Aboul Ella Hassanien – författare

Internet of Things (IoT) is a new platform of various physical objects or "things� equipped with sensors, electronics, smart devices, software, and network connections. IoT represents a new revolution of the Internet network which is driven by the recent advances of technologies such as sensor networks (wearable and implantable), mobile devices, networking, and cloud computing technologies. IoT permits these the smart devices to collect, store and analyze the collected data with limited storage and processing capacities.

Swarm Intelligence for Resource Management in the Internet of Things presents a new approach in Artificial Intelligence that can be used for resources management in IoT, which is considered a critical issue for this network. The authors demonstrate these resource management applications using swarm intelligence techniques. Currently, IoT can be used in many important applications which include healthcare, smart cities, smart homes, smart hospitals, environment monitoring, and video surveillance. IoT devices cannot perform complex on-site data processing due to their limited battery and processing. However, the major processing unit of an application can be transmitted to other nodes, which are more powerful in terms of storage and processing. By applying swarm intelligence algorithms for IoT devices, we can provide major advantages for energy saving in IoT devices. Swarm Intelligence for Resource Management in the Internet of Things shows the reader how to overcome the problems and challenges of creating and implementing swarm intelligence algorithms for each application

Examines the development and application of swarm intelligence systems in artificial intelligence as applied to the Internet of Things Discusses intelligent techniques for the implementation of swarm intelligence in IoT Prepared for researchers and specialists who are interested in the use and integration of IoT and cloud computing technologies

Artificial Intelligence and Industry 4.0 explores recent advancements in blockchain technology and artificial intelligence (AI) as well as their crucial impacts on realizing Industry 4.0 goals. The book explores AI applications in industry including Internet of Things (IoT) and Industrial Internet of Things (IIoT) technology. Chapters explore how AI (machine learning, smart cities, healthcare, Society 5.0, etc.) have numerous potential applications in the Industry 4.0 era. This book is a useful resource for researchers and graduate students in computer science researching and developing AI and the IIoT.

Explores artificial intelligence applications within the industrial manufacturing and communications sectors Presents a wide range of machine learning, computer vision, and digital twin applications across the IoT sector Explores how deep learning and cognitive computing tools enable processing vast data sets, precise and comprehensive forecast of risks, and delivering recommended actions

Throughout the industry, financial institutions seek to eliminate cumbersome authentication methods, such as PINs, passwords, and security questions, as these antiquated tactics prove increasingly weak. Thus, many organizations now aim to implement emerging technologies in an effort to validate identities with greater certainty. The near instantaneous nature of online banking, purchases, transactions, and payments puts tremendous pressure on banks to secure their operations and procedures.In order to reduce the risk of human error in financial domains, expert systems are seen to offer a great advantage in big data environments. Besides their efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems. Due to the increase in financial applications’ size, complexity, and number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emergence of new research areas is clear evidence of the rise of new demands and requirements of modern real-life applications to be more intelligent.This book provides an exhaustive review of the roles of expert systems within the financial sector, with particular reference to big data environments. In addition, it offers a collection of high-quality research that addresses broad challenges in both theoretical and application aspects of intelligent and expert systems in finance. The book serves to aid the continued efforts of the application of intelligent systems that respond to the problem of big data processing in a smart banking and financial environment.

Throughout the industry, financial institutions seek to eliminate cumbersome authentication methods, such as PINs, passwords, and security questions, as these antiquated tactics prove increasingly weak. Thus, many organizations now aim to implement emerging technologies in an effort to validate identities with greater certainty. The near instantaneous nature of online banking, purchases, transactions, and payments puts tremendous pressure on banks to secure their operations and procedures.In order to reduce the risk of human error in financial domains, expert systems are seen to offer a great advantage in big data environments. Besides their efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems. Due to the increase in financial applications’ size, complexity, and number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emergence of new research areas is clear evidence of the rise of new demands and requirements of modern real-life applications to be more intelligent.This book provides an exhaustive review of the roles of expert systems within the financial sector, with particular reference to big data environments. In addition, it offers a collection of high-quality research that addresses broad challenges in both theoretical and application aspects of intelligent and expert systems in finance. The book serves to aid the continued efforts of the application of intelligent systems that respond to the problem of big data processing in a smart banking and financial environment.

