Meenu Gupta – författare

Focusing on the challenges, directions, and future predictions with the role of 5G in smart healthcare monitoring, this book offers the fundamental concepts and analyses on the methods to apply Internet of Things (IoT) in monitoring devices for diagnosing and transferring data. It also discusses self-managing to help providers improve their patients' healthcare experience.Smart Healthcare Monitoring Using IoT with 5G: Challenges, Directions, and Future Predictions illustrates user-focused wearable devices such as Fitbit health monitors and smartwatches by which consumers can self-manage and self-monitor their own health. The book covers new points of security and privacy concerns, with the expectation of IoT devices gaining more popularity within the next ten years. Case studies depicting applications and best practices as well as future predictions of smart healthcare monitoring by way of a 5G network are also included.Interested readers of this book include anyone working or involved in research in the field of smart healthcare, such as healthcare specialists, computer science engineers, electronics engineers, and pharmaceutical practitioners.

Focusing on the challenges, directions, and future predictions with the role of 5G in smart healthcare monitoring, this book offers the fundamental concepts and analyses on the methods to apply Internet of Things (IoT) in monitoring devices for diagnosing and transferring data. It also discusses self-managing to help providers improve their patients' healthcare experience.Smart Healthcare Monitoring Using IoT with 5G: Challenges, Directions, and Future Predictions illustrates user-focused wearable devices such as Fitbit health monitors and smartwatches by which consumers can self-manage and self-monitor their own health. The book covers new points of security and privacy concerns, with the expectation of IoT devices gaining more popularity within the next ten years. Case studies depicting applications and best practices as well as future predictions of smart healthcare monitoring by way of a 5G network are also included.Interested readers of this book include anyone working or involved in research in the field of smart healthcare, such as healthcare specialists, computer science engineers, electronics engineers, and pharmaceutical practitioners.

Artificial Intelligence and Machine Learning for Women’s Health Issues discusses the applications, challenges, and solutions that machine learning can bring to women’s health challenges. The book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning which enhance the future healthcare system. This book's primary focus is on women’s health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women’s health issues.Provides fundamental concepts and analysis of machine learning algorithms used to aid in the diagnosis of women’s health issuesGuides researchers to specific ideas, tools, and practices most applicable to product/service development, innovation problems, and opportunitiesProvides hands-on chapters that describe frameworks, applications, best practices, and case studies of future directions of applied machine learning in women’s healthcare

Bone deformations can lead to musculoskeletal disorders and negatively impact on individual quality of life. Early and accurate detection of bone deformation is crucial for effective medical intervention. Diagnosing Musculoskeletal Conditions Using Artificial Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging outlines a comprehensive approach that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques to identify bone deformations promptly and accurately. By leveraging advanced image analysis and pattern recognition, this approach aims to revolutionize the field of orthopedic diagnostics. This book covers challenges, technologies, applications, and future trends of AI and ML in Bone Deformation, addressing advanced and innovative techniques, frameworks, methodologies, and practical implementations of machine learning to get early predictions of Bone deformation. Written by experts in the field for researchers, surgeons, students, and instructors interested in musculoskeletal conditions.Presents fundamental concepts and analysis of machine learning algorithms and diagnoses in bone deformationAddresses human health issues related to bone deformation using a different machine learning algorithmProvides innovative, new approaches for machine learning in musculoskeletal conditions with its future directions

