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: 3030339661
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Deep Learning Techniques for Biomedical and Health Informatics Book Excerpt:

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: 0128190620
Category: Science
Language: EN, FR, DE, ES & NL

Deep Learning Techniques for Biomedical and Health Informatics Book Excerpt:

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

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: 1000534057
Category: Computers
Language: EN, FR, DE, ES & NL

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

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: 1000429083
Category: Computers
Language: EN, FR, DE, ES & NL

Deep Learning in Biomedical and Health Informatics Book Excerpt:

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.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

Handbook of Deep Learning in Biomedical Engineering and Health Informatics
Author: Golden Julie,S. M. Jaisakthi,Y. Harold Robinson
Publsiher: Unknown
Total Pages: 135
Release: 2021
ISBN: 9781774638170
Category: Electronic Book
Language: EN, FR, DE, ES & NL

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

"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 the 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. The 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"--

Emerging Technologies in Data Mining and Information Security

Emerging Technologies in Data Mining and Information Security
Author: João Manuel R. S. Tavares,Satyajit Chakrabarti,Abhishek Bhattacharya,Sujata Ghatak
Publsiher: Springer Nature
Total Pages: 994
Release: 2021-05-04
ISBN: 981159774X
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Emerging Technologies in Data Mining and Information Security Book Excerpt:

This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of things (IoT), and information security.

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: 3110676141
Category: Computers
Language: EN, FR, DE, ES & NL

Predictive Intelligence in Biomedical and Health Informatics Book Excerpt:

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

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: 1000370496
Category: Medical
Language: EN, FR, DE, ES & NL

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

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 and Deep Learning Techniques for Medical Science

Machine Learning and Deep Learning Techniques for Medical Science
Author: K. Gayathri Devi,Kishore Balasubramanian,Le Anh Ngoc
Publsiher: CRC Press
Total Pages: 426
Release: 2022-05-12
ISBN: 1000583368
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

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

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).

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: 0128172932
Category: Computers
Language: EN, FR, DE, ES & NL

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

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

Research Anthology on Machine Learning Techniques Methods and Applications

Research Anthology on Machine Learning Techniques  Methods  and Applications
Author: Management Association, Information Resources
Publsiher: IGI Global
Total Pages: 1516
Release: 2022-05-13
ISBN: 1668462923
Category: Computers
Language: EN, FR, DE, ES & NL

Research Anthology on Machine Learning Techniques Methods and Applications Book Excerpt:

Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.

Computational Intelligence for Machine Learning and Healthcare Informatics

Computational Intelligence for Machine Learning and Healthcare Informatics
Author: Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 346
Release: 2020-06-22
ISBN: 3110648199
Category: Computers
Language: EN, FR, DE, ES & NL

Computational Intelligence for Machine Learning and Healthcare Informatics Book Excerpt:

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
Author: Sunil Kumar Dhal,Srinivas Prasad,Sudhir Kumar Mohapatra,Subhendu Kumar Pani
Publsiher: John Wiley & Sons
Total Pages: 352
Release: 2022-05-20
ISBN: 1119792355
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics Book Excerpt:

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

Statistics and Machine Learning Methods for EHR Data

Statistics and Machine Learning Methods for EHR Data
Author: Hulin Wu,Jose Miguel Yamal,Ashraf Yaseen,Vahed Maroufy
Publsiher: CRC Press
Total Pages: 313
Release: 2020-12-10
ISBN: 1000260968
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Statistics and Machine Learning Methods for EHR Data Book Excerpt:

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
Author: Sunil Kumar Dhal,Subhendu Kumar Pani,Srinivas Prasad,Sudhir Kumar Mohapatra
Publsiher: John Wiley & Sons
Total Pages: 352
Release: 2022-06-28
ISBN: 1119791731
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics Book Excerpt:

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

Big Data IoT and Machine Learning

Big Data  IoT  and Machine Learning
Author: Rashmi Agrawal,Marcin Paprzycki,Neha Gupta
Publsiher: CRC Press
Total Pages: 319
Release: 2020-09-01
ISBN: 1000098281
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data IoT and Machine Learning Book Excerpt:

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases

Machine Learning Used in Biomedical Computing and Intelligence Healthcare Volume II

Machine Learning Used in Biomedical Computing and Intelligence Healthcare  Volume II
Author: Honghao Gao,Ying Li,Zijian Zhang,Wenbing Zhao
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2022-05-27
ISBN: 2889762475
Category: Science
Language: EN, FR, DE, ES & NL

Machine Learning Used in Biomedical Computing and Intelligence Healthcare Volume II Book Excerpt:

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems
Author: Om Prakash Jena,Bharat Bhushan,Nitin Rakesh,Parma Nand Astya,Yousef Farhaoui
Publsiher: Emerging Trends in Biomedical Technologies and Health informatics
Total Pages: 400
Release: 2022
ISBN: 9781032036724
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems Book Excerpt:

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 insure high quality data processing, medical image and signal analysis, and improved healthcare application.

Design of Intelligent Applications using Machine Learning and Deep Learning Techniques

Design of Intelligent Applications using Machine Learning and Deep Learning Techniques
Author: Ramchandra Sharad Mangrulkar,Antonis Michalas,Narendra Shekokar,Meera Narvekar,Pallavi Vijay Chavan
Publsiher: CRC Press
Total Pages: 446
Release: 2021-08-15
ISBN: 1000423832
Category: Computers
Language: EN, FR, DE, ES & NL

Design of Intelligent Applications using Machine Learning and Deep Learning Techniques Book Excerpt:

Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL. · Comprehensive analyses and probabilistic approaches for ML and DL. · Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia. · Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals. This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.

Computational Intelligence for Machine Learning and Healthcare Informatics

Computational Intelligence for Machine Learning and Healthcare Informatics
Author: Rajshree Srivastava,Pradeep Kumar Mallick,Siddharth Swarup Rautaray,Manjusha Pandey
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 346
Release: 2020-06-22
ISBN: 3110649276
Category: Computers
Language: EN, FR, DE, ES & NL

Computational Intelligence for Machine Learning and Healthcare Informatics Book Excerpt:

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.