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• 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)

• 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)

• 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)

• Part I. Intelligence and decision making system
• A Context-Based Video Compression: A Quantum-Inspired Vector Quantization Approach
• An Enhanced Database Recovery Model Based on Game Theory for Mobile Applications
• Location Estimation of RF Emitting Source Using Supervised Machine Learning Technique
• An Effective Offloading Model Based on Genetic Markov Process for Cloud Mobile Applications
• Toward an Efficient CRWSN Node Based on Stochastic Threshold Spectrum Sensing
• Video Captioning Using Attention Based Visual Fusion with Bi-temporal Context and Bi-modal Semantic Feature Learning
• Matchmoving Previsualization Based on Artificial Marker Detection
• Research Method of Blind Path Recognition Based on DCGAN
• The Impact of the Behavioral Factors on Investment Decision-Making: A Systemic Review on Financial Institutions
• Part II. Deep learning technology and applications (4 papers)
• Part III. Document and sentiment analysis (8 papers)
• Part IV. Blockchain and Cyber Physical System (6 papers)
• Part V Health Informatics and AI Against COVID-19 (7 papers)
• Part VI. Big Data Analytics and Service Quality (4 papers)
• Part VII. Data Mining, Decision Making, and Intelligent Systems (7 papers)
• Part VIII. Power and control systems (10 papers)
• Part IX. Business Intelligence (6 papers)
• Part X. Social media and Digital transformation (5 papers)
• Part XI. Robotic, Control Design and Smart Systems (12 papers).

This book presents the proceedings of the 6th International Conference on Advanced Intelligent Systems and Informatics 2020 (AISI2020), which took place in Cairo, Egypt, from October 19 to 21, 2020. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: Intelligent Systems, Deep Learning Technology, Document and Sentiment Analysis, Blockchain and Cyber Physical System, Health Informatics and AI against COVID-19, Data Mining, Power and Control Systems, Business Intelligence, Social Media and Digital Transformation, Robotic, Control Design, and Smart Systems.
(source: Nielsen Book Data)

• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator.- Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition.- Face Recognition Benchmark with ID Photos.- Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition.- Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion.- A Demand-based Allocation Mechanism for Virtual Machine.- A Joint Hierarchy Model for Action Recognition Using Kinect.- QoS-Based Medical Program Evolution.- An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry.- The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography.- Combining CNN and MRF for Road Detection.- The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis.- A Diffeomorphic Demons Approach to Statistical Shape Modeling.
• (source: Nielsen Book Data)

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.
(source: Nielsen Book Data)

• Invited Paper.- Technical Session: Workflows.- Technical Session: Applications in Health.- Technical Session: New paradigms.- Technical Session: Explanations.- Technical Session: Deep learning.- Technical Session: Applications.- Technical Session: Retrieval and Adaptation.- Special Track Challenges & Promises.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 28th International Conference on Case-Based Reasoning Research and Development, ICCBR 2020, held in Salamanca, Spain*, in June 2020. The 20 full papers and 2 short papers presented in this book were carefully reviewed and selected from 64 submissions. The theme of ICCBR 2020, "CBR Across Bridges" was highlighted by several activities. These papers, which are included in the proceedings, address many themes related to the theory and application of case-based reasoning and its future direction. *The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Automated Reasoning and Data Mining
• Axiomatization of Classes of Domain Cases Based on FCA
• 1 Introduction
• 2 Preliminaries
• 2.1 Domain Cases
• 2.2 Formal Contexts of Axiomatizable Classes
• 2.3 Relatively Axiomatizable Classes
• 3 Axiomatization of Classes of Domain Cases
• 3.1 Sentences Permissible for Models
• 3.2 Theories of Classes of Models Having Different Signatures
• 3.3 Axiomatizable Classes and Decidability of Theories
• 4 Conclusion
• References

• The Combined Method of Automated Knowledge Acquisition from Various Sources: The Features of Development and Experimental Research of the Temporal Version
• 1 Introduction
• 2 The Evolution of the Combined Method of Knowledge Acquisition and Means of Its Implementation
• 3 Features of the Organization of Software Experiments Based on the Temporal Version of CMKA
• 3.1 General Organization of the Language Experiment
• 3.2 General Organization of Experiments with Temporal Database
• 3.3 Merging of Knowledge Field's Elements
• 4 Conclusion
• References