Climate change, biodiversity loss, resource depletion, and pollution are intensifying. Addressing these global issues requires fresh perspectives and cutting-edge solutions that integrate AI with environmental sustainability. AI and computational intelligence technologies are evolving at an unprecedented pace, offering new tools and methodologies for tackling environmental challenges. Yet existing resources often fail to capture the latest breakthroughs and interdisciplinary approaches needed to harness these innovations effectively.AI and Computational Intelligence for Environmental Sustainability showcases the potential of artificial intelligence and computational intelligence in tackling critical environmental challenges. As the urgency to combat climate change, optimize resource utilization, and promote sustainable practices grows, this book provides a comprehensive resource that bridges the gap between AI research and its practical applications in environmental sustainability, and offers insights into the latest developments and methodologies. It highlights real-world applications and case studies to inspire researchers, developers, and policymakers to create and implement AI-driven solutions for a more sustainable future, foster collaboration by bringing together experts from diverse fields to share knowledge and foster interdisciplinary collaboration in the pursuit of environmental sustainability. It also addresses the ethical and practical challenges of applying AI in sustainability, advocating for responsible AI development and low-energy computing practices.Explores AI applications across diverse environmental domains, offering a 360-degree view of the fieldProvides actionable methodologies, frameworks, and tools for deploying AI solutionsShowcases successful implementations, bridging theory and practiceHighlights emerging trends and future directions to inspire innovation

Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines.Features• Covers the fundamentals, history, reality and challenges of cancer• Presents concepts and analysis of different cancers in humans• Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer• Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction• Explores the future prospects in cancer prediction and treatmentReaders will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions.This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.

Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines.Features• Covers the fundamentals, history, reality and challenges of cancer• Presents concepts and analysis of different cancers in humans• Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer• Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction• Explores the future prospects in cancer prediction and treatmentReaders will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions.This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.

Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. Artificial intelligent systems offer great improvement in healthcare systems by providing more intelligent and convenient solutions and services assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing. Modern health treatments are faced with the challenge of acquiring, analysing, and applying the large amount of knowledge necessary to solve complex problems. AI techniques are being effectively used in the field of healthcare systems by extracting the useful information from the vast amounts of data by applying human expertise and CI methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods which have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice.This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with them. Contained in the book are state-of-the-art CI methods and other allied techniques used in healthcare systems as well as advances in different CI methods that confront the problem of effective data analysis and storage faced by healthcare institutions.The objective of this book is to provide the latest research related to the healthcare sector to researchers and engineers with a platform encompassing state-of-the-art innovations, research and design, and the implementation of methodologies.

Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. Artificial intelligent systems offer great improvement in healthcare systems by providing more intelligent and convenient solutions and services assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing. Modern health treatments are faced with the challenge of acquiring, analysing, and applying the large amount of knowledge necessary to solve complex problems. AI techniques are being effectively used in the field of healthcare systems by extracting the useful information from the vast amounts of data by applying human expertise and CI methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods which have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice.This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with them. Contained in the book are state-of-the-art CI methods and other allied techniques used in healthcare systems as well as advances in different CI methods that confront the problem of effective data analysis and storage faced by healthcare institutions.The objective of this book is to provide the latest research related to the healthcare sector to researchers and engineers with a platform encompassing state-of-the-art innovations, research and design, and the implementation of methodologies.

In today’s era, there is a need for a system that can automate the process of treatment for the patient if medical facilities are out of reach. Smart healthcare can step in to make the patient more self-dependent. 6G with its features can be seen as the future of smart healthcare with IoT and AI.6G-Enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis offers the fundamentals, history, reality, and challenges faced in the smart healthcare industry today. It discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used. The book details the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systems. The book goes on to presents applications of smart healthcare through various real-world examples and includes chapters on security and privacy in the 6G-enabled and IoT environment, as well as research on the future prospects of the smart healthcare industry.This book:Offers the fundamentals, history, reality, and the challenges faced in the smart healthcare industryDiscusses the concepts, tools, and techniques of smart healthcare as well as the analysis usedDetails the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systemsPresents applications of smart healthcare through various real-world examplesIncludes topics on security and privacy in 6G-enabled IoT, as well as research and future prospectus of the smart healthcare industryInterested readers of this book will include anyone working in or involved in smart healthcare research which includes, but is not limited to healthcare specialists, computer science engineers, electronics engineers, systems engineers, and pharmaceutical practitioners.

