The health technology has become a hot topic in academic research. It employs the theory of social networks into the different levels of the prediction and analysis and has brought new possibilities for the development of technology. This book is a descriptive summary of challenges and methods using disease identification with various case studies from diverse authors across the globe. One of the new buzzwords in healthcare sector that has become popular over years is health informatics. Healthcare professionals must deal with an increasing number of computers and computer programs in their daily work. With rapid growth of digital data, the role of analytics in healthcare has created a significant impact on healthcare professional’s life. Improvements in storage data, computational power and paral- lelization has also contributed to uptake this technology. This book is intended for use by researchers, health informatics professionals, academicians and undergraduate and postgraduate students interested in knowing more about health informatics. It aims to provide a brief overview about informatics, its history and area of practice, laws in health informatics, challenges and technologies in health informatics, applica- tion of informatics in various sectors and so on. Finally, the research avenues in health informatics along with some case studies are discussed.

Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. - Presents tools, connections and proactive solutions to take sustainability programs to the next level - Offers a practical guide for making students proficient in modern electronic data analysis and graphics - Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery

This book is a collection of selected papers presented at the Third Congress on Intelligent Systems (CIS 2022), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 5–6, 2022. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as the Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber-physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision-making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.

This book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics. The cancer has been known as a heterogeneous disease categorized in several different subtypes. According to WHO’s recent report, cancer is a leading cause of death worldwide, accounting for over 10 million deaths in the year 2020. Therefore, its early diagnosis, prognosis, and classification to a subtype have become necessary as it facilitates the subsequent clinical management and therapeutics plan. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning, and nature-inspired algorithms, have been widely utilized in various aspects of oncology research, viz. diagnosis, prognosis, therapeutics, and optimized clinical management. Appreciable progress has been made toward the understanding the hallmarks of cancer development, progression, and its effective therapeutics. However, notwithstanding the extrinsic and intrinsic factors which lead to drastic increment in incidence cases, the detection, diagnosis, prognosis, and therapeutics remain an apex challenge for the medical fraternity. With the advent in CI-based approaches, including nature-inspired techniques, and availability of clinical data from various high-throughput experiments, medical consultants, researchers, and oncologists have seen a hope to devise and employ CI in various aspects of oncology. The main aim of the book is to occupy state-of-the-art applications of CI methods which have been derived from core computer sciences to back medical oncology. This edited book covers artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary algorithms, various nature-inspired algorithms, and hybrid intelligent systems which are widely appreciated for the diagnosis, prognosis, and optimization of therapeutics of various cancers. Besides, this book also covers multi-omics exploration, gene expression analysis, gene signature identification of cancers, genomic characterization of tumors, anti-cancer drug design and discovery, drug response prediction by means of CI, and applications of IoT, IoMT, and blockchain technology in cancer research.

This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 45 selected papers from the 20th International Conference on Hybrid Intelligent Systems (HIS 2021) and 16 papers from the 17th International Conference on Information Assurance and Security, which was held online, from December 14 to 16, 2021. A premier conference in the field of artificial intelligence and machine learning applications, HIS-IAS 2021 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from over 20 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of computer science and engineering.

This book comprises select proceedings of the International Conference on Advances in Electrical and Computer Technologies 2020 (ICAECT 2020). The papers presented in this book are peer-reviewed and cover latest research in electrical, electronics, communication and computer engineering. Topics covered include smart grids, soft computing techniques in power systems, smart energy management systems, power electronics, feedback control systems, biomedical engineering, geo informative systems, grid computing, data mining, image and signal processing, video processing, computer vision, pattern recognition, cloud computing, pervasive computing, intelligent systems, artificial intelligence, neural network and fuzzy logic, broad band communication, mobile and optical communication, network security, VLSI, embedded systems, optical networks and wireless communication. The volume can be useful for students and researchers working in the different overlapping areas of electrical, electronics and communication engineering.

Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis