Biological Network Reconstruction Denoising And Applications In Cancer Classification

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Biological Network Reconstruction, Denoising, and Applications in Cancer Classification

Author : Chengwei Lei
Publisher :
ISBN 13 : 9781321194784
Total Pages : 84 pages
Book Rating : 4.1/5 (947 download)

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Book Synopsis Biological Network Reconstruction, Denoising, and Applications in Cancer Classification by : Chengwei Lei

Download or read book Biological Network Reconstruction, Denoising, and Applications in Cancer Classification written by Chengwei Lei and published by . This book was released on 2014 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in high-throughput technology have dramatically increased the amount of available experimental data in biological research, such as complete genome sequences, transcriptomic data under diverse conditions, and interaction networks among different components in the cell. However, the exponentially increasing data challenges the conventional gene-based paradigm to understand biology. Efficient and effective computational methods are needed to clean, analyze and model the data from a whole systems perspective. To achieve these goals, this research attempts to addresses several key challenging problems in bioinformatics that are associated with constructing functional gene networks and utilizing the networks for better understanding and prediction of cancer development and progression. Specifically, this dissertation has made significant contributions in three relatively independent but highly related sub-areas of bioinformatics. First, an optimization algorithm based on particle swarm intelligence has been developed to efficiently identify transcription factor binding sites (TFBS) motifs that often consist of two short DNA sequence patterns separated by a variable length gap. This work can help decipher the complex gene regulatory networks and understand gene functions. Second, a novel random walk based algorithm has been proposed to remove spurious protein-protein interactions and predict new interactions based solely on the basis of the topological properties of proteins in an existing protein-protein interaction network. Experimental results showed that the method can significantly improve the quality of existing protein-protein interaction networks in yeast and human, which in turn resulted in much better accuracy of protein complex prediction. Finally, new method has been developed to improve cancer prognosis by combining gene expression microarray data and protein-protein interaction networks. Utilizing a random walk algorithm, our method was able to identify novel biomarker genes that can significantly improve the prognosis accuracy of breast cancer metastasis. Importantly, these individual biomarkers are not differentially expressed and therefore would not be detectable by conventionally classification methods that treat individual genes as independent features. Taken together, the results achieved in these diverse sub-areas demonstrated the feasibility of using machine learning approaches to assist biological research at a systems level.

Applications of Machine Learning and Deep Learning on Biological Data

Author : Faheem Masoodi
Publisher : CRC Press
ISBN 13 : 1000833798
Total Pages : 233 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Applications of Machine Learning and Deep Learning on Biological Data by : Faheem Masoodi

Download or read book Applications of Machine Learning and Deep Learning on Biological Data written by Faheem Masoodi and published by CRC Press. This book was released on 2023-03-13 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms. Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment. Highlights include: Artificial Intelligence in treating and diagnosing schizophrenia An analysis of ML’s and DL’s financial effect on healthcare An XGBoost-based classification method for breast cancer classification Using ML to predict squamous diseases ML and DL applications in genomics and proteomics Applying ML and DL to biological data

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Data Classification

Author : Charu C. Aggarwal
Publisher : CRC Press
ISBN 13 : 1498760589
Total Pages : 710 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Data Classification by : Charu C. Aggarwal

Download or read book Data Classification written by Charu C. Aggarwal and published by CRC Press. This book was released on 2014-07-25 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi

Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics

Author : Sujata Dash
Publisher : CRC Press
ISBN 13 : 1000534006
Total Pages : 382 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics by : Sujata Dash

Download or read book Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics written by Sujata Dash and published by CRC Press. This book was released on 2022-02-10 with total page 382 pages. Available in PDF, EPUB and Kindle. 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

Biomedical Data Mining for Information Retrieval

Author : Sujata Dash
Publisher : John Wiley & Sons
ISBN 13 : 111971124X
Total Pages : 450 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Biomedical Data Mining for Information Retrieval by : Sujata Dash

Download or read book Biomedical Data Mining for Information Retrieval written by Sujata Dash and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval 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 healthcare 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 the vast amount of available 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 relating to healthcare. 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. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Pacific Symposium On Biocomputing 2015

Author : Russ B Altman
Publisher : World Scientific
ISBN 13 : 9814644749
Total Pages : 516 pages
Book Rating : 4.8/5 (146 download)

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Book Synopsis Pacific Symposium On Biocomputing 2015 by : Russ B Altman

Download or read book Pacific Symposium On Biocomputing 2015 written by Russ B Altman and published by World Scientific. This book was released on 2014-11-11 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Pacific Symposium on Biocomputing (PSB) 2015 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2015 will be held from January 4 - 8, 2015 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2015 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's “hot topics.” In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

