for Students in Grade 3-8 with an IEP.- Declarative Knowledge Extraction in the AC&NL Tutor.- On the Importance of Adaptive Operator Training in Human-Swarm Interaction.- The Mental Machine: Classifying Mental Workload State from Unobtrusive Heart Rate- measures Using Machine Learning.- Production Implementation of Recurrent Neural Networks in Adaptive Instructional Systems.- Pilot State Monitoring for Cursus Recommendation.- Experimental Evaluation of Heart-based Workload Measures as Related to Their Suitability for Real-time Applications.- EEG Covariance-based Estimation of Cooperative States in Teammates.- Adapting the Zone of Proximal Development to the Wicked Environments of Professional Practice.- An Adaptive Instructional System for the Retention of Complex Skills.- Learner Modeling in the Context of Caring Assessments.- Evaluating the Effectiveness of Adaptive Instructional Systems.- The Evolving Assessment Landscape and Adaptive Instructional Systems: Moving Beyond Good Intentions.- Contextual Barriers to Validity in Adaptive Instruction and Assessment.- Does Time Matter in Learning? A Computer Simulation of Carroll's Model of Learning.- Competency Development through Experiential Training: Mapping Scenarios with Assessments.- Does Gamification Work? Analyzing Effects of Game Features on Learning in an Adaptive Scenario-Based Trainer.- From "Knowing What" to "Knowing When": Exploring a Concept of Situation Awareness Synchrony for Evaluating SA Dynamics in Teams.- Exploring Video Engagement in an Intelligent Tutoring System.- Using a Non-Player Character to Improve Training Outcomes for Submarine Electronic Warfare Operators.- The Impact of Adaptive Activities in Acrobatiq Courseware: Investigating the Efficacy of Formative Adaptive Activities on Learning Estimates and Summative Assessment Scores.- A Mastery Approach to Flashcard-based Adaptive Training. .
• (source: Nielsen Book Data)

This volume constitutes the refereed proceedings of the Second International Conference on Adaptive Instructional Systems, AIS 2020, which was due to be held in July 2020 as part of HCI International 2020 in Copenhagen, Denmark. The conference was held virtually due to the COVID-19 pandemic. A total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings from a total of 6326 submissions. The 41 papers presented in this volume were organized in topical sections as follows: designing and developing adaptive instructional systems; learner modelling and methods of adaptation; evaluating the effectiveness of adaptive instructional systems. Chapter "Exploring Video Engagement in an Intelligent Tutoring System" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(source: Nielsen Book Data)

• 1st Workshop on Intelligent Data - From Data to Knowledge (DOING 2020).- 2nd Workshop on Modern Approaches in Data Engineering and Information System Design (MADEISD 2020).- 1st Workshop on Scientic Knowledge Graphs (SKG 2020).- 2nd Workshop of BI & Big Data Applications (BBIGAP 2020).- International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2020).- The 1st International Workshop on Assessing Impact and Merit in Science (AIMinScience 2020).- Doctoral Consortium.
• (source: Nielsen Book Data)

This book constitutes thoroughly reviewed and selected papers presented at Workshops and Doctoral Consortium of the 24th East-European Conference on Advances in Databases and Information Systems, ADBIS 2020, the 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, and the 16th Workshop on Business Intelligence and Big Data, EDA 2020, held in August 2020. Due to the COVID-19 the joint conference and satellite events were held online. The 26 full papers and 5 short papers were carefully reviewed and selected from 56 submissions. This volume presents the papers that have been accepted for the following satellite events: Workshop on Intelligent Data - From Data to Knowledge, DOING 2020; Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2020; Workshop on Scientic Knowledge Graphs, SKG 2020; Workshop of BI & Big Data Applications, BBIGAP 2020; International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2020; International Workshop on Assessing Impact and Merit in Science, AIMinScience 2020; Doctoral Consortium.
(source: Nielsen Book Data)

