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This book constitutes the thoroughly refereed post-workshop proceedings of the AVI 2020 Workshop on Road Mapping Infrastructures for Artificial Intelligence Supporting Advanced Visual Big Data Analysis, AVI-BDA 2020, held in Ischia, Italy, in June 2020, and the Second Italian Workshop on Visualization and Visual Analytics, held in Ischia, Italy, in September 2020. The 14 regular papers in this volume present topics such as big data collection, management and curation; big data analytics; big data interaction and perception; big data insight and effectuation; configuration and management of big data storage and compute infrastructures, services, and tools; advanced visual interaction in big data applications; user empowerment and meta design in big data applications; prediction and automation of big data analysis workflows; as well as data visualization; information visualization; visual analytics; infographics; and design.
This book constitutes the thoroughly refereed post-workshop proceedings of the AVI 2016 Workshop on Road Mapping Infrastructures for Advanced Visual Interfaces Supporting Big Data Applications in Virtual Research Environments, AVI-BDA 2016, held in Bari, Italy, in June 2016. The 10 revised full papers in this volume present the elaborated outcome of the initial position papers capturing the results oft the roadmapping discussions in the workshop at which comments of several external reviewers for these full publications were also integrated.
This book is aimed at managerial decision makers, practitioners in any field, and the academic community. The chapter authors have integrated theory with evidence-based practice to go beyond merely explaining cybersecurity topics. To accomplish this, the editors drew upon the combined cognitive intelligence of 46 scholars from 11 countries to present the state of the art in cybersecurity. Managers and leaders at all levels in organizations around the globe will find the explanations and suggestions useful for understanding cybersecurity risks as well as formulating strategies to mitigate future problems. Employees will find the examples and caveats both interesting as well as practical for everyday activities at the workplace and in their personal lives. Cybersecurity practitioners in computer science, programming, or espionage will find the literature and statistics fascinating and more than likely a confirmation of their own findings and assumptions. Government policymakers will find the book valuable to inform their new agenda of protecting citizens and infrastructure in any country around the world. Academic scholars, professors, instructors, and students will find the theories, models, frameworks, and discussions relevant and supportive to teaching as well as research.
Computers and machines were developed to reduce time consumption and manual human efforts to complete projects efficiently. With fast-growing technologies in the field, we have finally reached a stage where almost everyone in the world has access to these high technologies. However, this is just a starting phase because future development is taking a more advanced route in the shape of artificial intelligence (AI). Although AI is under the computer science umbrella, nowadays there is no field unaffected by this high technology. The overall aim of using intelligence learning methods is to train machines to think intelligently and make decisions in different situations the same as humans. Previously, machines were doing what they were programmed to do, but now with AI, devices can think and behave like a human being. This book aims to present the application of advanced analytics and AI in different industries as practical tools to develop prediction, optimization, and make decision models.
Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR. - Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics - Presents a roadmap of future trends suitable for innovative Data Science research and practice - Includes topics such as Blockchain technology for securing data at end user/server side - Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
In a rapidly evolving digital landscape, integrating emerging technologies presents unprecedented opportunities and complex challenges across various disciplines. As society navigates this transformation, there is a growing need for comprehensive insights into the implications of these advancements. This book serves as a vital resource, offering a multidimensional exploration of how emerging technologies are reshaping the social sciences, education, law and policy, tourism, health, environment, communication, business and management, and security. Focusing on the intersection of technology and society, the Future of Digital Technology and AI in Social Sectors addresses pressing issues such as ethical dilemmas in technological advancement, the impact of automation on employment, and the moral and legal challenges of AI and data analytics. By providing a platform for researchers and practitioners to delve into these topics, the book aims to foster a deeper understanding of emerging technologies' implications and opportunities across diverse fields.
The integration of fifth generation (5G) wireless technologies with distributed artificial intelligence (AI) is transforming network operations. AI is increasingly embedded in all network elements, from cloud and edge to terminal devices, enabling AI to function as a networking system. This convergence facilitates AI-based applications across the global network, with notable successes in various domains such as computer vision, natural language processing, and healthcare. Networked Artificial Intelligence: AI-Enabled 5G Networking a comprehensive framework for the deep integration of computing and communications, optimizing networks and applications as a unified system using AI. The book covers topics ranging from networked AI fundamentals to AI-enabled 5G networks, including agent modeling, machine learning (ML) algorithms, and network protocol architectures. It discusses how network service providers can leverage AI and ML techniques to customize network baselines, reduce noise, and accurately identify issues. It also looks at AI-driven networks that enable self-correction for maximum uptime and prescriptive actions for issue resolution, as well as troubleshooting by capturing and storing data before network events. The book presents a comprehensive approach to AI-enabled networking that offers unprecedented opportunities for efficiency, reliability, and innovation in telecommunications. It works through the approach’s five steps of connection, communication, collaboration, curation, and community. These steps enhance network effects, empowering operators with insights for trusted automation, cost reduction, and optimal user experiences. The book also discusses AI and ML capabilities that enable networks to continuously learn, self-optimize, and predict and rectify service degradations proactively, even with full automation.