• Multi-agent Systems, Intelligent Robots and Behavior Planning
• Multi-agent Path Finding with Kinematic Constraints via Conflict Based Search
• 1 Introduction
• 2 Problem Statement
• 3 Suggested Approach
• 3.1 Conflict Based Search Algorithm
• 3.2 Continuous Conflict Based Search Algorithm
• 3.3 Safe Interval Path Planning with Rotations
• 3.4 Implementation Details
• 4 Experimental Evaluation
• 4.1 CCBS vs CCBS-kc
• 4.2 AA-SIPP(m) vs CCBS-kc
• 4.3 Comparison of Heuristic Functions
• 5 Conclusion
• References

• Map-Merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation
• 1 Introduction
• 2 Related Work
• 2.1 vSLAM Algorithms
• 2.2 Map-Merging Algorithms
• 3 Problem Statement
• 4 Methods and Metrics Overview
• 5 Experimental Evaluation and Results
• 5.1 Setup
• 5.2 Results
• 6 Conclusion
• References
• Can a Robot Be a Moral Agent?
• 1 Introduction
• 2 A Moral Agent
• 3 Phenomena and Mechanisms
• 3.1 Emotions and Needs
• 3.2 Model of the World, "I"
• 3.3 Imitative Behavior
• 3.4 Empathy
• 3.5 Characteristic Properties of Moral Agents
• 4 Conclusion

This book constitutes the proceedings of the 18th Russian Conference on Artificial Intelligence, RCAI 2020, held in Moscow, Russia, in October 2020. The 27 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 140 submissions. The conference deals with a wide range of topics, including data mining and knowledge discovery, text mining, reasoning, decisionmaking, natural language processing, vision, intelligent robotics, multi-agent systems, machine learning, AI in applied systems, and ontology engineering.
(source: Nielsen Book Data)

