Aryan Chaudhary – författare

Connected Diagnoses: IoT, Healthcare, and Digital Forensics investigates the complex intersection of IoT, healthcare, and digital forensics. The book explores the intricate relationships between these fields, with a focus on cybersecurity, patient data ethics, and challenges in IoT investigations. It advances knowledge on leveraging IoT securely to enhance patient care and digital forensic analysis, providing significant insights from experts along with practical guidance for those operating at the crossroads of these critical disciplines. The book helps professionals grasp, adapt to, and capitalize on the interconnected nature of emerging technologies to ensure ethics, security, and safety. Users will find this to be a comprehensive resource that benefits researchers and practitioners who seek to understand the convergence of medical technology, interconnected devices, and digital forensics.Presents best practices for the authentication, encryption, and other security protocols to proactively safeguard patient data and healthcare infrastructureAddresses a critical gap in current literature by offering practical insights and solutions for professionals working at the intersection of IoT, healthcare, and digital forensicsExplains the complexities of interrelated medical technology and clarifies how they affect cybersecurity, digital forensic investigations, and patient care

The book showcases how advanced cybersecurity and forensic techniques can be applied to various computational issues. It further covers the advanced exploitation tools that are used in the domain of ethical hacking and penetration testing.• Focuses on tools used in performing mobile and SIM forensics, static and dynamic memory analysis, and deep web forensics• Covers advanced tools in the domain of data hiding and steganalysis• Discusses the role and application of artificial intelligence and big data in cybersecurity• Elaborates on the use of advanced cybersecurity and forensics techniques in computational issues• Includes numerous open-source tools such as NMAP, Autopsy, and Wireshark used in the domain of digital forensicsThe text is primarily written for senior undergraduates, graduate students, and academicresearchers, in the fields of computer science, electrical engineering, cybersecurity,and forensics.

Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization, and gradient-based non-linear optimization, and their applications.Discusses important topics including multi-component differential equations, geometric partial differential equations, and computational neural systemsCovers linear integer programming and network design problems, along with an application of the mixed integer problemsDiscusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domainElucidates the application of statistical models, probability models, and transfer learning conceptsShowcases the importance of multi-attribute decision modeling in the domain of image processing and soft computingThe text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science.

The book showcases how advanced cybersecurity and forensic techniques can be applied to various computational issues. It further covers the advanced exploitation tools that are used in the domain of ethical hacking and penetration testing.• Focuses on tools used in performing mobile and SIM forensics, static and dynamic memory analysis, and deep web forensics• Covers advanced tools in the domain of data hiding and steganalysis• Discusses the role and application of artificial intelligence and big data in cybersecurity• Elaborates on the use of advanced cybersecurity and forensics techniques in computational issues• Includes numerous open-source tools such as NMAP, Autopsy, and Wireshark used in the domain of digital forensicsThe text is primarily written for senior undergraduates, graduate students, and academicresearchers, in the fields of computer science, electrical engineering, cybersecurity,and forensics.

Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization, and gradient-based non-linear optimization, and their applications.Discusses important topics including multi-component differential equations, geometric partial differential equations, and computational neural systemsCovers linear integer programming and network design problems, along with an application of the mixed integer problemsDiscusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domainElucidates the application of statistical models, probability models, and transfer learning conceptsShowcases the importance of multi-attribute decision modeling in the domain of image processing and soft computingThe text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:An overview of applications and computing devicesDeep learning impacts in the field of AIDeep learning as state-of-the-art approach to AIExploring deep learning architecture for cutting-edge AI solutionsOperations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:An overview of applications and computing devicesDeep learning impacts in the field of AIDeep learning as state-of-the-art approach to AIExploring deep learning architecture for cutting-edge AI solutionsOperations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

