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Author

M. Alshar'e

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Open access Sep 2026

Cross-Modal Representation Learning for Integrating Heterogeneous Data in AI Systems

A cross-modal representation learning framework that aligns heterogeneous modalities within a shared latent representation space and exhibits strong robustness under missing modality conditions, with significantly lower performance degradation compared to baseline approaches is proposed.

I. Ibrahim, M. Alshar'e, I. Sanjaya et al. · 0 citations
Open access Sep 2026

Scalable AutoML Frameworks for End-to-End Optimization of Data Science Workflows

This study proposes a scalable end-to-end AutoML framework that integrates automated preprocessing, feature engineering, model selection, hyperparameter optimization, and adaptive resource allocation within a unified optimization architecture to improve optimization efficiency while maintaining predictive robustness.

Devi Udariansyah, M. Alshar'e, Puneet Kumar Yadav et al. · 0 citations
Open access Sep 2026

Continual Learning Frameworks for Long-Term Knowledge Retention in Evolving Data Streams

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 is proposed, suggesting that combining incremental learning with compact rehearsal memory provides a practical solution for b...

Fatmasari, Marwan Alshar'e, C. S. Kulkarni et al. · 0 citations
Open access Sep 2026

Multi-Objective Optimization in Machine Learning for Balancing Accuracy, Fairness and Efficiency

A multi-objective optimization framework that simultaneously integrates accuracy, fairness, and efficiency within the model development process using Pareto-based optimization techniques is proposed, enabling the development of models that are not only accurate but also fair and efficient.

Timur Dali Purwanto, Marwan Alshar'e, Anjali Bhardwaj et al. · 0 citations
Open access Sep 2026

Causal Machine Learning for Discovering Actionable Insights in Observational Data

A unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation and achieves higher causal discovery accuracy with improved precision and recall of causal edges.

Maria Ulfa, Marwan Alshar'e, Dharmesh Dhabliya et al. · 0 citations
Open access 2026

Self-Supervised Knowledge Representation for Rare Fraud and Operational Failure Detection in Multi-Channel Payment Systems

It is shown 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, enhancing a knowledge based perspective of when self-supervised repre...

Boumedyen Shannaq, N. Elshaiekh, Basel Bani-Ismail et al. · 0 citations

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