Surbhi Bhatia – författare

An Interdisciplinary Approach to Modern Network Security presents the latest methodologies and trends in detecting and preventing network threats. Investigating the potential of current and emerging security technologies, this publication is an all-inclusive reference source for academicians, researchers, students, professionals, practitioners, network analysts and technology specialists interested in the simulation and application of computer network protection. It presents theoretical frameworks and the latest research findings in network security technologies, while analyzing malicious threats which can compromise network integrity. It discusses the security and optimization of computer networks for use in a variety of disciplines and fields. Touching on such matters as mobile and VPN security, IP spoofing and intrusion detection, this edited collection emboldens the efforts of researchers, academics and network administrators working in both the public and private sectors. This edited compilation includes chapters covering topics such as attacks and countermeasures, mobile wireless networking, intrusion detection systems, next-generation firewalls, web security and much more. Information and communication systems are an essential component of our society, forcing us to become dependent on these infrastructures. At the same time, these systems are undergoing a convergence and interconnection process that has its benefits, but also raises specific threats to user interests. Citizens and organizations must feel safe when using cyberspace facilities in order to benefit from its advantages. This book is interdisciplinary in the sense that it covers a wide range of topics like network security threats, attacks, tools and procedures to mitigate the effects of malware and common network attacks, network security architecture and deep learning methods of intrusion detection.

An Interdisciplinary Approach to Modern Network Security presents the latest methodologies and trends in detecting and preventing network threats. Investigating the potential of current and emerging security technologies, this publication is an all-inclusive reference source for academicians, researchers, students, professionals, practitioners, network analysts and technology specialists interested in the simulation and application of computer network protection. It presents theoretical frameworks and the latest research findings in network security technologies, while analyzing malicious threats which can compromise network integrity. It discusses the security and optimization of computer networks for use in a variety of disciplines and fields. Touching on such matters as mobile and VPN security, IP spoofing and intrusion detection, this edited collection emboldens the efforts of researchers, academics and network administrators working in both the public and private sectors. This edited compilation includes chapters covering topics such as attacks and countermeasures, mobile wireless networking, intrusion detection systems, next-generation firewalls, web security and much more. Information and communication systems are an essential component of our society, forcing us to become dependent on these infrastructures. At the same time, these systems are undergoing a convergence and interconnection process that has its benefits, but also raises specific threats to user interests. Citizens and organizations must feel safe when using cyberspace facilities in order to benefit from its advantages. This book is interdisciplinary in the sense that it covers a wide range of topics like network security threats, attacks, tools and procedures to mitigate the effects of malware and common network attacks, network security architecture and deep learning methods of intrusion detection.

This book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining.Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics.This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.

This book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining.Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics.This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.

This edited book is a compilation of scholarly articles on the latest developments in the field of AI, Blockchain, and ML/DL in cloud security. This book is designed for security and risk assessment professionals, and to help undergraduate, postgraduate students, research scholars, academicians, and technology professionals who are interested in learning practical approaches to cloud security. It covers practical strategies for assessing the security and privacy of cloud infrastructure and applications and shows how to make cloud infrastructure secure to combat threats and attacks, and prevent data breaches. The chapters are designed with a granular framework, starting with the security concepts, followed by hands-on assessment techniques based on real-world studies. Readers will gain detailed information on cloud computing security that—until now—has been difficult to access.This book:• Covers topics such as AI, Blockchain, and ML/DL in cloud security.• Presents several case studies revealing how threat actors abuse and exploit cloud environments to spread threats.• Explains the privacy aspects you need to consider in the cloud, including how they compare with aspects considered in traditional computing models.• Examines security delivered as a service—a different facet of cloud security.