In today’s era, there is a need for a system that can automate the process of treatment for the patient if medical facilities are out of reach. Smart healthcare can step in to make the patient more self-dependent. 6G with its features can be seen as the future of smart healthcare with IoT and AI.6G-Enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis offers the fundamentals, history, reality, and challenges faced in the smart healthcare industry today. It discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used. The book details the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systems. The book goes on to presents applications of smart healthcare through various real-world examples and includes chapters on security and privacy in the 6G-enabled and IoT environment, as well as research on the future prospects of the smart healthcare industry.This book:Offers the fundamentals, history, reality, and the challenges faced in the smart healthcare industryDiscusses the concepts, tools, and techniques of smart healthcare as well as the analysis usedDetails the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systemsPresents applications of smart healthcare through various real-world examplesIncludes topics on security and privacy in 6G-enabled IoT, as well as research and future prospectus of the smart healthcare industryInterested readers of this book will include anyone working in or involved in smart healthcare research which includes, but is not limited to healthcare specialists, computer science engineers, electronics engineers, systems engineers, and pharmaceutical practitioners.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

This book provides a thorough exploration of the intersection between gender-based healthcare disparities and the transformative potential of artificial intelligence (AI) and machine learning (ML). It covers a wide range of topics from fundamental concepts to practical applications.Transforming Gender-Based Healthcare with AI and Machine Learning incorporates real-world case studies and success stories to illustrate how AI and ML are actively reshaping gender-based healthcare and offers examples that showcase tangible outcomes and the impact of technology in healthcare settings. The book delves into the ethical considerations surrounding the use of AI and ML in healthcare and addresses issues related to privacy, bias, and responsible technology implementation. Empasis is placed on patient-centered care, and the book discusses how technology empowers individuals to actively participate in their healthcare decisions and promotes a more engaged and informed patient population. Written to encourage interdisciplinary collaboration and highlight the importance of cooperation between health professionals, technologies, researchers, and policymakers, this book portrays how this collaborative approach is essential for achieving transformative goals and is not only for professionals but can also be used at the student level as well.

This book provides a thorough exploration of the intersection between gender-based healthcare disparities and the transformative potential of artificial intelligence (AI) and machine learning (ML). It covers a wide range of topics from fundamental concepts to practical applications.Transforming Gender-Based Healthcare with AI and Machine Learning incorporates real-world case studies and success stories to illustrate how AI and ML are actively reshaping gender-based healthcare and offers examples that showcase tangible outcomes and the impact of technology in healthcare settings. The book delves into the ethical considerations surrounding the use of AI and ML in healthcare and addresses issues related to privacy, bias, and responsible technology implementation. Empasis is placed on patient-centered care, and the book discusses how technology empowers individuals to actively participate in their healthcare decisions and promotes a more engaged and informed patient population. Written to encourage interdisciplinary collaboration and highlight the importance of cooperation between health professionals, technologies, researchers, and policymakers, this book portrays how this collaborative approach is essential for achieving transformative goals and is not only for professionals but can also be used at the student level as well.

Sophisticated data analysis is revolutionizing healthcare decision- making, personalized treatments, and operational efficiency. Innovations in HealthTech covers this impact and highlights the significance of robust security measures in safeguarding sensitive medical data and ensuring patient confidentiality. The book provides insights into the development, implementation, and future potential of telemedicine infrastructure.Explores the role of wearable technology in promoting patient engagement and wellness and addressing the critical issue of data security.Offers practical technical insights that provide a deeper understanding of the real-world applications and challenges in the healthcare technology landscape.Examines the role of telehealth and remote monitoring in healthcare accessibility, and use of artificial intelligence in augmenting clinical decision-making processes.Discusses frameworks and guidelines that enable different health technologies and systems to work together seamlessly, ensuring efficient data exchange and improved patient care.Presents the essential measures and strategies in place to protect sensitive healthcare data, ensuring the confidentiality and integrity of information.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and biomedical engineering.