• Exploring Artificial Jabbering For Automatic Text Comprehension Question Generation.- Digital Value-Adding Chains in Vocational Education: Automatic Keyword Extraction from Learning Videos to Provide Learning Resource Recommendations.- Human-centered design of a dashboard on students' revisions during writing.- An operational Framework for Evaluating the Performance of Learning Record Stores.- Does an e-mail reminder intervention with learning analytics reduce procrastination in a blended university course.- Designing an Online Self-Assessment for Informed Study Decisions: The User Perspective.- What Teachers Need for Orchestrating Robotic Classrooms.- Assessing Teacher's Discourse Effect on Students' Learning: A Keyword Centrality Approach.- For learners, with learners: Identifying indicators for an academic advising dashboard for students.- Living with learning difficulties: Two case studies exploring the relationship between emotion and performance in students with learning difficulties.- Learnersourcing Quality Assessment of Explanations for Peer Instruction.- Using Diffusion Network Analytics to Examine and Support Knowledge Construction in CSCL Settings.- Supporting Second Language Learners' Development of Affective Self-Regulated Learning Skills through the Use and Design of Mobile Technology.- We Know What You Did Last Semester: Learners' Perspectives on Screen Recordings as a Long-Term Data Source for Learning Analytics.- Teaching Simulation Literacy with Evacuations: Concept, Technology, and Material for a Novel Approach.- Design of conversational agents for CSCL: comparing two types of agent intervention strategies in a university classroom.- Exploring Human-AI Control over Dynamic Transitions between Individual and Collaborative Learning.- Exploring Student-Controlled Social Comparison.- New Measures for Offline Evaluation of Learning Path Recommenders.- Assessing the Impact of the combination of Self-directed learning, Immediate feedback and Visualizations on Student Engagement in Online Learning.- CGVis: A Visualization-Based Learning Platform for Computational Geometry Algorithms.- How to Design Effective Learning Analytics Indicators? A Human-Centered Design Approach.- Emergency Remote Teaching: Capturing Teacher Experiences in Spain with SELFIE.- Utilising Learnersourcing to Inform Design Loop Adaptivity.- Fooling it - Student Attacks on Automatic Short Answer Grading.- Beyond indicators: A scoping review of the academic literature related to SDG4 and educational technology.- Pedagogical underpinnings of open science, citizen science and open innovation activities: a state-of-the-art analysis.- Knowledge-Driven Wikipedia Article Recommendation for Electronic Textbooks.- InfoBiTS: A mobile application to foster digital competencies of senior citizens.- Student Awareness and Privacy Perception of Learning Analytics in Higher Education.- User Assistance for Serious Games Using Hidden Markov Model.- Guiding Socio-technical Reflection of Ethical Principles in TEL Software Development: The SREP Framework.- Git4School: a Dashboard for Supporting Teacher Interventions in Software Engineering Courses.- Exploring the design and impact of online exercises for teacher training about dynamic models in mathematics.- Interactive Concept Cartoons: Exploring an Instrument for Developing Scientific Literacy.- Quality Evaluation of Open Educational Resources.- Designing Digital Activities to Screen Locomotor Skills in Developing Children.- Towards Adaptive Social Comparison for Education.- Simulation Based Assessment of Epistemological Beliefs about Science.- An Approach to Support Interactive Activities in Live Stream Lectures.- Educational Escape Games for Mixed Reality.- Measuring Learning Progress for serving Immediate Feedback needs: Learning Process Quantification Framework (LPQF).- Data-driven Game Design: The Case of Difficulty in Educational Games.- Extracting Topics from Open Educational Resources.- Supporting Gamification with an Interactive Gamification Analytics Tool (IGAT).- OpenLAIR an Open Learning Analytics Indicator Repository Dashboard.- CasualLearn: A smart application to learn History of Art.- Applying Instructional Design Principles on Augmented Reality Cards for Computer Science Education.- Extending Patient Education with CLAIRE: An Interactive Virtual Reality and Voice User Interface Application.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, held in Heidelberg, Germany, in September 2020. The 24 research papers and 20 demo and 5 poster papers presented in this volume were carefully reviewed and selected from 91 submissions. The European Conference on Technology-Enhance Learning, which celebrates its 15th anniversary this year, is committed to address global challenges and quality education. The papers deal with the Sustainable Development Goals, particularly SDG 4 and SDG 10, to help to reduce the existing gaps and inequalities between countries and regions from around the world in terms of inclusiveness, equity, access, and quality of education. The chapters: "Designing an Online Self-Assessment for Informed Study Decisions: The User Perspective"; "Living with Learning Difficulties: Two Case Studies Exploring the Relationship Between Emotion and Performance in Students With Learning Difficulties"; "Applying Instructional Design Principles on Augmented Reality Cards for Computer Science Education"; and "Teaching Simulation Literacy With Evacuations - Concept, Technology, and Material for a Novel Approach" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. Due to the Corona pandemic EC-TEL 2020 was held as an virtual event.
(source: Nielsen Book Data)

• Robust Functional Regression for Outlier Detection
• Transform Learning Based Function Approximation for Regression and Forecasting
• Proactive Fiber Break Detection based on Quaternion Time Series and Automatic Variable Selection from Relational Data
• A fully automated periodicity detection in time series
• Conditional Forecasting of Water Level Time Series with RNNs
• Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories
• Localized Random Shapelets
• Feature-Based Gait Pattern Classification for a Robotic Walking Frame
• How to detect novelty in textual data streams? A comparative study of existing methods
• Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model
• Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets
• Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems
• Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets using Deep Learning
• Learning Stochastic Dynamical Systems via Bridge Sampling
• Quantifying Quality of Actions Using Wearable Sensor
• An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis.

