AI is revolutionizing healthcare, transforming diagnostics, treatment, and patient care. Applied Intelligence for Healthcare Informatics: Techniques and Applications explores cutting-edge AI solutions for disease prediction, medical imaging, mental health analysis, and healthcare decision-making.
Covering topics like heart disease prediction, COVID-19 detection, breast cancer classification, and ethical AI challenges, this book bridges theory with real-world applications. A must-read for researchers, practitioners, and innovators, it offers insights into the future of AI-driven healthcare.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic and Postgraduate
Illustrationen
13 s/w Abbildungen, 97 farbige Abbildungen, 1 s/w Photographie bzw. Rasterbild, 16 Farbfotos bzw. farbige Rasterbilder, 12 s/w Zeichnungen, 81 farbige Zeichnungen, 50 s/w Tabellen
50 Tables, black and white; 81 Line drawings, color; 12 Line drawings, black and white; 16 Halftones, color; 1 Halftones, black and white; 97 Illustrations, color; 13 Illustrations, black and white
Maße
Höhe: 254 mm
Breite: 178 mm
Gewicht
ISBN-13
978-1-032-95056-3 (9781032950563)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Dr. Nazmul Siddique is a researcher at the School of Computing, Engineering, and Intelligent Systems, Ulster University. He has published over 170 research papers and several books on cybernetics and computational intelligence. His editorial roles in top journals highlight his academic influence and contributions.
Dr. Mohammad Shamsul Arefin is a professor at the Department of CSE, CUET, and Dean of Electrical and Computer Engineering. He has over 170 publications in journals and conferences on data mining, distributed computing, and machine learning. His leadership has significantly fostered research growth and academic excellence in many aspects.
Dr. Md Zahid Hasan is an Associate Professor in the Department of Computer Science and Engineering at Daffodil International University. His research expertise includes machine learning, artificial intelligence, deep learning, bioinformatics, computer vision, health informatics, and decision theory. He has published over 60 articles in high-ranking journals and conferences and is the director of the Health Informatics Research Lab (HIRL).
Dr. M Shamim Kaiser is a professor and Chairman at the Institute of Information Technology, Jahangirnagar University. He has authored over 100 research papers on machine learning, cyber security, and cognitive radio networks. His leadership at IIT has driven academic and research excellence in ICT.
Herausgeber*in
University of Ulster, Magee, UK
Institute of Information Technology, Jahangirnagar University, Bangladesh
1. Explainable AI-based Heart Attack Prediction Model using Various Machine Learning and Ensemble Learning Approaches. 2. Predicting Heart Block and Its Type using Ensemble-based Machine Learning. 3. Towards COVID-19 Detection Information System based on AIoT. 4. Tomato Leaf Disease Detection Using Convolutional Neural Network. 5. An Automatic Recognition System for Thyroid Nodule in Ultrasound Images using Convolutional Neural Network. 6. Malnutrition prediction among under-five children using Machine learning techniques. 7. Prediction of Depression Severity via Feature Grouping and Machine Learning with Burn Depression Checklist. 8. Comparative Analysis of Machine Learning Methods for Symptoms based COVID-19 Detection. 9. BCNet-11: A dilated convolutional neural network for breast cancer classification using histopathology images. 10. Age Estimation from Human Facial Expression using Deep Neural Network. 11. Detecting Depression from Social Media Posts Using Comprehensive Machine Learning Approaches with TF-IDF and N-Grams. 12. An Ensemble Approach and Comparison of LSTM, Bi-LSTM, and GNN Models to Predict Protein Secondary Structure. 13. Psychological Stress Prediction of Human Using Machine Learning Algorithms. 14. Face Mask Detection Using CNN: Ensuring Safety During COVID-19. 15. Approximating the Survival Possibility of Post Thoracic Surgery for Lung Cancer using Machine Learning. 16. Time-Frequency Analysis using fNIRS Signal for Pain Detection on Hemodynamic Response. 17. A Study on Different SVM Kernels with Suitable Pre-Processing Technique and Parameter Optimization for Cardiovascular Disease Prediction.
