Topics

Original unpublished manuscripts, and not currently under review in another journal or conference, are solicited in relevant areas including, but not limited to, the following:

1. Data Modelling

• Advanced Data Modeling Techniques: Methodologies for constructing, analyzing, and validating complex data models.
• Big Data Analytics: Approaches and tools for handling and extracting insights from large-scale datasets.
• Data Visualization: Techniques for effectively visualizing and interpreting complex data.
• Predictive and Prescriptive Analytics: Models and algorithms for forecasting future trends and recommending actions.
• Data Integration and Fusion: Methods for integrating and synthesizing data from diverse sources.
• Handling Uncertainty in Data: Techniques for managing and mitigating uncertainty in data modeling.

2. Artificial Intelligence

• Machine Learning Algorithms: Advances in supervised, unsupervised, and reinforcement learning methods.
• Deep Learning: Innovations in neural networks, including convolutional and recurrent neural networks.
• Natural Language Processing (NLP): Techniques for processing and analyzing human language data.
• AI in Automation: Applications of AI in automating complex processes and tasks.
• Explainable AI (XAI): Methods for making AI models and decisions more transparent and interpretable.
• Ethics and Fairness in AI: Addressing issues related to bias, fairness, and ethical considerations in AI systems.

3. Economics

• Economic Forecasting: Techniques for predicting economic indicators and trends using data models.
• Behavioral Economics: Insights into how psychological factors influence economic decisions and market outcomes.
• Econometric Modeling: Advanced methods for statistical analysis and modeling of economic data.
• Policy Analysis: Using data and AI to evaluate the impact of economic policies and regulations.
• Economic Impact of AI: Exploring how AI technologies are transforming economic sectors and labor markets.

4. Finance

• Financial Modeling: Techniques for constructing models to analyze financial markets, instruments, and portfolios.
• Algorithmic Trading: Applications of AI and machine learning in developing trading strategies and systems.
• Risk Management: Advanced methods for assessing and managing financial risk using data-driven approaches.
• Fraud Detection: Using AI to detect and prevent financial fraud and anomalies.
• Fintech Innovations: Exploring the impact of AI and data analytics on financial technology and services.

5. Management

• Strategic Management: Leveraging data and AI to support strategic decision-making and business planning.
• Operational Efficiency: Using data modeling and AI to optimize operational processes and resource allocation.
• Supply Chain Management: Innovations in managing and optimizing supply chains through data-driven insights.
• Customer Relationship Management (CRM): Enhancing customer interactions and satisfaction using AI and data analytics.
• Change Management: Strategies for managing organizational change and transformation driven by data and AI technologies.

6. Interdisciplinary Topics

• AI and Data-Driven Policy Making: How AI and data modeling can inform and shape public policy and governance.
• Ethical and Societal Implications of AI: Exploring the broader impact of AI on society, including issues of privacy, employment, and inequality.
• Smart Cities and IoT: Applications of data modeling and AI in the development and management of smart cities and Internet of Things (IoT) systems.
• Healthcare Analytics: Using data and AI to improve healthcare outcomes, diagnostics, and personalized medicine.