The conference provides an opportunity for the researchers, engineers, developers, and practitioners from academia and industry to discuss and address the experimental, theoretical work and methods in solving problems related to data science.

Topics of Interest

Quantum Machine Intelligence & Computing

•Quantum algorithms for AI and machine learning
•Quantum neural networks and deep learning
•Quantum cryptography and secure computing
•Quantum-inspired optimization techniques
•Quantum simulation and modeling
•Hybrid quantum-classical computing frameworks
•Error correction and fault tolerance in quantum systems

Next-Gen Computing Architectures and Accelerators

•High-performance computing architectures
•Neuromorphic and brain-inspired architectures
•Embedded & real-time systems
•Cloud, edge, and fog computing architectures
•Energy-efficient computing systems
•Hardware acceleration for AI (GPU, TPU, FPGA)
•Scalable architectures for big data and AI workloads

Recent Advances in Computer Vision and Pattern Recognition

•Object detection, tracking, and recognition
•3D vision, stereoscopic imaging, and depth estimation
•Scene understanding and semantic segmentation
•Action and gesture recognition
•Vision-based biometrics and identity verification
•Image and video quality assessment and enhancement
•Vision for autonomous vehicles and robotics

Immersive Technologies: AR, VR, and Mixed Reality

•Virtual and augmented reality applications in education, healthcare, and industry
•Mixed reality interfaces and interaction design
•Human-computer interaction in immersive environments
•3D content creation, rendering, and visualization
•Immersive simulations and training systems
•Stereoscopic 3D and 360° video processing
•Perceptual quality and user experience in immersive systems

Innovations in Computational Intelligence and Machine Learning

•Deep learning architectures and optimization
•Reinforcement learning and decision-making systems
•Transfer learning and domain adaptation
•Generative models and diffusion-based learning
•Explainable AI (XAI) and interpretable models
•Federated and privacy-preserving machine learning
•Bio-inspired and swarm intelligence algorithms

Optimization Techniques and Operations Research

•Metaheuristic and evolutionary optimization algorithms
•Multi-objective and constraint-based optimization
•Combinatorial optimization and scheduling problems
•Stochastic and robust optimization methods
•Game theory and decision sciences
•Optimization in logistics, supply chain, and transportation
•AI-driven resource allocation and planning

Ethical, Legal, and Regulatory Frameworks for AI

•AI ethics, bias, and fairness
•Accountability, transparency, and trust in AI
•AI governance and policy frameworks
•Privacy, data protection, and compliance
•Legal aspects of autonomous systems
•Responsible AI deployment in industry
•Societal impact and sustainable AI adoption

AI for Sustainable Cities and Rural Development

•Smart city infrastructure and IoT for urban management
•AI in energy efficiency and renewable energy systems
•Sustainable agriculture and rural development technologies
•Intelligent transportation and traffic management
•Environmental monitoring and disaster management
•Water resource management using AI
•AI for education and healthcare in rural communities