The 2nd World Conference on Artificial Intelligence, Machine Learning, and Data Science will take place during October 19-20, 2023, in Paris, France. This would be one of the leading conferences in the fields of Artificial Intelligence, Machine Learning, and Data Science. The conference aims to gather scholars from all over the world to present the latest research outcomes and advances in relevant fields and provides an ideal environment for making new scientific collaborations.
The aim of the conference is to promote quality research and create an atmosphere of true international cooperation between Software Engineers, Delivery managers, and Data Science Professionals by bringing together world-class researchers, International Communities, and Industrial heads to discuss the latest developments and innovations in the fields of Artificial Intelligence, Machine Learning, and Data Science.
The 2nd World Conference on Artificial Intelligence, Machine Learning, and Data Science will increase the opportunities for researchers to network with colleagues from across the world in more focused sessions that will feature cutting-edge presentations, special panel discussions, and live interaction with industry leaders and experts.
Abstract Topics
Deep Learning Applications in Healthcare
Natural Language Processing for Information Extraction
Image Processing and Computer Vision
Recommender Systems and Personalization
Data Mining Techniques for Big Data Analysis
Machine Learning for Cybersecurity
Reinforcement Learning in Robotics
Bayesian Learning for Uncertainty Estimation
Time Series Analysis and Forecasting
Predictive Analytics for Business Intelligence
Transfer Learning for Model Generalization
Generative Adversarial Networks for Image Synthesis
Explainable AI and Interpretability
Federated Learning for Collaborative Intelligence
Neural Networks for Speech Recognition
Multimodal Learning for Multimedia Processing
Human-Machine Interaction and Collaboration
Knowledge Representation and Reasoning
Data Privacy and Security in Machine Learning
Graph Mining and Network Analysis
Active Learning for Efficient Data Collection
Cognitive Computing and Intelligent Agents
Sentiment Analysis and Opinion Mining
Unsupervised Learning for Clustering
Data Visualization and Exploration
Machine Learning for Medical Imaging
Online Learning for Streaming Data
Feature Selection and Dimensionality Reduction
Automated Machine Learning and AutoML
Multi-Task and Multi-Modal Learning
Robustness and Adversarial Attacks in Deep Learning
Real-Time and Embedded Systems for AI
Sequential Decision Making and Planning
Blockchain and Distributed Ledgers for Data Privacy