This edited book is a compilation of scholarly articles on the latest developments in the field of AI, Blockchain, and ML/DL in cloud security. This book is designed for security and risk assessment professionals, and to help undergraduate, postgraduate students, research scholars, academicians, and technology professionals who are interested in learning practical approaches to cloud security. It covers practical strategies for assessing the security and privacy of cloud infrastructure and applications and shows how to make cloud infrastructure secure to combat threats and attacks, and prevent data breaches. The chapters are designed with a granular framework, starting with the security concepts, followed by hands-on assessment techniques based on real-world studies. Readers will gain detailed information on cloud computing security that—until now—has been difficult to access.This book:• Covers topics such as AI, Blockchain, and ML/DL in cloud security.• Presents several case studies revealing how threat actors abuse and exploit cloud environments to spread threats.• Explains the privacy aspects you need to consider in the cloud, including how they compare with aspects considered in traditional computing models.• Examines security delivered as a service—a different facet of cloud security.

This book covers issues associated with smart systems due to the presenceof onboard nonlinear components. It discusses the advanced architectureof smart systems for power management units. It explores issues of powermanagement and identifies hazardous signals in the power managementunits of smart devices. It• Presents adaptive artificial intelligence and machine learning-basedcontrol strategies.• Discusses advanced simulations and data synthesis for various powermanagement issues.• Showcases solutions to the uncertainty and reliability issues in powermanagement units.• Identifies new power quality challenges in smart devices.• Explains hybrid active power filters, shunt hybrid active power filters,and the industrial internet of things in power quality management.This book comprehensively discusses advancements of traditional electricalgrids, the benefits of smart grids to customers and stakeholders, propertiesof smart grids, smart grid architecture, smart grid communication, andsmart grid security. It further covers the architecture of advance power managementunits (PMU) of smart devices, and the identification of harmonicdistortions with respect to various sensor-based technology. It will serve asan ideal reference text for senior undergraduate and graduate students, andacademic researchers in fields including electrical engineering, electronics,communications engineering, and computer engineering.

This book focuses on artificial intelligence (AI) and machine learning (ML) technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications in which they are being used.Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of AI and ML with various emerging technologies such as Internet of Things (IoT), healthcare, smart cities, robotics, and more. The book discusses various data collection and data transformation techniques and also maps the legal and ethical issues of data-driven e-healthcare systems while covering possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances.With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.

This book focuses on artificial intelligence (AI) and machine learning (ML) technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications in which they are being used.Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of AI and ML with various emerging technologies such as Internet of Things (IoT), healthcare, smart cities, robotics, and more. The book discusses various data collection and data transformation techniques and also maps the legal and ethical issues of data-driven e-healthcare systems while covering possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances.With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.

With the exponential growth of science and technology, the delivery of dental care has shifted from conventional methods to intelligent techniques. In addition to adapting intelligent techniques, sustainable dental practice is of the utmost importance. Eco-friendly dentistry, sustainable dentistry, or green dentistry are approaches that reduce the environmental impact of dental practice and help safeguard planetary and community well-being. This handbook provides the latest and most comprehensive evidence-based guidance on intelligent and sustainable approaches in dentistry.Handbook of Intelligent and Sustainable Smart Dentistry: Nature and Bio-Inspired Approaches, Processes, Materials, and Manufacturing highlights how Dentistry 4.0 has come to the rescue after COVID-19 and how it has helped in providing needed e-healthcare. This handbook bridges the gap between research and development in the field of smart dentistry for professionals and clinicians. Intelligent materials, equipment, instrumentation, and the latest behavior management techniques and how these techniques provide superior care and treatment to society are explored in detail. The scope of nature-inspired techniques and procession, along with green solutions, are also discussed in this one-of-a-kind handbook.This valuable handbook is a single-stop solution for practitioners, researchers, scholars, students, academicians, and clinicians interested in updating their knowledge on intelligent and sustainable dentistry. The handbook will bestow the readers with not only theoretical knowledge but will equip them with clinical skills as well.

