Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare
Author: Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
Publsiher: Academic Press
Total Pages: 292
Release: 2020-10-18
ISBN 10: 0128193158
ISBN 13: 9780128193150
Language: EN, FR, DE, ES & NL

Data Analytics in Biomedical Engineering and Healthcare Book Review:

Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Handbook of Data Science Approaches for Biomedical Engineering

Handbook of Data Science Approaches for Biomedical Engineering
Author: Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Publsiher: Academic Press
Total Pages: 318
Release: 2019-11-13
ISBN 10: 0128183195
ISBN 13: 9780128183199
Language: EN, FR, DE, ES & NL

Handbook of Data Science Approaches for Biomedical Engineering Book Review:

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare
Author: Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Publsiher: Academic Press
Total Pages: 396
Release: 2021-04-08
ISBN 10: 0128222611
ISBN 13: 9780128222614
Language: EN, FR, DE, ES & NL

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare Book Review:

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

Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications
Author: Hemanth D. Jude,Deepak Gupta,Valentina Emilia Balas
Publsiher: Academic Press
Total Pages: 294
Release: 2019-03-15
ISBN 10: 0128156430
ISBN 13: 9780128156438
Language: EN, FR, DE, ES & NL

Intelligent Data Analysis for Biomedical Applications Book Review:

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases. Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection Contains an analysis of medical databases to provide diagnostic expert systems Addresses the integration of intelligent data analysis techniques within biomedical information systems

Healthcare Data Analytics

Healthcare Data Analytics
Author: Chandan K. Reddy,Charu C. Aggarwal
Publsiher: CRC Press
Total Pages: 760
Release: 2015-06-23
ISBN 10: 148223212X
ISBN 13: 9781482232127
Language: EN, FR, DE, ES & NL

Healthcare Data Analytics Book Review:

At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available to solve healthcare problems. The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients. Written by prominent researchers and experts working in the healthcare domain, the book sheds light on many of the computational challenges in the field of medical informatics. Each chapter in the book is structured as a "survey-style" article discussing the prominent research issues and the advances made on that research topic. The book is divided into three major categories: Healthcare Data Sources and Basic Analytics - details the various healthcare data sources and analytical techniques used in the processing and analysis of such data Advanced Data Analytics for Healthcare - covers advanced analytical methods, including clinical prediction models, temporal pattern mining methods, and visual analytics Applications and Practical Systems for Healthcare - covers the applications of data analytics to pervasive healthcare, fraud detection, and drug discovery along with systems for medical imaging and decision support Computer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers have limited exposure to the data analytics area. The contents of this book will help to bring together these diverse communities by carefully and comprehensively discussing the most relevant contributions from each domain.

Exploratory Data Analytics for Healthcare

Exploratory Data Analytics for Healthcare
Author: R. Lakshmana Kumar,R. Indrakumari,B. Balamurugan,Achyut Shankar
Publsiher: CRC Press
Total Pages: 312
Release: 2021-12-24
ISBN 10: 1000527018
ISBN 13: 9781000527018
Language: EN, FR, DE, ES & NL

Exploratory Data Analytics for Healthcare Book Review:

Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications
Author: Om Prakash Jena,Bharat Bhushan,Utku Kose
Publsiher: CRC Press
Total Pages: 280
Release: 2022
ISBN 10: 9781032126876
ISBN 13: 1032126876
Language: EN, FR, DE, ES & NL

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications Book Review:

This book introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary ML/DL research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for healthcare sector, it depth, breadth, complexity, and diversity of this multi-disciplinary area. This book provides a comprehensive overview of Machine Learning (ML) and Deep Learning (DL) algorithms and explores the related use cases in enterprises such as computer aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. The book aims to endow different communities with their innovative advances in theory, analytical results, case studies, numerical simulation, modelling, and computational structuring in the field of ML/DL models for healthcare applications. This book will reveal different dimensions of ML/DL applications and will illustrate its use in the solution of assorted real world biomedical and healthcare problems. This book is a valuable source for information for researchers, scientists, healthcare professional, programmers and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.

Big Data Analytics in Bioinformatics and Healthcare

Big Data Analytics in Bioinformatics and Healthcare
Author: Wang, Baoying
Publsiher: IGI Global
Total Pages: 528
Release: 2014-10-31
ISBN 10: 1466666129
ISBN 13: 9781466666122
Language: EN, FR, DE, ES & NL

Big Data Analytics in Bioinformatics and Healthcare Book Review:

As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.

