This two-set volume CCIS 2653 and 2654 constitutes the refereed proceedings of the Second International Conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processing, AAIMB 2024, held in Chennai, India, during December 12 13, 2024.
The 59 full papers included in this book were carefully reviewed and selected from 434 submissions. The papers are organized in the following topical sections:
Part I: artificial intelligence and data analytics; machine learning.
Part II: artificial intelligence and data analytics; deep learning.
.- ARTIFICIAL INTELLIGENCE & DATA ANALYTICS.
.- Agent-Based Flood Evacuation Modeling: Optimizing Transportation, Shelter Allocation and Traffic Control.
.- PhysioAI - Smart physiotherapy solutions driven by AI technology.
.- Predictive Modelling For Network Threat Detection Using Artificial Intelligence.
.- Enhancing Generative AI with Retrieval-Augmented Generation : A Comparative Study.
.- AI-Based tool for preliminary diagnosis of Dermatological.
.- Integrating Artificial Intelligence and Machine Learning in Indian Manufacturing Industries: Pathways to Sustainable Growth.
.- Multimodal Mental Health Chatbot.
.- Leveraging Generative AI in Management Decision Making: Opportunities and Challenges.
.- Hybrid Contextual Embeddings with Attention Mechanisms for Enhanced Sentiment Analysis in COVID-19 Tweets.
.- A Major Role of Artificial Intelligence in Modern Healthcare.
.- Adopting AIML for Marketing of SME's.
.- Smart Recipe Using Gen-AI with BMI and Vegetables Detection.
.- MACHINE LEARNING.
.- Personality Prediction Machine Learning Model Using XGBoost To Facilitate Interpersonal Relationships.
.- Enhancing Emotion Detection Classification through Bagging Techniques.
.- Assistive Technology to Translate Hand Gestures to Text Using Lenet and Alexnet.
.- Analysis of Breast Cancer Detection with Gradient Boosted Tree using Rapid Miner.
.- Epileptical Seizure Classification from Electroencephalography Signals and 2-D Images: A Comprehensive Approach.
.- Evaluating Machine Learning Algorithms for Breast Cancer Detection: Logistic Regression, Naive Bayes, and Decision