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then:Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possibleDiscusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizersDepicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function designDetails the similarities, differences, weaknesses, and strengths of each swarm optimization methodDraws parallels between the operators and searching manners of the different algorithmsSwarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB® package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools.This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

Securing Next-Generation Connected Healthcare Systems: Artificial Intelligence Technologies focuses on the crucial aspects of IoT security in a connected environment, which will not only benefit from cutting-edge methodological approaches but also assist in the rapid scalability and improvement of these systems. This book shows how to utilize technologies like blockchain and its integration with IoT for communication, data security, and trust management. It introduces the security aspect of next generation technologies for healthcare, covering a wide range of security and computing methodologies.Researchers, data scientists, students, and professionals interested in the application of artificial intelligence in healthcare management, data security of connected healthcare systems and related fields, specifically on data intensive secured systems and computing environments, will finds this to be a welcomed resource.Covers the latest next generation connected healthcare technologies using parallel computingPresents all the security aspects in next-generation technologies for healthcareUtilizes technologies such as blockchain and its integration with IoT for communication, data security, and trust managementDiscusses privacy and security issues and challenges in data intensive cloud computing environmentDives into the concept of parallel and distributed computing technologies and their applications in the real world

Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools.This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

The main objective of pervasive computing systems is to create environments where computers become invisible by being seamlessly integrated and connected into our everyday environment, where such embedded computers can then provide inf- mation and exercise intelligent control when needed, but without being obtrusive. Pervasive computing and intelligent multimedia technologies are becoming incre- ingly important to the modern way of living. However, many of their potential applications have not yet been fully realized. Intelligent multimedia allows dynamic selection, composition and presentation of the most appropriate multimedia content based on user preferences. A variety of applications of pervasive computing and - telligent multimedia are being developed for all walks of personal and business life. Pervasive computing (often synonymously called ubiquitous computing, palpable computing or ambient intelligence) is an emerging ?eld of research that brings in revolutionary paradigms for computing models in the 21st century. Pervasive c- puting is the trend towards increasingly ubiquitous connected computing devices in the environment, a trend being brought about by a convergence of advanced el- tronic – and particularly, wireless – technologies and the Internet. Recent advances in pervasive computers, networks, telecommunications and information technology, along with the proliferation of multimedia mobile devices – such as laptops, iPods, personal digital assistants (PDAs) and cellular telephones – have further stimulated the development of intelligent pervasive multimedia applications. These key te- nologiesarecreatingamultimediarevolutionthatwillhavesigni?cantimpactacross a wide spectrum of consumer, business, healthcare and governmental domains.

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities.This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems.Topics and Features: Provides an overview social network tools, and explores methods for discovering key players in social networks, designing self-organizing search systems, and clustering blog sites, surveys techniques for exploratory analysis and text mining of social networks, approaches to tracking online community interaction, and examines how the topological features of a system affects the flow of information, reviews the models of network evolution, covering scientific co-citation networks, nature-inspired frameworks, latent social networks in e-Learning systems, and compound communities, examines the relationship between the intent of web pages, their architecture and the communities who take part in their usage and creation, discusses team selection based on members’ social context, presents social network applications, including music recommendation and face recognition in photographs, explores the use of social networks in web services that focus on the discovery stage in the life cycleof these web services.This useful and comprehensive volume will be indispensible to senior undergraduate and postgraduate students taking courses in Social Intelligence, as well as to researchers, developers, and postgraduates interested in intelligent-social-networks research and related areas.

