Aims and Scope

Aims

    The Journal of Data Analytics and Intelligent Decision-Making (JDAID) is an international, interdisciplinary, peer-reviewed, open-access publication dedicated to advancing the foundational field of data analytics and the theories, methods, and applications of intelligent decision-making. Our core mission is to synthesize artificial intelligence (AI), machine learning, data analytics, and decision theory with human cognitive and affective dimensions, thereby fostering innovation in intelligent decision-making systems.

    JDAID aims to serve as a global platform for researchers, practitioners, and policymakers to exchange innovative methodologies, empirical research, and case studies that enhance data-driven decision-making across various sectors. By combining theoretical insights with practical applications, the journal fosters research that improves decision quality and outcomes in complex, uncertain, and dynamic environments. We seek to promote the development of intelligent systems that bridge the gap between theory and real-world application, driving the next generation of solutions for business, healthcare, engineering, public policy, and finance.

    The journal supports interdisciplinary collaboration among data scientists, statisticians, mathematicians, business strategists, managers, policymakers, and experts in computer science and AI. This collaboration is focused on expanding the frontiers of knowledge, solving human problems, and driving meaningful progress. JDAID is committed to publishing scholarly research, theory, and developmental applications that address various facets of decision-making and intelligent systems, ultimately facilitating the exchange of knowledge, methodologies, and best practices that drive the evolution of the field.

 

Scope

    The Journal of Data Analytics and Intelligent Decision-Making (JDAID) welcomes high-quality, original research and review manuscripts that explore innovative theories, methodologies, and applications at the intersection of data science, artificial intelligence, and decision-making processes. The journal covers a broad, interdisciplinary range of topics, including but not limited to:

1. Foundational Theories and Methodologies:

  • Data Analytics: Statistics, machine learning, predictive modeling, time series analysis, data mining, and visualization.

  • Decision Analysis and Optimization: Data envelopment analysis (DEA), performance evaluation, mathematical modeling, simulation, forecasting, and prescriptive methods.

  • Artificial Intelligence in Decision-Making: AI-based decision support systems, reinforcement learning, natural language processing, intelligent automation, and robotics.

  • Modeling and Problem-Solving: Predictive models, scenario analysis, optimization, foresight, and future studies.

2. Behavioral, Cognitive, and Human-Centric Perspectives:

  • Behavioral and cognitive sciences in intelligent decision-making, including human-computer interaction, cognitive biases, behavioral economics, and behavioral finance.

  • The integration of neuroscience in decision-making, covering leadership, marketing, and strategic neuroscience, as well as bio-inspired decision models.

3. Applied Domains and Sector-Specific Analytics:

  • Healthcare and Biomedical Decision Support: Disease diagnosis, clinical decision support, personalized medicine, and medical image analysis.

  • Financial and Economic Analytics: Portfolio management, economic forecasting, fraud detection, and AI-driven investment strategies.

  • Business, Strategic, and Competitive Intelligence: Market analysis, customer segmentation, performance optimization, and risk analysis for strategic decision-making.

  • Manufacturing, Operations, and Logistics: Applications of data analytics for process optimization and service efficiency.

  • Data-Driven Governance and Policy-Making: Data management in government, smart cities, cybersecurity, and the ethics of AI.

4. Technological Innovations and Tools:

  • Blockchain-based data analytics and secure data handling.

  • Automated analytics and AutoML.

  • Open-source tools and platforms for data analytics (e.g., Python, R, Julia, Apache Spark).

  • Data visualization, dashboards, storytelling, and interactive analytics for user-driven data exploration.

  • Decision Support Systems (DSS), multi-agent architectures, and autonomous systems.

5. Ethics, Governance, and Accountability:

  • Transparency, fairness, bias mitigation, and legal frameworks in intelligent decision-making.

  • Interdisciplinary approaches that integrate cognitive science, psychology, neuroscience, and artificial intelligence to address these critical issues.

    The Journal of Data Analytics and Intelligent Decision-Making (JDAID) encourages submissions that demonstrate a clear integration of theory and practice, offering novel insights and solutions for real-world challenges. All manuscripts undergo a rigorous double-blind peer-review process to ensure originality, validity, significance, and scientific value.