Deep Learning for Biomedical Applications

Author : Utku Kose
Publisher : CRC Press
ISBN 13 : 1000406423
Total Pages : 365 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Deep Learning for Biomedical Applications by : Utku Kose

Download or read book Deep Learning for Biomedical Applications written by Utku Kose and published by CRC Press. This book was released on 2021-07-19 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Automated breast cancer detection and classification using ultrasound images: A survey

Author : H.D.Cheng
Publisher : Infinite Study
ISBN 13 :
Total Pages : 19 pages
Book Rating : 4./5 ( download)

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Book Synopsis Automated breast cancer detection and classification using ultrasound images: A survey by : H.D.Cheng

Download or read book Automated breast cancer detection and classification using ultrasound images: A survey written by H.D.Cheng and published by Infinite Study. This book was released on with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast.

Deep Learning for Medical Image Analysis

Author : S. Kevin Zhou
Publisher : Academic Press
ISBN 13 : 0323858880
Total Pages : 544 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Deep Learning for Medical Image Analysis by : S. Kevin Zhou

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-11-23 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Handbook of Research on Natural Computing for Optimization Problems

Author : Mandal, Jyotsna Kumar
Publisher : IGI Global
ISBN 13 : 1522500596
Total Pages : 1199 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Handbook of Research on Natural Computing for Optimization Problems by : Mandal, Jyotsna Kumar

Download or read book Handbook of Research on Natural Computing for Optimization Problems written by Mandal, Jyotsna Kumar and published by IGI Global. This book was released on 2016-05-25 with total page 1199 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nature-inspired computation is an interdisciplinary topic area that connects the natural sciences to computer science. Since natural computing is utilized in a variety of disciplines, it is imperative to research its capabilities in solving optimization issues. The Handbook of Research on Natural Computing for Optimization Problems discusses nascent optimization procedures in nature-inspired computation and the innovative tools and techniques being utilized in the field. Highlighting empirical research and best practices concerning various optimization issues, this publication is a comprehensive reference for researchers, academicians, students, scientists, and technology developers interested in a multidisciplinary perspective on natural computational systems.

Deep Learning in Medical Image Analysis

Author : Gobert Lee
Publisher : Springer Nature
ISBN 13 : 3030331288
Total Pages : 184 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Deep Learning in Medical Image Analysis by : Gobert Lee

Download or read book Deep Learning in Medical Image Analysis written by Gobert Lee and published by Springer Nature. This book was released on 2020-02-06 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Deep Learning in Biology and Medicine

Author : Davide Bacciu
Publisher : World Scientific Publishing Europe Limited
ISBN 13 : 9781800610934
Total Pages : 0 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Deep Learning in Biology and Medicine by : Davide Bacciu

Download or read book Deep Learning in Biology and Medicine written by Davide Bacciu and published by World Scientific Publishing Europe Limited. This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.

Artificial Intelligence in HCI

Author : Helmut Degen
Publisher : Springer Nature
ISBN 13 : 3030503348
Total Pages : 461 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Artificial Intelligence in HCI by : Helmut Degen

Download or read book Artificial Intelligence in HCI written by Helmut Degen and published by Springer Nature. This book was released on 2020-07-10 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Conference on Artificial Intelligence in HCI, AI-HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020, in July 2020. The conference was planned to be held in Copenhagen, Denmark, but had to change to a virtual conference mode due to the COVID-19 pandemic. The conference presents results from academic and industrial research, as well as industrial experiences, on the use of Artificial Intelligence technologies to enhance Human-Computer Interaction. From a total of 6326 submissions, a total of 1439 papers and 238 posters has been accepted for publication in the HCII 2020 proceedings. The 30 papers presented in this volume were organized in topical sections as follows: Human-Centered AI; and AI Applications in HCI.pical sections as follows: Human-Centered AI; and AI Applications in HCI.

