This book constitutes the refereed proceedings of the Second International Conference on Recent Trends in AI Enabled Technologies, ThinkAI 2024, which took place in Hyderabad, India, during December 27-28, 2024.
The 18 full papers in this book were carefully reviewed and selected from 75 submissions. These papers focus on topics of AI enabled technologies, including machine learning, soft computing, and deep learning algorithms.
.- Advancements in Generative AI for Image Classification: Applications, Chal lenges, and Future Directions.
.- Points of Interest Reviews Summarization for YELP Dataset using Natural
Language Processing.
.- Multi-Stream CNN for Salient Object Detection.
.- An Optimized Convolutional Neural Network for Accurate and Efficient De[1]tection of Spine Fractures in CT Scans.
.- A Systematic Review of Deep Learning Models for Intrusion Detection: From
CNN to Hybrid Architectures.
.- Leveraging Artificial Intelligence for Real-Time Decision-Making in Emer gency Healthcare System.
.- Enhancing Mental Health Diagnostics with Advanced Machine Learning
Techniques: A Comparative Study.
.- Hybrid Machine Learning Approaches for Enhanced Insurance Fraud Detec tion.
.- EXPLAINABLE ARTIFICIAL INTELLIGENCE(XAI) IN CARDIAC DIS EASE : Using SHAP Technique.
.- Lightweight Deep Learning Framework for Efficient Breast Cancer Classifi[1]cation in Ultrasound Imaging.
.- Efficient Multi-Cancer Detection: A Unified CNN Approach Leveraging
Transfer Learning and Depthwise Convolutions.
.- A Review and Potential Gaps in News Article Classification.
.- DEEP ENSEMBLE TRANSFER LEARNING FRAMEWORK FOR EN HANCED ENDOSCOPIC IMAGE CLASSIFICATION.
.- Impact of COVID-19 on Stock Market Prediction: A Study Using LSTM
and STANN Approaches.
.- Sentiment Analysis Techniques for Social Media and Customer Reviews.
.- Attention-Enhanced EfficientNet for Accurate Skin Cancer Diagnosis: A
Deep Learning Approach.
.- Enhancing Remaining Useful Life Prediction: A Comparative Study of Clas sical Machine Learning and Generative AI.
.- UrduDigitsCNN: Bridging the Gap in Numeral Recognition.
