This book of Advances in Deep Learning, Artificial Intelligence and Robotics (proceedings of ICDLAIR 2020) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics. Deep Learning, AI and robotics represent key ingredients for the 4th Industrial Revolution. Their extensive application is dramatically changing products and services, with a large impact on labour, economy and society at all. The research and reports of new technologies and applications in DL, AI and robotics like biometric recognition systems, medical diagnosis, industries, telecommunications, AI petri nets model-based diagnosis, gaming, stock trading, intelligent aerospace systems, robot control and web intelligence aim to bridge the gap between these non-coherent disciplines of knowledge and fosters unified development in next-generation computational models for machine intelligence.

This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example-a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.

This book constitutes the refereed proceedings of the 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021, which took place during November 25-27, 2021. The conference was originally planned to take place in Manchester, UK, but was held virtually due to the COVID-19 pandemic.The 61 full papers included in this book were carefully reviewed and selected from 85 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

This book contributes to the body of scholarly knowledge by exploring the main ideas of wireless networks of past, present, and future, trends in the field of networking, the capabilities of 5G and technologies that are potential enablers of 6G, potential 6G applications and requirements, as well as unique challenges and opportunities that 6G research is going to offer over the next decade. It covers research topics such as communication via millimeter-waves, terahertz waves and visible light to enable faster speeds, as well as research into achieving other basic requirements of 6G networks. These include low end-to-end latency, high energy efficiency, coverage that is ubiquitous and always-on, integration of terrestrial wireless with non-terrestrial networks, network management that is made more effective by connected intelligence with machine learning capabilities, as well as support for the evolution of old service classes and support for new ones.

This book constitutes refereed proceedings of the 7th International Conference on Technologies and Innovation, CITI 2021, held in Guayaquil, Ecuador, in November 2021.The 14 full papers presented in this volume were carefully reviewed and selected from 36 submissions. They are organized in topical sections named: semantic technologies and machine learning; natural language processing; mobile and collaborative technologies; networks and IoT technologies; ICT for agronomy and environment.

This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community.

This book constitutes the proceedings of the 10th International Conference on Health Information Science, HIS 2021, which took place in Melbourne, Australia, in October 2021. The 16 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 56 submissions. They are organized in topical sections named: COVID-19, EEG data processing, Medical Data Analysis, Medical Record Mining (I), Medical Data Mining (II), Medical Data Processing.

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.

Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings. Machine Learning for Auditors provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization. What You Will Learn Understand the role of auditors as trusted advisors Perform exploratory data analysis to gain a deeper understanding of your organization Build machine learning predictive models that detect fraudulent vendor payments and expenses Integrate data analytics with existing and new technologies Leverage storytelling to communicate and validate your findings effectively Apply practical implementation use cases within your organization Who This Book Is For AI Auditing is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes.

This book features a collection of high-quality research papers presented at the International Conference on Intelligent and Cloud Computing (ICICC 2019), held at Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India, on December 20, 2019. Including contributions on system and network design that can support existing and future applications and services, it covers topics such as cloud computing system and network design, optimization for cloud computing, networking, and applications, green cloud system design, cloud storage design and networking, storage security, cloud system models, big data storage, intra-cloud computing, mobile cloud system design, real-time resource reporting and monitoring for cloud management, machine learning, data mining for cloud computing, data-driven methodology and architecture, and networking for machine learning systems.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers' insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis. This book is one of a series of textbooks in machine learning by the same Author. Other titles are: Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679) Statistical Learning with Math and Pyth (https://www.springer.com/gp/book/9789811578762) Sparse Estimation with Math and R

This book constitutes the refereed proceedings of the 10th International Conference on Theory and Practice of Natural Computing, TPNC 2021, held virtually, in December 2021. The 9 full papers presented together with 3 invited talks, in this book were carefully reviewed and selected from 14 submissions. The papers are organized in topical sections named Applications of Natural Computing, Deep Learning and Transfer Learning, Evolutionary and Swarm Algorithms.

This book constitutes the refereed post-conference proceedings of the 11th EAI International Conference on Research in Computer science and its Applications, CNRIA 2021, held in June 2021. Due to COVID-19 pandemic the conference was held virtually. The 11 full papers presented were selected from 24 submissions and issue different problems in underserved and unserved areas. The papers are arranged in 3 tracks: data science and artificial intelligence; telecom and artificial intelligence; IoT and ICT applications.

This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. What You Will Learn Understand widespread supervised and unsupervised learning, including key dimension reduction techniques Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning Integrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworks Design, build, test, and validate skilled machine models and deep learning models Optimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration Who This Book Is For Data scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics

This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains, machine learning, intelligent information retrieval and business intelligence, intelligent information system development using design science principles, intelligent web mining and knowledge discovery systems.