Sophisticated data analysis is revolutionizing healthcare decision- making, personalized treatments, and operational efficiency. Innovations in HealthTech covers this impact and highlights the significance of robust security measures in safeguarding sensitive medical data and ensuring patient confidentiality. The book provides insights into the development, implementation, and future potential of telemedicine infrastructure.Explores the role of wearable technology in promoting patient engagement and wellness and addressing the critical issue of data security.Offers practical technical insights that provide a deeper understanding of the real-world applications and challenges in the healthcare technology landscape.Examines the role of telehealth and remote monitoring in healthcare accessibility, and use of artificial intelligence in augmenting clinical decision-making processes.Discusses frameworks and guidelines that enable different health technologies and systems to work together seamlessly, ensuring efficient data exchange and improved patient care.Presents the essential measures and strategies in place to protect sensitive healthcare data, ensuring the confidentiality and integrity of information.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and biomedical engineering.

This book compiles the proceedings of the International Conference on Smart Technologies and Intelligent Computing (INCSTIC 2025), organized by the School of Computer Science and Engineering, Geeta University, Panipat, Haryana, India.It brings together innovative insights and practical solutions in diverse domains such as Smart Technologies, Intelligent Computing, Artificial Intelligence, Machine Learning, Internet of Things (IoT), Cloud Computing, Big Data Analytics, Advanced Networking Solutions, System Design and Methodologies, and ICT for Sustainability. With novel approaches and real-world applications, this collection of peer-reviewed papers aims to address emerging challenges in smart and intelligent systems.This volume serves as a valuable resource for researchers, academicians, industry professionals, and students in the fields of Computer Science, Artificial Intelligence, Smart Systems, and Multidisciplinary Technologies. It aims to inspire collaboration and knowledge exchange among academia and industry, fostering innovation that drives the future of sustainable computing.

AIoT for Smart Transportation: Transforming the future of mobility explores the convergence of artificial intelligence (AI) and the internet of things (IoT) technologies in the context of modern transportation systems.Intelligent transportation systems (ITS) leverage cutting-edge information, electronic, sensing, communication, control, and management technologies to transform raw traffic data, gathered from individuals, vehicles, and road infrastructure within the transportation network, into valuable and actionable information. By facilitating real-time communication and seamless connectivity through robust communication systems, ITS can enhance the synergy among people, vehicles, and road infrastructure.This book explores the dynamic intersection of AIoT technologies and their pivotal role in shaping the future of transportation systems. It looks at the critical challenges posed by urbanization, and the demand for efficient, safe, and sustainable mobility, featuring chapters exploring the challenges, technologies, applications, and future trends of AIoT in realizing intelligent transportation. Topics include: AIoT for real-time traffic analysis and optimisation, the role of data and sensors, security and safety, and sustainable mobility.This book aims to help readers understand and leverage the potential of AIoT in modern transportation, providing insights and practical knowledge for those working, or interested, in the evolving landscape of smart transportation. It is intended to be a useful resource for researchers from industry and academia, scientists, and engineers in the fields of ICT and networking, IoT, smart technologies, and transportation.

Although some IoT systems are built for simple event control where a sensor signal triggers a corresponding reaction, many events are far more complex, requiring applications to interpret the event using analytical techniques to initiate proper actions. Artificial intelligence of things (AIoT) applies intelligence to the edge and gives devices the ability to understand the data, observe the environment around them, and decide what to do best with minimum human intervention. With the power of AI, AIoT devices are not just messengers feeding information to control centers. They have evolved into intelligent machines capable of performing self-driven analytics and acting independently. A smart environment uses technologies such as wearable devices, IoT, and mobile internet to dynamically access information, connect people, materials and institutions, and then actively manages and responds to the ecosystem's needs in an intelligent manner.In this edited book, the contributors present challenges, technologies, applications and future trends of AIoT in realizing smart and intelligent environments, including frameworks and methodologies for applying AIoT in monitoring devices and environments, tools and practices most applicable to product or service development to solve innovation problems, advanced and innovative techniques, and practical implementations to enhance future smart environment systems. Chapters cover a broad range of applications including smart cities, smart transportation and smart agriculture.This book is a valuable resource for industry and academic researchers, scientists, engineers and advanced students in the fields of ICTs and networking, IoT, AI and machine and deep learning, data science, sensing, robotics, automation and smart technologies and smart environments.

This book provides an overview of AI-powered nutritional interventions for the management of Polycystic Ovary Syndrome (PCOS).