About the Journal

About the Journal of Data Intelligence and Predictive Systems (Sample Overview)

Title: Journal of Data Intelligence and Predictive Systems
Type: Peer-reviewed, scientific journal
Frequency: Quarterly / Biannually (depends on publisher)

Aim and Scope

The Journal of Data Intelligence and Predictive Systems is dedicated to publishing high-quality research on the theory, development, and application of intelligent systems that leverage data to predict, learn, and adapt.

It serves as a platform for researchers, practitioners, and developers to share innovations in:

• Data Intelligence: Including data mining, big data analytics, knowledge discovery, and decision support.

• Predictive Systems: Encompassing AI, machine learning, statistical models, forecasting, and real-time prediction.

• Applications: In smart systems, cybersecurity, finance, healthcare, industrial automation, and IoT.

Topics of Interest

• Machine learning & deep learning for prediction

• Predictive modeling in smart cities or cyber-physical systems

• Data-driven decision support systems

• Real-time analytics and stream processing

• AI for forecasting in healthcare, climate, industry

• Data privacy and security in predictive systems

• Hybrid systems: AI + statistics + optimization

Types of Papers Published

• Original research articles

• Review papers

• Technical reports

• Case studies

• Short communications

• Special issues on emerging topics (e.g., AI for predictive cybersecurity)

Target Audience

• Researchers in AI, data science, and systems engineering

• Professionals working on data-driven automation or predictive maintenance

• Graduate students in computer science, statistics, and applied fields

• Industry R&D teams applying data analytics and forecasting models