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Author: Sujata Dash
Publisher: CRC Press
ISBN: 1000534057
Category : Computers
Languages : en
Pages : 407
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Book Description
Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Author: Sujata Dash
Publisher: CRC Press
ISBN: 1000534057
Category : Computers
Languages : en
Pages : 407
Get Book Here
Book Description
Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Author: Pardeep Kumar
Publisher: Academic Press
ISBN: 0128217812
Category : Computers
Languages : en
Pages : 460
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Book Description
Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. - Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. - Includes several privacy preservation techniques for medical data. - Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. - Offers case studies and applications relating to machine learning, big data, and health care analysis.
Author: Sujata Dash
Publisher: CRC Press
ISBN: 1000534006
Category : Computers
Languages : en
Pages : 382
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Book Description
Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems
Author: Krishna Kant Singh
Publisher: Academic Press
ISBN: 012823217X
Category : Science
Languages : en
Pages : 290
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Book Description
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
Author: Basant Agarwal
Publisher: Academic Press
ISBN: 0128190620
Category : Science
Languages : en
Pages : 370
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Book Description
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
Author: Akash Kumar Bhoi
Publisher: Springer Nature
ISBN: 9811629722
Category : Technology & Engineering
Languages : en
Pages : 341
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Book Description
This book covers applications for hybrid artificial intelligence (AI) and Internet of Things (IoT) for integrated approach and problem solving in the areas of radiology, drug interactions, creation of new drugs, imaging, electronic health records, disease diagnosis, telehealth, and mobility-related problems in healthcare. The book discusses the convergence of AI and the hybrid approaches in healthcare which optimizes the possible solutions and better treatment. Internet of Things (IoT) in healthcare is the next-gen technologies which automate the healthcare facility by mobility solutions are discussed in detail. It also discusses hybrid AI with bio-inspired techniques, genetic algorithm, neuro-fuzzy algorithms, and soft computing approaches which significantly improves the prediction of critical cardiovascular abnormalities and other healthcare solutions to the ongoing challenging research.
Author: Arun Kumar Sangaiah
Publisher: Academic Press
ISBN: 0128172932
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Author: Om Prakash Jena
Publisher: CRC Press
ISBN: 1000486826
Category : Computers
Languages : en
Pages : 321
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Book Description
The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications. FEATURES Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems Provides guidance in developing intelligence-based diagnostic systems, efficient models and cost-effective machines Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real testbeds and empirical characterization of security and privacy interoperability issues in healthcare applications Explores and illustrates the current and future impacts of pandemics and mitigates risk in healthcare with advanced analytics This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.
Author: Monika Mangla
Publisher: John Wiley & Sons
ISBN: 1119791723
Category : Computers
Languages : de
Pages : 418
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Book Description
?Emerging Technologies for Healthcare? beginnt mit einer IoT-basierten Lösung für die Automatisierung im Gesundheitssektor, wodurch Verfahren auf Grundlage von fortschrittlichen Deep-Learning-Techniken ermöglicht werden. Praktische Lösungen, die auf verschiedenen Ansätzen des maschinellen Lernens beruhen, werden vorgestellt und auf die Analyse und Vorhersage von Krankheiten angewandt. Ein Beispiel ist die Nutzung einer dreidimensionalen Matrix für die Behandlung chronischer Nierenerkrankungen, die Diagnose und Prognose des erworbenen demyelinisierenden Syndroms und von Autismus-Spektrum-Störungen sowie die Erkennung von Lungenentzündungen. Außerdem werden verschiedene geeignete Ansätze vorgestellt, wie die Gesundheitssysteme mit COVID-19-Fällen umgehen können. Daneben wird ein detaillierter Erkennungsmechanismus dargelegt, mit dessen Hilfe Lösungen entwickelt werden können, um von der Handschrift auf die Persönlichkeit zu schließen, und es werden neuartige Ansätze für die Stimmungsanalyse aufgezeigt, die mit ausreichenden Daten und verschiedenen Betrachtungsweisen untermauert sind. Dieses Buch enthält nicht nur theoretische Ansätze und Algorithmen, sondern zeigt auch auf, welche Schritte bei der Problemanalyse mithilfe von Daten, Prozessen, Berichten und Optimierungstechniken durchlaufen werden. Es ist ein umfassendes Nachschlagewerk für die Lösung verschiedener Probleme anhand von Algorithmen für das maschinelle Lernen.
Author: Qiang Yang
Publisher: Springer Nature
ISBN: 3030630765
Category : Computers
Languages : en
Pages : 291
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Book Description
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
