This book describes important methodologies, tools and techniques from the fields of artificial intelligence, basically those which are based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web, with many new topics. The contributions are concerned with machine learning, big data, data processing in medicine, similarity processing in ontologies, semantic image analysis, as well as many applications including the use of machine leaning techniques for cloud security, artificial intelligence techniques for detecting COVID-19, the Internet of things, etc. The book is meant to be a very important and useful source of information for researchers and doctoral students in data analysis, Semantic Web, big data, machine learning, computer engineering and related disciplines, as well as for postgraduate students who want to integrate the doctoral cycle.

The Semantic Web is a young discipline, even if only in comparison to other areas of computer science. Nonetheless, it already exhibits an interesting history and evolution. This book is a reflection on this evolution, aiming to take a snapshot of where we are at this specific point in time, and also showing what might be the focus of future research. This book provides both a conceptual and practical view of this evolution, especially targeted at readers who are starting research in this area and as support material for their supervisors. From a conceptual point of view, it highlights and discusses key questions that have animated the research community: what does it mean to be a Semantic Web system and how is it different from other types of systems, such as knowledge systems or web-based information systems? From a more practical point of view, the core of the book introduces a simple conceptual framework which characterizes Intelligent Semantic Web Systems. We describe this framework, the components it includes, and give pointers to some of the approaches and technologies that might be used to implement them. We also look in detail at concrete systems falling under the category of Intelligent Semantic Web Systems, according to the proposed framework, allowing us to compare them, analyze their strengths and weaknesses, and identify the key fundamental challenges still open for researchers to tackle.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing. This book: Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

In the last few years, there has been an increased advancement and evolution in semantic web and information systems in a variety of fields. The integration of these approaches to ontology engineering, sophisticated methods and algorithms for open linked data extraction, and advanced decision-making creates new opportunities for a bright future. Innovations, Developments, and Applications of Semantic Web and Information Systems is a critical scholarly resource that discusses integrated methods of research and analytics in information technology. Featuring coverage on a broad range of topics, such as cognitive computing, artificial intelligence, machine learning, data analysis, and algorithms, this book is geared towards researchers, academicians, and professionals seeking current information on semantic web and information systems.

This book contains the latest researches on advanced intelligent systems applied in the field of education presented during the second edition of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) held on July 08–11, 2019, in Marrakech, Morocco. The book proposes new approaches and innovative strategies for the manipulation of data and big data collected from the educational environment, exploiting the analysis tools, algorithms of artificial intelligence, and machine learning techniques, in order to extract results, which allow improving the performance and effectiveness of the education field, which is a strategic lever for sustainable development. The book deals with concepts, strategies, and approaches developed on various current axes of scientific research in the field of education, such as smart e-learning, smart education (smart classroom, smart assessment and smart teaching and learning technologies), massive open online courses (MOOC), courseware design, and development for smart learning, cloud learning, and mobile learning. The book is intended for all actors in the educational sector, namely students, professors, academic researchers, and stakeholders. It constitutes a large-scale forum for the exchange of ideas, approaches, and innovative techniques between these actors on the development and innovation of the field of education with the revolution 4.0. The authors of each chapter report the state of the art of the various topics addressed and present results of their own research, laboratory experiments, and successful applications. The purpose of this session is to share the idea of advanced intelligent systems with appropriate tools and techniques for modeling, management, and decision support in the field of education.

The success of the World Wide Web depends on the ability of users to store, p- cess and retrieve digital information regardless of distance boundaries, languages and domains of knowledge. The universality and ?exibility of the World Wide Web have also enabled the rapid growth of a variety of new services and applications based on human–machine interaction. The semantics of exchanged information and services should be useful not only for human to human communications, but also in that machines would be able to understand and automatically process web content. Semanticsgive well-de?nedmeaningto web content and enable computersand p- ple to work in cooperation. Today, the crucial challenge becomes the development of languages to express information in a machine processable format. Now more than ever, new advanced techniques and intelligent approaches are required to tra- form the Web into a universal reasoning and computing machine. Web intelligence attempts to deal with this challenge by exploiting information technologies and - ti?cial intelligence approaches to design the next generation of web-empowered systems and services.

Technological Advancements in Data Processing for Next Generation Intelligent Systems presents an in-depth exploration of cutting-edge data processing technologies that drive the development of next-generation intelligent systems in the context of the digital transformation era. This comprehensive book delves into the role data plays as a critical asset for organizations across diverse industries, and how recent technological breakthroughs have unlocked unprecedented potential for handling vast data volumes and real-time analysis. The book begins by providing a thorough overview of novel technologies such as artificial intelligence (AI) or machine learning (ML), edge computing, federated learning, quantum computing, and more. These revolutionary technologies, when integrated with big data frameworks, in-memory computing, and AI/ML algorithms, have transformed data processing capabilities, enabling the creation of intelligent systems that fuel innovation, optimize operations, and deliver personalized experiences. The ultimate aim of this integration is to empower devices with the ability to make autonomous intelligent decisions, maximizing computing power. This book serves as a valuable resource for research scholars, academicians, and industry professionals working towards the future advancement of optimized intelligent systems and intelligent data processing approaches. The chapters encompass a wide range of topics, including architecture and frameworks for intelligent systems, applications in diverse domains, cloud-based solutions, quantum processing, federated learning, in-memory data processing, real-time stream processing, trustworthy AI for Internet of Things (IoT) sensory data, and more.

This book constitutes the refereed proceedings of the 10th Extended Semantic Web Conference, ESWC 2013, held in Montpellier, France, in May 2013. The 42 revised full papers presented together with three invited talks were carefully reviewed and selected from 162 submissions. They are organized in tracks on ontologies; linked open data; semantic data management; mobile Web, sensors and semantic streams; reasoning; natural language processing and information retrieval; machine learning; social Web and Web science; cognition and semantic Web; and in-use and industrial tracks. The book also includes 17 PhD papers presented at the PhD Symposium.

Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way humans do. Data Science with Semantic Technologies: Deployment and Exploration, the second volume of a two-volume handbook set, provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book offers the answer to various questions like: What makes a technology semantic as opposed to other approaches to data science? What is knowledge data science? How does knowledge data science relate to other fields? This book explores the optimal use of these technologies to provide the highest benefit to the user under one comprehensive source and title. As there is no dedicated book available in the market on this topic at this time, this book becomes a unique resource for scholars, researchers, data scientists, professionals, and practitioners. This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation.