Handbook Of Data Science Approaches For Biomedical Engineering

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Handbook of Data Science Approaches for Biomedical Engineering

Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Handbook of Data Science Approaches for Biomedical Engineering Book PDF

Author : Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
Publisher : Academic Press
Page : 320 pages
File Size : 41,7 Mb
Release : 2019-11-13
Category : Science
ISBN : 9780128183199

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Handbook of Data Science Approaches for Biomedical Engineering by Valentina Emilia Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari Pdf

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 Deep Learning in Biomedical Engineering

Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Handbook of Deep Learning in Biomedical Engineering Book PDF

Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Publisher : Academic Press
Page : 320 pages
File Size : 49,8 Mb
Release : 2020-11-12
Category : Science
ISBN : 9780128230473

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Handbook of Deep Learning in Biomedical Engineering by Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar Pdf

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Handbook of Computational Intelligence in Biomedical Engineering and Healthcare Book PDF

Author : Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
Publisher : Academic Press
Page : 398 pages
File Size : 49,9 Mb
Release : 2021-04-08
Category : Science
ISBN : 9780128222614

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Handbook of Computational Intelligence in Biomedical Engineering and Healthcare by Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das Pdf

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

Handbook of Artificial Intelligence in Biomedical Engineering

Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G.S. Mahalakshmi
Handbook of Artificial Intelligence in Biomedical Engineering Book PDF

Author : Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G.S. Mahalakshmi
Publisher : CRC Press
Page : 538 pages
File Size : 54,9 Mb
Release : 2021-03-30
Category : Computers
ISBN : 9781000067675

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Handbook of Artificial Intelligence in Biomedical Engineering by Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G.S. Mahalakshmi Pdf

Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert’s knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.

Data Deduplication Approaches

Tin Thein Thwel,G. R. Sinha
Data Deduplication Approaches Book PDF

Author : Tin Thein Thwel,G. R. Sinha
Publisher : Academic Press
Page : 406 pages
File Size : 49,6 Mb
Release : 2020-11-25
Category : Science
ISBN : 9780128236338

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Data Deduplication Approaches by Tin Thein Thwel,G. R. Sinha Pdf

In the age of data science, the rapidly increasing amount of data is a major concern in numerous applications of computing operations and data storage. Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strategies, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated segments or chunks of data within the associated files. Due to ever-increasing data duplication, its deduplication has become an especially useful field of research for storage environments, in particular persistent data storage. Data Deduplication Approaches provides readers with an overview of the concepts and background of data deduplication approaches, then proceeds to demonstrate in technical detail the strategies and challenges of real-time implementations of handling big data, data science, data backup, and recovery. The book also includes future research directions, case studies, and real-world applications of data deduplication, focusing on reduced storage, backup, recovery, and reliability. Includes data deduplication methods for a wide variety of applications Includes concepts and implementation strategies that will help the reader to use the suggested methods Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable methods for their applications Focuses on reduced storage, backup, recovery, and reliability, which are the most important aspects of implementing data deduplication approaches Includes case studies

Efficient Data Handling for Massive Internet of Medical Things

Chinmay Chakraborty,Uttam Ghosh,Vinayakumar Ravi,Yogesh Shelke
Efficient Data Handling for Massive Internet of Medical Things Book PDF

Author : Chinmay Chakraborty,Uttam Ghosh,Vinayakumar Ravi,Yogesh Shelke
Publisher : Springer Nature
Page : 398 pages
File Size : 51,5 Mb
Release : 2021-09-01
Category : Technology & Engineering
ISBN : 9783030666330

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Efficient Data Handling for Massive Internet of Medical Things by Chinmay Chakraborty,Uttam Ghosh,Vinayakumar Ravi,Yogesh Shelke Pdf

This book focuses on recent advances and different research areas in multi-modal data fusion under healthcare informatics and seeks out theoretical, methodological, well-established and validated empirical work dealing with these different topics. This book brings together the latest industrial and academic progress, research, and development efforts within the rapidly maturing health informatics ecosystem. Contributions highlight emerging data fusion topics that support prospective healthcare applications. The book also presents various technologies and concerns regarding energy aware and secure sensors and how they can reduce energy consumption in health care applications. It also discusses the life cycle of sensor devices and protocols with the help of energy-aware design, production, and utilization, as well as the Internet of Things technologies such as tags, sensors, sensing networks, and Internet technologies. In a nutshell, this book gives a comprehensive overview of the state-of-the-art theories and techniques for massive data handling and access in medical data and smart health in IoT, and provides useful guidelines for the design of massive Internet of Medical Things.

Handbook on Intelligent Healthcare Analytics

A. Jaya,K. Kalaiselvi,Dinesh Goyal,Dhiya Al-Jumeily
Handbook on Intelligent Healthcare Analytics Book PDF

Author : A. Jaya,K. Kalaiselvi,Dinesh Goyal,Dhiya Al-Jumeily
Publisher : John Wiley & Sons
Page : 452 pages
File Size : 51,5 Mb
Release : 2022-06-01
Category : Technology & Engineering
ISBN : 9781119791799

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Handbook on Intelligent Healthcare Analytics by A. Jaya,K. Kalaiselvi,Dinesh Goyal,Dhiya Al-Jumeily Pdf

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; An exploration of predictive analytics in healthcare; The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics. Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Impact of AI and Data Science in Response to Coronavirus Pandemic

Sushruta Mishra,Pradeep Kumar Mallick,Hrudaya Kumar Tripathy,Gyoo-Soo Chae,Bhabani Shankar Prasad Mishra
Impact of AI and Data Science in Response to Coronavirus Pandemic Book PDF

Author : Sushruta Mishra,Pradeep Kumar Mallick,Hrudaya Kumar Tripathy,Gyoo-Soo Chae,Bhabani Shankar Prasad Mishra
Publisher : Springer Nature
Page : 324 pages
File Size : 41,9 Mb
Release : 2021-07-22
Category : Technology & Engineering
ISBN : 9789811627866

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Impact of AI and Data Science in Response to Coronavirus Pandemic by Sushruta Mishra,Pradeep Kumar Mallick,Hrudaya Kumar Tripathy,Gyoo-Soo Chae,Bhabani Shankar Prasad Mishra Pdf

The book presents advanced AI based technologies in dealing with COVID-19 outbreak and provides an in-depth analysis of variety of COVID-19 datasets throughout globe. It discusses recent artificial intelligence based algorithms and models for data analysis of COVID-19 symptoms and its possible remedies. It provides a unique opportunity to present the work on state-of-the-art of modern artificial intelligence tools and technologies to track and forecast COVID-19 cases. It indicates insights and viewpoints from scholars regarding risk and resilience analytics for policy making and operations of large-scale systems on this epidemic. A snapshot of the latest architectures, frameworks in machine learning and data science are also highlighted to gather and aggregate data records related to COVID-19 and to diagnose the virus. It delivers significant research outcomes and inspiring new real-world applications with respect to feasible AI based solutions in COVID-19 outbreak. In addition, it discusses strong preventive measures to control such pandemic.

Handbook of Deep Learning in Biomedical Engineering and Health Informatics

E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Handbook of Deep Learning in Biomedical Engineering and Health Informatics Book PDF

Author : E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publisher : CRC Press
Page : 366 pages
File Size : 42,8 Mb
Release : 2021-09-22
Category : Medical
ISBN : 9781000370492

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Handbook of Deep Learning in Biomedical Engineering and Health Informatics by E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi Pdf

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.

Data Analytics in Biomedical Engineering and Healthcare

Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
Data Analytics in Biomedical Engineering and Healthcare Book PDF

Author : Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
Publisher : Academic Press
Page : 298 pages
File Size : 40,7 Mb
Release : 2020-10-18
Category : Science
ISBN : 9780128193150

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Data Analytics in Biomedical Engineering and Healthcare by Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar Pdf

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

Big Data Analytics and Computational Intelligence for Cybersecurity

Mariya Ouaissa,Zakaria Boulouard,Mariyam Ouaissa,Inam Ullah Khan,Mohammed Kaosar
Big Data Analytics and Computational Intelligence for Cybersecurity Book PDF

Author : Mariya Ouaissa,Zakaria Boulouard,Mariyam Ouaissa,Inam Ullah Khan,Mohammed Kaosar
Publisher : Springer Nature
Page : 336 pages
File Size : 51,8 Mb
Release : 2022-09-01
Category : Computers
ISBN : 9783031057526

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Big Data Analytics and Computational Intelligence for Cybersecurity by Mariya Ouaissa,Zakaria Boulouard,Mariyam Ouaissa,Inam Ullah Khan,Mohammed Kaosar Pdf