The three-volume set CCIS 1467, CCIS 1468, and CCIS 1469 constitutes the thoroughly refereed proceedings of the 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and of the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, held in Hangzhou, China, in October 2021. The 159 revised papers presented were carefully reviewed and selected from over 430 submissions. The papers of this volume are organized in topical sections on: Medical Imaging and Analysis Using Intelligence Computing; Biomedical signal processing, imaging, visualization and surgical robotics; Computational method in taxonomy study and neural dynamics; Intelligent medical apparatus, clinical applications and intelligent design of biochips; Power and Energy Systems; Computational Intelligence in Utilization of Clean and Renewable Energy Resources, and Intelligent Modelling, Control and Supervision for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Control Methods in Energy Infrastructure Development and Distributed Power Generation Systems; Intelligent Modeling, Simulation and Control of Power Electronics and Power Networks; Intelligent Techniques for Sustainable Energy and Green Built Environment, Water Treatment and Waste Management; Intelligent Robot and Simulation; Intelligent Data Processing, Analysis and Control in Complex Systems; Advanced Neural Network Theory and Algorithms; Advanced Computational Methods and Applications; Fuzzy, Neural, and Fuzzy-neuro Hybrids; Intelligent Modelling, Monitoring, and Control of Complex Nonlinear Systems; Intelligent manufacturing, autonomous systems, intelligent robotic systems; Computational Intelligence and Applications.

This two-volume book constitutes the post-conference proceedings of the 5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021, held in Nashik, India, in April 2021.* The 103 full papers were carefully reviewed and selected from 781 submissions. The papers in Part I and II are centered around topics like distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations database management system engines, data mining, information retrieval query processing, database and storage security, ubiquitous and mobile computing, parallel computing methodologies, and others. *The conference was held virtually due to the COVID-19 pandemic.

This book gathers the latest quality research work of Ph.D. students working on the current areas presented in the Doctoral Symposium on Computational Intelligence (DoSCI 2020). The book includes works in the areas of artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum-inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high-performance computing, biomedical computing, decision support and decision making. The book is useful for researchers, students, engineers, practitioners and academicians in their advance studies.

This three-volume set CCIS 1467, CCIS 1468, and CCIS 1469 constitutes the thoroughly refereed proceedings of the 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and of the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, held in Hangzhou, China, in October 2021. The 159 revised papers presented were carefully reviewed and selected from over 430 submissions. The papers of this volume are organized in topical sections on: Medical Imaging and Analysis Using Intelligence Computing; Biomedical signal processing, imaging, visualization and surgical robotics; Computational method in taxonomy study and neural dynamics; Intelligent medical apparatus, clinical applications and intelligent design of biochips; Power and Energy Systems; Computational Intelligence in Utilization of Clean and Renewable Energy Resources, and Intelligent Modelling, Control and Supervision for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Control Methods in Energy Infrastructure Development and Distributed Power Generation Systems; Intelligent Modeling, Simulation and Control of Power Electronics and Power Networks; Intelligent Techniques for Sustainable Energy and Green Built Environment, Water Treatment and Waste Management; Intelligent Robot and Simulation; Intelligent Data Processing, Analysis and Control in Complex Systems; Advanced Neural Network Theory and Algorithms; Advanced Computational Methods and Applications; Fuzzy, Neural, and Fuzzy-neuro Hybrids; Intelligent Modelling, Monitoring, and Control of Complex Nonlinear Systems; Intelligent manufacturing, autonomous systems, intelligent robotic systems; Computational Intelligence and Applications.

This two-volume book constitutes the post-conference proceedings of the 5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021, held in Nashik, India, in April 2021.* The 103 full papers were carefully reviewed and selected from 781 submissions. Part II is devoted to data sciences, organizing principles, medical technologies, computational linguistics etc. *The conference was held virtually due to the COVID-19 pandemic.

This book constitutes the thoroughly refereed papers of the 4th International Conference on Applied Informatics, ICAI 2021, held in Buenos Aires, Argentina, in October, 2021.The 35 full papers were carefully reviewed and selected from 89 submissions. The papers are organized in topical sections on artificial intelligence; data analysis; decision systems; health care information systems; image processing; security services; simulation and emulation; smart cities; software and systems modeling; software design engineering.

The book first explores the cybersecurity's landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law.

This book addresses the principles and applications of metaheuristic approaches in engineering and related fields. The first part covers metaheuristics tools and techniques such as ant colony optimization and Tabu search, and their applications to several classes of optimization problems. In turn, the book's second part focuses on a wide variety of metaheuristics applications in engineering and/or the applied sciences, e.g. in smart grids and renewable energy. In addition, the simulation codes for the problems discussed are included in an appendix for ready reference. Intended for researchers aspiring to learn and apply metaheuristic techniques, and gathering contributions by prominent experts in the field, the book offers readers an essential introduction to metaheuristics, its theoretical aspects and applications.

This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: "Recent Developments in Metaheuristics" and "Metaheuristics for Production Systems", books in Springer Series in Operations Research/Computer Science Interfaces. The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.