Machine Learning Applications for Data Analysis in Healthcare Systems
Editat de Sudeshna Chakraborty, Jyotsna Singh, Praveen Kumar Shukla, Prasenjit Chatterjeeen Limba Engleză Hardback – 17 noi 2025
Preț: 1071.63 lei
Preț vechi: 1306.86 lei
-18% Nou
Puncte Express: 1607
Preț estimativ în valută:
189.72€ • 220.91$ • 164.81£
189.72€ • 220.91$ • 164.81£
Carte tipărită la comandă
Livrare economică 23 februarie-09 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781779643186
ISBN-10: 1779643187
Pagini: 250
Ilustrații: 124
Dimensiuni: 152 x 229 x 18 mm
Greutate: 0.62 kg
Ediția:1
Editura: Apple Academic Press Inc.
Colecția Apple Academic Press
ISBN-10: 1779643187
Pagini: 250
Ilustrații: 124
Dimensiuni: 152 x 229 x 18 mm
Greutate: 0.62 kg
Ediția:1
Editura: Apple Academic Press Inc.
Colecția Apple Academic Press
Public țintă
Academic and PostgraduateCuprins
1. Classification of In-Hospital Mortality for Heart-Failure Patient Using a Resource Constraint Dataset 2. Predicting the Pandemic Outbreak (Covid-19) Using Facebook Prophet 3. Mobile App for Analyzing and Predicting Depression in Human Beings 4. Machine Learning-Based Analytics for Premature Rheumatoid Arthritis and Osteoarthritis Detection in Clinical Practices: A Review 5. Performance Evaluation of Heart Disease Classification Using Deep and Machine Learning-Based Methods 6. Application of Artificial Intelligence in Healthcare Sector: Benefits and Challenges 7. Design and Development of Anticancerous Drug Molecules by Structure and Ligand-Based Drug Designing Computational Approaches 8. Smart Healthcare Systems: An Exigency of Current Era 9. Technology-Enabled Smart Healthcare toward Smart Society 5.0 10. ELM-HC: An Approach for Heartbeat Classification Based on a Machine-Human Interaction Model 11. Bow-Tie Construction from Accident Narratives: A Text-Mining Approach 12. Finger Knuckle Print: an Emerging Person Recognition Trait for Online Applications 13. Face Mask Detection Using Deep Learning and Image Processing Algorithms
Notă biografică
Sudeshna Chakraborty, PhD, is a Professor and Research Group Head of data analytics and deep learning at Galgotias University, India. With over 20 years of academic and industry experience, she has received several awards. She has been a keynote speaker, an organizing member of international conferences, a member of review committees, session chair, speaker at training and faculty development programs, etc. She has filed eight patents in the field of robotic, solar energy, and sensors and has published in Scopus- and SCI-indexed journals and international conferences.
Jyotsna Singh, PhD, is Chairperson of the School of Technology Management and Engineering at NMIMS, India. In her more than 21-year career in education, she has been Director, Dean of Students, etc., with institutions including NIT Kurukshetra, Northcap University, Amity University, Lloyd Group, IILM, and others. She has participated in workshops, has undertaken government-funded projects, and has initiated dozens of university-related programs. She has published and presented research papers in journals and conferences as well as several departmental books.
Praveen Kumar Shukla, PhD, is an Assistant Professor with the Department of IoT and Intelligent Systems at Manipal University Jaipur, India. Dr. Shukla’s research interests focus on brain computer interfacing, medical image processing, and robotics. He has published 40 research articles and is a reviewer for the several IEEE journals. He is currently supervising PhD students. He has six patents to his name. He has received four best paper awards and a best thesis award.
Prasenjit Chatterjee, PhD, is Dean (Research and Consultancy) at the MCKV Institute of Engineering, West Bengal, India. He has published over 130 research papers and has authored and edited more than 15 books on intelligent decision-making, supply chain management, optimization techniques, risk, and sustainability modeling and is also an editor for several book series. He has received numerous awards for his work. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods: Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).
Jyotsna Singh, PhD, is Chairperson of the School of Technology Management and Engineering at NMIMS, India. In her more than 21-year career in education, she has been Director, Dean of Students, etc., with institutions including NIT Kurukshetra, Northcap University, Amity University, Lloyd Group, IILM, and others. She has participated in workshops, has undertaken government-funded projects, and has initiated dozens of university-related programs. She has published and presented research papers in journals and conferences as well as several departmental books.
Praveen Kumar Shukla, PhD, is an Assistant Professor with the Department of IoT and Intelligent Systems at Manipal University Jaipur, India. Dr. Shukla’s research interests focus on brain computer interfacing, medical image processing, and robotics. He has published 40 research articles and is a reviewer for the several IEEE journals. He is currently supervising PhD students. He has six patents to his name. He has received four best paper awards and a best thesis award.
Prasenjit Chatterjee, PhD, is Dean (Research and Consultancy) at the MCKV Institute of Engineering, West Bengal, India. He has published over 130 research papers and has authored and edited more than 15 books on intelligent decision-making, supply chain management, optimization techniques, risk, and sustainability modeling and is also an editor for several book series. He has received numerous awards for his work. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods: Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).
Descriere
Discusses machine learning and its potential in outbreak prediction, design and development of anti-cancerous drug molecules. Delves into heartbeat classification based on a machine-human interaction model. Looks at role of machine learning in clinical decision-making, predictive modeling, public health management, and more.