Mohd Anas Wajid – författare

This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis.This book:Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computationExamines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real lifeServes as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of ThingsCovers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cyberneticsIllustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industriesThe text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

The text employs computational techniques and large-scale data analysis to study complex social phenomena and human behavior. It discusses diverse methodologies, including agent-based modeling, network analysis, natural language processing, and machine learning, to gain insights into topics ranging from social network dynamics and opinion formation to economic trends and public health crises.Features:Discusses the theoretical background of each algorithm in detail and presents the applications of each method.Presents artificial intelligence implications, sustainable artificial intelligence, and the importance of artificial intelligence in agriculture, and energy.Explains the use of predictive modeling in computational social science and applications of computational social science.Showcases the framework for social network analysis, application program interface, data collection methods, and data preprocessing.Covers topics such as density-based spatial clustering of applications with noise, the role of clustering in computational social science, and clustering in network structure.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

This book discusses the concept of the digital twin, which has the potential to change how systems are managed and created. It also discusses the metaverse as a new technology with literary roots, cross-platform avatars, and artificial intelligence-related cybersecurity risks.Digital Twin for Smart Cities and Urban Planning: From Virtual to Reality provides practitioners with concrete problem-solving methodologies while covering the most recent and cutting-edge digital twin application technologies in diverse fields. It highlights the benefits of digital twins in terms of data visualization, real-time data analytics, and learning, which leads to increased confidence in decision-making. The book discusses the metaverse as a new technology with literary roots, cross-platform avatars, and artificial intelligence-related cybersecurity risks. It also evaluates the opportunities that DT can provide for smart cities and discusses the prerequisites for secure, safe, and sustainable smart cities. It also explores the mix between the industrial Internet of Things, artificial intelligence, machine learning, and software analytics with spatial network graphs to construct living digital simulation models that update and alter in response to changes in their physical counterparts. The chapters also focus on digital twin driven smart design which establishes a foundation for the adoption of digital twin technology in product design by drawing on the most recent industry practice and research.The book is an excellent resource for practitioners and scholars in manufacturing, operations research, and communications who are thinking about digitizing their assets and related services. It is also a helpful resource for graduate students and academics looking to better understand pioneering digital twins technologies.

This reference text covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and engages in a discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication. It further presents concepts and theories related to high-speed communication systems, which are important in intelligent wireless communications.Features:• Discusses machine learning-based network management strategy in wireless systems, and machine learning-inspired big data analytics frameworks for wireless network applications.• Presents high-speed communication systems, deep learning for wireless networks, security aspects in wireless networks, and decision-making for wireless networks.• Highlights the importance of using deep reinforcement learning in intelligent wireless networks and deep reinforcement learning-based mobile data offloading frameworks.• Covers novel network architectures for distributed edge learning, and privacy issues in distributed edge learning.• Illustrates experimentation and deep learning-based simulations in networking systems, deep learning-based communication frameworks for multiuser detection and sparse channel estimation.The book is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

This book explores how data science, which involves preparing, analyzing, visualizing, and interpreting data, can revolutionize the field of education. The authors delve into how schools and universities can analyze data to improve teaching methods, enhance student learning, and design effective evaluations.Learning Analytics: Shaping the Future of Education with Data Science examines how machine learning algorithms can analyze individual student performance data to tailor personalized adaptive learning paths, ensuring the best educational experience. Through real-world examples, this book discusses how valuable insights and opportunities can be gained through the application of data science in educational environments. The authors discuss the application of natural language processing (NLP) to analyze educational content, providing insights into language usage, comprehension levels, and improving the effectiveness of instructional materials and examines computer vision in classroom dynamics to measure student engagement. The book also exposes the reader to the crucial role of cybersecurity in safeguarding sensitive student and institutional information, ensuring a secure learning environment, and protecting against cyber threats. It also addresses the ethical considerations and privacy concerns associated with collecting, analyzing, and making decisions from educational data. Finally, it emphasizes the importance of responsible practices to protect the rights and well-being of students and educators.The book is intended for engineers from computer science, government policymakers, institutions, and educational stakeholders. It shows how computer science, statistics, and data can personalize learning, improve educational tools, enhance classroom dynamics, secure academic records with blockchain, and ensure online safety.