Computational Intelligence in Medical Decision Making and Diagnosis (eBook, PDF)

Techniques and Applications
Redaktion: Tamrakar, Sitendra; Choubey, Abhishek; Choubey, Shruti Bhargava

• Format: PDF

Produktbeschreibung

This book explains different aspects of the current research on computational intelligence technologies applied in the field of medical diagnosis. It discusses critical issues related to the medical diagnosis like uncertainties in the medical domain, problems in the medical data especially dealing with time-stamped data.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 286
• Erscheinungstermin: 31. März 2023
• Englisch
• ISBN-13: 9781000853896
• Artikelnr.: 67428136

Autorenporträt

Dr. Sitendra Tamrakar is working as an associate professor and research coordinator in the Department of Computer Science and Engineering at Nalla Malla Reddy Engineering College, Hyderabad, Telangana, India. He has more than 17 years of experience in the field of teaching and research. He has guided 5 PhD and 19 MTech dissertations. He has authored a total of 92 publications which include books, research papers, and book chapters which have been published nationally and internationally. He has 5 patents published and granted with IP Australia and IP India. He had delivered 15 invited talks in various national and international conferences and seminars. He has been appointed as reviewer in various journals and conferences. He has attended 35 FDP/workshops and organized 7 conferences, FDPs, and workshops. His research interests are focused on the area of artificial intelligence, cloud computing, and computer networks. He is an active member of the Computer Society of India (CSI), Hyderabad Chapter, and ACM CSTA. Dr. Shruti Bhargava Choubey has received her BE with honors (2007) from RGPV Bhopal and her MTech degree in Digital Communication Engineering (2010) from RGPV Bhopal; subsequently, she carried out her research from Dr. K. N. Modi University Banasthali Rajasthan and was awarded PhD in 2015. Presently, she is working as an associate professor and dean of innovation and research in the Department of Electronics and Communication at Sreenidhi Institute of Science and Technology, Hyderabad. She has published more than 100 papers (5 SCI, 18 Scopus) of national and international repute. She has been a member of many selection committees for recruitment of staff and faculty. Her research areas include signal processing, image processing, and biomedical engineering. She has produced 17 MTech degrees and guided more than 70 BTech projects. She is a senior member of IEEE and a member of IETE, New Delhi, and International Association of Engineers (IAENG). She worked in different positions, like dean of academics and HOD, with numerous capacities. She was awarded MP Young Scientist fellowship in 2015 and received MP Council fellowship in 2014 for her contribution to research. Dr. Abhishek Choubey has received his PhD degree in the field of VLSI for digital signal processing from Jayppe University of Engineering and Technology, Guna MP, in 2017. He is currently associated with Sreenidhi Institute of Science and Technology, Hyderabad, as an associate professor. He has published nearly 70 technical articles. His research interest includes reconfigurable architectures, approximate computating, algorithm design, and implementation of high-performance VLSI systems for signal processing applications. He was a recipient of the Sydney R. Parker and M. N. S. Swamy Best Paper Award for Circuits, Systems, and Signal Processing in 2018.

Inhaltsangabe

1 Prediction of Diseases Using Machine Learning Techniques; 2 A Novel
Virtual Medicinal Care Model for Remote Treatments; 3 Artificial
Intelligence in Future Telepsychiatry and Psychotherapy for E-Mental Health
Revolution; 4 Optimized Convolutional Neural Network for Classification of
Tumors from MR Brain Images; 5 Predictive Modeling of Epidemic Diseases
Based on Vector-Borne Diseases Using Artificial Intelligence Techniques; 6
Hybrid Neural Network-Based Fuzzy Inference System Combined with Machine
Learning to Detect and Segment Kidney Tumor; 7 Classification of Breast
Tumor from Histopathological Images with Transfer Learning; 8 Performance
of IoT-Enabled Devices in Remote Health Monitoring Applications; 9
Applying Machine Learning Logistic Regression Model for Predicting Diabetes
in Women; 10 Compressive Sensing-Based Medical Imaging Techniques to Detect
the Type of Pneumonia in Lungs; 11 Electroencephalogram (EEG) Signal
Denoising Using Optimized Wavelet Transform (WT): A Study; 12 Predicting
Diabetes in Women by Applying the Support Vector Machine (SVM) Model; 13
Data Mining Approaches on EHR System: A Survey; 14 Chest Tumor
Identification in Mammograms by Selected Features Employing SVM; 15 A Novel
Optimum Clustering Method Using Variant of NOA; 16 Role of Artificial
Intelligence and Neural Network in the Health-Care Sector: An Important
Guide for Health Prominence