Sandeep Kumar – författare

Advances in Nanosensors for Biological and Environmental Analysis presents the current state-of-art in nanosensors for biological and environmental analysis, also covering commercial aspects. Broadly, the book provides detailed information on the emergence of different types of nanomaterials as transduction platforms used in the development of nanosensors. These include carbon nanotubes, graphene, 2-D transition metal dichalcogenides, conducting polymers and metal organic frameworks. Additional topics include sections on the way nanosensors have inspired new product development in various types of biological and environmental applications that are currently available and on the horizon.

Engineered Nanostructures for Therapeutics and Biomedical Applications offers a single reference for a diverse biomedical readership to learn about the application of nanotechnology in biomedicine and biomedical engineering, from past developments to current research and future prospects. This book sets out a broad selection of biomedical and therapeutic applications for nanostructures, including bioimaging, nanorobotics, orthopedics, and tissue engineering, offering a useful, multidisciplinary approach. Each chapter discusses challenges faced in each discipline, including limiting factors, biocompatibility, and toxicity, thus enabling the reader to make informed decisions in their research.This book is a comprehensive, broad overview of the role and significance of nanomaterials and their composites that also includes discussions of key aspects in the field of biomedicine. It will be of significant interest to academics and researchers in materials science and engineering, biomedicine and biomedical engineering, chemical engineering, pharmaceutics, bioimaging, and nanorobotics.

Cyber-Physical Systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS).

The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture.

Advances in Legume-based Agroecoystem for Sustainable Intensification explores current research and future strategies for ensuring capacity growth and socioeconomic improvement through the utilization of legume crop cultivation and production in the achievement of sustainability development goals (SDGs). Sections cover the role of legumes in addressing issues of food security, improving nitrogen in the environment, environmental sustainability, economic-environmentally optimized systems, the importance and impact of nitrogen, organic production, and biomass potential, legume production, biology, breeding improvement, cropping systems, and the use of legumes for eco-friendly weed management.

This book is an important resource for scientists, researchers and advanced students interested in championing the effective utilization of legumes for agronomic and ecological benefit.

Agroforestry for Carbon and Ecosystem Management is a comprehensive overview of current research, issues, challenges, and case studies in the area of agroforestry. The book focuses specifically on carbon source-sink relationship and management through agroforestry practices with a goal of improving overall environmental sustainability. Through expert insights and case studies, the book promotes carbon management, greenhouse gas emission reduction, forest, and ecosystem services management, along with relevant sustainable approaches for natural resources conservation. Users will find insights into novel approaches for natural resource management, with specific attention given to technologies related to carbon capture and management.In addition, the book addresses the knowledge gap in relation to agroforestry, sustainability, and agroecosystem management and explores the application of remote sensing and geospatial technologies for agroforestry management.

This book provides good coverage of the powerful numerical techniques namely, finite element and wavelets, for the solution of partial differential equation to the scientists and engineers with a modest mathematical background. The objective of the book is to provide the necessary mathematical foundation for the advanced level applications of these numerical techniques. The book begins with the description of the steps involved in finite element and wavelets-Galerkin methods. The knowledge of Hilbert and Sobolev spaces is needed to understand the theory of finite element and wavelet-based methods. Therefore, an overview of essential content such as vector spaces, norm, inner product, linear operators, spectral theory, dual space, and distribution theory, etc. with relevant theorems are presented in a coherent and accessible manner. For the graduate students and researchers with diverse educational background, the authors have focused on the applications of numerical techniques which are developed in the last few decades. This includes the wavelet-Galerkin method, lifting scheme, and error estimation technique, etc.

Features:

• Computer programs in Mathematica/Matlab are incorporated for easy understanding of wavelets.

• Presents a range of workout examples for better comprehension of spaces and operators.

• Algorithms are presented to facilitate computer programming.

• Contains the error estimation techniques necessary for adaptive finite element method.

