This book offers a unique balance between a basic introductory knowledge of bioinformatics and a detailed study of algorithmic techniques. Bioinformatics and RNA: A Practice-Based Approach is a complete guide on the fundamental concepts, applications, algorithms, protocols, new trends, challenges, and research results in the area of bioinformatics and RNA. The book offers a broad introduction to the explosively growing new discipline of bioinformatics. It covers theoretical topics along with computational algorithms. It explores RNA bioinformatics, which contribute to therapeutics and drug discovery. Implementation of algorithms in a DotNet Framework with code and complete insight on the state-of-the-art and recent advancements are presented in detail. The book targets both novice readers as well as practitioners in the field. FEATURES Offers a broad introduction to the explosively growing new discipline of bioinformatics Covers theoretical topics and computational algorithms Explores RNA bioinformatics to unleash the potential from therapeutics to drug discovery Discusses implementation of algorithms in DotNet Frameworks with code Presents insights into the state of the art and recent advancements in bioinformatics The book is useful to undergraduate students with engineering, science, mathematics, or biology backgrounds. Researchers will be equally interested.

This book offers a unique balance between a basic introductory knowledge of bioinformatics and a detailed study of algorithmic techniques. Bioinformatics and RNA: A Practice-Based Approach is a complete guide on the fundamental concepts, applications, algorithms, protocols, new trends, challenges, and research results in the area of bioinformatics and RNA. The book offers a broad introduction to the explosively growing new discipline of bioinformatics. It covers theoretical topics along with computational algorithms. It explores RNA bioinformatics, which contribute to therapeutics and drug discovery. Implementation of algorithms in a DotNet Framework with code and complete insight on the state-of-the-art and recent advancements are presented in detail. The book targets both novice readers as well as practitioners in the field. FEATURES Offers a broad introduction to the explosively growing new discipline of bioinformatics Covers theoretical topics and computational algorithms Explores RNA bioinformatics to unleash the potential from therapeutics to drug discovery Discusses implementation of algorithms in DotNet Frameworks with code Presents insights into the state of the art and recent advancements in bioinformatics The book is useful to undergraduate students with engineering, science, mathematics, or biology backgrounds. Researchers will be equally interested.

This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.

Most events and activities in today's world are ordinarily captured using photos, videos and other multimedia content. Such content has some limitation of storing data and fetching them effectively. Three-dimensional continuous PC animation is the most proper media to simulate these occasions and activities. This book focuses on futuristic trends and innovations in multimedia systems using big data, IoT and cloud technologies. The authors present recent advancements in multimedia systems as they relate to various application areas such as healthcare services and agriculture-related industries. The authors also discuss human-machine interface design, graphics modelling, rendering/animation, image/graphics techniques/systems and visualization. They then go on to explore multimedia content adaptation for interoperable delivery. Finally, the book covers cultural heritage, philosophical/ethical/societal/international issues, standards-related virtual technology and multimedia uses. This book is intended for computer engineers and computer scientists developing applications for multimedia and virtual reality and professionals working in object design and visualization, transformation, modelling and animation of the real world. Features: Focuses on futuristic trends and innovations in multimedia systems using big data, IoT and cloud technologies Offers opportunity for state-of-the-art approaches, methodologies and systems, and innovative use of multimedia-based emerging technology services in different application areas Discusses human-machine interface design, graphics modelling, rendering/animation, image/graphics techniques/systems and visualization Covers cultural heritage, philosophical/ethical/societal/international issues, standards-related virtual technology and multimedia uses Explores multimedia content adaptation for interoperable delivery and recent advancements in multimedia systems in context to various application areas such as healthcare services and agriculture-related fields Rajeev Tiwari is a Senior Associate Professor in the School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India. Neelam Duhan is an Associate Professor in the Department of Computer Engineering at J. C. Bose University of Science and Technology, YMCA, Faridabad, India. Mamta Mittal has 18 years of teaching experience, and her research areas include data mining, big data, machine learning, soft computing and data structure. Abhineet Anand is a Professor in the Computer Science and Engineering Department at Chitkara University, Punjab, India. Muhammad Attique Khan is a lecturer of the Computer Science Department at HITEC University, Taxila, Pakistan.

This book explores the inputs with regard to individuals and companies who have developed technologies and innovative solutions, bioinformatics, datasets, apps for diagnosis, etc., that can be leveraged for strengthening the fight against coronavirus. It focuses on technology solutions to stop Covid-19 outbreak and mitigate the risk. The book contains innovative ideas from active researchers who are presently working to find solutions, and they give insights to other researchers to explore the innovative methods and predictive modeling techniques. The novel applications and techniques of established technologies like artificial intelligence (AI), Internet of things (IoT), big data, computer vision and machine learning are discussed to fight the spread of this disease, Covid-19. This pandemic has triggered an unprecedented demand for digital health technology solutions and unleashing information technology to win over this pandemic.

This book focuses on sustainability issues post COVID-19 outbreak, discusses ways to restrict global spread of the pandemic, and also how to survive holistically in the environment. It also discusses the economic impacts on the world due to the coronavirus outbreak. There is a strong need for monitoring and analysis of pandemics for sustainability like epidemic risk analysis by using pattern recognition or the mental health challenges during an outbreak. This book presents ways to find solutions and gives insights to explore innovative methods and predictive modeling techniques, such that masses are prevented from pandemics.

This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.

This book explores the inputs with regard to individuals and companies who have developed technologies and innovative solutions, bioinformatics, datasets, apps for diagnosis, etc., that can be leveraged for strengthening the fight against coronavirus. It focuses on technology solutions to stop Covid-19 outbreak and mitigate the risk. The book contains innovative ideas from active researchers who are presently working to find solutions, and they give insights to other researchers to explore the innovative methods and predictive modeling techniques. The novel applications and techniques of established technologies like artificial intelligence (AI), Internet of things (IoT), big data, computer vision and machine learning are discussed to fight the spread of this disease, Covid-19. This pandemic has triggered an unprecedented demand for digital health technology solutions and unleashing information technology to win over this pandemic.