This book presents a collection of state-of-the-art artificial intelligence and big data analytics approaches to cybersecurity intelligence. It illustrates the latest trends in AI/ML-based strategic defense mechanisms against malware, vulnerabilities, cyber threats, as well as proactive countermeasures. It also introduces other trending technologies, such as blockchain, SDN, and IoT, and discusses their possible impact on improving security. The book discusses the convergence of AI/ML and big data in cybersecurity by providing an overview of theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in different approaches of security. It also displays solutions that will help analyze complex patterns in user data and ultimately improve productivity. This book can be a source for researchers, students, and practitioners interested in the fields of artificial intelligence, cybersecurity, data analytics, and recent trends of networks.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Pradeep N,Sandeep Kautish,Sheng-Lung Peng
Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics Book PDF

Author : Pradeep N,Sandeep Kautish,Sheng-Lung Peng
Publisher : Academic Press
Page : 374 pages
File Size : 49,9 Mb
Release : 2021-06-10
Category : Science
ISBN : 9780128220443

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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics by Pradeep N,Sandeep Kautish,Sheng-Lung Peng Pdf

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

AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management

Kautish, Sandeep,Dhiman, Gaurav
AI Enabled Multiple Criteria Decision Making Approaches for Healthcare Management Book PDF

Author : Kautish, Sandeep,Dhiman, Gaurav
Publisher : IGI Global
Page : 294 pages
File Size : 50,5 Mb
Release : 2022-06-30
Category : Computers
ISBN : 9781668444078

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AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management by Kautish, Sandeep,Dhiman, Gaurav Pdf

Multiple-criteria decision making, including multiple rule-based decision making, multiple-objective decision making, and multiple-attribute decision making, is used by clinical decision makers to analyze healthcare issues from various perspectives. In practical healthcare cases, semi-structured and unstructured decision-making issues involve multiple criteria that may conflict with each other. Thus, the use of multiple-criteria decision making is a promising source of practical solutions for such problems. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management investigates the contributions of practical multiple-criteria decision analysis applications and cases for healthcare management. The book also considers the best practices and tactics for utilizing multiple-criteria decision making to ensure the technology is utilized appropriately. Covering key topics such as fuzzy data, augmented reality, blockchain, and data transmission, this reference work is ideal for computer scientists, healthcare professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, educators, and students.

Internet of Things for Healthcare Technologies

Chinmay Chakraborty,Amit Banerjee,Maheshkumar H. Kolekar,Lalit Garg,Basabi Chakraborty
Internet of Things for Healthcare Technologies Book PDF

Author : Chinmay Chakraborty,Amit Banerjee,Maheshkumar H. Kolekar,Lalit Garg,Basabi Chakraborty
Publisher : Springer Nature
Page : 332 pages
File Size : 47,6 Mb
Release : 2020-06-08
Category : Technology & Engineering
ISBN : 9789811541124

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Internet of Things for Healthcare Technologies by Chinmay Chakraborty,Amit Banerjee,Maheshkumar H. Kolekar,Lalit Garg,Basabi Chakraborty Pdf

This book focuses on recent advances in the Internet of Things (IoT) in biomedical and healthcare technologies, presenting theoretical, methodological, well-established, and validated empirical work in these fields. Artificial intelligence and IoT are set to revolutionize all industries, but perhaps none so much as health care. Both biomedicine and machine learning applications are capable of analyzing data stored in national health databases in order to identify potential health problems, complications and effective protocols, and a range of wearable devices for biomedical and healthcare applications far beyond tracking individuals’ steps each day has emerged. These prosthetic technologies have made significant strides in recent decades with the advances in materials and development. As a result, more flexible, more mobile chip-enabled prosthetics or other robotic devices are on the horizon. For example, IoT-enabled wireless ECG sensors that reduce healthcare cost, and lead to better quality of life for cardiac patients. This book focuses on three current trends that are likely to have a significant impact on future healthcare: Advanced Medical Imaging and Signal Processing; Biomedical Sensors; and Biotechnological and Healthcare Advances. It also presents new methods of evaluating medical data, and diagnosing diseases in order to improve general quality of life.

Artificial Intelligence Techniques in IoT Sensor Networks

Mohamed Elhoseny,K Shankar,Mohamed Abdel-Basset
Artificial Intelligence Techniques in IoT Sensor Networks Book PDF

Author : Mohamed Elhoseny,K Shankar,Mohamed Abdel-Basset
Publisher : CRC Press
Page : 221 pages
File Size : 42,8 Mb
Release : 2020-12-18
Category : Computers
ISBN : 9781000318708

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Artificial Intelligence Techniques in IoT Sensor Networks by Mohamed Elhoseny,K Shankar,Mohamed Abdel-Basset Pdf

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks. This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master’s students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications. This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.