Topics

We invite submission of papers focusing on all aspects of computing and data science ranging from theoretical foundations to novel models and algorithms. We focus on challenges and applications in the fields of computing, machine learning, artificial Intelligence and data science. The topics of interest include but are not limited to:

Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Deep Learning
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Generative Adversarial Networks
  • Transfer Learning
  • Ensemble Learning
  • Explainable AI
  • Natural Language Processing
  • Speech Recognition
  • Image Recognition
  • Recommendation Systems
  • Anomaly Detection
  • Cluster Analysis
  • Dimensionality Reduction
  • Feature Engineering
  • Model Evaluation

Automation

  • AI-Assisted Design
  • Automated Machine Learning
  • Clustering and Classification
  • Collaborative Filtering and Recommendation Systems
  • Computer Vision
  • Cyber-Physical Systems
  • Data Preprocessing Methods
  • Feature Selection Approaches
  • Graph and Network Data
  • Home Automation
  • Regression with Machine Learning
  • Robotic Process Automation
  • Sensor Technology
  • Warehouse Automation
  • Hyperparameter Optimization
  • Industrial Automation
  • IoT (Internet of Things)
  • Machine-to-Machine Communication
  • Meta-learning
  • Model Interpretability Techniques
  • Model Selection Strategies
  • Neural Architecture Search
  • Pipeline Generation Methods
  • Predictive Maintenance
  • Process Automation
  • Supply Chain Automation
  • Time Series Analysis

Robotics and Intelligent Systems

  • Aerial and Underwater Robotics
  • Assistive Devices and Exoskeletons
  • Autonomous Vehicles
  • Deep Learning for Robotic Vision
  • Drones
  • Human-Robot Collaboration and Learning
  • Human-Robot Interaction
  • Imitation Learning and Learning from Demonstration
  • Intelligent Transportation Systems
  • Learning-Based Control and Planning Algorithms
  • Multi-Robot Systems and Learning
  • Reinforcement Learning for Robotics
  • Robotic Manipulation and Grasping
  • Robot Navigation and Path Planning
  • Robot Perception
  • Transfer Learning and Domain Adaptation