The book discusses major technical advances and research findings in the field of machine intelligence in medical image analysis. It examines the latest technologies and that have been implemented in clinical practice, such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy. This book provides insights into the basic science involved in processing, analysing, and utilising all aspects of advanced computational intelligence in medical decision-making based on medical imaging.

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.

The book discusses major technical advances and research findings in the field of machine intelligence in medical image analysis. It examines the latest technologies and that have been implemented in clinical practice, such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy. This book provides insights into the basic science involved in processing, analysing, and utilising all aspects of advanced computational intelligence in medical decision-making based on medical imaging.

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.

The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data's immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments.

Predictive Modeling in Biomedical Data Mining and Analysis presents major technical advancements and research findings in the field of machine learning in biomedical image and data analysis. The book examines recent technologies and studies in preclinical and clinical practice in computational intelligence. The authors present leading-edge research in the science of processing, analyzing and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis. As the application of machine learning is spreading to a variety of biomedical problems, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments, this is an ideal reference. Machine Learning techniques are used as predictive models for many types of applications, including biomedical applications. These techniques have shown impressive results across a variety of domains in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood, hence the need for new resources and information.

This book focuses on energy efficiency concerns in fog-edge computing and the requirements related to Industry 4.0 and next-generation networks like 5G and 6G. This book guides the research community about practical approaches, methodological, and moral questions in any nations' journey to conserve energy in fog-edge computing environments. It discusses a detailed approach required to conserve energy and comparative case studies with respect to various performance evaluation metrics, such as energy conservation, resource allocation strategies, task allocation strategies, VM migration, and load-sharing strategies with state-of-the-art approaches, with fog and edge networks.

As the application of smart technologies for monitoring environmental activities becomes more widespread, there is a growing demand for solutions that can help analyze the risk factors and impacts on the environment by focusing on energy consumption, storage, and management. This book is designed to serve as a knowledge-sharing platform, focusing on the emerging models, architectures, and algorithms being developed for smart computational technologies that can lead to efficient energy conservation and environmental sustainability.

This book focuses on sustainability issues post COVID-19 outbreak, discusses ways to restrict global spread of the pandemic, and also how to survive holistically in the environment. It also discusses the economic impacts on the world due to the coronavirus outbreak. There is a strong need for monitoring and analysis of pandemics for sustainability like epidemic risk analysis by using pattern recognition or the mental health challenges during an outbreak. This book presents ways to find solutions and gives insights to explore innovative methods and predictive modeling techniques, such that masses are prevented from pandemics.

This book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.

Cognitive Computing for Human-Robot Interaction: Principles and Practices explores the efforts that should ultimately enable society to take advantage of the often-heralded potential of robots to provide economical and sustainable computing applications. This book discusses each of these applications, presents working implementations, and combines coherent and original deliberative architecture for human-robot interactions (HRI). Supported by experimental results, it shows how explicit knowledge management promises to be instrumental in building richer and more natural HRI, by pushing for pervasive, human-level semantics within the robot's deliberative system for sustainable computing applications. This book will be of special interest to academics, postgraduate students, and researchers working in the area of artificial intelligence and machine learning. Key features: Introduces several new contributions to the representation and management of humans in autonomous robotic systems; Explores the potential of cognitive computing, robots, and HRI to generate a deeper understanding and to provide a better contribution from robots to society; Engages with the potential repercussions of cognitive computing and HRI in the real world.

This book addresses the Internet of Things (IoT), an essential topic in the technology industry, policy, and engineering circles, and one that has become headline news in both the specialty press and the popular media. The book focuses on energy efficiency concerns in IoT and the requirements related to Industry 4.0. It is the first-ever "how-to" guide on frequently overlooked practical, methodological, and moral questions in any nations' journey to reducing energy consumption in IoT devices. The book discusses several examples of energy-efficient IoT, ranging from simple devices like indoor temperature sensors, to more complex sensors (e.g. electrical power measuring devices), actuators (e.g. HVAC room controllers, motors) and devices (e.g. industrial circuit-breakers, PLC for home, building or industrial automation). It provides a detailed approach to conserving energy in IoT devices, and comparative case studies on performance evaluation metrics, state-of-the-art approaches, and IoT legislation.

This book addresses the Internet of Things (IoT), an essential topic in the technology industry, policy, and engineering circles, and one that has become headline news in both the specialty press and the popular media. The book focuses on energy efficiency concerns in IoT and the requirements related to Industry 4.0. It is the first-ever "how-to" guide on frequently overlooked practical, methodological, and moral questions in any nations' journey to reducing energy consumption in IoT devices. The book discusses several examples of energy-efficient IoT, ranging from simple devices like indoor temperature sensors, to more complex sensors (e.g. electrical power measuring devices), actuators (e.g. HVAC room controllers, motors) and devices (e.g. industrial circuit-breakers, PLC for home, building or industrial automation). It provides a detailed approach to conserving energy in IoT devices, and comparative case studies on performance evaluation metrics, state-of-the-art approaches, and IoT legislation.

The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data's immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments.

As the application of smart technologies for monitoring environmental activities becomes more widespread, there is a growing demand for solutions that can help analyze the risk factors and impacts on the environment by focusing on energy consumption, storage, and management. This book is designed to serve as a knowledge-sharing platform, focusing on the emerging models, architectures, and algorithms being developed for smart computational technologies that can lead to efficient energy conservation and environmental sustainability.