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.

Features detailed information on various types of biological and environmental nanosensors Gives particular attention to the different categories of advanced functional interfaces, processes for their development, and application areas Includes the current state-of-the-art in terms of commercial aspects

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.

Provides a broad overview of the many applications of nanomaterials and nanotechnology in biomedicine and engineering Offers a multidisciplinary approach that will appeal to a diverse readership, including those in biomedical engineering, materials science, biomedicine, and pharmaceutics Includes challenges faced and limiting factors for each application, allowing readers to make an informed decision when using nanomaterials in their research

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.

Offers perspectives on the design, development and commissioning of intelligent applications Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of COVID-19 Puts forth insights on how future illnesses can be supported using intelligent corona virus monitoring techniques

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.

Focuses on opportunities for agricultural impact and sustainability Presents insights into both agricultural sustainability and eco-intensification Includes the impact of legume production on societal impacts such as health and wealth management

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.

Presents the latest insights in agroforestry and ecosystem management to achieve Sustainable Development Goals (SDGS) for a green futureIncludes both theoretical and practical approaches to agroforestry practicesPresents expert insights on the multidisciplinary challenges and opportunities of agroforestry for carbon and other ecological impactsExplores the integration of technological interfaces for improving the potential of agroforestry practices

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: Highlights the importance and applications of Swarm Intelligence and Evolutionary Algorithms in Healthcare industry. Elaborates Swarm Intelligence and Evolutionary Algorithms for Cancer Detection. In-depth coverage of computational methodologies, approaches and techniques based on Swarm Intelligence and Evolutionary Algorithms for detecting Brain Tumour including deep learning to optimize brain tumor diagnosis. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms. Focuses on applying Swarm Intelligence and Evolutionary Algorithms for Heart Disease detection and diagnosis. Comprehensively covers the role of Swarm Intelligence and Evolutionary Algorithms for Drug Design and Discovery.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.

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 Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field

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 Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field

Agriculture Toward Net Zero Emissions explores how agriculture has historically contributed to carbon emissions and then takes the reader forward, offering insights into an integrated approach to reducing those emissions toward the COP26 goal. The dual challenge of increasing production to meet population and nutrition food demands while reducing the traditional emissions generated by production practices is significant. It requires understanding the foundation of current practices and then revising those underlying principles to reflect the resources and greater insights of today.This book provides an overview of the current state of the science, explores the development of policies and plans to improve carbon management, and provides examples of technology and agroecosystem management practices. It includes the latest updates in carbon neutral farming, carbon and energy management, and addresses the knowledge gap between input management, livestock management and agroecosystem management.Advancing agroecosystem science through a roadmap for improving capacity, Agriculture Toward Net Zero Emissions is a valuable resource for those seeking to develop and apply new agricultural best practices.Provides insights into agriculture’s role in reaching Sustainable Development Goals through improved practicesIncludes diverse agroecosystems for broad and translational insights and applicationsPromotes transition to cleaner energy sources, including the role of regulation

Economic incentives intended to catalyze environmentally responsible behavior have assumed a central role in confronting climate change, food insecurity, and natural resource degradation. Notwithstanding their promise, many programs underperform owing to methodological gaps, limited transparency, and insufficient regulatory oversight. Carbon Incentives, Plans, and Policies in Agriculture undertakes a critical appraisal of these constraints and advances evidence-based propositions for strengthening carbon credit mechanisms across agricultural systems and value chains.The volume assembles a globally diverse authorship to synthesize emergent strategies and forward trajectories in carbon financing and monetization, green accounting, ecological and environmental economics, and carbon credit trading, while situating these within the broader sustainability imperatives of agroecosystems. Distinctive emphasis is placed on measurement and methodological rigor, including quantification protocols, MRV frameworks, and domain-specific estimation approaches for soils, croplands, grasslands, and agroforestry, together with field-level interventions (e.g., energy and water use efficiency, best management practices, and green manuring) and technological enablement (notably, AI-supported assessment). Integrating scientific, policy, and economic perspectives, this book furnishes a conceptual and operational foundation for scholars, researchers, industry practitioners, and policymakers seeking to design effective, transparent, and equitable governance and planning protocols for a low-carbon agricultural transition.Surfaces the carbon-storage contribution of perennial fruit-tree systems and tree-crop ecologies, extending sequestration analysis beyond annual croppingConsolidates expertise spanning agronomy, soil microbiology, forestry, environmental law and economics, and AI-enabled analytics, yielding multi-scalar insightsProvides methodologies for carbon credit assessment across different land use types, highlighting ecosystem-specific constraints and opportunities that inform context-sensitive decision-making and carbon incentive policy frameworksExamines the evolving role of private sector actors in agricultural carbon markets and financing, documenting novel industry-level initiatives and investment patterns that shape real-world adoption and successful implementation of carbon incentives in agriculture

