Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
Author: Sujata Dash,Biswa Ranjan Acharya,Mamta Mittal,Ajith Abraham,Arpad Kelemen
Publsiher: Springer Nature
Total Pages: 383
Release: 2019-11-14
ISBN 10: 3030339661
ISBN 13: 9783030339661
Language: EN, FR, DE, ES & NL

Deep Learning Techniques for Biomedical and Health Informatics Book Review:

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.

Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
Author: Basant Agarwal,Valentina Emilia Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
Publsiher: Academic Press
Total Pages: 367
Release: 2020-01-14
ISBN 10: 0128190620
ISBN 13: 9780128190623
Language: EN, FR, DE, ES & NL

Deep Learning Techniques for Biomedical and Health Informatics Book Review:

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

Biomedical Data Mining for Information Retrieval

Biomedical Data Mining for Information Retrieval
Author: Subhendu Kumar Pani,Sujata Dash,S. Balamurugan,Ajith Abraham
Publsiher: John Wiley & Sons
Total Pages: 448
Release: 2021-08-06
ISBN 10: 1119711266
ISBN 13: 9781119711261
Language: EN, FR, DE, ES & NL

Biomedical Data Mining for Information Retrieval Book Review:

This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical Image Mining, a novel research area, due to its large amount of biomedical images increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.

Deep Learning Machine Learning and IoT in Biomedical and Health Informatics

Deep Learning  Machine Learning and IoT in Biomedical and Health Informatics
Author: Sujata Dash,Subhendu Kumar Pani,Joel J. P. C. Rodrigues,Babita Majhi
Publsiher: CRC Press
Total Pages: 382
Release: 2022-02-11
ISBN 10: 1000534057
ISBN 13: 9781000534054
Language: EN, FR, DE, ES & NL

Deep Learning Machine Learning and IoT in Biomedical and Health Informatics Book Review:

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

Deep Learning in Biomedical and Health Informatics

Deep Learning in Biomedical and Health Informatics
Author: M. A. Jabbar,Ajith Abraham,Onur Dogan,Ana Maria Madureira,Sanju Tiwari
Publsiher: CRC Press
Total Pages: 224
Release: 2021-09-26
ISBN 10: 1000429083
ISBN 13: 9781000429084
Language: EN, FR, DE, ES & NL

Deep Learning in Biomedical and Health Informatics Book Review:

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

Deep Learning in Bioinformatics

Deep Learning in Bioinformatics
Author: Habib Izadkhah
Publsiher: Academic Press
Total Pages: 380
Release: 2022-01-17
ISBN 10: 0128238364
ISBN 13: 9780128238363
Language: EN, FR, DE, ES & NL

Deep Learning in Bioinformatics Book Review:

Deep Learning in Bioinformatics: Techniques and Applications in Practice introduces the topic in an easy-to-understand way, exploring how it can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology. The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions. Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies. Introduces deep learning in an easy-to-understand way Presents how deep learning can be utilized for addressing some important problems in bioinformatics Presents the state-of-the-art algorithms in deep learning and bioinformatics Introduces deep learning libraries in bioinformatics

Emerging Technologies for Healthcare

Emerging Technologies for Healthcare
Author: Monika Mangla,Nonita Sharma,Poonam Garg,Vaishali Wadhwa,Thirunavukkarasu K,Shahnawaz Khan
Publsiher: John Wiley & Sons
Total Pages: 442
Release: 2021-07-20
ISBN 10: 1119792320
ISBN 13: 9781119792321
Language: EN, FR, DE, ES & NL

Emerging Technologies for Healthcare Book Review:

“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques. The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions. This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
Author: E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publsiher: CRC Press
Total Pages: 344
Release: 2021-09-22
ISBN 10: 1000370496
ISBN 13: 9781000370492
Language: EN, FR, DE, ES & NL

Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book Review:

This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.

Machine Learning for Non Less Invasive Methods in Health Informatics

Machine Learning for Non Less Invasive Methods in Health Informatics
Author: Kun Qian,Liang Zhang,Kezhi Li,Juan Liu
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2021-11-26
ISBN 10: 2889717089
ISBN 13: 9782889717088
Language: EN, FR, DE, ES & NL

Machine Learning for Non Less Invasive Methods in Health Informatics Book Review:

Machine Learning and Deep Learning Techniques for Medical Science

Machine Learning and Deep Learning Techniques for Medical Science
Author: K. Gayathri Devi
Publsiher: CRC Press
Total Pages: 410
Release: 2022
ISBN 10: 9781003217497
ISBN 13: 1003217494
Language: EN, FR, DE, ES & NL

Machine Learning and Deep Learning Techniques for Medical Science Book Review:

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

Machine Learning for Health Informatics

Machine Learning for Health Informatics
Author: Andreas Holzinger
Publsiher: Springer
Total Pages: 481
Release: 2016-12-09
ISBN 10: 3319504789
ISBN 13: 9783319504780
Language: EN, FR, DE, ES & NL

Machine Learning for Health Informatics Book Review:

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.