• Fast Texture Image Retrieval Using Learning Copula Model of Multiple DTCWT.- Joint Generic Learning and Multi-SourceDomain Adaptation on Unsupervised FaceRecognition.- Reset Attack: An Attack Against Homomorphic Encryption-based Privacy-Preserving Deep Learning System.- Intelligent Contract for Power Market Transaction Based on the BlockChain.- Survey on Zero-trust Network Security.- A Novel Consensus Algorithm for Alliance Chain.- A Novel Thematic Network Data Collector Based on Simulated Annealing Hybrid Genetic Algorithm.- The Study of Fog Computing Security Situation Prediction in Industrial Internet.- A novel keypoints-based image registration method with fully convolutional neural network.- Image super-resolution reconstruction with dense residual attention module.- GF-2 image blue roof building extraction method based on object-oriented classification technology.- Optimization and Parallelization of the cosmological N-body Simulation on Many-core Processor.- Parallel optimization of stencil computation base on Sunway TaihuLight.- Chinese News Data Extraction System Based On Readability Algorithm.- Parallel Acceleration Improvement of Physical Quantity Gradient Algorithm Based on CFD.- Using Process Visualization and Early Warning Based on Learning Analytics to Enhance Teaching and Learning.- Research Progress of Knowledge Graph Embedding.- A Robust Framework for High-quality Voice Conversion with Conditional Generative Adversarial Network.- Effective Android Malware Detection based on Deep Learning.- Research on Tampering Detection of Material Gene Data Based on Fragile Watermarking.- Quantitative Evaluation Of Promotion Of Traditional National Sports Events Based On Probity Multiple Regression Analysis.- Research on Leisure Sports Activities Based on Decision Tree Algorithm.- Semantic Integrity Analysis Based on Transformer.- An improvement of Coherent Semantic Attention for Image Inpainting.- Formal Verification of the Correctness of Operating System on Assembly Layer in Isabelle/HoL.- Nonlinear Correction Based on Depth Neural Network.- Spatial-temporal co-attention network for action recognition.- Improved SSD for object detection.- Sentiment Analysis on Weibo Platform for Stock Prediction.- Refinement Measurement and Evaluation of Streets in Shanghai Old City.- A Traceability Architecture for the Fresh Food Supply Chain based on Blockchain Technology in China.- Research Progress in Blind Forensics of Digital Image Smooth Filtering.- Research on video violence detection technology of UAV on cloud platform.- A Generative Steganography Method Based on WGAN-GP.- A deep learning network for coarse-to-fine deformable medical image registration.- Research on Cross-lingual Machine Reading Comprehension Technology Based on Non-parallel Corpus.- Research on the data reference model of NMIS.- A Real-time Recommender System Design Based on Spark Streaming.- A Novel Clone Detection Scheme Based on Generative Adversarial Networks.- Design of Vehicle Remote Monitoring System Based on LoRa Technology and QT Platform.- Unifying Domain Adaptation into Simultaneous Discriminant Analysis: a Method for Low-resolution Face Recognition with Single Sample per Person.- An exploratory study of blending Smart classroom in database course.- Optimization and simulation of pre-warehouse multi-objective stock levels based on the Weibull distribution function.- Research and Analysis of Fine Management of Engineering Cost Based on BIM.- Survey of Software-Defined Network Security Issues.- A Fall Detection Algorithm Based on a Support Vector Machine.- A Multi-AGV Optimal Scheduling Algorithm Based on Particle Swarm Optimization.- An efficient quantum private comparison protocol based on conjugate coding.- Deliberated Vulnerable Environment Generating based on Container and Structured Vulnerability Intelligence.- Deep neural network watermarking based on texture analysis.- A Robust Reversible Image Watermarking Scheme based on Singular Value Decomposition and Quantization Technique.- Boolean Function Decomposition Based on Grover Algorithm and Its Simulation Using Quantum Language Quipper.- Research on Emotional Recognition of EEG Signals in Experimental Paradigm.- Research on Rasterized Population Evaluation Method Based on Multi-class Machine Learning Method.- Salary Satisfaction of University Teachers with Different Professional Positions.- Defining Cost Function for Robust Embedding.- Efficient Identity-Based Signature authentication scheme for smart home system.- A Resource Allocation Mechanism for Edge-blockchain.- Remote sensing image zero-watermarking algorithm based on accurate polar harmonic Fourier moment.- Multi-layer quantum secret sharing based on GHZ states.- Robot scheduling system based on semantic recognition.- Pedestrian Counting without Tracking for the Security Application of IoT Framework.
• (source: Nielsen Book Data)

The 3-volume set CCIS 1252 until CCIS 1254 constitutes the refereed proceedings of the 6th International Conference on Artificial Intelligence and Security, ICAIS 2020, which was held in Hohhot, China, in July 2020. The conference was formerly called "International Conference on Cloud Computing and Security" with the acronym ICCCS. The total of 178 full papers and 8 short papers presented in this 3-volume proceedings was carefully reviewed and selected from 1064 submissions. The papers were organized in topical sections as follows: Part I: artificial intelligence; Part II: artificial intelligence; Internet of things; information security; Part III: information security; big data and cloud computing; information processing.
(source: Nielsen Book Data)