The book provides a comprehensive understanding of how blockchain technology can revolutionize healthcare by improving patient outcomes, enhancing data privacy, and driving innovative solutions to industry challenges. Blockchain in Health Sciences is an essential roadmap for navigating the complex landscape of blockchain technology in healthcare. From foundational concepts to real-world applications, this book empowers understanding to harness the potential of blockchain to improve patient outcomes, enhance data privacy, and optimize healthcare delivery. Delve into the integration of blockchain with the Internet of Things and AI to uncover groundbreaking solutions for challenges faced by the healthcare industry. Gain insights into the regulatory and ethical implications of blockchain in healthcare, ensuring responsible and effective implementation. Each chapter unveils the current uses of blockchain in drug discovery, drug and device tracking, real-world data collection, and increased patient engagement, used to unlock opportunities to advance health sciences research. This book is an essential guide for readers exploring opportunities to empower and enable data in health science research. Readers will find the volume: Introduces the fundamentals of blockchain and its integration with IoT in healthcare;Provides practical applications across patient records, drug supply chains, and genomics research;Explores the synergy of AI, IoT, and blockchain for unprecedented healthcare advancements.Audience Healthcare professionals, researchers, policymakers, IT experts, and anyone interested in the future of healthcare.

The recent explosion of technology in healthcare has rapidly changed the healthcare sector. Technologies such as artificial intelligence and machine learning along with the integration of the Internet of Medical Things have evolved to tackle the need for remote healthcare systems, augmenting them in a self-sustainable way. This new volume explores the many important smart technologies that can make healthcare delivery and monitoring faster, more efficient, and less invasive. It looks at computational tactics as applied to the development of biomedical applications using artificial intelligence, machine learning, signal analysis, computer-aided design, robotics and automation, biomedical imaging, telemedicine, and other technologies. The book provides a solid framework to give the modern class of medical gearheads information on the innovative applications of computational mechanisms for improving and expediting patient-friendly automation in healthcare.

Digital analytics is an emerging new trend used in education to measure, collect, analyze, and report on data about learners and their contexts and to understand and optimize learning and learning environments. Taking into consideration the UN's Sustainable Development Goal 4, which aims to "ensure inclusive and equitable quality education and promote lifelong learning opportunities for all," this new book looks at digital technologies as a means to foster sustainable educational innovations for improving the teaching, learning, and assessment from K-12 to higher education. It demonstrates how artificial intelligence, deep learning, cloud computing, big data, and machine learning can be used to assess, evaluate, record, and predict student progress, participation, performance, personalization, and empowerment in academic and curricular activities.

Technology is an indispensable part of the business world with firms using digital platforms to gain/create, maintain and deliver customer experience, especially post-pandemic. Digitization involves the comprehensive integration of digital technology into all facets of a company’s operations, yielding heightened value for customers. This book, Digital Transformation in the Customer Experience, looks at how digital transformation can help both service and manufacturing firms to deliver better customer experience.This book will enhance readers’ understanding of the disruptive technologies that have brought businesses to their current digital state, including social media, artificial intelligence (AI), big data, machine learning, and the Internet of Things (IoT). Numerous examples and exhibits are employed to illustrate the book’s comprehensive digital framework. It presents case studies of digital implementation across diverse industries and provides implementation templates for businesses. Designed to support careers in consulting and the execution of digital strategies within companies, the book covers all essential aspects. To offer insights into practical digital transformation in real-world scenarios, the book incorporates case studies and examples spanning various industries. Its organized structure makes it accessible to students, instructors, and junior and mid-level executives. Furthermore, the book seeks to disseminate novel technical concepts and features that can be integrated into daily life for the betterment of society.The book addresses important questions such as the relevance of e-service quality in customer satisfaction and loyalty, impact and effect of adware, malware and spyware on the digital environment, the Internet of Everything in marketing, digital entertainment, digital transformation in healthcare and more. The book also covers the impact of digitalization on education, finance and banking as well as the hospitality industry. Sensitive topics like the influence of Facebook on consumer engagement and the impact of social media marketing communications on teenagers’ attitudes, etc. are also addressed in the book.

