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El. knyga: Intelligent Systems Design and Applications: Smart Healthcare, Volume 1

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This book highlights recent research on intelligent systems and nature-inspired computing. It presents 47 selected papers focused on Smart Healthcare from the 23rd International Conference on Intelligent Systems Design and Applications (ISDA 2023), which was held in 5 different cities namely Olten, Switzerland; Porto, Portugal; Kaunas, Lithuania; Greater Noida, India; Kochi, India, and in online mode. The ISDA is a premier conference in the field of artificial intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. ISDA 2023 had contributions by authors from 64 countries. This book offers a valuable reference guide for all medical doctors, scientists, academicians, researchers, students, and practitioners in the field of artificial intelligence and smart health care.

Identifying Lung Cancer from CT Scan Images with VGG16 Convolutional Neural Net.- Cause and Effect of Dementia on Women in Technological Environment.- Machine Learning Techniques for Pancreatic Cancer Detection.- Integrating Artificial Intelligence and Data Analytics for Enhanced Healthcare Management Innovations and Challenges.- An Intelligent model for post covid hearing loss.- Generalized Skin Cancer Image Classification Performance using Xception Model.- Analysis of Magnetic Resonance Imaging for Parkinsons Disease.- Study on Health Issue Identification Using Deep Learning and Convolutional Neural Networks.- Early stage Lung Cancer Prediction A machine Learning Approach.- Convolutional Neural Network Based Brain Tumor Segmentation using Detectron.- Deep Learning based Histopathological Analysis for Colon Cancer Diagnosis A Comparative Study of CNN and Transformer Models with Image Preprocessing Techniques.- Detecting Parkinsons Disease at an Early Stage through Machine Learning Analysis of Brain MRI Images.- Early Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration.- Comprehensive Comparative Analysis of Breast Cancer Forecasting Using Machine Learning Algorithms and Feature Selection Methods.

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