This book reports on research and developments in human-technology interaction. A special emphasis is given to human-computer interaction, and its implementation for a wide range of purposes such as healthcare, manufacturing, transportation, and education, among others. The human aspects are analyzed in detail. Innovative studies related to human-centered design, wearable technologies, augmented, virtual and mixed reality simulation, as well as developments and applications of machine learning and AI for different purposes, represent the core of the book. Emerging issues in business, security, and infrastructure are also critically examined, thus offering a timely, scientifically-grounded, but also professionally-oriented snapshot of the current state of the field. The book is based on contributions presented at the 4th International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET-AI 2021, held on April 28-30, 2021, in Strasbourg, France. It offers a timely survey and a practice-oriented reference guide to researchers and professionals dealing with design and/or management of the new generation of service systems.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

This book focuses on essential aspects of the theoretical foundations that support blended learning (BL) as a teaching training modality in tertiary education. Analyzing the changes in the world of education that lead to new ways of thinking and learning, it redefines the concept of blended learning at a time of constant growth in many universities around the world. This involves a shared reflection on the role of technology in the current university teacher education programs, as well as on the role that pedagogy plays in increasingly technology-driven contexts. Furthermore, the book presents pedagogical approaches to guide university professors in the design and implementation of blended learning courses. To this end, it describes some of the major models and approaches to BL instructional design, and examines issues related to the quality of BL training and the indicators to measure it, in order to identify those models that contribute to a better understanding of the dimensions that increase its effectiveness.

This book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our understanding and management of 'soil'. The judicious utilization of a piece of land is one of the biggest and most important current challenges, especially in light of the rapid global urbanization, which requires continuous monitoring of resource consumption. The book provides a clear overview of how machine learning can be used to analyze remote sensing data to monitor the key parameters, below, at, and above the surface. It not only offers insights into the approaches, but also allows readers to learn about the challenges and issues associated with the digital mapping of these parameters and to gain a better understanding of the selection of data to represent soil-landscape relationships as well as the complex and interconnected links between soil-landscape parameters under a range of soil and climatic conditions. Lastly, the book sheds light on using the network of satellite-based Earth observations to provide solutions toward smart farming and smart land management.

* A useful guide to financial product modeling and to minimizing business risk and uncertainty * Looks at wide range of financial assets and markets and correlates them with enterprises' profitability * Introduces advanced and novel machine learning techniques in finance such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and applies them to analyze finance data sets * Real world applicable examples to further understanding

This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991. During this period the area has developed into the main forum for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state-of-the-art applications in real-world domains. The international contributors represent leaders in the field from prestigious institutions in Europe, North America and Asia.Graduate students and researchers in this field will find this book highly useful as it provides an up-to-date insight into the key sub-areas of implementation and theory of ILP. For academics and researchers in the field of artificial intelligence and natural sciences, the book demonstrates how ILP is being used in areas as diverse as the learning of game strategies, robotics, natural language understanding, query search, drug design and protein modelling.

Understand the current and future research into technologies that underpin the increasing capabilities of automation technologies and their impact on the working world of the future. Rapid advances in automation and robotics technologies are often reported in the trade and general media, often relying on scary headlines such as "Jobs Lost to Robots." It is certainly true that work will change with the advent of smarter and faster automated workers; however, the scope and scale of the changes is still unknown. Automation may seem to be here already, but we are only at the early stages. Automation and Collaborative Robotics explores the output of current research projects that are improving the building blocks of an automated world. Research into collaborative robotics (cobotics) is merging digital, audio, and visual data to generate a commonly held view between cobots and their human collaborators. Low-power machine learning at the edge of the network can deliver decision making on cobots or to their manipulations. Topics covered in this book include: Robotic process automation, chatbots, and their impact in the near future The hype of automation and headlines leading to concerns over the future of work Component technologies that are still in the research labs Foundational technologies and collaboration that will enable many tasks to be automated with human workers being re-skilled and displaced rather than replaced What You Will Learn Be aware of the technologies currently being researched to improve or deliver automation Understand the impact of robotics, other automation technologies, and the impact of AI on automation Get an idea of how far we are from implementation of an automated future Know what work will look like in the future with the deployment of these technologies Who This Book Is For Technical and business managers interested in the future of automation and robotics, and the impact it will have on their organizations, customers, and the business world in general

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

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.

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Wurzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.

This two-volume set (CCIS 1147, CCIS 1148) constitutes the refereed proceedings of the 4th International Conference on Computer Vision and Image Processing. held in Jaipur, India, in September 2019. The 73 full papers and 10 short papers were carefully reviewed and selected from 202 submissions. The papers are organized by the topical headings in two parts. Part I: Biometrics; Computer Forensic; Computer Vision; Dimension Reduction; Healthcare Information Systems; Image Processing; Image segmentation; Information Retrieval; Instance based learning; Machine Learning.Part II: Neural Network; Object Detection; Object Recognition; Online Handwriting Recognition; Optical Character Recognition; Security and Privacy; Unsupervised Clustering.

This book features selected papers presented at Second International Conference on International Conference on Information Management & Machine Intelligence (ICIMMI 2020) held at Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India during 24 - 25 July 2020. It covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation; applications of artificial intelligence in soft computing and pattern recognition; cloud-based applications for machine learning; application of IoT in power distribution systems; as well as wireless sensor networks and adaptive wireless communication.