Youmans and Winn Neurological Surgery E-Book

Author : H. Richard Winn
Publisher : Elsevier Health Sciences
ISBN 13 : 0323674992
Total Pages : 7439 pages
Book Rating : 4.3/5 (236 download)

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Book Synopsis Youmans and Winn Neurological Surgery E-Book by : H. Richard Winn

Download or read book Youmans and Winn Neurological Surgery E-Book written by H. Richard Winn and published by Elsevier Health Sciences. This book was released on 2022-01-21 with total page 7439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Widely regarded as the definitive reference in the field, Youmans and Winn Neurological Surgery offers unparalleled, multimedia coverage of the entirety of this complex specialty. Fully updated to reflect recent advances in the basic and clinical neurosciences, the 8th Edition covers everything you need to know about functional and restorative neurosurgery, deep brain stimulation, stem cell biology, radiological and nuclear imaging, and neuro-oncology, as well as minimally invasive surgeries in spine and peripheral nerve surgery, and endoscopic and other approaches for cranial procedures and cerebrovascular diseases. In four comprehensive volumes, Dr. H. Richard Winn and his expert team of editors and authors provide updated content, a significantly expanded video library, and hundreds of new video lectures that help you master new procedures, new technologies, and essential anatomic knowledge in neurosurgery. - Discusses current topics such as diffusion tensor imaging, brain and spine robotic surgery, augmented reality as an aid in neurosurgery, AI and big data in neurosurgery, and neuroimaging in stereotactic functional neurosurgery. - 55 new chapters provide cutting-edge information on Surgical Anatomy of the Spine, Precision Medicine in Neurosurgery, The Geriatric Patient, Neuroanesthesia During Pregnancy, Laser Interstitial Thermal Therapy for Epilepsy, Fetal Surgery for Myelomeningocele, Rehabilitation of Acute Spinal Cord Injury, Surgical Considerations for Patients with Polytrauma, Endovascular Approaches to Intracranial Aneurysms, and much more. - Hundreds of all-new video lectures clarify key concepts in techniques, cases, and surgical management and evaluation. Notable lecture videos include multiple videos on Thalamotomy for Focal Hand Dystonia and a video to accompany a new chapter on the Basic Science of Brain Metastases. - An extensive video library contains stunning anatomy videos and videos demonstrating intraoperative procedures with more than 800 videos in all. - Each clinical section contains chapters on technology specific to a clinical area. - Each section contains a chapter providing an overview from experienced Section Editors, including a report on ongoing controversies within that subspecialty. - Enhanced eBook version included with purchase. Your enhanced eBook allows you to access all of the text, figures, and references from the book on a variety of devices.

Big Data Analysis and Artificial Intelligence for Medical Sciences

Author : Paola Lecca
Publisher : John Wiley & Sons
ISBN 13 : 1119846536
Total Pages : 437 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Big Data Analysis and Artificial Intelligence for Medical Sciences by : Paola Lecca

Download or read book Big Data Analysis and Artificial Intelligence for Medical Sciences written by Paola Lecca and published by John Wiley & Sons. This book was released on 2024-07-29 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analysis and Artificial Intelligence for Medical Sciences Overview of the current state of the art on the use of artificial intelligence in medicine and biology Big Data Analysis and Artificial Intelligence for Medical Sciences demonstrates the efforts made in the fields of Computational Biology and medical sciences to design and implement robust, accurate, and efficient computer algorithms for modeling the behavior of complex biological systems much faster than using traditional modeling approaches based solely on theory. With chapters written by international experts in the field of medical and biological research, Big Data Analysis and Artificial Intelligence for Medical Sciences includes information on: Studies conducted by the authors which are the result of years of interdisciplinary collaborations with clinicians, computer scientists, mathematicians, and engineers Differences between traditional computational approaches to data processing (those of mathematical biology) versus the experiment-data-theory-model-validation cycle Existing approaches to the use of big data in the healthcare industry, such as through IBM’s Watson Oncology, Microsoft’s Hanover, and Google’s DeepMind Difficulties in the field that have arisen as a result of technological changes, and potential future directions these changes may take A timely and up-to-date resource on the integration of artificial intelligence in medicine and biology, Big Data Analysis and Artificial Intelligence for Medical Sciences is of great benefit not only to professional scholars, but also MSc or PhD program students eager to explore advancement in the field.

Intelligent System Design

Author : Vikrant Bhateja
Publisher : Springer Nature
ISBN 13 : 9811948631
Total Pages : 577 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Intelligent System Design by : Vikrant Bhateja

Download or read book Intelligent System Design written by Vikrant Bhateja and published by Springer Nature. This book was released on 2022-10-27 with total page 577 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of high-quality, peer-reviewed research papers from the 7th International Conference on Information System Design and Intelligent Applications (India 2022), held at BVRIT Hyderabad College of Engineering for Women, Hyderabad, Telangana, India, from February 25 to 26, 2022. It covers a wide range of topics in computer science and information technology, including data mining and data warehousing, high-performance computing, parallel and distributed computing, computational intelligence, soft computing, big data, cloud computing, grid computing and cognitive computing.