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.

• Part I. Artificial Intelligence and Machine Learning
• Part II. Artificial Intelligence User Research
• Part III. Artificial Intelligence Applications
• Part IV. Applications in Software and Systems Engineering
• Part V. Applications in Supply Chain Management and Business
• Part VI. Cognitive Computing and Internet of Things
• Part VII. Human Factors in Energy: Oil, Gas, Nuclear and Electric Power Industries.

This book addresses emerging issues concerning the integration of artificial intelligence systems in our daily lives. It focuses on the cognitive, visual, social and analytical aspects of computing and intelligent technologies, and highlights ways to improve the acceptance, effectiveness, and efficiency of said technologies. Topics such as responsibility, integration and training are discussed throughout. The book also reports on the latest advances in systems engineering, with a focus on societal challenges and next-generation systems and applications for meeting them. Based on the AHFE 2020 Virtual Conference on Software and Systems Engineering, and the AHFE 2020 Virtual Conference on Artificial Intelligence and Social Computing, held on July 16-20, 2020, it provides readers with extensive information on current research and future challenges in these fields, together with practical insights into the development of innovative services for various purposes.
(source: Nielsen Book Data)

This book discusses the latest advances in the development of artificial intelligence systems and their applications in various fields, from medicine and technology to education. It comprises papers presented at the Third International Conference of Artificial Intelligence, Medical Engineering, Education (AIMEE2019), held at the Mechanical Engineering Institute of the Russian Academy of Sciences, Moscow, Russia, on 1-3 October 2019. Covering topics such as mathematics and biomathematics; medical approaches; and technological and educational approaches, it is intended for the growing number of specialists and students in this field, as well as other readers interested in discovering where artificial intelligence systems can be applied in the future.

• Natural Language Processing.- Image Processing and Pattern Recognition.- Intelligent Applications and Robotics.
• (source: Nielsen Book Data)

The two-volume set LNAI 12468 and 12469 constitutes the proceedings of the 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, held in Mexico City, Mexico, in October 2020. The total of 77 papers presented in these two volumes was carefully reviewed and selected from 186 submissions. The contributions are organized in topical as follows: Part I: machine and deep learning, evolutionary and metaheuristic algorithms, and soft computing. Part II: natural language processing, image processing and pattern recognition, and intelligent applications and robotics.
(source: Nielsen Book Data)

