Bioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms /
BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information...
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| Format: | text |
| Language: | eng |
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Hoboken, NJ : Wiley : Beverly, MA : Scrivener Publishing,
2022
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| _version_ | 1796765458757582848 |
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| author | Suresh, A., editor 651286 Vimal, S., editor 651287 Robinson, Y. Harold, editor 651288 Ramaswami, Dhinesh Kumar, editor 651289 Udendhran, R., editor 651290 |
| author_facet | Suresh, A., editor 651286 Vimal, S., editor 651287 Robinson, Y. Harold, editor 651288 Ramaswami, Dhinesh Kumar, editor 651289 Udendhran, R., editor 651290 |
| author_sort | Suresh, A., editor 651286 |
| collection | OCEAN |
| description | BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician's important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues. |
| first_indexed | 2024-03-05T17:24:43Z |
| format | text |
| id | KOHA-OAI-TEST:608075 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T17:24:43Z |
| publishDate | 2022 |
| publisher | Hoboken, NJ : Wiley : Beverly, MA : Scrivener Publishing, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6080752023-10-12T03:44:35ZBioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms / Suresh, A., editor 651286 Vimal, S., editor 651287 Robinson, Y. Harold, editor 651288 Ramaswami, Dhinesh Kumar, editor 651289 Udendhran, R., editor 651290 textHoboken, NJ : Wiley : Beverly, MA : Scrivener Publishing,©20222022 engBIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician's important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.Includes bibliographical references and index1. Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction -- 2. Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach -- 3. Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case -- 4. Deep Learning in Gait Abnormality Detection: Principles and Illustrations -- 5. Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health -- 6. Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier -- 7. Deep Learning for Medical Informatics and Public Health -- 8. An Insight Into Human Pose Estimation and Its Applications -- 9. Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare -- 10. Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century -- 11. An Economical Machine Learning Approach for Anomaly Detection in IoT Environment -- 12. ndian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach -- 13. Collection and Analysis of Big Data From Emerging Technologies in Healthcare -- 14. A Complete Overview of Sign Language Recognition and Translation Systems.BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician's important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.Medical informaticsBioinformaticsBig dataDeep learning (Machine learning)URN:ISBN:9781119791836 |
| spellingShingle | Medical informatics Bioinformatics Big data Deep learning (Machine learning) Suresh, A., editor 651286 Vimal, S., editor 651287 Robinson, Y. Harold, editor 651288 Ramaswami, Dhinesh Kumar, editor 651289 Udendhran, R., editor 651290 Bioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms / |
| title | Bioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms / |
| title_full | Bioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms / |
| title_fullStr | Bioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms / |
| title_full_unstemmed | Bioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms / |
| title_short | Bioinformatics and Medical Applications : Big Data Using Deep Learning Algorithms / |
| title_sort | bioinformatics and medical applications big data using deep learning algorithms |
| topic | Medical informatics Bioinformatics Big data Deep learning (Machine learning) |
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