International Conference on AI for Data Science and Decision Making
(ICAIDM-27)

This track focuses on the integration of artificial intelligence in decision support systems to enhance operational efficiency. Papers should explore innovative methodologies that leverage AI for improved decision-making processes across various industries.

This session invites contributions that investigate advanced machine learning techniques for predictive analytics. Emphasis will be placed on novel algorithms and their applications in real-world scenarios.

This track aims to discuss the role of optimization algorithms in solving complex data science problems. Submissions should highlight practical applications and theoretical advancements in optimization techniques.

This session will explore the application of reinforcement learning in enhancing business intelligence frameworks. Papers should present case studies or theoretical insights that demonstrate the effectiveness of reinforcement learning in decision-making.

This track focuses on the development of real-time analytics solutions that support smart decision systems. Contributions should address challenges and innovations in processing and analyzing data in real-time.

This session invites research on the application of AI techniques in risk assessment models. Papers should discuss methodologies that improve risk evaluation and management in various sectors.

This track will delve into the significance of knowledge representation in artificial intelligence applications for data science. Submissions should explore frameworks and models that facilitate effective knowledge management.

This session aims to bridge the gap between artificial intelligence and operations research methodologies. Papers should present interdisciplinary approaches that leverage AI to enhance operations research outcomes.

This track addresses the ethical implications of using AI in decision-making processes. Contributions should explore frameworks for ensuring responsible AI deployment in data-driven environments.

This session invites submissions that present innovative machine learning models and their applications. Emphasis will be placed on novel approaches that push the boundaries of current machine learning capabilities.

This track focuses on how AI and data science can transform business strategies and decision-making. Papers should provide insights into the practical applications of AI in driving business innovation and efficiency.