Sub- and Supercritical Hydrothermal Technology: Industrial Applications offers a practical view of a variety of industrial applications and their challenges, offering a deep understanding of the application of sub- and supercritical fluids and their techno-economic viability.This book covers a wide range of applications of hydrothermal processing that result in almost zero waste, high energy efficiency, sustainable chemical processes, and minimal impact over the life cycle. These applications include processing of hazardous waste, bioproducts, coal, lipids, heavy oil and bitumen, and carbon materials. The use of hot-compressed water instead of different organic solvents, such as methanol, acetone, and hexane, is an environmentally benign, green, and sustainable option which can help to design chemical processes that support green chemistry and engineering.This book is pertinent for researchers and professionals in the fields of chemical engineering, industrial chemistry, environmental engineering, materials engineering, and manufacturing.

Biopharmaceuticals are emerging as frontline medicines to combat several life-threatening and chronic diseases. However, such medicines are expensive to develop and produce on a commercial scale, contributing to rising healthcare costs. Developability of Biotherapeutics: Computational Approaches describes applications of computational and molecular modeling techniques that improve the overall process of discovery and development by removing empiricism.The concept of developability involves making rational choices at the pre-clinical stages of biopharmaceutical drug development that could positively impact clinical outcomes. The book also addresses a general lack of awareness of the many different contributions that computation can make to biopharmaceutical drug development.This informative and practical reference is a valuable resource for professionals engaged in industrial research and development, scientists working with regulatory agencies, and pharmacy, medicine, and life science students and educators. It focuses primarily on the developability of monoclonal antibody candidates, but the principles described can also be extended to other modalities such as recombinant proteins, fusion proteins, antibody drug conjugates and vaccines.The book is organized into two sections. The first discusses principles and applications of computational approaches toward discovering and developing biopharmaceutical drugs. The second presents best practices in developability assessments of early-stage biopharmaceutical drug candidates.In addition to raising awareness of the promise of computational research, this book also discusses solutions required to improve the success rate of translating biologic drug candidates into products available in the clinic. As such, it is a rich source of information on current principles and practices as well as a starting point for finding innovative applications of computation towards biopharmaceutical drug development.

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.

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

Despite the phenomenal clinical success of antibody-based biopharmaceuticals in recent years, discovery and development of these novel biomedicines remains a costly, time-consuming, and risky endeavor with low probability of success. To bring better biomedicines to patients faster, we have come up with a strategic vision of Biopharmaceutical Informatics which calls for syncretic use of computation and experiment at all stages of biologic drug discovery and pre-clinical development cycles to improve probability of successful clinical outcomes. Biopharmaceutical Informatics also encourages industry and academic scientists supporting various aspects of biotherapeutic drug discovery and development cycles to learn from our collective experiences of successes and, more importantly, failures. The insights gained from such learnings shall help us improve the rate of successful translation of drug discoveries into drug products available to clinicians and patients, reduce costs, and increase the speed of biologic drug discovery and development. Hopefully, the efficiencies gained from implementing such insights shall make novel biomedicines more affordable for patients.This unique volume describes ways to invent and commercialize biomedicines more efficiently:Calls for digital transformation of biopharmaceutical industry by appropriately collecting, curating, and making available discovery and pre-clinical development project data using FAIR principlesDescribes applications of artificial intelligence and machine learning (AIML) in discovery of antibodies in silico (DAbI) starting with antigen design, constructing inherently developable antibody libraries, finding hits, identifying lead candidates, and optimizing themDetails applications of AIML, physics-based computational design methods, and other bioinformatics tools in fields such as developability assessments, formulation and excipient design, analytical and bioprocess development, and pharmacologyPresents pharmacokinetics/pharmacodynamics (PK/PD) and Quantitative Systems Pharmacology (QSP) models for biopharmaceuticalsDescribes uses of AIML in bispecific and multi-specific formatsDr Sandeep Kumar has also edited a collection of articles dedicated to this topic which can be found in the Taylor and Francis journal mAbs.

