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Abu-Shareha, Ahmad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Mitigating Healthcare Information Overload: a Trust-aware Multi-Criteria Collaborative Filtering Model Shambour, Qusai Y; Abualhaj, Mosleh M; Abu-Shareha, Ahmad; Hussein, Abdelrahman H; Kharma, Qasem M
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.297

Abstract

The rapid growth of online health information resources has made it difficult for users, as well as providers of healthcare, to cope with large volumes of information that are becoming increasingly complex. Hence, there is an urgent demand for developing new advanced recommendation techniques in the healthcare domain to enhance decision-making processes. However, most current health recommendation systems, which recommend personalized healthcare services and items such as diagnoses, medications, and doctors based on users' health conditions and needs, are hindered by the data sparsity issue that compromises the reliability of their recommendations. In this paper, we intend to address this issue by proposing a Trust-aware Multi-Criteria Collaborative Filtering model for recommendation services in the healthcare domain. This model leverages multi-criteria ratings and integrates user-item trust relationships to improve the precision and coverage of recommendations, thus facilitating more informed healthcare choices that align closely with their individual needs. Our empirical analysis on two healthcare multicriteria rating datasets, including those with sparse data, shows the proposed model's superior performance over existing baseline methods. On the RateMDs dataset, our model improved the average MAE by 24% and RMSE by 19% compared to baseline methods. For the WebMD dataset, it enhanced the average MAE by 6% and RMSE by 2%. In sparse data scenarios, the model boosted the average MAE by 18% and Coverage by 6% compared to baseline approaches.
Enhancing Spam Detection Using Hybrid of Harris Hawks and Firefly Optimization Algorithms Abualhaj, Mosleh M.; Shambour, Qusai Y.; Alsaaidah, Adeeb; Abu-Shareha, Ahmad; Al-Khatib, Sumaya; Hiari, Mohammad O.
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.279

Abstract

The emergence of the modern Internet has presented numerous opportunities for attackers to profit illegally by distributing spam mail. Spam refers to irrelevant or inappropriate messages that are sent on the Internet to numerous recipients. Many researchers use many classification methods in machine learning to filter spam messages. However, more research is still needed to assess using metaheuristic optimization algorithms to classify spam emails in feature selection. In this paper, we endorse fighting spam emails by employing a union of Firefly Optimization Algorithm (FOA) and Harris Hawks Optimization (HHO) algorithms to classify spam emails, along with one of the most well-known and efficient methods in this area, the Random Forest (RF) classifier. In this process, the experimental studies on the ISCX-URL2016 spam dataset yield promising results. For instance, the union of HHO and FOA, along with using an RF classifier, achieved an accuracy of 99.83% in detecting spam emails.
Co-Authors Abualhaj, Mosleh M Abualhaj, Mosleh M. Al-Khatib, Sumaya Alsaaidah, Adeeb Hiari, Mohammad O. Hussein, Abdelrahman H Kharma, Qasem M Shambour, Qusai Y Shambour, Qusai Y.