Download or read book Machine Learning Used in Biomedical Computing and Intelligence Healthcare Volume II written by Honghao Gao and published by Frontiers Media SA. This book was released on 2022-05-27 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Intelligence and Healthcare Informatics written by Om Prakash Jena and published by John Wiley & Sons. This book was released on 2021-10-19 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Machine Learning Used in Biomedical Computing and Intelligence Healthcare Volume I written by Honghao Gao and published by Frontiers Media SA. This book was released on 2021-06-17 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Computational Intelligence in Biomedical Engineering and Healthcare written by Janmenjoy Nayak and published by Academic Press. This book was released on 2021-04-08 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques. Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis. - Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence - Helps readers analyze and do advanced research in specialty healthcare applications - Includes links to websites, videos, articles and other online content to expand and support primary learning objectives
Download or read book Machine Learning and the Internet of Medical Things in Healthcare written by Krishna Kant Singh and published by Academic Press. This book was released on 2021-04-14 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. - Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning - Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics - Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies
Download or read book Medical Imaging written by K.C. Santosh and published by CRC Press. This book was released on 2019-08-20 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
Download or read book Handbook of Deep Learning in Biomedical Engineering written by Valentina Emilia Balas and published by Academic Press. This book was released on 2020-11-12 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis. - Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT - Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis - Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks - Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer's, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography
Download or read book Proceedings of the Future Technologies Conference FTC 2022 Volume 3 written by Kohei Arai and published by Springer Nature. This book was released on 2022-10-13 with total page 829 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seventh Future Technologies Conference 2022 was organized in a hybrid mode. It received a total of 511 submissions from learned scholars, academicians, engineers, scientists and students across many countries. The papers included the wide arena of studies like Computing, Artificial Intelligence, Machine Vision, Ambient Intelligence and Security and their jaw- breaking application to the real world. After a double-blind peer review process 177 submissions have been selected to be included in these proceedings. One of the prominent contributions of this conference is the confluence of distinguished researchers who not only enthralled us by their priceless studies but also paved way for future area of research. The papers provide amicable solutions to many vexing problems across diverse fields. They also are a window to the future world which is completely governed by technology and its multiple applications. We hope that the readers find this volume interesting and inspiring and render their enthusiastic support towards it.
Download or read book Deep Learning Techniques for Biomedical and Health Informatics written by Basant Agarwal and published by Academic Press. This book was released on 2020-01-14 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. - Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring - Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making - Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis
Download or read book Artificial Intelligence in Medicine written by David Riaño and published by Springer. This book was released on 2019-06-19 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence Blockchain Computing and Security Volume 2 written by Arvind Dagur and published by CRC Press. This book was released on 2023-10-23 with total page 1009 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the conference proceedings of ICABCS 2023, a non-profit conference with the objective to provide a platform that allows academicians, researchers, scholars and students from various institutions, universities and industries in India and abroad to exchange their research and innovative ideas in the field of Artificial Intelligence, Blockchain, Computing and Security. It explores the recent advancement in field of Artificial Intelligence, Blockchain, Communication and Security in this digital era for novice to profound knowledge about cutting edges in artificial intelligence, financial, secure transaction, monitoring, real time assistance and security for advanced stage learners/ researchers/ academicians. The key features of this book are: Broad knowledge and research trends in artificial intelligence and blockchain with security and their role in smart living assistance Depiction of system model and architecture for clear picture of AI in real life Discussion on the role of Artificial Intelligence and Blockchain in various real-life problems across sectors including banking, healthcare, navigation, communication, security Explanation of the challenges and opportunities in AI and Blockchain based healthcare, education, banking, and related industries This book will be of great interest to researchers, academicians, undergraduate students, postgraduate students, research scholars, industry professionals, technologists, and entrepreneurs.
Download or read book Adoption of Artificial Intelligence in Human and Clinical Genomics volume II written by Li Zhang and published by Frontiers Media SA. This book was released on 2024-06-21 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large databases are created by genomics for the discovery, study, and development of novel treatments all around the world. It's not hard to conceive that artificial intelligence (AI) might currently study the 3 billion base pairs that make up humanoid genetic makeup in order to uncover genetic disparities among the population. By 2026, large pharmaceutical companies hope to have researched up to 2 million genomes and analyzed massive amounts of patient data from clinical drug studies. As new equipment is introduced, AI will be employed in genomics for a variety of omics investigations, including transcriptomics. To aid in the classification of potentially clinically significant genes, AI is used to combine data from genomic research with literature analysis. Machine learning is now a critical component of the genomics industry's growth. AI and Machine learning in genomics is already having an impact on a number of areas, including genetic testing, medical care delivery, and genomics accessibility for people interested in learning more about how their genes influence their health. The purpose of this research is to explore AI and Machine learning applications in gene technology and their roles in paving the way for future genomics machine learning applications.
Download or read book Machine Learning and AI for Healthcare written by Arjun Panesar and published by Apress. This book was released on 2019-02-04 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Download or read book Predictive Intelligence in Biomedical and Health Informatics written by Rajshree Srivastava and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-10-12 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning for Health Informatics written by Andreas Holzinger and published by Springer. This book was released on 2016-12-09 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.
Download or read book Computer Intelligence Against Pandemics written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-08-07 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the most recent research and innovative developments regarding the new strains of COVID-19. While medical and natural sciences have been working instantly on deriving solutions and trying to protect humankind against such virus types, there is also a great focus on technological developments for improving the mechanism – momentum of science for effective and efficient solutions. At this point, computational intelligence is the most powerful tools for researchers to fight against COVID-19. Thanks to instant data-analyze and predictive techniques by computational intelligence, it is possible to get positive results and introduce revolutionary solutions against related medical diseases. By running capabilities – resources for rising the computational intelligence, technological fields like Artificial Intelligence (with Machine / Deep Learning), Data Mining, Applied Mathematics are essential components for processing data, recognizing patterns, modelling new techniques and improving the advantages of the computational intelligence more. Nowadays, there is a great interest in the application potentials of computational intelligence to be an effective approach for taking humankind more step away, after COVID-19 and before pandemics similar to the COVID-19 many appear.