Data Analytics in Bioinformatics

Data Analytics in Bioinformatics
Author: Rabinarayan Satpathy,Tanupriya Choudhury,Suneeta Satpathy,Sachi Nandan Mohanty,Xiaobo Zhang
Publsiher: John Wiley & Sons
Total Pages: 544
Release: 2021-01-20
ISBN 10: 111978560X
ISBN 13: 9781119785606
Language: EN, FR, DE, ES & NL

Data Analytics in Bioinformatics Book Review:

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics
Author: Sushmita Mitra,Sujay Datta,Theodore Perkins,George Michailidis
Publsiher: CRC Press
Total Pages: 384
Release: 2008-06-05
ISBN 10: 1420011782
ISBN 13: 9781420011784
Language: EN, FR, DE, ES & NL

Introduction to Machine Learning and Bioinformatics Book Review:

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

Computational Intelligence and Healthcare Informatics

Computational Intelligence and Healthcare Informatics
Author: Om Prakash Jena,Alok Ranjan Tripathy,Ahmed A. Elngar,Zdzislaw Polkowski
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2021-10-19
ISBN 10: 1119818680
ISBN 13: 9781119818687
Language: EN, FR, DE, ES & NL

Computational Intelligence and Healthcare Informatics Book Review:

AI techniques are being successfully used in the fields of health to increase the efficacy of therapies and avoid the risks of false diagnosis, therapeutic decision-making, and outcome prediction in many clinical cases, thanks to the rapid advancement of technology. The acquisition, analysis, and application of a vast amount of information required to solve complex problems is a challenge for modern health therapies. The 21 chapters in this integrate several aspects of computational intelligence like machine learning and deep learning from diversified perspectives. The purpose of the book is to endow to different communities with their innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modeling, advanced deployment, case studies, analytical results, computational structuring and significance progress in healthcare applications.

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques  Tools  and Applications
Author: K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar
Publsiher: Springer Nature
Total Pages: 317
Release: 2020-01-30
ISBN 10: 9811524459
ISBN 13: 9789811524455
Language: EN, FR, DE, ES & NL

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications Book Review:

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

Machine Learning and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare
Author: Krishna Kant Singh,Mohamed Elhoseny,Akansha Singh,Ahmed A. Elngar
Publsiher: Academic Press
Total Pages: 290
Release: 2021-04-26
ISBN 10: 012823217X
ISBN 13: 9780128232170
Language: EN, FR, DE, ES & NL

Machine Learning and the Internet of Medical Things in Healthcare Book Review:

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

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems
Author: Arun Kumar Sangaiah
Publsiher: Academic Press
Total Pages: 280
Release: 2019-07-26
ISBN 10: 0128172932
ISBN 13: 9780128172933
Language: EN, FR, DE, ES & NL

Deep Learning and Parallel Computing Environment for Bioengineering Systems Book Review:

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

Computational Analysis and Deep Learning for Medical Care

Computational Analysis and Deep Learning for Medical Care
Author: Amit Kumar Tyagi
Publsiher: John Wiley & Sons
Total Pages: 528
Release: 2021-08-24
ISBN 10: 1119785723
ISBN 13: 9781119785729
Language: EN, FR, DE, ES & NL

Computational Analysis and Deep Learning for Medical Care Book Review:

This book discuss how deep learning can help healthcare images or text data in making useful decisions”. For that, the need of reliable deep learning models like Neural networks, Convolutional neural network, Backpropagation, Recurrent neural network is increasing in medical image processing, i.e., in Colorization of Black and white images of X-Ray, automatic machine translation, object classification in photographs / images (CT-SCAN), character or useful generation (ECG), image caption generation, etc. Hence, Reliable Deep Learning methods for perception or producing belter results are highly effective for e-healthcare applications, which is the challenge of today. For that, this book provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, this book will cover introduction, requirement, importance, issues and challenges, etc., faced in available current deep learning models (also include innovative deep learning algorithms/ models for curing disease in Medicare) and provide opportunities for several research communities with including several research gaps in deep learning models (for healthcare applications).

Advanced AI Techniques and Applications in Bioinformatics

Advanced AI Techniques and Applications in Bioinformatics
Author: Loveleen Gaur,Arun Solanki,Samuel Fosso Wamba,Noor Zaman Jhanjhi
Publsiher: CRC Press
Total Pages: 282
Release: 2021-10-18
ISBN 10: 1000462986
ISBN 13: 9781000462982
Language: EN, FR, DE, ES & NL

Advanced AI Techniques and Applications in Bioinformatics Book Review:

The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers

Machine Learning Big Data and IoT for Medical Informatics

Machine Learning  Big Data  and IoT for Medical Informatics
Author: Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid
Publsiher: Academic Press
Total Pages: 458
Release: 2021-06-13
ISBN 10: 0128217812
ISBN 13: 9780128217818
Language: EN, FR, DE, ES & NL

Machine Learning Big Data and IoT for Medical Informatics Book Review:

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.