• Invited Talks.- Complementing Behavioural Modeling with Cognitive Modeling for Better Recommendations.- Nominees for Best Paper Award.- The Construction of Action Rules to Raise Artwork Prices.- Metric-Guided Multi-Task Learning.- Sentiment Analysis with Contextual Embeddings and Self-Attention.- Nominees for Best Student Paper Award.- Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings.- Decision-Making with Probabilistic Reasoning in Engineering Design.- Hyperbolic embeddings for hierarchical multi-label classification.- Natural Language Processing.- Joint Multiclass Debiasing of Word Embeddings.- Recursive Neural Text Classification using Discourse Tree Structure for Argumentation Mining and Sentiment Analysis Tasks.- Named Entity Recommendations to Enhance Multilingual Retrieval in Europeana.eu.- A Deep Learning Approach to Fake News Detection.- Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets.- Deep Learning and Embeddings.- Comparing state-of-the-art neural network ensemble methods in soccer predictions.- Static Music Emotion Recognition Using Recurrent Neural Networks.- Saliency detection in hyperspectral images using autoencoder-based data reconstruction.- Mesoscale Anisotropically-Connected Learning.- Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering.- Neural spike sorting using unsupervised adversarial learning.- Digital Signal Processing.- Poriferal Vision: Classifying benthic sponge spicules to assess historical impacts of marine climate change.- Experimental Evaluation of GAN-Based One-Class Anomaly Detection on Office Monitoring.- Ranking Speech Features for their Usage in Singing Classification.- Leveraging Machine Learning in IoT to predict the trustworthiness of Mobile Crowd Sensing data.- A Hierarchical-Based Web-Platform for Crowdsourcing Distinguishable Image Patches.- Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs.- Modelling and Reasoning.- CatIO - Framework for Model Based Diagnosis of Cyber-Physical Systems.- Data Publishing: Availibility of data under Security Policies - Preliminary work.- Matrix Factorization based Heuristics Learning for Solving Constraint Satisfaction Problems.- Explaining object motion using answer set programming.- The GraphBRAIN System for Knowledge Graph Management and Advanced Fruition.- Mining Exceptional Mediation Models.- Machine Learning Applications.- Multivariate predictive clustering trees for classification.- Comparison of Machine Learning Methods to Detect Anomalies in the Activity of Dairy Cows.- Clustering Algorithm Consistency in Fixed Dimensional Spaces.- Estimating the Importance of Relational Features by using Gradient Boosting.- Multi-objective Discrete Moth-Flame Optimization for Complex Network Clustering.- Predicting associations between proteins and multiple diseases.- Short Papers.- Exploiting Answer Set Programming for Building Explainable Recommendations.- Tailoring Random Forest for Requirements Classification.- On the Design of a Natural Logic System for Knowledge Bases.- Evaluation of post-hoc XAI approaches through synthetic tabular data.- SimLoss: Class Similarities in Cross Entropy.- Efficient and Precise Classification of CT Scannings of Renal Tumors using Convolutional Neural Networks.- Deep Autoencoder Ensembles for Anomaly Detection on Blockchain.- A Parallelized Variant of Junker's QUICKXPLAIN Algorithm.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 25th International Symposium on Foundations of Intelligent Systems, ISMIS 2020, held in Graz, Austria, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 35 full and 8 short papers presented in this volume were carefully reviewed and selected from 79 submissions. Included is also one invited talk. The papers deal with topics such as natural language processing; deep learning and embeddings; digital signal processing; modelling and reasoning; and machine learning applications.
(source: Nielsen Book Data)

• FOUNDATIONS COMPUTATIONAL INTELLIGENCE EVOLUTIONARY COMPUTATION CONCEPTS AND PARADIGMS EVOLUTIONARY COMPUTATION IMPLEMENTATIONS NEURAL NETWORK CONCEPTS AND PARADIGMS NEURAL NETWORK IMPLEMENTATIONS FUZZY SYSTEMS CONCEPTS AND PARADIGMS FUZZY SYSTEMS IMPLEMENTATIONS COMPUTATIONAL INTELLIGENCE IMPLEMENTATIONS PERFORMANCE METRICS ANALYSIS AND EXPLANATION CASE STUDY SUMMARIES.
• (source: Nielsen Book Data)

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
(source: Nielsen Book Data)

• Introduction.- Part I: The Components.- Computers.- People.- Thought.- Part II: The Synergies.- Computers and People.- People and Thought.- Computers and Thought.- Computers, People, and Thought.- Part III: The Wider Picture.- Ethical, Societal, Philosophical, and Spiritual Issues.- Jobs and Education.- Superintelligence.- Lethal Autonomous Weapons Systems.- The Right to Privacy as a Cornerstone of Human Individuality and Freedom?.- The Future of Society: Choices, Decisions, and Consequences.- App. A: What Is the Most Efficient Number Base?.- App. B: Ternary State Machine Design.- References.
• (source: Nielsen Book Data)

