Machine learning systems are commonly developed under the assumption of static data distributions, limiting their effectiveness in real-world environments where data evolve continuously. In dynamic domains such as cybersecurity monitoring, financial analytics, and intelligent recommendation systems, models must adapt to new information while preserving previously acquired knowledge. However, sequential learning often leads to catastrophic forgetting, where newly learned information overwrites earlier knowledge representations. Existing continual learning approaches partially address this issue but frequently rely on large memory buffers, explicit task boundaries, or computationally expensive retraining strategies, which limit their scalability in real-world streaming environments. To address this gap, this study proposes a modular continual learning framework that integrates incremental model updates with a memory-based rehearsal mechanism designed to preserve representative samples from previously learned tasks. The framework enables models to adapt to evolving data streams while maintaining knowledge retention with minimal memory overhead. Experiments were conducted using sequential learning benchmarks that simulate realistic data evolution scenarios with varying levels of task similarity and data drift. The results demonstrate that the proposed approach maintains stable predictive performance across tasks while significantly reducing catastrophic forgetting compared with conventional sequential training strategies. In particular, the framework achieves consistent task accuracy with substantially lower forgetting rates and reduced retraining cost, highlighting its effectiveness for long-term adaptive learning. The findings suggest that combining incremental learning with compact rehearsal memory provides a practical solution for building adaptive AI systems capable of sustained learning in evolving data environments.
Fatmasari, M. Alshar'e, C. S. Kulkarni et al.· Journal of Data Science· 0 citations
Machine learning models are traditionally optimized for predictive accuracy, often overlooking critical aspects such as fairness and computational efficiency, which are essential for real-world deployment in socially sensitive and resource-constrained environments. This creates a significant research gap, as existing approaches typically address fairness or efficiency in isolation, lacking a unified framework that systematically balances multiple objectives. To address this limitation, this study proposes a multi-objective optimization framework that simultaneously integrates accuracy, fairness, and efficiency within the model development process using Pareto-based optimization techniques. The methodology involves training multiple machine learning models across benchmark datasets containing sensitive attributes, enabling the evaluation of trade-offs between objectives. The framework employs fairness metrics such as demographic parity and equal opportunity, alongside computational efficiency indicators including training time and resource utilization. Pareto front analysis is used to identify optimal model configurations that achieve balanced performance across competing criteria. The results demonstrate that the proposed approach achieves accuracy levels within 1–3% of the best-performing single-objective models, while reducing fairness disparities by up to 40% and computational cost by approximately 20–30%. Statistical analysis confirms that improvements in fairness and efficiency are significant (p < 0.01), with no statistically significant loss in accuracy. These findings highlight the effectiveness of multi-objective optimization in producing balanced and deployable machine learning systems. This study aims to advance a holistic optimization paradigm for responsible AI, enabling the development of models that are not only accurate but also fair and efficient, thereby aligning machine learning practices with ethical and operational requirements
Timur Dali Purwanto, M. Alshar'e, Anjali Bhardwaj et al.· Journal of Data Science· 0 citations
Traditional machine learning models achieve strong predictive performance but often are unable to reliably uncover causal relationships required for reliable decision-making, particularly in observational data where controlled experiments are not feasible. This limitation creates a critical gap between prediction and actionable insight, as correlation-based models are vulnerable to confounding bias and poor generalization under distributional shifts. To address this challenge, this study proposes a unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation. The methodology combines hybrid causal structure learning (constraint-based and score-based approaches) with advanced causal effect estimation methods, including propensity score techniques and doubly robust estimators. The framework is evaluated on both synthetic datasets with known causal structures and real-world datasets to assess its accuracy, robustness, and interpretability. Experiments are conducted using multiple runs with controlled settings to ensure reproducibility and statistical validity. The results demonstrate that the proposed framework significantly outperforms traditional predictive models and standalone causal methods. It achieves higher causal discovery accuracy with improved precision and recall of causal edges, reduces estimation error in Average Treatment Effect (ATE), and maintains stable predictive performance under distributional shifts. Statistical analysis confirms significant improvements (p < 0.01) with large effect sizes, indicating strong reliability and robustness. This research aims to bridge the gap between prediction and explanation by enabling machine learning systems to generate actionable, interpretable, and causally valid insights. The findings highlight the importance of integrating causal reasoning into data science workflows to support informed decision-making, intervention planning, and trustworthy AI development.
Maria Ulfa, M. Alshar'e, Dharmesh Dhabliya et al.· Journal of Data Science· 0 citations
In modern high-volume payment systems, detecting fraud is still essentially confined by abhorrent class imbalance, changing transaction patterns, and lack of dependably labelled fraud occurrences. The current research questions the issue of whether self-supervised learning (SSL) can add to the extraction of the knowledge related to fraud in comparison with the capability of strong supervised baselines in the multi-channel payment setting. Using a real world banking dataset of over 13.3 million transactions in the 2010-2019 period, we perform an extensive analysis, including supervised machine learning, anomaly-based SSL, and methods of integrating knowledge into machine learning strategies. Gradient-boosting models are able to build a strong base (F1 = 0.86, ROC-auc = 0.99) that suggests that the trained model has a near-saturation discriminative ability that is solely based on tabular transaction characteristics. We show that naive, generic, SSL-based anomaly detectors lead to reduced precision, and task-adapted representations of supervised models, stacked with task-adapted representations, can increase fraud recall by up to 4.9 with a small F1 increase ( +0.6). However, with strict operationally imposed conditions of accuracy ≥ 0.90, the added benefits of the use of SSL are not experienced, highlighting inherent thresholds of representation based improvement. Such results enhance a knowledge based perspective of when self-supervised representations add value to the decision making and when supervised models have already acquired adequate information about fraud meaning thereby guiding the design of financial fraud knowledge-management models in a robust way.
Boumedyen Shannaq, N. Elshaiekh, Basel Bani-Ismail et al.· ITEGAM- Journal of Engineeri...· 0 citations
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