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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Muhammet Esad Kuloğlu
Abdullah Gül University
Computer Science & IT

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Constraint-Aware Machine Learning for Ensuring Feasible Predictions in Operational Data Science Wu Shukun; Tri Basuki Kurniawan; Muhammet Esad Kuloğlu
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.652

Abstract

Background: Machine learning models deployed in operational environments often demonstrate high predictive accuracy during benchmark evaluation. However, their practical reliability is frequently compromised when predictions violate domain-specific operational constraints.Aims: This study aims to address the problem of infeasible predictions by proposing a unified framework that integrates operational constraints directly into the learning and inference processes.Methods: The CALF framework incorporates operational constraints through a dual mechanism consisting of correction-based learning and regularization-based penalty functions. These mechanisms are embedded directly within the training and inference objectives, allowing the model to learn constraint-compliant predictions during optimization. The framework was evaluated by comparing predictive error and operational feasibility against an unconstrained baseline model. Sensitivity analysis was also conducted to examine the stability and flexibility of the constraint penalties under varying operational thresholds.Result: Experimental results demonstrate that CALF achieved predictive error levels comparable to the unconstrained baseline while maintaining full operational feasibility. The framework reached 100% operational compliance, indicating that all generated predictions satisfied the defined constraints. Sensitivity analysis further showed that the regularization penalties operated within acceptable thresholds, allowing the model to maintain predictive flexibility while enforcing constraint adherence.Conclusion: The findings highlight the importance of integrating operational constraints directly into machine learning model design. By embedding feasibility constraints within the optimization process, the CALF framework ensures that predictive outputs remain both accurate and operationally compliant. This approach repositions operational constraints as intrinsic components of predictive modeling and contributes to the development of reliable and deployable AI systems in real-world environments.
Adaptive Curriculum Learning for Accelerating Convergence in Data-Driven Model Training Eko Risdianto; Muhammet Esad Kuloğlu; Maria Ulfa
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i2.863

Abstract

Background: Efficient training of machine learning models remains a critical challenge in data-driven artificial intelligence systems, particularly when training samples vary in difficulty, noise level, and representational complexity.Aims: This study proposes and evaluates an adaptive curriculum learning framework that accelerates convergence in machine learning model training by dynamically adjusting sample ordering based on the model's evolving competence.Methods: The proposed approach introduces a feedback-driven curriculum mechanism that integrates difficulty estimation and model competence evaluation. Training samples are initially ranked by estimated difficulty and are progressively updated during training using validation-based performance signals. Experiments are conducted on three benchmark datasets, including UCI Adult Income, Breast Cancer Wisconsin, and CIFAR-10, using multilayer perceptron and convolutional neural network architectures. The framework is compared against random sampling, static curriculum learning, and hard-first sampling strategies. Performance is evaluated based on convergence speed, predictive accuracy, robustness to noise, class imbalance handling, and computational overhead.Result: The proposed adaptive curriculum learning framework consistently accelerates convergence across all datasets compared to baseline methods. The model achieves faster stabilization of training dynamics while maintaining or slightly improving predictive performance on accuracy, F1-score, and AUC. Furthermore, the framework demonstrates improved robustness under noisy-label conditions and class-imbalance scenarios, with reduced performance degradation compared to conventional sampling strategies. The computational overhead introduced by the adaptive mechanism remains minimal compared with the gains in training efficiency.Conclusion: The study concludes that aligning sample ordering with evolving model competence significantly improves training efficiency in machine learning systems.
Co-Authors Eko Risdianto Maria Ulfa Tri Basuki Kurniawan Wu Shukun