In this book the author discusses synergies between computers and thought, related to the field of Artificial Intelligence; between people and thought, leading to questions of consciousness and our existence as humans; and between computers and people, leading to the recent remarkable advances in the field of humanoid robots. He then looks toward the implications of intelligent 'conscious' humanoid robots with superior intellects, able to operate in our human environments. After presenting the basic engineering components and supporting logic of computer systems, and giving an overview of the contributions of pioneering scientists in the domains of computing, logic, and robotics, in the core of the book the author examines the meaning of thought and intelligence in the context of specific tasks and successful AI approaches. In the final part of the book he introduces related societal and ethical implications. The book will be a useful accompanying text in courses on artificial intelligence, robotics, intelligent systems, games, and evolutionary computing. It will also be valuable for general readers and historians of technology.
(source: Nielsen Book Data)

• Chapter 1: An Overview of Artificial Intelligence
• Chapter 2: An Overview of Machine Learning
• Chapter 3: Introduction to Deep Learning
• Chapter 4: Machine Learning Versus Deep Learning
• Chapter 5: Machine Learning with Python
• Chapter 6: Introduction to Jupyter Notebooks
• Chapter 7: Python Programming on the Jupyter Notebook
• Chapter 8: The Tensorflow Machine Learning Library
• Chapter 9: Programming with Tensorflow 1.0
• Chapter 10: Introducing TensorFlow 2.0
• Chapter 11: Machine Learning Programming with TensorFlow 2.0.

Create, execute, modify, and share machine learning applications with Python in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebooks instead of a text editor or a regular IDE. Youll start by learning fundamental concepts in Python necessary for working with machine learning application development. Then use Jupyter Notebooks to improve the way you program with Python. After grounding your skills in working with Python in Jupyter Notebooks, youll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can start in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. You will: Program machine learning models in Python Tackle basic machine learning obstacles Develop in the Jupyter Notebooks environment.

• Chapter 1
• Welcome to TensorFlow.js Headings
• What is TensorFlow.js?
• TensorFlow.js API
• Tensors Operations Variables
• How to install it
• Use cases Chapter 2 Building your First Model Headings
• Building a logistic regression classification model
• Building a linear regression model
• Doing unsupervised learning with k-means
• Dimensionality reduction and visualization with t-SNE and d3.js
• Our first neural network Chapter 3 Create a drawing app to predict handwritten digits using Convolutional Neural Networks and MNIST Headings
• Convolutional Neural Networks
• The MNIST Dataset
• Design the model architecture
• Train the model
• Evaluate the model
• Build the drawing app
• Integrate the model within the app
• Chapter 4 "Move your body!" A game featuring PoseNet, a pose estimator model Headings
• What is PoseNet?
• Loading the model
• Interpreting the result
• Building a game around it Chapter 5 Detect yourself in real-time using an object detection model trained in Google Cloud's AutoML Headings
• TensorFlow Object Detection API
• Google Cloud's AutoML
• Training the model
• Exporting the model and importing it in TensorFlow.js
• Building the webcam app Chapter 6 Transfer Learning with Image Classifier and Voice Recognition Headings
• What's Transfer Learning?
• MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set)
• Transferring the knowledge
• Re-training the model
• Testing the model with a video Chapter 7 Censor food you do not like with pix2pix, Generative Adversarial Networks, and ml5.js Headings
• Introduction to Generative Adversarial Networks
• What is image translation?
• Training your custom image translator with pix2pix
• Deploying the model with ml5.js Chapter 8 Detect toxic words from a Chrome Extension using a Universal Sentence Encoder Headings
• Toxicity classifier
• Training the model
• Testing the model
• Integrating the model in a Chrome Extension Chapter 9 Time Series Analysis and Text Generation with Recurrent Neural Networks Headings
• Recurrent Neural Networks
• Example 1: Building an RNN for time series analysis
• Example 2: Building an RNN to generate text Chapter 10 Best practices, integrations with other platforms, remarks and final words Headings
• Best practices
• Integration with other platforms
• Materials for further practice
• Conclusion.
• (source: Nielsen Book Data)

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard , ml5js , tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
(source: Nielsen Book Data)