Richard Chbeir, Youakim Badr, Ajith Abraham, and Aboul-Ella Hassanien Abstract As the Web continues to grow and evolve, more and more data are becoming available. Particularly, multimedia and XML-based data are produced regularly and in increasing way in our daily digital activities, and their retrieval and access must be explored and studied in this emergent web-based era. This book provides reviews of the cutting-edge technologies and insights of various topics related to XML-based and multimedia information access and retrieval under the umbrella of Web Intelligence and reporting how organizations can gain compe- tive advantages by applying new different emergent techniques in the real-world scenarios. The primary target audience for the book includes researchers, scholars, postgraduate students and developers who are interested in advanced information retrieval on the web research and related issues. 1 Introduction Since the last two decades, Internet has changed our daily life by rede?ning the meanings and processes of business, commerce, marketing, ?nance, publishing, R. Chbeir Universite ' de Bourgogne, LE2I-UMR CNRS 5158, Fac. de Sciences Mirande, 21078 Dijon Cedex, France e-mail: richard.chbeir@u-bourgogne. fr Y. Badr INSA de Lyon, Universite ' de Lyon, Depart ' ement Informatique, 7 avenue Jean Capelle, 69621 Villeurbanne CX, France e-mail: youakim. badr@insa-lyon. fr A. Abraham Norwegian University of Science & Technology, Center for Quanti?able Quality of Service in Communication Systems, O. S. Bragstads plass 2E, 7491 Trondheim, Norway e-mail: ajith. abraham@ieee. org A. -E. Hassanien Kuwait University, College of Business & Administration, Dept.

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.

This book is the first of three volumes that illustrate the concept of social networks from a computational point of view. describes simulations of social networks, the representation and analysis of social networks, and the use of semantic networks in knowledge discovery and visualization;

This book is the first of three volumes that illustrate the concept of social networks from a computational point of view. The book contains contributions from a international selection of world-class experts, with a specific focus on practical tools, applications, and open avenues for further research (the other two volumes review issues of Security and Privacy, and Mining and Visualization in CSNs). Topics and features: presents the latest advances in CSNs, and illustrates how organizations can gain a competitive advantage by applying these ideas in real-world scenarios; discusses the design and use of a wide range of computational tools and software for social network analysis; describes simulations of social networks, the representation and analysis of social networks, and the use of semantic networks in knowledge discovery and visualization; provides experience reports, survey articles, and intelligence techniques and theories relating to specific problems in networktechnology.

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then:Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possibleDiscusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizersDepicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function designDetails the similarities, differences, weaknesses, and strengths of each swarm optimization methodDraws parallels between the operators and searching manners of the different algorithmsSwarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB® package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities.This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems.Topics and Features: Provides an overview social network tools, and explores methods for discovering key players in social networks, designing self-organizing search systems, and clustering blog sites, surveys techniques for exploratory analysis and text mining of social networks, approaches to tracking online community interaction, and examines how the topological features of a system affects the flow of information, reviews the models of network evolution, covering scientific co-citation networks, nature-inspired frameworks, latent social networks in e-Learning systems, and compound communities, examines the relationship between the intent of web pages, their architecture and the communities who take part in their usage and creation, discusses team selection based on members’ social context, presents social network applications, including music recommendation and face recognition in photographs, explores the use of social networks in web services that focus on the discovery stage in the life cycleof these web services.This useful and comprehensive volume will be indispensible to senior undergraduate and postgraduate students taking courses in Social Intelligence, as well as to researchers, developers, and postgraduates interested in intelligent-social-networks research and related areas.

The main objective of pervasive computing systems is to create environments where computers become invisible by being seamlessly integrated and connected into our everyday environment, where such embedded computers can then provide inf- mation and exercise intelligent control when needed, but without being obtrusive. Pervasive computing and intelligent multimedia technologies are becoming incre- ingly important to the modern way of living. However, many of their potential applications have not yet been fully realized. Intelligent multimedia allows dynamic selection, composition and presentation of the most appropriate multimedia content based on user preferences. A variety of applications of pervasive computing and - telligent multimedia are being developed for all walks of personal and business life. Pervasive computing (often synonymously called ubiquitous computing, palpable computing or ambient intelligence) is an emerging ?eld of research that brings in revolutionary paradigms for computing models in the 21st century. Pervasive c- puting is the trend towards increasingly ubiquitous connected computing devices in the environment, a trend being brought about by a convergence of advanced el- tronic – and particularly, wireless – technologies and the Internet. Recent advances in pervasive computers, networks, telecommunications and information technology, along with the proliferation of multimedia mobile devices – such as laptops, iPods, personal digital assistants (PDAs) and cellular telephones – have further stimulated the development of intelligent pervasive multimedia applications. These key te- nologiesarecreatingamultimediarevolutionthatwillhavesigni?cantimpactacross a wide spectrum of consumer, business, healthcare and governmental domains.