Predictive Intelligence in Biomedical and Health Informatics

Predictive Intelligence in Biomedical and Health Informatics
Author: Rajshree Srivastava,Nhu Gia Nguyen,Ashish Khanna,Siddhartha Bhattacharyya
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 180
Release: 2020-10-12
ISBN 10: 3110676125
ISBN 13: 9783110676129
Language: EN, FR, DE, ES & NL

Predictive Intelligence in Biomedical and Health Informatics Book Review:

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.

Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications
Author: Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
Publsiher: Academic Press
Total Pages: 310
Release: 2019-11-29
ISBN 10: 0128144831
ISBN 13: 9780128144831
Language: EN, FR, DE, ES & NL

Computational Learning Approaches to Data Analytics in Biomedical Applications Book Review:

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

Deep Learning for Data Analytics

Deep Learning for Data Analytics
Author: Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
Publsiher: Academic Press
Total Pages: 218
Release: 2020-05-29
ISBN 10: 0128226080
ISBN 13: 9780128226087
Language: EN, FR, DE, ES & NL

Deep Learning for Data Analytics Book Review:

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning

Smart Computational Intelligence in Biomedical and Health Informatics

Smart Computational Intelligence in Biomedical and Health Informatics
Author: Amit Kumar Manocha,Mandeep Singh,Shruti Jain,Vishal Jain
Publsiher: CRC Press
Total Pages: 202
Release: 2021-09-27
ISBN 10: 1000434370
ISBN 13: 9781000434378
Language: EN, FR, DE, ES & NL

Smart Computational Intelligence in Biomedical and Health Informatics Book Review:

Smart Computational Intelligence in Biomedical and Health Informatics presents state-of-the-art innovations; research, design, and implementation of methodological and algorithmic solutions to data processing problems, including analysis of evolving trends in health informatics and computer-aided diagnosis. This book describes practical, applications-led research regarding the use of methods and devices in clinical diagnosis, disease prevention, and patient monitoring and management. It also covers simulation and modeling, measurement and control, analysis, information extraction and monitoring of physiological data in clinical medicine and the biological sciences. FEATURES Covers evolutionary approaches to solve optimization problems in biomedical engineering Discusses IoT, Cloud computing, and data analytics in healthcare informatics Provides computational intelligence-based solution for diagnosis of diseases Reviews modelling and simulations in designing of biomedical equipment Promotes machine learning-based approaches to improvements in biomedical engineering problems This book is for researchers, graduate students in healthcare, biomedical engineers, and those interested in health informatics, computational intelligence, and machine learning.

Data Science and Predictive Analytics

Data Science and Predictive Analytics
Author: Ivo D. Dinov
Publsiher: Springer
Total Pages: 832
Release: 2018-08-27
ISBN 10: 3319723472
ISBN 13: 9783319723471
Language: EN, FR, DE, ES & NL

Data Science and Predictive Analytics Book Review:

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. The two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). This can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook. • A geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. To determine if the patient may suffer from Parkinson’s disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (Big Data). Most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. A learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. The results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis. • To improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (Big Data). The organization’s data-analytics team is tasked with developing a protocol that identifies, aggregates, harmonizes, models and analyzes these heterogeneous data elements to generate a trend forecast. This system needs to provide an automated, adaptive, scalable, and reliable prediction of the optimal investment, e.g., R&D allocation, that maximizes the company’s bottom line. A reader that complete a course of study using this textbook will be able to ingest the observed structured and unstructured data, mathematically represent the data as a computable object, apply appropriate model-based and model-free prediction techniques. The results of these techniques may be used to forecast the expected relation between the company’s investment, product supply, general demand of healthcare (providers and patients), and estimate the return on initial investments.

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

Demystifying Big Data  Machine Learning  and Deep Learning for Healthcare Analytics
Author: Pradeep N,Sandeep Kautish,Sheng-Lung Peng
Publsiher: Academic Press
Total Pages: 372
Release: 2021-06-25
ISBN 10: 0128220449
ISBN 13: 9780128220443
Language: EN, FR, DE, ES & NL

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics Book Review:

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

Handbook of Research on Engineering Business and Healthcare Applications of Data Science and Analytics

Handbook of Research on Engineering  Business  and Healthcare Applications of Data Science and Analytics
Author: Patil, Bhushan,Vohra, Manisha
Publsiher: IGI Global
Total Pages: 583
Release: 2020-10-23
ISBN 10: 1799830543
ISBN 13: 9781799830542
Language: EN, FR, DE, ES & NL

Handbook of Research on Engineering Business and Healthcare Applications of Data Science and Analytics Book Review:

Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies.