In recent years, the urgency to address climate change and transition toward cleaner, more sustainable energy sources has intensified. At the same time, the rapid advancements in artificial intelligence (AI) technologies have opened up new possibilities for optimizing renewable energy systems and enhancing their efficiency, reliability, and affordability. Recognizing the immense potential of this synergy, this new volume, Green Energy Technologies and AI for Sustainable Development, explores and systematically examines the synergies between renewable energy and AI and their transformative potential for fostering sustainability and innovation.The chapters delve into the intricate intersections of renewable energy and AI across diverse fields, offering a holistic examination of foundational technologies, renewable energy applications, and sustainable development initiatives.The book first lays the foundation by looking at the fundamental technologies and applications driving the integration of renewable energy and AI. From maximizing the power output of solar photovoltaic systems to optimizing green infrastructure management through data-driven approaches, the book provides a comprehensive overview of the synergies.The book then shifts focus to specific renewable energy technologies, examining challenges and solutions in solar PV system integration, the environmental impact of solar panels, power converter topologies in wind energy conversion systems, and emerging technologies for wind power generation, among others. The volume explores the role of renewable energy and AI in rural development, sustainable agriculture, smart homes, green transportation, and more. From empowering rural communities through bio-energy solutions to harnessing AI for energy security in developing nations, the volume highlights the potential for transformative change.This volume will serve as valuable resource for researchers, policymakers, industry professionals, and others seeking to navigate the complex dynamics of renewable energy and artificial intelligence in pursuing sustainable development.

This new book, Demystifying Sustainable Farming Using Smart Technologies, helps to bridge the gap between traditional agricultural practices and the technological innovations that are revolutionizing the field, focusing on the transformative potential of artificial intelligence (AI) and machine learning (ML) in agriculture.Covering myriad topics that encompass the essence of smart agriculture, the book highlights the concept of Agriculture 5.0, covering its scope and challenges. It looks at sustainable agriculture from various angles, proposing technological means for increasing efficiency with regard to supply chain, pest control, farming techniques, climate change, irrigation, and more. From precision farming and predictive analytics to automated machinery and IoT-enabled devices, each chapter unveils the practical applications of technology in agriculture. These innovations are not only making farming more efficient but are also paving the way for more resilient and adaptable agricultural systems.Technologies covered in the book include agricultural drones and robotics, the Internet of Things, blockchain technology, image processing-based applications, smart monitoring devices, augmented and virtual reality applications, data-driven mobile apps, and more.By harnessing the power of these technologies, we can optimize crop yields, enhance resource efficiency, and make informed decisions that promote sustainability. This volume highlights the integration of AI and ML into farming practices to address some of the most pressing challenges in agriculture today.

Photonic sensors are the most promising and rapidly advancing areas of photonics today, with applications in healthcare, environmental monitoring, and industrial applications. This new book provides an overview of a wide range of new and innovative technologies and current applications of photonic sensors for biomedical applications. Beginning with an overview of basic elements that make up the photonic sensors, the authors lay out the principles of light-matter interaction to the complexities surrounding optical waveguides and then delve into the richest part of the field of sensors: biosensors that are made for detecting and measuring certain biochemical substances. The book covers biosensors, surface plasmon resonance sensors, one- and two-dimensional photonic crystals for sensing and detection applications, muscle strength monitoring using optical sensors, AI in photonics, versatile fiber Bragg gratings sensors, highly sensitive surface plasmon resonance sensors, etc. The exciting realm of MEMS optical sensors that illustrate the union of miniaturization and photonics in sensing technology is also covered, along with case studies. The authors also take us through using artificial intelligence with photonics sensors that help improve data analysis, pattern recognition, and decision-making.

Fuzzy logic, a set of rules that can be used to reach logical conclusions from fuzzy sets of data, is a useful way of determining relevant relationships from imprecise data and has many uses in real-life applications. This new volume provides in-depth information and research on fuzzy logic and its many beneficial and versatile applications in diverse fields of engineering and other areas. It explores the varied applications of fuzzy logic in many sectors, such as in manufacturing and in the automotive industry, in the healthcare and allied health sciences, in mathematics and allied domains, in business and finance, in electronics and computer science, and more.

In this internet age of security challenges and threats from cybercrime, enhanced security measures are necessary. The zero trust model—the IT security model that requires strict identity verification for every person and device trying to access resources on a private network—helps to meet these everincreasing and evolving security challenges. This new volume offers a comprehensive overview of the zero trust security model and its application in the field of cybersecurity, covering the principles, technologies, and best practices for implementing a zero trust approach, equipping readers with the knowledge and tools to secure their digital environments effectively. This book stands out by providing a holistic view of the zero trust security model, combining practical guidance for professionals with educational insights for both professionals and students and offering real-world examples and case studies that bridge the gap between learning and implementation.

