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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Abualhaj, Mosleh M.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
A novel approach for e-health recommender systems Alsaaidah, Adeeb M.; Shambour, Qusai Y.; Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7749

Abstract

The increasing use of the internet for health information brings challenges due to the complexity and abundance of data, leading to information overload. This highlights the necessity of implementing recommender systems (RSs) within the healthcare domain, with the aim of facilitating more effective and precise healthcare-related decisions for both healthcare providers and users. Health recommendation systems can suggest suitable healthcare items or services based on users' health conditions and needs, including medications, diagnoses, hospitals, doctors, and healthcare services. Despite their potential benefits, RSs encounter significant limitations, including data sparsity, which can lead to recommendations that are unreliable and misleading. Considering the increasing significance of health recommendation systems and the challenge of sparse data, we propose an effective approach to improve precision and coverage in recommending healthcare items or services. This aims to assist users and healthcare practitioners in making informed decisions tailored to their unique needs and health conditions. Empirical testing on two healthcare rating datasets, including sparse datasets, illustrate that our proposed approach outperforms baseline recommendation methods. It excels in improving both the precision and coverage of health-related recommendations, demonstrating effective handling of extremely sparse datasets.
Enhancing spyware detection by utilizing decision trees with hyperparameter optimization Abualhaj, Mosleh M.; Al-Shamayleh, Ahmad Sami; Munther, Alhamza; Alkhatib, Sumaya Nabil; Hiari, Mohammad O.; Anbar, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7939

Abstract

In the realm of cybersecurity, spyware has emerged as a formidable adversary due to its persistent and stealthy nature. This study delves deeply into the multifaceted impact of spyware, meticulously examining its implications for individuals and organizations. This work introduces a systematic approach to spyware detection, leveraging decision trees (DT), a machine-learning classifier renowned for its analytical prowess. A pivotal aspect of this research involves the meticulous optimization of DT's hyperparameters, a critical operation for enhancing the precision of spyware threat identification. To evaluate the efficacy of the proposed methodology, the study employs the Obfuscated-MalMem2022 dataset, well-regarded for its comprehensive and detailed spyware-related data. The model is implemented using the Python programming language. Significantly, the findings of this study consistently demonstrate the superiority of the DT classifier over other methods. With an accuracy rate of 99.97%, the DT proves its exceptional effectiveness in detecting spyware, particularly in the face of more intricate threats. By advancing our understanding of spyware and providing a potent detection mechanism, this research equips cybersecurity professionals with a valuable tool to combat this persistent online menace.
Detecting spam using Harris Hawks optimizer as a feature selection algorithm Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Nabil Alkhatib, Sumaya; Shambour, Qusai Y.; Alsaaidah, Adeeb M.
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9198

Abstract

The Harris Hawks optimization (HHO) was used in this study to enhance spam identification. Only the features with a high influence on spam detection have been selected using the HHO metaheuristic technique. The HHO technique's assessment of the selected features was conducted using the ISCX-URL2016 dataset. The ISCX-URL2016 dataset has 72 features, but the HHO technique reduces that to just 10 features. Extra tree (ET), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques are used to complete the classification assignment. 99.81% accuracy is attained by the ET, 99.60% by XGBoost, and 98.74% by SVM. As we can see, with the ET, XGBoost, and k-nearest neighbor (KNN) techniques, the HHO technique achieves accuracy above 98%. Nonetheless, the ET technique outperforms the XGBoost and KNN techniques. ET outperforms other methods due to its robust ensemble approach, which benefits from the diverse and relevant feature subset selected by HHO. HHO's effective reduction of noisy or redundant features enhances ET's ability to generalize and avoid overfitting, making it a highly efficient combination for spam detection. Thus, it looks promising to combat spam emails by combining the ET technique for classification with the HHO technique for feature selection.
Co-Authors Abu-Shareha, Ahmad Abu-Shareha, Ahmad A. Abu-Shareha, Ahmad Adel Al-Khatib, Sumaya Al-Khatib, Sumaya N Al-Khatib, Sumaya N. Al-Khatib, Sumaya Nabil Al-Shamayleh, Ahmad Sami Al-Zyoud, Mahran M. Alalousi, Alabass Alkhatib, Sumaya Nabil Alsaaidah, Adeeb Alsaaidah, Adeeb M. Alsharaiah, Mohammad A Amer, Amal Anbar, Mohammed Fadhil, Hilal A. Hiari, Mohammad O Hiari, Mohammad O. Munther, Alhamza Nabil Alkhatib, Sumaya Rateb, Roqia Shambour, Qusai Y.