Bishwajeet Kumar Pandey – författare

Network Evolution and Applications provides a comprehensive, integrative, and easy approach to understanding the technologies, concepts, and milestones in the history of networking. It provides an overview of different aspects involved in the networking arena that includes the core technologies that are essential for communication and important in our day-to-day life. It throws some light on certain past networking concepts and technologies that have been revolutionary in the history of science and technology and have been highly impactful. It expands on various concepts like Artificial Intelligence, Software Defined Networking, Cloud Computing, and Internet of Things, which are very popular at present. This book focuses on the evolutions made in the world of networking. One can’t imagine the world without the Internet today; with the Internet and the present- day networking, distance doesn’t matter at all. The COVID-19 pandemic has resulted in a tough time worldwide, with global lockdown, locked homes, empty streets, stores without consumers, and offices with no or fewer staff. Thanks to the modern digital networks, the culture of work from home (WFH) or working remotely with the network/Internet connection has come to the fore, with even school and university classes going online. Although WFH is not new, the COVID-19 pandemic has given it a new look, and industries are now willfully exploring WFH to extend it in the future. The aim of this book is to present the timeline of networking to show the developments made and the milestones that were achieved due to these developments.

Network Evolution and Applications provides a comprehensive, integrative, and easy approach to understanding the technologies, concepts, and milestones in the history of networking. It provides an overview of different aspects involved in the networking arena that includes the core technologies that are essential for communication and important in our day-to-day life. It throws some light on certain past networking concepts and technologies that have been revolutionary in the history of science and technology and have been highly impactful. It expands on various concepts like Artificial Intelligence, Software Defined Networking, Cloud Computing, and Internet of Things, which are very popular at present. This book focuses on the evolutions made in the world of networking. One can’t imagine the world without the Internet today; with the Internet and the present- day networking, distance doesn’t matter at all. The COVID-19 pandemic has resulted in a tough time worldwide, with global lockdown, locked homes, empty streets, stores without consumers, and offices with no or fewer staff. Thanks to the modern digital networks, the culture of work from home (WFH) or working remotely with the network/Internet connection has come to the fore, with even school and university classes going online. Although WFH is not new, the COVID-19 pandemic has given it a new look, and industries are now willfully exploring WFH to extend it in the future. The aim of this book is to present the timeline of networking to show the developments made and the milestones that were achieved due to these developments.

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.Features:Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.Explains computing models using real-world examples and dataset-based experiments.Includes case studies, quality diagrams, and demonstrations in each chapter.Describes modifications and optimization of existing technologies along with the novel big data computing models.Explores references to machine learning, deep learning, and graph processing.This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.Features:Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.Explains computing models using real-world examples and dataset-based experiments.Includes case studies, quality diagrams, and demonstrations in each chapter.Describes modifications and optimization of existing technologies along with the novel big data computing models.Explores references to machine learning, deep learning, and graph processing.This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.

This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.

This book gives readers a deep insight into cryptography and discusses the various types of cryptography algorithms used for the encryption and decryption of data. It also covers the mathematics behind the use of algorithms for encryption and decryption.FeaturesPresents clear insight to the readers about the various security algorithms and the different mechanisms used for data encryption.Discusses algorithms such as symmetric encryption, asymmetric encryption, digital signatures, and hash functions used for encryption.Covers techniques and methods to optimize the mathematical steps of security algorithms to make those algorithms lightweight, which can be suitable for voice encryption.Illustrates software methods to implement cryptography algorithms.Highlights a comparative analysis of models that are used in implementing cryptography algorithms.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.

The text offers practical insights, real-world examples, and actionable steps to implement Zero Trust principles effectively in cloud infrastructure. It covers key regulatory frameworks such as GDPR, HIPAA, and NIST 800-207, highlighting how Zero Trust aligns with compliance requirements. The book will provide readers with hands-on implementation walkthroughs tailored for major cloud providers, including AWS, Azure, Google Cloud, and Oracle Cloud.This book:Covers a detailed exploration of identity and access management (IAM), access control models, and shared responsibility in cloud environments. Bridges the gap between general cybersecurity principles and the unique challenges of cloud ecosystems. Discusses identity-centric security in cloud environments, continuous authentication, adaptive access, and managing identities across multi-cloud and hybrid environmentsHighlights the implementation, benefits, and limitations of role-based access control in traditional and cloud-native workloads. Showcases innovations in identity and access management, the role of blockchain and decentralized identity, and zero trust for edge computing. It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, edge computing, artificial intelligence, cloud computing, and information technology.

AI-Powered Cybersecurity Essentials: Protecting Enterprise Networks explores how modern AI and machine learning safeguard complex, hybrid enterprise environments. The book progresses from cybersecurity and ML foundations to applied defenses: AI-enhanced intrusion detection, malware and APT discovery, automated incident response, and predictive threat intelligence with risk analysis. It then covers securing cloud and hybrid networks, protecting IoT and edge devices, rigorous evaluation and validation of AI-driven solutions, and the ethical and regulatory guardrails that govern responsible deployment, closing with actionable future trends.Written for security architects, SOC analysts, network engineers, and researchers, the book blends principles with practical patterns, reference workflows, and implementation checklists. Readers will learn to design and tune AI-assisted controls, integrate them with existing stacks, operationalize detection and response, measure effectiveness, and navigate governance and compliance—so they can confidently deploy resilient, human-centered, AI-enabled defenses across the enterprise.