This book presents an overview of the latest smart transportation systems, IoV connectivity frameworks, issues of security and safety in VANETs, future developments in the IoV, technical solutions to address key challenges, and other related topics. A connected vehicle is a vehicle equipped with Internet access and wireless LAN, which allows the sharing of data through various devices, inside as well as outside the vehicle. The ad-hoc network of such vehicles, often referred to as VANET or the Internet of vehicles (IoV), is an application of IoT technology, and may be regarded as an integration of three types of networks: inter-vehicle, intra-vehicle, and vehicular mobile networks. VANET involves several varieties of vehicle connectivity mechanisms, including vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-cloud (V2C), and vehicle-to-everything (V2X). According to one survey, it is expected that there will be approximately 380 million connected cars on the roads by 2020. IoV is an important aspect of the new vision for smart transportation. The book is divided into three parts: examining the evolution of IoV (basic concepts, principles, technologies, and architectures), connectivity of vehicles in the IoT (protocols, frameworks, and methodologies), connected vehicle environments and advanced topics in VANETs (security and safety issues, autonomous operations, machine learning, sensor technology, and AI). By providing scientific contributions and workable suggestions from researchers and practitioners in the areas of IoT, IoV, and security, this valuable reference aims to extend the body of existing knowledge.

Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT. What You Will Learn Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is For AI and machine learning developers and natural language processing developers.

This book highlights recent advances and emerging technologies that utilize computational intelligence in signal processing, computing, imaging science, artificial intelligence, and their applications. It covers all branches of artificial intelligence and machine learning that are based on computation at some level, e.g. artificial neural networks, evolutionary algorithms, fuzzy systems, and automatic medical identification systems. Exploring recent trends in research and applications, the book offers a valuable resource for professors, researchers, and engineers alike.

Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code! You will learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience. You will even discover when not to use machine learning-and the alternative approaches that might be cheaper and more effective. When you're done working through this toolbox guide, you will be able to reliably deliver cost-effective solutions for organizations big and small alike. Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt.

Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary "plumbing" that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not. Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations. What You Will Learn Create a machine learning model using only the C# language Build confidence in your understanding of machine learning algorithms Painlessly implement algorithms Begin using the ML.NET library software Recognize the many opportunities to utilize ML.NET to your advantage Apply and reuse code samples from the book Utilize the bonus algorithm selection quick references available online Who This Book Is For Developers who want to learn how to use and apply machine learning to enrich their applications

This book includes selected papers from the International Conference on Machine Learning and Information Processing (ICMLIP 2019), held at ISB&M School of Technology, Pune, Maharashtra, India, from December 27 to 28, 2019. It presents the latest developments and technical solutions in the areas of advanced computing and data sciences, covering machine learning, artificial intelligence, human-computer interaction, IoT, deep learning, image processing and pattern recognition, and signal and speech processing.

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Wurzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019. The chapter "Supervised Human-guided Data Exploration" is published open access under a Creative Commons Attribution 4.0 International license (CC BY).

This book is based on the International Conference on Decision Economics (DECON 2019). Highlighting the fact that important decision-making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, psychology, small and international business, management, operations, and production, the book focuses on analytics as an emerging synthesis of sophisticated methodology and large data systems used to guide economic decision-making in an increasingly complex business environment. DECON 2019 was organised by the University of Chieti-Pescara (Italy), the National Chengchi University of Taipei (Taiwan), and the University of Salamanca (Spain), and was held at the Escuela politecnica Superior de Avila, Spain, from 26th to 28th June, 2019. Sponsored by IEEE Systems Man and Cybernetics Society, Spain Section Chapter, and IEEE Spain Section (Technical Co-Sponsor), IBM, Indra, Viewnext, Global Exchange, AEPIA-and-APPIA, with the funding supporting of the Junta de Castilla y Leon, Spain (ID: SA267P18-Project co-financed with FEDER funds)

The book aims to outline the issues of AI and COVID-19, involving predictions,medical support decision-making, and possible impact on human life. Starting withmajor COVID-19 issues and challenges, it takes possible AI-based solutions forseveral problems, such as public health surveillance, early (epidemic) prediction,COVID-19 positive case detection, and robotics integration against COVID-19.Beside mathematical modeling, it includes the necessity of changes in innovationsand possible COVID-19 impacts. The book covers a clear understanding of AI-driven tools and techniques, where pattern recognition, anomaly detection, machinelearning, and data analytics are considered. It aims to include the wide range ofaudiences from computer science and engineering to healthcare professionals.

This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.

The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.*For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 papers included in this volume set were organized in topical sections as follows: data mining; healthcare analytics-improving healthcare outcomes using big data analytics; human activity recognition; image processing and computer vision; natural language processing; recommender systems; the 13th international workshop on artificial intelligence and cybersecurity; computational intelligence; machine learning; neural network models; robotics and control; and time series analysis. * The conference was held virtually due to the COVID-19 pandemic.

The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.*For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 papers included in this volume set were organized in topical sections as follows: data mining; healthcare analytics-improving healthcare outcomes using big data analytics; human activity recognition; image processing and computer vision; natural language processing; recommender systems; the 13th international workshop on artificial intelligence and cybersecurity; computational intelligence; machine learning; neural network models; robotics and control; and time series analysis. * The conference was held virtually due to the COVID-19 pandemic.