• Decision Support Model for Assessing Projects by a Group of Investors with Regards of Multi-factors
• 7-Dimensional Optimization Task: PBO-Nature-Inspired Optimizer Versus 10-Years-Old Differential Evolution Based Optimizer 3rd Generation EPSDE
• A Hand Contour Classification Using Ensemble of Natural Features: A Large Comparative Study
• Energy Consumption Reduction in Real Time Multiprocessor Embedded Systems with Uncertain Data
• Developing an Efficient Method for Automatic Threshold Detection Based on Hybrid Feature Selection Approach
• Comparison of Hybrid ACO-k-Means Algorithm and Graph Cut for MRI Images Segmentation
• Parallel Deep Neural Network for Motor Imagery EEG Recognition with Spatiotemporal Features.

This book gathers the refereed proceedings of the Artificial Intelligence and Bioinspired Computational Methods Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Artificial intelligence and bioinspired computational methods now represent crucial areas of computer science research. The topics presented here reflect the current discussion on cutting-edge hybrid and bioinspired algorithms and their applications. .
(source: Nielsen Book Data)

• 1: Introduction
• 2: Fundamentals of Edge Computing
• 2.1 Paradigms of Edge Computing
• Cloudlet and Micro Data Centers
• Fog Computing
• Mobile (Multi-access) Edge Computing (MEC)
• Definition of Edge Computing Terminologies
• Collaborative End-Edge-Cloud Computing
• 2.2 Hardware for Edge Computing
• AI Hardware for Edge Computing
• Integrated Commodities Potentially for Edge Nodes
• Edge Computing Frameworks
• 2.3 Virtualizing the Edge
• Virtualization Techniques
• Network Virtualization
• Network Slicing
• 3. Fundamentals of Deep Learning
• 3.1 Neural Networks in Deep Learning
• Fully Connected Neural Network (FCNN)
• Auto-Encoder (AE)
• Convolutional Neural Network (CNN)
• Generative Adversarial Network (GAN)
• Recurrent Neural Network (RNN)
• Transfer Learning (TL)
• 3.2 Deep Reinforcement Learning (DRL)
• Value-based DRL
• Policy-gradient-based DRL
• 3.3 Distributed DL Training
• 3.4 Potential DL Libraries for Edge
• 4. Deep Learning Applications on Edge
• 4.1 Real-time Video Analytic
• 4.2 Autonomous Internet of Vehicles (IoVs)
• 4.3 Intelligent Manufacturing
• 4.4 Smart Home and City
• 5. Deep Learning Inference in Edge
• 5.1 Optimization of DL Models in Edge
• General Methods for Model Optimization
• Model Optimization for Edge Devices
• 5.2 Segmentation of DL Models
• 5.3 Early Exit of Inference (EEoI)
• 5.4 Sharing of DL Computation
• 6. Edge Computing for Deep Learning
• 6.1 Edge Hardware for DL
• Mobile CPUs and GPUs
• FPGA-based Solutions
• 6.2 Communication and Computation Modes for Edge DL
• Integral Offloading
• Partial Offloading
• Vertical Collaboration
• Horizontal Collaboration
• 6.3 Tailoring Edge Frameworks for DL
• 6.4 Performance Evaluation for Edge DL
• 7. Deep Learning Training at Edge
• 7.1 Distributed Training at Edge
• 7.2 Vanilla Federated Learning at Edge
• 7.3 Communication-efficient FL
• 7.4 Resource-optimized FL
• 7.5 Security-enhanced FL
• 8. Deep Learning for Optimizing Edge
• 8.1 DL for Adaptive Edge Caching
• 8.2 DL for Optimizing Edge Task Offloading
• 8.3 DL for Edge Management and Maintenance
• Edge Communication
• Edge Security
• Joint Edge Optimization
• 9. Lessons Learned and Open Challenges
• 9.1 More Promising Applications
• 9.2 General DL Model for Inference
• 9.3 Complete Edge Architecture for DL
• 9.4 Practical Training Principles at Edge
• 9.5 Deployment and Improvement of Intelligent Edge
• .
• (source: Nielsen Book Data)

As an important enabler for changing people's lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
(source: Nielsen Book Data)

