This area will track advanced data-based models, methods and frameworks to efficiently extract relevant insights. Topics of interest include but are not limited to:

• Big data trends to process huge heterogeneous data
• Data science models
• Large Language Models (LLMs)
• Data integration architectures
• Inferences models
• Intelligent systems design, development and implementation
• Distributed Intelligent systems
• Intelligent systems for Sustainable development
• Intelligent Systems for Renewable Energy
• Intelligent systems for computer security
• Cloud computing for scalable data analytics
• Ethical AI and explainable machine learning in decision-making
• Applications

This area will cover topics related to the application of data-driven analytics in financial technology. Topics of interest include but are not limited to:

• AI-driven innovations in supply chain management and logistics
• Data-driven strategies for digital transformation in business
• Predictive modeling and forecasting
• Customer behavior analysis and segmentation
• Operational efficiency and process optimization
• Risk management and mitigation strategies
• The microstructure of modern financial markets: algorithmic /high frequency trading, market liquidity, dark trading, blockchain settlements etc.
• Behavioural economics
• FinTech and decision making
• Open banking , insurTech, SupTech, PayTech and embedded FinTech
• Micro FinTech and Financial Inclusion
• FinTech and Sustainable Finance
• FinTech and Gender

This area will cover topics related to the use of data-driven analytics in healthcare. Topics of interest include but are not limited to:

• Drug discovery
• Computational biology
• Health informatics
• Telehealth
• Sensor informatics and medical imaging
• QSAR / QSPR modeling
• Disease diagnosis and treatment prediction
• Medical image and signal analysis
• Healthcare resource allocation and optimization
• Electronic health records analysis
• Patient outcome prediction

This area will cover topics related to the application of data-driven analytics in the context of Industry 4.0. Topics of interest include but are not limited to:

• Data-driven decision making in Industry 4.0
• Data analytics for smart manufacturing
• Data analytics for supply chain optimization
• Big data analytics for Industry 4.0
• Data-driven quality control
• Data analytics for energy management in Industry 4.0
• Data analytics for predictive maintenance
• Machine learning for Industry 4.0
• Blockchain - AI and robotics for enhancing productivity

This area will cover topics related to the application of data-driven analytics in the field of education. Topics of interest include but are not limited to:

• Artificial Intelligence in Education;
• Learning Analytics for adaptive learning;
• Scalable Data Driven architectures for Smart Learning;
• e-Assessment;
• Development of AI and new roles for teachers;
• Best practices and case studies on smart teaching and learning.
• Ethical implications of AI in education and data privacy concerns
• Gamification and AI-driven engagement strategies

This area will cover topics related to the application of data-driven analytics in the field of Agriculture. Topics of interest include but are not limited to:

• Predicting and mitigating climate change impact through data analytics
• Predictive modeling of crop yield and quality using sensor data
• Crop health monitoring with satellite and drone imagery and sensor data
• Holistic farm management through data integration
• Visualization tools for complex agricultural data
• Resource management through data-driven analytics
• Ethical and privacy considerations in agricultural data collection and use
• Sharing and integration of agricultural data across stakeholders
• Evaluation and improvement of agricultural data platforms and tools
• Data-driven solutions for global food security
• Blockchain for transparency in agricultural supply chains