With the exponential growth of science and technology, the delivery of dental care has shifted from conventional methods to intelligent techniques. In addition to adapting intelligent techniques, sustainable dental practice is of the utmost importance. Eco-friendly dentistry, sustainable dentistry, or green dentistry are approaches that reduce the environmental impact of dental practice and help safeguard planetary and community well-being. This handbook provides the latest and most comprehensive evidence-based guidance on intelligent and sustainable approaches in dentistry.Handbook of Intelligent and Sustainable Smart Dentistry: Nature and Bio-Inspired Approaches, Processes, Materials, and Manufacturing highlights how Dentistry 4.0 has come to the rescue after COVID-19 and how it has helped in providing needed e-healthcare. This handbook bridges the gap between research and development in the field of smart dentistry for professionals and clinicians. Intelligent materials, equipment, instrumentation, and the latest behavior management techniques and how these techniques provide superior care and treatment to society are explored in detail. The scope of nature-inspired techniques and procession, along with green solutions, are also discussed in this one-of-a-kind handbook.This valuable handbook is a single-stop solution for practitioners, researchers, scholars, students, academicians, and clinicians interested in updating their knowledge on intelligent and sustainable dentistry. The handbook will bestow the readers with not only theoretical knowledge but will equip them with clinical skills as well.

This book covers issues associated with smart systems due to the presenceof onboard nonlinear components. It discusses the advanced architectureof smart systems for power management units. It explores issues of powermanagement and identifies hazardous signals in the power managementunits of smart devices. It• Presents adaptive artificial intelligence and machine learning-basedcontrol strategies.• Discusses advanced simulations and data synthesis for various powermanagement issues.• Showcases solutions to the uncertainty and reliability issues in powermanagement units.• Identifies new power quality challenges in smart devices.• Explains hybrid active power filters, shunt hybrid active power filters,and the industrial internet of things in power quality management.This book comprehensively discusses advancements of traditional electricalgrids, the benefits of smart grids to customers and stakeholders, propertiesof smart grids, smart grid architecture, smart grid communication, andsmart grid security. It further covers the architecture of advance power managementunits (PMU) of smart devices, and the identification of harmonicdistortions with respect to various sensor-based technology. It will serve asan ideal reference text for senior undergraduate and graduate students, andacademic researchers in fields including electrical engineering, electronics,communications engineering, and computer engineering.

The objective of this book is to teach what IoT is, how it works, and how it can be successfully utilized in business. This book helps to develop and implement a powerful IoT strategy for business transformation as well as project execution. Digital change, business creation/change and upgrades in the ways and manners in which we work, live, and engage with our clients and customers, are all enveloped by the Internet of Things which is now named "Industry 5.0" or "Industrial Internet of Things." The sheer number of IoT(a billion+), demonstrates the advent of an advanced business society led by sustainable robotics and business intelligence. This book will be an indispensable asset in helping businesses to understand the new technology and thrive.

DEEP LEARNING AND ITS APPLICATIONS USING PYTHON This practical book gives a detailed description of deep learning models and their implementation using Python programming relating to computer vision, natural language processing, and other applications. This book thoroughly explains deep learning models and how to use Python programming to implement them in applications such as NLP, face detection, face recognition, face analysis, and virtual assistance (chatbot, machine translation, etc.). It provides hands-on guidance in using Python for implementing deep learning application models. It also identifies future research directions for deep learning. Readers/users will discover A precise description of deep learning history, fundamental concepts, and background information relating to deep learning;A detailed introduction to several concepts including tensorflow and keras, starting from the fundamentals to the application-based concept implementation using Python;Explanations of multilayer perceptron, convolutional neural network, recurrent neural network, and long short-term memory in terms of applications like chatbot, face detection and recognition;Advanced deep learning concepts along with their future research advancements;Assist in building the reader’s understanding through intuitive explanations and practical examples by exploring challenging concepts in the related applications of computer vision, natural language processing, and other models.Audience The book is ideal for computer science researchers, industry professionals, as well as postgraduate and undergraduate students who want to learn how to program deep learning models using Python.

