Machine Learning for Predictive Analytics: Models, Applications, and Challenges

Dear Colleagues,

The MDPI Information journal invites submissions to a Special Issue on "Machine Learning for Predictive Analytics: Models, Applications, and Challenges".

Machine learning (ML) continues to revolutionize predictive capabilities across scientific and industrial domains. While achieving remarkable success, ML-based prediction systems face persistent challenges in interpretability, generalization, computational efficiency, and ethical implementation. This Special Issue seeks to advance the field by publishing innovative research that bridges theoretical developments with practical solutions across the predictive analytics pipeline.

Contributions are invited across (but are not limited to) the following themes:

1. Model Development and Innovation

• Novel architectures (transformers, graph neural networks, neurosymbolic systems);
• Time-series, spatial–temporal, and multimodal forecasting;
• Uncertainty quantification and confidence calibration;
• Federated and distributed learning approaches.

2. Domain-Specific Applications

• Healthcare: clinical outcome prediction and medical imaging analytics;
• Cybersecurity: threat detection and adversarial attack forecasting;
• Engineering: predictive maintenance and structural health monitoring;
• Climate Science: extreme weather modeling and carbon emission prediction;
• Finance (FinTech): algorithmic trading, fraud detection systems, and credit risk assessment;
• Education (EdTech): learning outcome prediction, adaptive learning systems, student performance analytics, and educational resource optimization;
• Smart Cities: traffic flow optimization;
• Agriculture: precision farming and crop yield forecasting;
• Social Good: poverty mapping, disaster response optimization, and computer forensic analytics.

3. Critical Challenges and Solutions

• Explainable AI (XAI) for high-consequence decisions;
• Bias detection and fairness-aware modeling;
• Edge deployment and resource-efficient inference;
• Hybrid modeling;
• Data scarcity solutions.

4. Evaluation and Reproducibility

• Benchmark datasets and metrics;
• Reproducibility frameworks;
• Real-world validation studies.

We welcome original research and reviews that demonstrate rigorous methodology with clear practical implications. Interdisciplinary contributions connecting ML theory with domain expertise are particularly encouraged. Join us in shaping the future of predictive analytics—submit your work to advance methodologies, tools, and applications that empower equitable and sustainable decision-making.

Dr. Raza Hasan
Dr. Bacha Rehman
Prof. Dr. Wei Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.