Green Computing and Predictive Analytics for Healthcare

Green Computing and Predictive Analytics for Healthcare
Author: Sourav Banerjee,Chinmay Chakraborty,Kousik Dasgupta
Publsiher: CRC Press
Total Pages: 190
Release: 2020-12-03
ISBN 10: 9780367322007
ISBN 13: 0367322005
Language: EN, FR, DE, ES & NL

Green Computing and Predictive Analytics for Healthcare Book Review:

"The emergent trends in Green Cloud Computing lead to new developments in various application domains, mainly in healthcare. The aim of this book is to collect innovative and high-quality research contributions related to the advances in the energy-aware cloud-enabled healthcare domain"-- Provided by publisher.

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
Author: Ervin Sejdic,Tiago H. Falk
Publsiher: CRC Press
Total Pages: 606
Release: 2018-07-04
ISBN 10: 1351061216
ISBN 13: 9781351061216
Language: EN, FR, DE, ES & NL

Signal Processing and Machine Learning for Biomedical Big Data Book Review:

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.

Terahertz Biomedical and Healthcare Technologies

Terahertz Biomedical and Healthcare Technologies
Author: Amit Banerjee,Basabi Chakraborty,Hiroshi Inokawa,Jitendra Nath Roy
Publsiher: Elsevier
Total Pages: 262
Release: 2020-08-11
ISBN 10: 0128185570
ISBN 13: 9780128185575
Language: EN, FR, DE, ES & NL

Terahertz Biomedical and Healthcare Technologies Book Review:

Terahertz Biomedical and Healthcare Technologies: Materials to Devices reviews emerging advances in terahertz biomedical and healthcare technologies, including advances in fundamental materials science research, device design and fabrication, applications, and challenges and opportunities for improved performance. In addition, the improvement of materials, optical elements, and measuring techniques are also explored. Other sections cover the design and development of wide bandgap semiconductors for terahertz device applications, including their physics, device modeling, characterization and fabrication concepts. Finally, the book touches on potential defense, medical imaging, internet of things, and the machine learning applications of terahertz technologies. Reviews the latest advances in the fundamental and applied research of terahertz technologies, covering key topics in materials science, biomedical engineering and healthcare informatics Includes applications of terahertz technologies in medical imaging, diagnosis and treatment Provides readers with an understanding of the machine learning, pattern recognition, and data analytics research utilized to enhance the effectiveness of terahertz technologies

Internet of Things in Biomedical Engineering

Internet of Things in Biomedical Engineering
Author: Valentina E. Balas,Le Hoang Son,Sudan Jha,Manju Khari,Raghvendra Kumar
Publsiher: Academic Press
Total Pages: 379
Release: 2019-06-14
ISBN 10: 0128173572
ISBN 13: 9780128173572
Language: EN, FR, DE, ES & NL

Internet of Things in Biomedical Engineering Book Review:

Internet of Things in Biomedical Engineering presents the most current research in Internet of Things (IoT) applications for clinical patient monitoring and treatment. The book takes a systems-level approach for both human-factors and the technical aspects of networking, databases and privacy. Sections delve into the latest advances and cutting-edge technologies, starting with an overview of the Internet of Things and biomedical engineering, as well as a focus on ‘daily life.’ Contributors from various experts then discuss ‘computer assisted anthropology,’ CLOUDFALL, and image guided surgery, as well as bio-informatics and data mining. This comprehensive coverage of the industry and technology is a perfect resource for students and researchers interested in the topic. Presents recent advances in IoT for biomedical engineering, covering biometrics, bioinformatics, artificial intelligence, computer vision and various network applications Discusses big data and data mining in healthcare and other IoT based biomedical data analysis Includes discussions on a variety of IoT applications and medical information systems Includes case studies and applications, as well as examples on how to automate data analysis with Perl R in IoT

IoT Based Data Analytics for the Healthcare Industry

IoT Based Data Analytics for the Healthcare Industry
Author: Sanjay Kumar Singh,Ravi Shankar Singh,Anil Kumar Pandey,Sandeep S Udmale,Ankit Chaudhary
Publsiher: Academic Press
Total Pages: 340
Release: 2020-12-01
ISBN 10: 0128214767
ISBN 13: 9780128214763
Language: EN, FR, DE, ES & NL

IoT Based Data Analytics for the Healthcare Industry Book Review:

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization. The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand. Provides state-of-art methods and current trends in data analytics for the healthcare industry Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques Discusses several potential AI techniques developed using IoT for the healthcare industry Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages