The Engineering Computation and Intelligence Group (ECI Group) at Bethlehem University continues to contribute to impactful, interdisciplinary research addressing real-world challenges in healthcare analytics, intelligent systems, and digital performance evaluation.

Led by Dr. Suhail Odeh, Associate Professor at the Department of Technology, the group brings together faculty expertise in artificial intelligence, machine learning, and engineering computation. The research team includes Dr. Anas Samara, Assistant Professor and Chairperson of the Department of Technology, Dr. Mohammed Abu Ayyash, Assistant Professor at the Department, and Dr. Mohammed Ghattas, Instructor at the Department, whose collaborative work has resulted in three recent peer-reviewed research studies.

Enhancing Early Disease Detection through Intelligent Ensemble Models

One of the group’s studies focuses on improving early liver disease prediction using advanced machine learning techniques. The research proposes a weighted soft-voting ensemble model that combines multiple traditional classifiers to achieve higher accuracy, robustness, and transparency in medical decision-making.

By evaluating the models on a large-scale clinical dataset within a leakage-free validation framework, the study demonstrates how ensemble intelligence can outperform individual models while maintaining interpretability, an essential requirement for clinical applications. This work highlights the potential of explainable AI in supporting physicians and improving patient outcomes.

Advancing Knowledge in Website Performance Evaluation

In another contribution, the ECI Group conducted a systematic literature review examining website performance metrics and prediction methods over more than a decade of research. Following the PRISMA methodology, the study analyzed hundreds of academic sources and identified key gaps in the adoption of intelligent prediction models.

The research consolidates dozens of performance indicators into a validated set of core metrics, offering practical guidance for researchers and practitioners seeking scalable, data-driven approaches to website evaluation. The findings emphasize the need to integrate machine learning techniques into performance assessment frameworks across sectors such as education, e-government, healthcare, and industry.

Emerging Research on Intelligent Systems and Applied AI

The group’s third study, published in an international open-access journal, further explores the application of intelligent and computational methods to complex engineering problems. This research reflects the group’s broader vision of leveraging artificial intelligence to support optimization, prediction, and decision-making across multiple domains.

The ECI Group is one of the officially formed research groups at Bethlehem University under the Deanship of Graduate Studies and Scientific Research, in line with the University’s strategic vision to strengthen interdisciplinary, collaborative, and impact-driven research.

Together, these studies reflect the ECI Group’s commitment to high-quality, applied research that bridges theory and practice. Through interdisciplinary collaboration and international publication, the group continues to strengthen Bethlehem University’s role in advancing innovation, knowledge production, and socially relevant engineering research.