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This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

COMPUTATION IN BIOINFORMATICS Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design. The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development. Audience Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients'biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.