Despite the phenomenal clinical success of antibody-based biopharmaceuticals in recent years, discovery and development of these novel biomedicines remains a costly, time-consuming, and risky endeavor with low probability of success. To bring better biomedicines to patients faster, we have come up with a strategic vision of Biopharmaceutical Informatics which calls for syncretic use of computation and experiment at all stages of biologic drug discovery and pre-clinical development cycles to improve probability of successful clinical outcomes. Biopharmaceutical Informatics also encourages industry and academic scientists supporting various aspects of biotherapeutic drug discovery and development cycles to learn from our collective experiences of successes and, more importantly, failures. The insights gained from such learnings shall help us improve the rate of successful translation of drug discoveries into drug products available to clinicians and patients, reduce costs, and increase the speed of biologic drug discovery and development. Hopefully, the efficiencies gained from implementing such insights shall make novel biomedicines more affordable for patients.This unique volume describes ways to invent and commercialize biomedicines more efficiently:Calls for digital transformation of biopharmaceutical industry by appropriately collecting, curating, and making available discovery and pre-clinical development project data using FAIR principlesDescribes applications of artificial intelligence and machine learning (AIML) in discovery of antibodies in silico (DAbI) starting with antigen design, constructing inherently developable antibody libraries, finding hits, identifying lead candidates, and optimizing themDetails applications of AIML, physics-based computational design methods, and other bioinformatics tools in fields such as developability assessments, formulation and excipient design, analytical and bioprocess development, and pharmacologyPresents pharmacokinetics/pharmacodynamics (PK/PD) and Quantitative Systems Pharmacology (QSP) models for biopharmaceuticalsDescribes uses of AIML in bispecific and multi-specific formatsDr Sandeep Kumar has also edited a collection of articles dedicated to this topic which can be found in the Taylor and Francis journal mAbs.

A changing climate is causing challenges for soil and water management in many parts of the world. Current soil management practices need to be redesigned to effectively address present and future fluctuating climates. This book explores how soil management practices impact soil hydrological characteristics, and how we can improve our understanding of soil and water management under changing conditions. This is a valuable resource for land managers, soil conservationists, researchers and others who wish to understand how different management practices affect soil and water dynamics and how these practices can be adjusted to enhance agricultural sustainability and environmental quality. Features: • Explores the impact of changing climate on soil hydrology and soil water dynamics. • Examines how effective soil management can improve soil health, water infiltration and soil water retention. • Discusses relationships of conservation tillage systems, diversified cropping systems, crop residue management, conservation buffers, and forest land use with soil hydrology. • Provides cases studies of modelling of soil water management.

The self-contained properties of discotic liquid crystals (DLCs) render them powerful functional materials for many semiconducting device applications and models for energy and charge migration in self-organized dynamic functional soft materials. The past three decades have seen tremendous interest in this area, fueled primarily by the possibility of creating a new generation of organic semiconductors and wide viewing displays using DLCs. While a number of books on classical calamitic liquid crystals are available, there are, as yet, no books that are dedicated exclusively to the basic design principles, synthesis, and physical properties of DLCs. The first reference book to cover DLCs, Chemistry of Discotic Liquid Crystals: From Monomers to Polymers highlights the chemistry and thermal behavior of DLCs. Divided into six chapters, each with a general description, background, and context for the concepts involved, the book begins with a basic introduction to liquid crystals, describing molecular self-assembly and various types of liquid crystals. It outlines their classification, covers their history and general applications, and focuses on DLCs and their discovery, structure, characterization, and alignment. The book goes on to examine the chemistry and physical properties of various monomeric DLCs, including 25 sections describing the synthesis and mesomorphic properties of monomeric DLCs formed by different cores. The bulk of the book covers the chemistry and mesomorphism of discotic dimers, oligomers, and polymers and concludes with a look at some applicable properties of DLCs. A comprehensive and up-to-date resource, this book is designed to be accessible and of value not just for students and researchers but also to the directors and principal investigators working in this field, providing the foundation and fuel to advance this fast-growing technological field.

The book highlights how technologies including artificial intelligence and machine learning are transforming renewable energy technologies and enabling the development of new solutions. It further discusses how smart technologies are employed to optimize energy production and storage, enhance energy efficiency, and improve the overall sustainability of energy systems.This book:Discusses artificial intelligence-based techniques, namely, neural networks, fuzzy expert systems, optimization techniques, and operational researchShowcases the importance of artificial intelligence and machine learning in the energy market, demand analysis, and forecasting of renewable energy applicationsIllustrates strategies for sustainable development using artificial intelligence and machine learning applicationsPresents applications of artificial intelligence in the domain of electronics transformation and development, smart cities, and renewable energy utilizationHighlights the role of artificial intelligence in solving problems such as image and signal processing, smart weather monitoring, smart farming, and distributed energy sourcesIt is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical, electronics and communications, energy, and environmental engineering.

The book highlights how technologies including artificial intelligence and machine learning are transforming renewable energy technologies and enabling the development of new solutions. It further discusses how smart technologies are employed to optimize energy production and storage, enhance energy efficiency, and improve the overall sustainability of energy systems.This book:Discusses artificial intelligence-based techniques, namely, neural networks, fuzzy expert systems, optimization techniques, and operational researchShowcases the importance of artificial intelligence and machine learning in the energy market, demand analysis, and forecasting of renewable energy applicationsIllustrates strategies for sustainable development using artificial intelligence and machine learning applicationsPresents applications of artificial intelligence in the domain of electronics transformation and development, smart cities, and renewable energy utilizationHighlights the role of artificial intelligence in solving problems such as image and signal processing, smart weather monitoring, smart farming, and distributed energy sourcesIt is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical, electronics and communications, energy, and environmental engineering.

This book examines heterocyclic compounds with focus on classification, nomenclature, and structural characteristics. Chapters address synthesis methods for five-membered heterocycles including pyrrole, furan, and thiophene through Paal-Knorr, Hantzsch, and Knorr syntheses. The book covers electrophilic and nucleophilic substitution reactions with detailed mechanisms and orientation patterns, and also:Covers classification, nomenclature, and numbering systems of heterocyclic compounds with focus on structural and aromatic characteristics.Examines synthesis methods for five-membered heterocycles including Paal-Knorr, Hantzsch, Knorr, and Piloty-Robinson syntheses for pyrrole, furan, and thiophene.Addresses electrophilic and nucleophilic substitution reactions in heterocyclic systems with detailed mechanisms and orientation patterns.Discusses synthesis of six-membered heterocycles through Skraup, Doebner-Miller, Friedlander, and Pfitzinger methods for quinoline and isoquinoline derivatives.Presents chemical properties including halogenation, nitration, sulfonation, acylation, reduction, and oxidation reactions of heterocyclic compounds.This title has been co-published with Manakin Press. T&F does not sell or distribute the print editions in Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri lanka.

MULTIMODAL BIOMETRIC AND MACHINE LEARNING TECHNOLOGIES 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.AudienceResearchers 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.

COGNITIVE BEHAVIOR AND HUMAN COMPUTER INTERACTION BASED ON MACHINE LEARNING ALGORITHMS The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design. Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas. This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come. Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning,A review and description of the learning methods in human-computer interaction,Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time,The scope and implementation of a majority of data mining and machine learning strategies.A discussion of real-time problems.Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

RENEWABLE ENERGY SYSTEMS Providing updated and state-of-the-art coverage of a rapidly changing science, this groundbreaking new volume presents the latest technologies, processes, and equipment in renewable energy systems for practical applications. This groundbreaking new volume examines recent advances in the area of renewable energy systems, including modeling and optimization using different methods like GAMS, HOMER, AI techniques and MATLAB Simulink, and others. Covering extensively diverse topics ranging from solar radiation prediction model to improving solar power output by studying the tilt and orientation angle of rooftop-mounted systems, a multitude of practical applications are covered, offering solutions to everyday problems, as well as the theory and concepts behind the technology. Among these applications are increasing the longevity of PV by studying its degradation and its use by operating an electrolyzer for hydrogen production, using biodiesel as a green energy resource as an alternative to diesel fuel, concentrating the black liquor-based biomass as a source from multiple stage evaporator along with thermo-vapour compressor, and the real-time problems of modeling and optimizing renewable energy sources. Written and edited by a global team of experts, this groundbreaking new volume from Scrivener Publishing presents recent advances in the study of renewable energy systems across a variety of fields and sources. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.

AMBIENT INTELLIGENCE AND INTERNET OF THINGS The book explores long-term implementation techniques and research paths of ambient intelligence and the Internet of Things that meet the design and application requirements of a variety of modern and real-time applications.Working environments based on the emerging technologies of ambient intelligence (AmI) and the Internet of Things (IoT) are available for current and future use in the diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them to make our lives easier. It is asserted that Am I can successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques and that it will transparently and proactively change the environment to conform to the requirements of the user. Over time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.The 13 chapters in Ambient Intelligence and Internet of Things: Convergent Technologies provide a comprehensive knowledge of the fundamental structure of innovative cutting-edge AmI and IoT technologies as well as practical applications.AudienceThe book will appeal to researchers, industry engineers, and students in artificial and ambient intelligence, the Internet of Things, intelligent systems, electronics and communication, electronics instrumentations, and computer science.