Yazar "Aktas, Muhammet" seçeneğine göre listele

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    Automated test design using swarm and evolutionary intelligence algorithms
    (Wiley, 2022) Aktas, Muhammet; Yetgin, Zeki; Kılıç, Fatih; Sunbul, Önder
    The world's increasing dependence on computer-assisted education systems has raised significant challenges about student assessment methods, such as automated test design. The exam questions should test the students' potential from various aspects, such as their intellectual and cognitive levels, which can be defined as attributes of the questions to assess student knowledge. Test design is challenging when various question attributes, such as category, learning outcomes, difficulty, and so forth, are considered with the exam constraints, such as exam difficulty and duration. In this paper, four contributions are provided to overcome test design challenges for the student assessment. First, a tool is developed to generate a synthetic question pool. Second, an objective function is designed based on the considered attributes. Third, the popular swarm and evolutionary optimization methods, namely particle swarm optimization, genetic algorithm, artificial bee colony, differential search algorithm are comparatively studied with novel methodologies applied to them. Finally, as the state of the art methods, artificial bee colony, and differential search algorithm are further modified to improve the solution of the test design. To perform the proposed algorithms, a dataset of 1000 questions is built with the proposed question attributes of the test design. Algorithms are evaluated in terms of their successes in both minimizing the objective function and running time. Additionally, Friedman's test and Wilcoxon rank-sum statistical tests are applied to statistically compare the algorithms' performances. The results show that the improved artificial bee colony and the improved differential search provide better results than others in terms of optimization error and running time.
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    Öğe
    Discrete Starfish Optimization Algorithm for Symmetric Travelling Salesman Problem
    (IEEE-Inst Electrical Electronics Engineers Inc, 2025) Aktas, Muhammet; Kilic, Fatih
    This paper introduces a new discrete StarFish Optimization Algorithm (D-SFOA) to solve a complex discrete Symmetric Travelling Salesman Problem (STSP). The discrete SFOA algorithm is initialized with the initial population of classical SFOA, and the continuous values of the individuals in the population are converted to the discrete version using the random key method. Ten neighbourhood methods used in this study provide diversity to the starfish population, and the 2-opt local search algorithm allows the study to find shorter tours. The performance of D-SFOA is tested on STSP samples ranging in size from 30 to 1084 from TSPLIB. Discrete versions of the Grey Wolf Optimizer (D-GWO) and Harris Hawk Optimization (D-HHO) algorithms are applied with the same parameters to compare the performance of the proposed algorithm. The algorithm uses descriptive statistics such as average tour, best tour, percentage of deviation of the mean tour, percentage of deviation of the best tour, and execution time to ensure a fair comparison. The Wilcoxon signed-rank test and Ablation test are applied to measure the significant difference in the values of the algorithms and to observe the performance effect of the main components used in the proposed algorithm on tour length and execution time, respectively. This study's numerical and statistical results show that D-SFOA has significantly outperformed other alternative algorithms and provided better solutions than the best-known solution.
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    Öğe
    Enhanced Phishing Website Detection Through Effective Feature Selection With Time-Varying Mirrored S-Shaped Transfer Function
    (Wiley, 2025) Kilic, Fatih; Erdi, Kemal; Aktas, Muhammet
    The increasing use of websites and the increasing sophistication of cybercrimes have highlighted the need for effective prevention mechanisms. Phishing attacks represent a significant cybersecurity threat, as they exploit human weaknesses and deceptive web-based tactics to achieve unauthorized access to sensitive user information. Due to the evolving and increasingly sophisticated methodologies cybercriminals use, there is an urgent demand for advanced and innovative approaches to detect and mitigate these threats accurately. In this paper, a novel approach is proposed by incorporating a time-varying mirrored S-shaped transfer function into three prominent binary optimization algorithms: Binary Grey Wolf Optimization (TVBGWO), Binary Particle Swarm Optimization (TVBPSO), and Binary Harris Hawk Optimization (TVBHHO). The proposed models are rigorously evaluated using a publicly available phishing website dataset. Using a time-varying mirrored S-shaped transfer function for feature selection enables metaheuristic algorithms to improve the balance between exploration and exploitation, thus enhancing their ability to classify phishing websites accurately. The Friedman test confirms statistically significant differences among the compared methods (X-2 = 70.3, p = 8.87 x 10(-14)). The Wilcoxon Rank-Sum test is applied to determine the difference in the performance of the time-varying methods among the standard BGWO, BHHO, and BPSO algorithms in this study. TV-based variants of BPSO and BGWO showed a solid and balanced performance, achieving the highest test accuracy (90.3% and 90.2%) and F1-scores (0.91%). Proposed models achieve promising high accuracy and F1-score performance in detecting phishing websites using fewer features and suggesting their potential.