This book is structured to transform in step by step manner the students without any knowledge of finite element, wavelet and functional analysis to the students of strong theoretical understanding who will be ready to take many challenging research problems in this area.

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.

Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.

This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.

This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.

FEATURES

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.

Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.

This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.

This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.

FEATURES

This reference text discusses advances in wireless communication, design challenges, and future research directions to design reliable wireless communication.

The text discusses emerging technologies including wireless sensor networks, Internet of Things (IoT), cloud computing, mm-Wave, Massive MIMO, cognitive radios (CR), visible light communication (VLC), wireless optical communication, signal processing, and channel modeling. The text covers artificial intelligence-based applications in wireless communication, machine learning techniques and challenges in wireless sensor networks, and deep learning for channel and bandwidth estimation during optical wireless communication.

The text will be useful for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, and electronics and communication engineering.

This reference text discusses advances in wireless communication, design challenges, and future research directions to design reliable wireless communication.

The text discusses emerging technologies including wireless sensor networks, Internet of Things (IoT), cloud computing, mm-Wave, Massive MIMO, cognitive radios (CR), visible light communication (VLC), wireless optical communication, signal processing, and channel modeling. The text covers artificial intelligence-based applications in wireless communication, machine learning techniques and challenges in wireless sensor networks, and deep learning for channel and bandwidth estimation during optical wireless communication.

The text will be useful for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, and electronics and communication engineering.

Healthcare sector is characterized by difficulty, dynamism and variety. In 21st century, healthcare domain is surrounded by tons of challenges in terms of Disease detection, prevention, high costs, skilled technicians and better infrastructure. In order to handle these challenges, Intelligent Healthcare management technologies are required to play an effective role in improvising patient’s life. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data for various purposes for saving lives, reducing medical operations errors, enhancing efficiency, reducing costs and making the whole world a healthy world.

Applying Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development is essential nowadays. The objective of this book is to highlight various Swarm Intelligence and Evolutionary Algorithms techniques for various medical issues in terms of Cancer Diagnosis, Brain Tumor, Diabetic Retinopathy, Heart disease as well as drug design and development. The book will act as one-stop reference for readers to think and explore Swarm Intelligence and Evolutionary Algorithms seriously for real-time patient diagnosis, as the book provides solutions to various complex diseases found critical for medical practitioners to diagnose in real-world.

Key Features:

The book will play a significant role for Researchers, Medical Practitioners, Healthcare Professionals and Industrial Healthcare Research and Development wings to conduct advanced research in Healthcare using Swarm Intelligence and Evolutionary Algorithms techniques.

Healthcare sector is characterized by difficulty, dynamism and variety. In 21st century, healthcare domain is surrounded by tons of challenges in terms of Disease detection, prevention, high costs, skilled technicians and better infrastructure. In order to handle these challenges, Intelligent Healthcare management technologies are required to play an effective role in improvising patient’s life. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data for various purposes for saving lives, reducing medical operations errors, enhancing efficiency, reducing costs and making the whole world a healthy world.

Applying Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development is essential nowadays. The objective of this book is to highlight various Swarm Intelligence and Evolutionary Algorithms techniques for various medical issues in terms of Cancer Diagnosis, Brain Tumor, Diabetic Retinopathy, Heart disease as well as drug design and development. The book will act as one-stop reference for readers to think and explore Swarm Intelligence and Evolutionary Algorithms seriously for real-time patient diagnosis, as the book provides solutions to various complex diseases found critical for medical practitioners to diagnose in real-world.

Key Features:

The book will play a significant role for Researchers, Medical Practitioners, Healthcare Professionals and Industrial Healthcare Research and Development wings to conduct advanced research in Healthcare using Swarm Intelligence and Evolutionary Algorithms techniques.

This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining.Features:• Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation• Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data• Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining• Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing• Covers latest datasets for natural language processing and information retrieval for social media like Twitter

The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.

This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining.Features:• Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation• Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data• Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining• Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing• Covers latest datasets for natural language processing and information retrieval for social media like Twitter

The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.