Written and edited by a team of experts in the field, this exciting new volume explores the real-world applications and methods for using Internet of Things (IoT) to make homes and buildings smart and sustainable and to continue working toward a “greener” world. Sustainable Smart Homes and Buildings with Internet of Things (IoT) is a book that explores the integration of renewable energy sources and IoT technology in the design and management of smart homes and buildings. The book covers various topics related to the subject, including energy efficiency, real-time monitoring, control and optimization of renewable energy sources, smart grid integration, energy storage systems, and microgrids. The book explains how IoT technology can be used to collect data from various sensors and devices installed in smart homes and buildings to create a real-time monitoring and control system for renewable energy sources, which can help optimize energy usage and reduce waste. It also discusses the challenges and opportunities associated with the integration of renewable energy sources in smart homes and buildings, and how these challenges can be addressed through the use of IoT technology. The book is intended for architects, engineers, building managers, energy professionals, and researchers interested in the design and management of sustainable smart homes and buildings. It provides practical insights, case studies, and examples that illustrate the benefits of using renewable energy sources and IoT technology to create energy-efficient, environmentally friendly, and comfortable living spaces.

A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs.Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions discusses deep learning algorithms in medical diagnosis, including applications such as Covid-19 detection, dementia detection, and predicting chemotherapy outcomes on breast cancer tumours. Smart healthcare monitoring frameworks using IoT with big data analytics are explored and the latest trends in predictive technology for solving real-time health care problems are examined. By using real-time data inputs to build predictive models, this new technology can literally 'see' your future health and allow clinicians to intervene as needed.This book is suitable reading for researchers interested in healthcare technology, big data analytics, and artificial intelligence.

This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare. The authors discuss the proliferation of huge data sources (e.g. genomes, electronic health records (EHRs), mobile diagnostics, and wearable devices) and breakthroughs in artificial intelligence applications, which have unlocked the doors for diagnosing and treating multitudes of rare diseases. The contributors show how the widespread adoption of intelligent health based systems could help overcome challenges, such as shortages of staff and supplies, accessibility barriers, lack of awareness on certain health issues, identification of patient needs, and early detection and diagnosis of illnesses. This book is a small yet significant step towards exploring recent advances, disseminating state-of-the-art techniquesand deploying novel technologies in intelligent healthcare services and applications.Describes the advances of computing methodologies for life and medical science data;Presents applications of artificial intelligence in healthcare along with case studies and datasets;Provides an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare. The authors discuss the proliferation of huge data sources (e.g. genomes, electronic health records (EHRs), mobile diagnostics, and wearable devices) and breakthroughs in artificial intelligence applications, which have unlocked the doors for diagnosing and treating multitudes of rare diseases. The contributors show how the widespread adoption of intelligent health based systems could help overcome challenges, such as shortages of staff and supplies, accessibility barriers, lack of awareness on certain health issues, identification of patient needs, and early detection and diagnosis of illnesses. This book is a small yet significant step towards exploring recent advances, disseminating state-of-the-art techniquesand deploying novel technologies in intelligent healthcare services and applications.Describes the advances of computing methodologies for life and medical science data;Presents applications of artificial intelligence in healthcare along with case studies and datasets;Provides an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

This book is to explore and explain the strategically sound capabilities at the synchronization between quantum computing and artificial intelligence (AI). The reader will be presented with an introduction and a deeper review of the technological trends and transitions being unearthed in the quantum computing and AI domains.

Quantum computing systems are powerful for allowing a client to perform any quantum computations from a remote quantum server while concealing the structure and content of the computation fall under the category of blind quantum computing (BQC). In BQC, the client delegates the quantum processing to one or more powerful quantum servers while retaining privacy over the input, computation and output. This makes it suitable for secure quantum cloud computing. This feature is powerful to ensure that even untrusted servers cannot learn the details of the user's computation. With quantum computing, there is a fast-growing need to transition from general-purpose quantum systems to customized architectures tailored to specific application requirements. This transition is critical while considering sustainability goals and financial limitations. With this advanced computing architecture, a custom system can optimize energy use, hardware complexity, and resource allocation to better serve individual user needs while staying within budgetary boundaries.