This book highlights recent research on intelligent systems and machine learning based solutions. It presents 46 selected papers focused on Industrial Applications 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 industrial specialists, scientists, academicians, researchers, students, and practitioners in the field of artificial intelligence and industrial applications.
Tabla de materias
Comparative Study of Image Compression Methods using Artificial Neural Networks based on Semi Log Quantization.- Granular Clustering for Maritime Situation Awareness.- Hybrid Approach for medical decision-making: Integrating Res Net Darknet19 based Transfer Learning with Radiomics Features for COVID 19 classification.- Hate speech recognition using Deep Learning.- Classification of Cardiac Arrhythmia using Machine Learning Algorithms.- Exploring Machine Learning Approaches for Precipitation Prediction Post Processing of Daily Accumulated North American Forecasts.- Deep Learning Based Classification of Conference Paper Reviews Accept or Reject.- A Layout Independent Deep Learning Framework for Recognition of Courtesy Amount in Bank Cheque Image.- Convolutional Neural Network CNN classifiers used in Land Use Land Cover Monitoring and Classification A review.- Classification of Obesity Level using Deep neural Networks.- Analysis of Deepfake Attacks and Detection Techniques in Smart City Applications.- Deep-Net Brain Lesion Segmentation with 3D CNN and Residual Connections.- A Bimodal Autism Spectrum Disorder Detection using f MRI Images.
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