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Machine Learning and Big Data Analytics
This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2022) is intended to be used as a reference book for researchers and professionals to share their research and reports of new technologies and applications in Machine Learning and Big Data Analytics like biometric Recognition Systems, medical diagnosis, industries, telecommunications, AI Petri Nets Model-Based Diagnosis, gaming, stock trading, Intelligent Aerospace Systems, robot control, law, remote sensing and scientific discovery agents and multiagent systems; and natural language and Web intelligence. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the advanced Scientific Technologies, provide a correlation of multidisciplinary areas and become a point of great interest for Data Scientists, systems architects, developers, new researchers and graduate level students. This volume provides cutting-edge research from around the globe on this field. Current status, trends, future directions, opportunities, etc. are discussed, making it friendly for beginners and young researchers.
Artificial Intelligence Revolutionizing Cancer Care
In the ever-evolving landscape of cancer treatment, the fusion of artificial intelligence (AI) with medical science marks a groundbreaking shift toward more precise, efficient, and personalized healthcare. Artificial Intelligence Revolutionizing Cancer Care: Precision Diagnosis and Patient-Centric Healthcare delves into the transformative power of AI, offering a comprehensive exploration of its role in enhancing cancer diagnosis, treatment, and patient management. This edited volume brings together leading experts and researchers who illuminate the latest advancements in AI technologies applied to oncology. From machine learning algorithms that predict cancer progression to sophisticated ima...
Multimedia Data Processing and Computing
This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, manufacturing, data science, automation in electronics industries, and many more relevant fields. Multimedia Data Processing and Computing provides a complete introduction to machine learning concepts, as well as practical guidance on how to use machine learning tools and techniques in real-world data engineering situations. It is divided into three sections. In this book on multimedia data engineering and machine learning, the reader will learn how...
Selective Oxidation by Heterogeneous Catalysis
Selective Oxidation by Heterogeneous Catalysis covers one of the major areas of industrial petrochemical production, outlining open questions and new opportunities. It gives keys for the interpretation and analysis of data and design of new catalysts and reactions, and provides guidelines for future research. A distinctive feature of this book is the use of concept by example. Rather than reporting an overview of the literature results, the authors have selected some representative examples, the in-depth analysis of which makes it possible to clarify the fundamental, but new concepts necessary for a better understanding of the new opportunities in this field and the design of new catalysts o...
Artificial Intelligence Using Federated Learning
Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA. Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount. The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students.
Structural Integrity Assessment
This volume contains selected papers from the Second Quadrennial International Conference on Structural Integrity (ICONS-2018). The papers cover important topics related to structural integrity of critical installations, such as power plants, aircrafts, spacecrafts, defense and civilian components. The focus is on assuring safety of operations with high levels of reliability and structural integrity. This volume will be of interest to plant operators working with safety critical equipment, engineering solution providers, software professionals working on engineering analysis, as well as academics working in the area.
Advances in Design Engineering III
This book contains the papers presented at the XXXI International Congress INGEGRAF “Graphic Expression: reunion, reflection, representation,” held on June 29–30 and July 1, 2021, in Málaga, Spain. It reports on cutting-edge topics in product design and manufacturing, such as industrial methods for integrated product and process design, innovative design and computer-aided design. Further topics covered include virtual simulation and reverse engineering, additive manufacturing, product manufacturing,engineering methods in medicine and education, representation techniques and nautical, engineering and construction, aeronautics and aerospace design and modeling. The book is divided into six main sections, reflecting the focus and primary themes of the conference. The contributions presented here provide researchers, engineers and experts in a range of industrial engineering subfields with extensive information to support their daily work; but also they are intended to stimulate new research directions, advanced applications of the methods discussed and future interdisciplinary collaborations.
Deep Learning Applications in Translational Bioinformatics
Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, various applications of deep learning in translational bioinformatics including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics to foster future research and development in ensemble deep learning and inspire new bioinformatics applications that cannot be attained by using traditional machine learning models. - Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challenges - Presents integrative and multidisciplinary approaches to ubiquitous healthcare - Includes case studies to illustrate the concepts discussed
Internet of Things in Bioelectronics
This book provides a comprehensive exploration of the exciting intersection between technology and biology and delves into the principles, applications, and future directions of IoT in the realm of bioelectronics; it serves as both an introduction for those new to the field and as a detailed reference for experienced professionals seeking to deepen their knowledge. The rapid convergence of technology and biology heralds a new era of evolution in the Internet of Things (IoT), a transformative force enabling interconnected devices to communicate and operate with unparalleled synergy. This is particularly true in the groundbreaking field of bioelectronics, where the fusion of biological systems...
