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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Al-Zyoud, Mahran M.
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A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations Shambour, Qusai Y.; Al-Zyoud, Mahran M.; Alsaaidah, Adeeb M.; Abualhaj, Mosleh M.; Abu-Shareha, Ahmad A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1919-1928

Abstract

The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry.
Comparative analysis of whale and Harris Hawks optimization for feature selection in intrusion detection Abualhaj, Mosleh M.; Hiari, Mohammad O.; Alsaaidah, Adeeb; Al-Zyoud, Mahran M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp179-185

Abstract

This research paper explores the efficacy of two nature-inspired optimization algorithms, the whale optimization algorithm (WOA) and Harris Hawks optimization (HHO), for feature selection in the context of intrusion detection and prevention systems (IDPS). Leveraging the NSL-KDD dataset as a benchmark, our study employs Python for implementation and uses decision tree (DT) as the classification model. The objective is to assess the impact of the HHO and WOA optimization techniques on the performance of IDPS through feature selection. The WOA and HHO techniques were able to lessen the features from 40 to 16 and 13, respectively. Results indicate that DT integrated with HHO achieves an impressive accuracy of 97.59%, outperforming the WOA-enhanced model, which attains an accuracy of 97.5%. This study contributes valuable insights into the comparative effectiveness of WOA and HHO optimization algorithms in enhancing the accuracy of IDPSs, shedding light on their potential applications in the realm of cybersecurity.
Co-Authors Abu-Shareha, Ahmad A. Abualhaj, Mosleh M. Alsaaidah, Adeeb Alsaaidah, Adeeb M. Hiari, Mohammad O. Shambour, Qusai Y.