Swarm Intelligence and its Applications in Biomedical Informatics


Book Description

Swarm Intelligence and Its Applications in Biomedical Informatics discusses Artificial Intelligence (AI) applications in medicine and biology, as well as challenges and opportunities presented in these arenas. It covers healthcare big data analytics, mobile health, personalized medicine, and clinical trial data management. This book shows how AI can be used for early disease diagnosis, prediction, and prognosis, and it offers healthcare case studies that demonstrate the application of AI and Machine Learning. Key Features: • Covers all major topics of swarm intelligence research and development such as novel-based search methods and novel optimization algorithm: applications of swarm intelligence to management problems and swarm intelligence for real-world application. • Provides a unique insight into the complex problems of bioinformatics and the innovative solutions which make up ‘intelligent bioinformatics’. • Covers a wide range of topics on the role of AI, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. • Explores applications in different areas of healthcare and highlights the current research. This book is designed as a reference text, and it aims primarily at advanced undergraduates and postgraduate students studying computer science and bioinformatics. Researchers and professionals will find this book useful.




Swarm Intelligence and Its Applications in Biomedical Informatics


Book Description

"Swarm Intelligence and its Applications in Biomedical Informatics discusses Artificial Intelligence applications in medicine and biology, as well as challenges and opportunities presented in these arenas. It covers healthcare big data analytics, mobile health, personalized medicine, and clinical trial data management. The book shows how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis, and offers health care case studies that demonstrate the application of AI and Machine Learning"--




Artificial Intelligence in Healthcare


Book Description

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data




Computational Intelligence and Healthcare Informatics


Book Description

COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.




Artificial Intelligence in Bioinformatics


Book Description

Artificial Intelligence in Bioinformatics: From Omics Analysis to Deep Learning and Network Mining reviews the main applications of the topic, from omics analysis to deep learning and network mining. The book includes a rigorous introduction on bioinformatics, also reviewing how methods are incorporated in tasks and processes. In addition, it presents methods and theory, including content for emergent fields such as Sentiment Analysis and Network Alignment. Other sections survey how Artificial Intelligence is exploited in bioinformatics applications, including sequence analysis, structure analysis, functional analysis, protein classification, omics analysis, biomarker discovery, integrative bioinformatics, protein interaction analysis, metabolic networks analysis, and much more. - Bridges the gap between computer science and bioinformatics, combining an introduction to Artificial Intelligence methods with a systematic review of its applications in the life sciences - Brings readers up-to-speed on current trends and methods in a dynamic and growing field - Provides academic teachers with a complete resource, covering fundamental concepts as well as applications




Predictive Intelligence in Biomedical and Health Informatics


Book Description

Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.




Concepts of Artificial Intelligence and its Application in Modern Healthcare Systems


Book Description

This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and solutions of using advanced techniques like wearable technologies and image processing in the sector. Features: Focuses on the use of artificial intelligence (AI) in healthcare with issues, applications, and prospects Presents the application of artificial intelligence in medical imaging, fractionalization of early lung tumour detection using a low intricacy approach, etc Discusses an artificial intelligence perspective on wearable technology Analyses cardiac dynamics and assessment of arrhythmia by classifying heartbeat using electrocardiogram (ECG) Elaborates machine learning models for early diagnosis of depressive mental affliction This book serves as a reference for students and researchers analyzing healthcare data. It can also be used by graduate and post graduate students as an elective course.




Explainable Artificial Intelligence for Biomedical and Healthcare Applications


Book Description

This reference text helps us understand how the concepts of explainable artificial intelligence (XAI) are used in the medical and healthcare sectors. The text discusses medical robotic systems using XAI and physical devices having autonomous behaviors for medical operations. It explores the usage of XAI for analyzing different types of unique data sets for medical image analysis, medical image registration, medical data synthesis, and information discovery. It covers important topics including XAI for biometric security, genomics, and medical disease diagnosis. This book: • Provides an excellent foundation for the core concepts and principles of explainable AI in biomedical and healthcare applications. • Covers explainable AI for robotics and autonomous systems. • Discusses usage of explainable AI in medical image analysis, medical image registration, and medical data synthesis. • Examines biometrics security-assisted applications and their integration using explainable AI. The text will be useful for graduate students, professionals, and academic researchers in diverse areas such as electrical engineering, electronics and communication engineering, biomedical engineering, and computer science.




Artificial Intelligence in Medicine


Book Description

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.




The Fusion of Artificial Intelligence and Soft Computing Techniques for Cybersecurity


Book Description

With the ever-increasing threat of cyber-attacks, especially as the COVID-19 pandemic helped to ramp up the use of digital communications technology, there is a continued need to find new ways to maintain and improve cybersecurity. This new volume investigates the advances in artificial intelligence and soft computing techniques in cybersecurity. It specifically looks at cybersecurity during the COVID-19 pandemic, the use of cybersecurity for cloud intelligent systems, applications of cybersecurity techniques for web applications, and cybersecurity for cyber-physical systems. A diverse array of technologies and techniques are explored for cybersecurity applications, such as the Internet of Things, edge computing, cloud computing, artificial intelligence, soft computing, machine learning, cross-site scripting in web-based services, neural gas (GNG) clustering technique, and more.