AI-Based Supervised Prediction Models
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 June 2026 | Viewed by 1283
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue, titled "AI-based Supervised Prediction Models", focuses on the development, analysis, and application of supervised machine learning (ML) techniques in diverse real-world domains. Supervised learning, one of the most widely used branches of artificial intelligence (AI), involves training algorithms on labeled datasets to make accurate predictions or classifications. This Special Issue provides a platform for researchers and practitioners to explore innovative methodologies, architectures, and evaluation strategies that enhance the predictive capabilities and interpretability of AI models.
This Special Issue welcomes contributions that cover a wide range of supervised learning models, including classical algorithms, as well as modern approaches such as deep learning, ensemble models, and hybrid frameworks. Special emphasis is placed on applications in healthcare, education, finance, cybersecurity, and smart systems, where predictive accuracy and robustness are critical. Moreover, this Special Issue encourages submissions addressing challenges, such as feature selection, imbalanced datasets, model explainability, and the integration of domain knowledge into predictive modeling.
Papers that demonstrate the use of supervised AI models to uncover insights from complex datasets, improve decision-making, or personalize user experiences are particularly encouraged. Additionally, comparative studies highlighting the performance of various supervised techniques and papers proposing novel metrics for evaluating model effectiveness are of high interest. Overall, this Special Issue aims to showcase state-of-the-art research that advances the field of supervised AI and contributes to the creation of intelligent, adaptive, and trustworthy predictive systems.
Dr. Maria Trigka
Guest Editor
Manuscript Submission Information
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Keywords
- supervised learning
- predictive modeling
- machine learning
- feature selection
- classification algorithms
- model interpretability
- artificial intelligence
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