• A Prescriptive Analytical Logic Model Incorporated with Analytic Hierarchy Process for Software Application Error Analysis
• Simplified Framework of Natural Language Processing for Structure Management of Current-Age Data
• Design and Software Implementation of Heuristic and Suboptimal Strategies for the Mancala/Kalah Game
• Sector-Selective Hybrid Scheme Facilitating Hardware Supportability Over Image Compression
• Performance Evaluation of Joint Rate-Distortion Model of Video Codec
• To Defeat DDoS Attacks in Cloud Computing Environment Using Software Defined Networking (SDN)
• Oriented Petri Nets as Means of Describing a Human-Computer Interaction
• FPGA Based Transient Fault Generate and Fault Tolerant for Asynchronous and Synchronous Circuits
• Designing an Energy Efficient Routing for Subsystems Sensors in Internet of Things Eco-System Using Distributed Approach
• The Compare of Solo Programming and Pair Programming Strategies in a Scrum Team: A Multi-agent Simulation
• Fair Random Access with Track-Based SNR Scheduling for Full-Duplex Wireless Powered Communication Networks
• A DTN Gateway-Based Architecture for Web Access in Space Internet
• Multi-objective Scheduling Optimization for ETL Tasks in Cluster Environment
• An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
• Analysis of Social Engineering Attacks Using Exploit Kits
• Bipolar Disorder: A Pathway Towards Research Progress in Identification and Classification
• Managing Electronic Waste with Recycling: A Review of Developing and Developed Regions
• Trajectory Modeling in a Pursuit Problem with Curvature Restrictions
• An Approach to Using Templates for Modeling Exceptions in Terms of Petri Nets
• Performance Bottleneck Analysis and Optimization Framework in Concurrent Environments
• Matlab Code Generation and Consumption
• The Problem of Preventing the Development of Critical Combinations of Events in Large-Scale Systems
• Using Software Package "Multimeat-Expert" for Modeling and Optimization of Composition Chopped Meat Product with Vegetable Additive
• Application of Machine Learning for Document Classification and Processing in Adaptive Information Systems
• SNR-Based TDMA Scheduling with Continuous Energy Transfer for Wireless Powered Communication Networks
• Comparative Analysis of Software Development Life Cycle Models (SDLC). Plus 27 more papers.

This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented. .
(source: Nielsen Book Data)

• Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications
• Part I Deep Learning in Sensing
• Vision to Language: Methods, Metrics and Datasets
• Deep Learning Techniques for Geospatial Data Analysis
• Deep Learning Approaches in Food Recognition
• Part II Deep Learning in Social Media and IOT
• Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding
• A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks
• Part III Deep Learning in the Medical Field
• Survey on Deep Learning Techniques for Medical Imaging Application Area
• Deep Learning Methods in Electroencephalography
• Part IV Deep Learning in Systems Control
• The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neural Network Fuzzy Inference System Models-A MIMO Centrifugal Chiller case study
• A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System
• Part V Deep Learning in Feature Vector Processing
• Stock Market Forecasting by Using Support Vector Machines
• An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction
• Deep Dense Neural Network for Early Prediction of Failure-Prone Students
• Part VI Evaluation of Algorithm Performance
• Non-parametric Performance Measurement with Artificial Neural Networks
• A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies
• Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms.

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
(source: Nielsen Book Data)

• Aggregation Operators and Decision Making.- Data science and Data Mining.
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This book constitutes the refereed proceedings of the 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020, held in Sant Cugat, Spain, in September 2020.* The 24 papers presented in this volume were carefully reviewed and selected from 46 submissions. They discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: aggregation operators and decision making, and data science and data mining. * The conference was canceled due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Knowledge Bases.- Conceptual Structures.- Reasoning Models.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 25th International Conference on Conceptual Structures, ICCS 2020, held in Bolzano, Italy, in September 2020.* The 10 full papers, 5 short papers and 1 poster paper presented were carefully reviewed and selected from 24 submissions. The proceedings also include two keynotes and two tutorials. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. The topics of this year's conference range from formal concept analysis to decision making, from machine learning to natural language processing. *The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)