Track 2: Machine Learning and its Applications

Track Co-chair(s):
1. Usman Yaseen, COMSATS University Islamabad (CUI), Pakistan

Machine Learning (ML) lies at the heart of modern artificial intelligence, driving innovation across diverse sectors by enabling systems to learn from data and make intelligent decisions. As a core discipline, ML encompasses supervised, unsupervised, and reinforcement learning techniques that empower machines to identify patterns, make predictions, and improve performance over time without being explicitly programmed. This track invites high-quality, original research contributions that advance the theory, methodology, and practical applications of machine learning.

We welcome submissions addressing innovations across supervised, unsupervised, and reinforcement learning, as well as their applications in diverse domains such as healthcare, finance, agriculture, manufacturing, transportation, and smart cities.

Key Topics of Interest Include (but are not limited to):

• Novel ML algorithms and architectures
• Supervised, unsupervised, and semi-supervised learning
• Reinforcement and online learning
• Ensemble methods and boosting techniques
• Feature selection and dimensionality reduction
• Explainable and interpretable ML
• ML for edge and cloud computing
• Transfer learning and domain adaptation
• Scalable and efficient ML models
• Optimization techniques for training ML models
• Ethical and responsible use of ML models
• ML applications in:
• Healthcare and bioinformatics
• Financial modeling and fraud detection
• Smart infrastructure and urban planning
• Industrial automation and predictive maintenance