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Call For Papers

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Prospective authors are invited to contribute high-quality papers by the submission deadline through the online submission system. The submission of a paper implies that the paper is original and has not been submitted under review or is not copyright-protected elsewhere and will be presented by an author if accepted. All submitted papers will be refereed by experts in the field based on the criteria of originality, significance, quality and clarity.

Tracks

Tracks

  • • Evolution of Generative Models
  • • Introduction to generative models (GANs, VAEs, etc.).
  • • Development and evolution of generative models.
  • • Transfer learning and fine-tuning for generative models.
  • • Progressive GANs and training stability.
  • • Prompt Engineering and Generative AI.
  • • Reinforcement learning in generative models.
  • • Using generative models for creative design processes.
  • • Generative AI for interactive experiences.
  • • Human-AI collaboration in creative processes.
  • • Making generative models more interpretable.
  • • Understanding and explaining decisions made by generative models.
  • • Cross-disciplinary Collaboration between generative AI and other fields (e.g., art, science, finance).
  • • Time Series Analysis with Generative Models.
  • • Bayesian Generative Models.
  • • Quantum Generative Models.
  • • Generative AI and Internet of Things.
  • • Generative AI and Big Data Analytics.

  • • Image synthesis and manipulation.
  • • Generative AI in content creation.
  • • Text generation and natural language processing.
  • • Video generation and deepfake technology.
  • • Music and audio synthesis.
  • • Generative AI in healthcare.
  • • Robotics and automation.
  • • Applications of generative design in architecture and engineering.
  • • Solving optimization problems using generative models.
  • • Decision support systems with generative AI.
  • • Augmented Reality and generative AI.
  • • Generative AI in Education.
  • • Graph Neural Networks (GNNs) and Applications.
  • • Generative AI for Personalisation and Recommendation Systems.
  • • Generative AI for Sustainable Development.

  • • Ethical issues in generative models.
  • • Addressing biases in generated content.
  • • Ethical implications of deepfakes.
  • • Detecting and preventing malicious use of generative models.
  • • Security challenges in the era of deepfakes.
  • • Emerging trends in generative AI research.
  • • Potential applications on the horizon.
  • • Differential Privacy in Machine Learning

  • • Foundations of Data Science
  • • Learning Techniques and Applications
  • • Big Data Analytics and Processing
  • • Predictive Modeling and Forecasting
  • • Natural Language Processing and Text Analytics
  • • Computer Vision and Image Analytics
  • • Explainable AI and Model Interpretability
  • • Ethics and Responsible AI in Predictive Analytics
  • • Deep Learning Advances
  • • Data Visualization and Storytelling
  • • Human-AI Collaboration and Augmented Analytics
  • • Real-Time Analytics and Stream Processing
  • • Data Engineering and Pipeline Automation
  • • Reinforcement Learning and Optimization
  • • Data Ethics, Privacy, and Security
  • • Personalization and Recommendation Systems
  • • Geospatial Data Science and GIS Analytics
  • • Predictive Analytics in Healthcare
  • • Data Science for Smart Cities and IoT
  • • Emerging Trends in Predictive Analytics

  • • Cryptography in Mobile and Wireless Communications
  • • Watermarking and Steganography
  • • Multimedia Security
  • • Public key and conventional algorithms and their implementations
  • • Cryptanalytic attacks and Cryptographic protocols
  • • Pseudo-random sequences
  • • Digital signature and key management
  • • Privacy and security in healthcare
  • • IOT security
  • • Cryptography in Financial Technologies
  • • Quantum Cryptography
  • • Differential Privacy