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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Atoum, Issa
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Computer Science & IT Electrical & Electronics Engineering Engineering

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The main weaknesses of using Manhattan distance for solving sliding tile puzzles Nayef Al Refai, Mohammed; Mohammed Jamhawi, Zeyad; Ali Otoom, Ahmed; Al-Momani, Adai; Khafajeh, Hayel; Atoum, Issa
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v14.i3.pp2423-2432

Abstract

Heuristics are a big improvement over blind searching in pathfinding. The node's test, run, and finish time are reasonable. Artificial intelligence (AI) uses Manhattan distance (MD), a good and simple heuristic, in various subjects to reduce the number of exploring nodes while requiring fewer calculations. The MD heuristics examined approximately 25 times fewer states than the blind search. Unfortunately, can’t reach the goal of pathfinding when the domain size increases, as it becomes similar to brute force or blind search algorithm results. Previous studies have concentrated on MD's weakness, specifically its low bound value for calculation results, and attempted to improve this value in various ways. Unfortunately, to our knowledge, none of the presented research has been able to find the optimal path for all slide tile puzzle sizes. This work discusses the detailed reasons for the low bound value and other related factors that contribute to its weakness. This paper discovered that the distribution of MD values within the domain, not lowbound values, is the critical issue that complicates the search. The MD's summation method for all tiles has an impact on the calculated duplication values. The total number of nodes in the optimal path also affects the search performance.
A powerful machine learning method for detecting phishing threats Baklizi, Mahmoud; Zraqou, Jamal; Alkhazaleh, Mohammad; Atoum, Issa; Alzyoud, Faisal; B. Alzghoul, Musab
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9579

Abstract

Phishing threats exploit social engineering and deceptive web infrastructure to steal sensitive personal information, often by mimicking legitimate websites. With the proliferation of online services and the increasing prevalence of cybercrime, detecting phishing websites has become a critical challenge. This study presents a comprehensive machine learning (ML)-based approach for detecting phishing websites. A total of 48 discriminative features were extracted from 10,000 web pages—comprising 5,000 phishing and 5,000 legitimate sites. Nine ML classifiers were initially evaluated, including random forest (RF), support vector machine (SVM), and XGBoost. Ensemble models based on soft voting and stacking were then constructed to improve detection performance. Among the models, the soft voting classifier (VC) achieved the best performance with an accuracy and F1-score of 98.82%. The results indicate that ensemble learning offers a robust solution for the automated detection of phishing websites.
Co-Authors Al-Momani, Adai Ali Otoom, Ahmed Alkhazaleh, Mohammad Alzyoud, Faisal B. Alzghoul, Musab Baklizi, Mahmoud Khafajeh, Hayel Mohammed Jamhawi, Zeyad Nayef Al Refai, Mohammed Zraqou, Jamal