Deepika Ghai – författare

This reference text presents statistical information, causes and impacts of coronavirus on populations, economics, and environment.

The text includes machine learning and deep learning techniques to understand exponential behavior as well as predicting the future reachability of the COVID-19 outbreak. It discusses important concepts including smart sensors for early stage diagnosis, diagnosis of COVID-19 using low power IoT-enabled systems, biomedical imaging and sensor fusion, and electronic solutions for diagnosis, monitoring, and treatment of diseases. Aimed at graduate students and professionals in the field of electrical engineering, electronics and communications engineering, biomedical engineering and nanomaterials, this book

This reference text presents statistical information, causes and impacts of coronavirus on populations, economics, and environment.

The text includes machine learning and deep learning techniques to understand exponential behavior as well as predicting the future reachability of the COVID-19 outbreak. It discusses important concepts including smart sensors for early stage diagnosis, diagnosis of COVID-19 using low power IoT-enabled systems, biomedical imaging and sensor fusion, and electronic solutions for diagnosis, monitoring, and treatment of diseases. Aimed at graduate students and professionals in the field of electrical engineering, electronics and communications engineering, biomedical engineering and nanomaterials, this book

How can the efficiency of the algorithms be increased with a lesser number of computations as well as optimized with the resources for cost-effective solutions? Artificial Intelligence Techniques for Sustainable Development provides an answer. Further, it discusses important concepts such as green communication network design and implementation for the Internet of Things ecosystem, green computing in network security, and artificial intelligence models for remote sensing applications.

Key features:

The book is aimed at senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, and communications engineering, computer science and engineering, environmental engineering, and biomedical engineering.

How can the efficiency of the algorithms be increased with a lesser number of computations as well as optimized with the resources for cost-effective solutions? Artificial Intelligence Techniques for Sustainable Development provides an answer. Further, it discusses important concepts such as green communication network design and implementation for the Internet of Things ecosystem, green computing in network security, and artificial intelligence models for remote sensing applications.

Key features:

The book is aimed at senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, and communications engineering, computer science and engineering, environmental engineering, and biomedical engineering.

This book explores the transformative potential of quantum computing in artificial intelligence (AI) and its impact on various fields. It delves into the foundational concepts, mathematical frameworks, and algorithmic paradigms, providing a roadmap for interdisciplinary research of quantum computing in AI. With a focus on practical applications and prospects, this book bridges the gap between theory and practice, empowering readers to harness the transformative power of quantum technologies for societal and technological advancement.

Key Features

• Examines the architecture, foundations, mathematics, algorithms, programming, encryption techniques, predictive data analysis, tools/software, and applications associated with quantum computing in AI.

• Explores the challenges of quantum hardware scalability and stability, and outlines error correction and mitigation strategies in quantum computing.

• Discusses applications in industries like finance, healthcare, cybersecurity, and transportation, offering innovative solutions to complex challenges.

• Provides analysis of quantum encryption methods for enhanced data security in AI systems.

• Offers an evaluation of the cost‑effectiveness and practicality of quantum computing solutions.

It is a reference work for academics, scholars, and researchers interested in quantum computing in AI.

This book explores the transformative potential of quantum computing in artificial intelligence (AI) and its impact on various fields. It delves into the foundational concepts, mathematical frameworks, and algorithmic paradigms, providing a roadmap for interdisciplinary research of quantum computing in AI. With a focus on practical applications and prospects, this book bridges the gap between theory and practice, empowering readers to harness the transformative power of quantum technologies for societal and technological advancement.

Key Features

• Examines the architecture, foundations, mathematics, algorithms, programming, encryption techniques, predictive data analysis, tools/software, and applications associated with quantum computing in AI.

• Explores the challenges of quantum hardware scalability and stability, and outlines error correction and mitigation strategies in quantum computing.

• Discusses applications in industries like finance, healthcare, cybersecurity, and transportation, offering innovative solutions to complex challenges.

• Provides analysis of quantum encryption methods for enhanced data security in AI systems.

• Offers an evaluation of the cost‑effectiveness and practicality of quantum computing solutions.

It is a reference work for academics, scholars, and researchers interested in quantum computing in AI.

With an increasing demand for biometric systems in various industries, this book on multimodal biometric systems, answers the call for increased resources to help researchers, developers, and practitioners.

Multimodal biometric and machine learning technologies have revolutionized the field of security and authentication. These technologies utilize multiple sources of information, such as facial recognition, voice recognition, and fingerprint scanning, to verify an individual''s identity. The need for enhanced security and authentication has become increasingly important, and with the rise of digital technologies, cyber-attacks and identity theft have increased exponentially. Traditional authentication methods, such as passwords and PINs, have become less secure as hackers devise new ways to bypass them. In this context, multimodal biometric and machine learning technologies offer a more secure and reliable approach to authentication.

This book provides relevant information on multimodal biometric and machine learning technologies and focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity. The book provides content on the theory of multimodal biometric design, evaluation, and user diversity, and explains the underlying causes of the social and organizational problems that are typically devoted to descriptions of rehabilitation methods for specific processes. Furthermore, the book describes new algorithms for modeling accessible to scientists of all varieties.

Audience

Researchers in computer science and biometrics, developers who are designing and implementing biometric systems, and practitioners who are using biometric systems in their work, such as law enforcement personnel or healthcare professionals.

With an increasing demand for biometric systems in various industries, this book on multimodal biometric systems, answers the call for increased resources to help researchers, developers, and practitioners.

Multimodal biometric and machine learning technologies have revolutionized the field of security and authentication. These technologies utilize multiple sources of information, such as facial recognition, voice recognition, and fingerprint scanning, to verify an individual''s identity. The need for enhanced security and authentication has become increasingly important, and with the rise of digital technologies, cyber-attacks and identity theft have increased exponentially. Traditional authentication methods, such as passwords and PINs, have become less secure as hackers devise new ways to bypass them. In this context, multimodal biometric and machine learning technologies offer a more secure and reliable approach to authentication.

This book provides relevant information on multimodal biometric and machine learning technologies and focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity. The book provides content on the theory of multimodal biometric design, evaluation, and user diversity, and explains the underlying causes of the social and organizational problems that are typically devoted to descriptions of rehabilitation methods for specific processes. Furthermore, the book describes new algorithms for modeling accessible to scientists of all varieties.

Audience

Researchers in computer science and biometrics, developers who are designing and implementing biometric systems, and practitioners who are using biometric systems in their work, such as law enforcement personnel or healthcare professionals.

Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing

Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks.

Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:

Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing

Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks.

Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:

Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.