Intelligent Systems Design and Applications
Description
This book highlights recent research on intelligent systems and nature-inspired computing. It presents 47 selected papers focused on Deep Learning 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 scientists, academicians, researchers, students, and practitioners in the field of artificial intelligence and deep learning.
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Content
Deep Learning Approach for Autonomous Spacecraft Landing.- Deep Learning Approach for Flood Mapping Using Satellite Images Dataset.- Large Language Models for Named Entity Recognition NER of Skills in Job Postings in German.- Machine Learning Approaches for Investing Strategies in Stock Market.- OP FedELM One pass Privacy-preserving Federated Classification via Evolving Clustering Method and Extreme Learning Machine hybrid.- Gamma Corrected Pyramid Pix2pix - Breast Cancer HE to IHC Image Generation.- Unveiling Deepfakes Customized Convolutional Neural Networks for Detection.- The Nasdaq Composite Index Prediction Using LSTM and Bi LSTM Multivariate Deep Learning Approaches.- PlastOcean Detecting Floating Marine Macro Litter FMML using Deep Learning Models.- Data Augmentation Using Generative Neural Networks Based on Fourier Feature Mapping.- Delay Risk Detection in Road Construction Projects Utilizing Large Language Model.- Unlocking The Potential of Novel LSTM in Airline Recommendation Prediction.- Pylung a supporting tool for comparative study of ViT and CNN based models used for lung nodules classification.- Deep Learning model for predicting rice plant disease identification and classification for improving the yield.