Richard Chbeir, Youakim Badr, Ajith Abraham, and Aboul-Ella Hassanien Abstract As the Web continues to grow and evolve, more and more data are becoming available. Particularly, multimedia and XML-based data are produced regularly and in increasing way in our daily digital activities, and their retrieval and access must be explored and studied in this emergent web-based era. This book provides reviews of the cutting-edge technologies and insights of various topics related to XML-based and multimedia information access and retrieval under the umbrella of Web Intelligence and reporting how organizations can gain compe- tive advantages by applying new different emergent techniques in the real-world scenarios. The primary target audience for the book includes researchers, scholars, postgraduate students and developers who are interested in advanced information retrieval on the web research and related issues. 1 Introduction Since the last two decades, Internet has changed our daily life by rede?ning the meanings and processes of business, commerce, marketing, ?nance, publishing, R. Chbeir Universite ' de Bourgogne, LE2I-UMR CNRS 5158, Fac. de Sciences Mirande, 21078 Dijon Cedex, France e-mail: richard.chbeir@u-bourgogne. fr Y. Badr INSA de Lyon, Universite ' de Lyon, Depart ' ement Informatique, 7 avenue Jean Capelle, 69621 Villeurbanne CX, France e-mail: youakim. badr@insa-lyon. fr A. Abraham Norwegian University of Science & Technology, Center for Quanti?able Quality of Service in Communication Systems, O. S. Bragstads plass 2E, 7491 Trondheim, Norway e-mail: ajith. abraham@ieee. org A. -E. Hassanien Kuwait University, College of Business & Administration, Dept.

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.

This book offers an essential guide to IoT Security, Smart Cities, IoT Applications, etc. In addition, it presents a structured introduction to the subject of destination marketing and an exhaustive review on the challenges of information security in smart and intelligent applications, especially for IoT and big data contexts. Highlighting the latest research on security in smart cities, it addresses essential models, applications, and challenges.Written in plain and straightforward language, the book offers a self-contained resource for readers with no prior background in the field. Primarily intended for students in Information Security and IoT applications (including smart cities systems and data heterogeneity), it will also greatly benefit academic researchers, IT professionals, policymakers and legislators. It is well suited as a reference book for both undergraduate and graduate courses on information security approaches, the Internet of Things, andreal-world intelligent applications.

The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.

This book provides a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of dynamic wireless sensor networks (WSNs) for intelligent and smart applications in a variety of environments. It presents the most central concepts associated with Dynamic Wireless Sensor Networks applications, and discusses issues surrounding Wireless Sensor Network Structures for complex and mobile-based applications. The book subsequently discusses several topics related to energy management in dynamic WSNs, and reviews the steps involved in building a secure and trusted data transmission model using the WSN applications of intelligent environments. Lastly, it discusses the applications of WSNs in live data systems such as SCADA systems. Readers will discover a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of WSNs for intelligent real-life applications. In addition, the book presents original research on the application of a dynamic WSN to solve the problem of energy consumption in a secure WSN during the process of data aggregation and transmission. Written by respected experts in the field, the book will stimulate further efforts in the application of the intelligent WSNs model, helping to solve the problem of data processing in a limited resource WSN-based environment.

This book offers an essential guide to IoT Security, Smart Cities, IoT Applications, etc. Primarily intended for students in Information Security and IoT applications (including smart cities systems and data heterogeneity), it will also greatly benefit academic researchers, IT professionals, policymakers and legislators.

This book presents the peer-reviewed proceedings of the 4th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2019), held in Cairo, Egypt, on March 28–30, 2019, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover the latest research on machine learning, deep learning, biomedical engineering, control and chaotic systems, text mining, summarization and language identification, machine learning in image processing, renewable energy, cyber security, and intelligence swarms and optimization.