• Extraction of Tumor Chunk Using Image Segmentation - Thresholding and HSV Color Space.- Predictive & Interactive IOT Diagnosis System with AI and ML Tools: Review.- Multi Agent Data Mining using Asymmetric AES Algorithm in Cloud Computing.- A Novel Architecture for a Two-Pass Opinion Mining Classifier.- QoS based Routing Algorithm for Software Defined Network using Ant Colony optimization.- An Integrated AdaBoost Algorithm for Down Syndrome Disease Recognition.- A Comprehensive Healthcare Model for a Smart Left Ventricle Assisting Device.- Moving Hand Segmentation from H.264 Compressed Sign Language Videos.- CNN-Based Real-time Indian Sign Language Recognition System.- Sarcasm Detection using Multiple Occurrences of Emojis and Hashtagged Words.- Multiclass Multiple Object Tracking.- Dimensionality Reduction in Hyperspectral Images using Auto-encoders.- Improved Registration of Infrared Images using EOH Descriptor.- Fine Grained Analysis of Bridging Social Capital with Triadic Analysis in a Co-Author Network.- Accumulation of Methodologies for Intensifying the Lifetime of the Wireless Sensor Networks.- A Multifactor Biometric Based User Authentication Protocol for IOT Networks.- Leveraging Topic Models with Novel Word Embeddings for Effective Document Clustering.- Survey on Access Control Mechanisms in Cloud Environments.- Sales Analysis and Performance of Super Store using Qlik Geo Analytics.- Assessment of Crop Rotation Using Multi Polarized Sentinel-1 Temporal SAR Data Over Parts of Kumaun Region of Uttarakhand.- Spatiotemporal and Delay Dynamics on a Prey Predator Fishery Model.- An Efficient Intrusion Detection System with Convolutional Neural Network.- Supervised Machine Learning Approach for Identification of Malicious URLs.- A Unified Framework for Stress Forecasting Using Machine Learning Algorithms.- Investigation and Analysis of Crop Maintenance Using IoT & KNN Techniques.- Enhancement of Anisotropic Diffusion Filtered Cardiac MR Images using Contrast based Fuzzy Approach.- Social Media Ecosystem: Review on Social Media Profile's Security and Introduce a New Approach.- An Investigation on Managing Patient Flow at Hospital Emergency Care Unit using Tree-Based Data Mining Techniques.- A Path Based Model for Link Prediction in Location Based Social Networks.- Predicting the Cost of Pre-owned Cars using Linear Regression Mechanisms in Machine Learning.- Bio-Inspired High Utility Item Framework based Particle Swarm Optimization Tree Algorithms for Mining High Utility Itemset.- On Atom-Bond Connectivity Invariant of Graphs.- Theoretical Design and Experimental Study for Urban Data Management using Energy-Saved IoT Big Data.- An Effective Approach for Security Attacks Based on Machine Learning Algorithms.- Class Based Associative Classification using Super Subset to Predict the By-Diseases in Thyroid Disorders.- Recommendation of Diet using Hybrid Collaborative Filtering Learning Methods.- Classification of Natural Images Using Machine Learning Classifiers on Graph Based Approaches.- Convolutional Neural Network Based Diagnosis of Alzheimer's Disease using Time Frequency features.- Humanly Evolving Robotics with Angular Sensor Control (HERASC).- Post-occupancy Evaluation of Building Facilities in a University Community using an Electronic Platform.- Internet of Things: Demystifying Smart Cities & Communities.- Comparative Analysis of Optimizations of Antecedents and Consequents of Fuzzy Inference System Rules Lists using Genetic Algorithm Operations.- Uses and Impact of Social Media on Work Performance of Low Literate People.- A Comparative Analysis on Rumor Microblogs Detection from Social Network Sites.- Smart Sprint Methodology for Financial Management System Development.- A Novel Infrared Image Segmentation Method for Identifying the Faults in the Systems.
• (source: Nielsen Book Data)

This book is a collection of outstanding papers presented at the 1st International Conference on Advances in Computational Intelligence and Informatics (ICACII 2019), organized by the Department of Computer Science & Engineering, Anurag Group of Institutions (AGI), Hyderabad, on 20-21 December 2019. It includes innovative ideas and new research findings in the field of Computational Intelligence and Informatics that will benefit researchers, scientists, technocrats, academics and engineers alike. The areas covered include high-performance systems, data science and analytics, computational intelligence and expert systems, cloud computing, computer networks and emerging technologies.
(source: Nielsen Book Data)

• [I]. Advanced computing: A computer vision based approach for the analysis of acuteness of garbage
• The moderating effect of demographic factors acceptance virtual reality learning in developing countries in the Middle East
• Table tennis forehand and backhand stroke recognition based on neural network
• An effective vision based framework for the identification of tuberculosis in chest X-ray images
• User assisted clustering based key frame extraction
• A threat towards the neonatal mortality
• Digital marketing effectiveness using incrementality
• Explainable artificial intelligence for falls prediction
• Enhanced UML use case meta-model semantics from cognitive and utility perspectives
• The impact of mobile augmented reality design implementation on user engagement
• Intelligent mobile edge computing : a deep learning based approach
• Analysis of clustering algorithms in machine learning for healthcare data
• Securing mobile agents migration using tree parity machine with new tiny encryption algorithm
• An approach to waste segregation and management using convolutional neural networks
• Open source intelligence initiating efficient investigation and reliable web searching
• A neural network based hybrid model for depression detection in Twitter
• Unleashing the VEP tirplet count of virtually created 3D Bangla alphabet to integrate with augmented reality application
• A hybrid machine learning framework for prediction of software effect at the initial phase of software development
• Chronic disease prediction using deep learning
• A deep learning based method to discriminate between photorealistic computer generated images and photographic images
• Load balancing algorithm in cloud computing using mutatino based PSO algorithm
• Statistical model for qualitative grading of milled rice
• Measuring the effectiveness of software code review comments

• Proposed model for feature extraction for vehicle detection
• Analysis of feature selection methods for P2P botnet detection
• ELM-MVD : an extreme learning machine trained model for malware variants detection
• Real-time biometric system for security and surveillance using face recognition
• An effective block-chain based authentication technique for cloud based IoT
• Early detection of autism spectrum disorder in children using supervised machine learning
• Auto segmentation of lung in non-small cell lung cancer using deep convolution neural network
• Multiwavelet based unmanned aerial vehicle thermal image fusion for surveillance and target location
• Investigating movement detection in unedited camera footage
• Time series forecasting using machine learning
• Improving packet queues using selective epidemic routing protocol in opportunistic networks (SERPO)
• Heart disease prediction system using classification algorithms.

• [II]. Data sciences: Graph database and relational database performance comparison on a transportation network
• Optimizing creative allocations in digital marketing
• Big data analytics for customer relationship management : a systematic review and research agenda
• Agricultural field analysis using satellite surface reflectance data and machine learning technique
• Sponsored data connectivity at the network edge
• Dynamic bidding with contextual bid decision trees in digital advertisement
• MOOC performance prediction by deep learning from raw clickstream data
• UDHR : unified decentralized health repository
• Mining massive time series data : with dimensionality reduction techniques
• Comparative analysis of data mining techniques to predict heart disease for diabetic patients.

This book constitutes the post-conference proceedings of the 4th International Conference on Advances in Computing and Data Sciences, ICACDS 2020, held in Valletta, Malta, in April 2020.* The 46 full papers were carefully reviewed and selected from 354 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations. * The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Internet of Things, Industry and Blockchain, and Cryptology
• Digital Forensics and Surveillance, Botnet and Malware, and DDoS and Intrusion Detection/Prevention
• Ambient Cloud and Edge Computing, Wireless and Cellular Communication.

This book presents refereed proceedings of the First International Conference on Advances in Cyber Security, ACeS 2019, held in Penang, Malaysia, in July-August 2019. The 25 full papers and 1 short paper were carefully reviewed and selected from 87 submissions. The papers are organized in topical sections on internet of things, industry and blockchain, and cryptology; digital forensics and surveillance, botnet and malware, and DDoS and intrusion detection/prevention; ambient cloud and edge computing, wireless and cellular communication.

• Keynote Extended Abstracts.- Blockchains and Databases: Opportunities and Challenges for the Permissioned and the Permissionless.- Processing Temporal and Time Series Data: Present State and Future Challenges.- Integrating (Very) Heterogeneous Data Sources: a Structured and an Unstructured Perspective.- Data Access and Database Performance.- Upper Bound on the Size of FP-tree.- An Efficient Index for Reachability Queries in Public Transport Networks.- Context-Free Path Querying by Kronecker Product.- Pattern Sampling in Distributed Databases.-Can We Probabilistically Generate Uniformly Distributed Relation Instances Efficiently?.- Machine Learning.- Towards Proximity Graph Auto-Configuration: an Approach Based on Meta-Learning.- Fake News Detection Based on Subjective Opinions.- Improving on Coalitional Prediction Explanation.- Data Processing.- JSON Functionally.- Semantic Web.- Template-Based Multi-Solution for Data-to-Text Generation (on RDF).- Distributed Tree-Pattern Matching in Big Data Analytics Systems.- Data Analytics.- Iterations and Propensity Score Matching in MonetDB.- The Tell-Tale Cube.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 24th European Conference on Advances in Databases and Information Systems, ADBIS 2020, held in Lyon, France, in August 2020.* The 13 full papers presented were carefully reviewed and selected from 82 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They are organized in the following topical sections: keynote extended abstracts; data access and database performance; machine learning; semantic web; data processing; and data analytics. * The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Symmetric-Key Cryptography 1.- Malicious Activity Detection 1.- Multiparty Computation.- Malicious Activity Detection 2.- Post Quantum Cryptography.- Symmetric-Key Cryptography 2.- Privacy.- Pairing-Based Cryptography.- Machine Learning.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Workshop on Security, IWSEC 2020, held in Fukui, Japan, in September 2020.* The 15 regular papers and 1 short paper presented in this volume were carefully reviewed and selected from 47 submissions. They were organized in topical sections named: symmetric-key cryptography, malicious activity detection, multiparty computation, post quantum cryptography, privacy, pairing-based cryptography, and machine learning. * The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Childrens Age and Gender Recognition from Raw Speech Waveform using DNN
• Inter-Vehicular Communication in Urban Traffic Scenario
• Distributed Estimation of IIR Systems Parameter in Sensor Network using Multihop Diffusion LMS Algorithm
• Implementation of Raspberry Pi for fault detection in optic fibre line
• Distributed Optical Fiber Sensing System Performance Improvement using Signal Processing Techniques.

This book features high-quality research papers presented at the 2nd International Conference on Intelligent Computing and Advances in Communication (ICAC 2019), held at Siksha 'O Anusandhan Deemed to be University, Bhubaneswar, Odisha, India, in November 2019. Covering a wide variety of topics, including management of clean and smart energy systems and environmental challenges, it is a valuable resource for researchers and practicing engineers working in various fields of renewable energy generation, and clean and smart energy management.