This comprehensive book brings together a diverse collection of insights from experts in medicine, computer science, and data analytics to explore the evolving role of artificial intelligence in the healthcare sector. The chapters delve into various facets of AI applications, from legal considerations and mathematical foundations to the practical implementations of machine learning and deep learning in healthcare.

Natural language processing enables communication between people and computers as well as automatic translation to facilitate easy interaction with others around the world. This new volume focuses on natural language processing in conjunction with artificial intelligence and allied areas, discussing advanced applications, approaches, and techniques for computational models of information and how they are presented by language (artificial, human, or natural in other ways). It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes.Key features:Addresses the functional frameworks and workflow that are trending in NLP and AILooks at the latest technologies and the major challenges, issues, and advances in NLP and AIExplores an intelligent field monitoring and automated system through AI with NLP and its implications for the real worldDiscusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP

This open access book constitutes the proceedings of the Second International Conference on Data Science and Exploration in Artificial Intelligence, CODE-AI 2025, which took place in Dubai, UAE, during April 7-8, 2025.The 67 full papers included in these proceedings were carefully reviewed and selected from 750 submissions.The thematic scope of CODE-AI 2025 spanned intelligent computing methods (including genetic algorithms, simulated annealing, artificial fish-swarm algorithms, quantum computing and fuzzy logic), advanced AI applications (such as biometrics, pattern recognition, computer and machine vision, speech recognition and smart robotics) and data-science topics (deep learning, decision-making frameworks, IoT/edge data integration and visualization.

This open access book constitutes the proceedings of the Second International Conference on Data Science and Exploration in Artificial Intelligence, CODE-AI 2025, which took place in Dubai, UAE, during April 7-8, 2025..The 67 full papers included in these proceedings were carefully reviewed and selected from 750 submissions.The thematic scope of CODE-AI 2025 spanned intelligent computing methods (including genetic algorithms, simulated annealing, artificial fish-swarm algorithms, quantum computing and fuzzy logic), advanced AI applications (such as biometrics, pattern recognition, computer and machine vision, speech recognition and smart robotics) and data-science topics (deep learning, decision-making frameworks, IoT/edge data integration and visualization.

Manufacturing is undergoing a profound digital transformation, and two technologies are at the forefront of this shift: photonics and the Industrial Internet of Things (IIoT). This book offers a comprehensive perspective on how these technologies intersect to create smarter, safer, and more productive industrial environments. By bridging the science of light generation, manipulation, and detection with intelligent, interconnected industrial systems, this work highlights how photonics-enabled IIoT can deliver unprecedented accuracy, efficiency, and automation.Beginning with clear introductions to the foundational principles of photonics and IoT, the book builds toward their practical integration in industrial environments. It explores how photonic technologies support precision measurement, quality assurance, and process optimization, and it explains the communication protocols and standards that enable secure and reliable machine-to-machine interaction.You’ll gain insight into how data gathered from photonic sensors can be processed using machine learning for enhanced analytics, predictive maintenance, and informed decision-making. Real-world case studies illustrate both the tangible benefits and implementation challenges of photonics-based IIoT systems across manufacturing sectors.The book concludes with a forward-looking analysis of emerging trends, research directions, and expected advancements in photonics and industrial connectivity. As global demand for smart manufacturing accelerates, this timely work underscores the pivotal role of photonics in enabling high-speed, high-precision, and cost-efficient industrial operations—ultimately driving productivity, safety, and competitiveness in the era of intelligent automation.What You Will LearnGain an understanding of photonics technology and its application within the IIoT ecosystemDelve into the methods of integrating photonics with existing industrial processesReview the latest innovations in manufacturing that have been made possible through photonics and IIoTFind out the critical aspects of security and safety in photonics-integrated IIoT systems, providing readers with strategies to mitigate risksWho This Book Is ForIndividuals with a foundational understanding of industrial processes and a keen interest in IoT technological advancements in IoT looking to integrate it with photonics into their operations