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    Empirical comparison of regression test selection algorithms by Mansour, Nashat

    Published 2001
    “…The empirical results show that the five algorithms can be used for different requirements of regression testing. …”
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    A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and Levy flight methods by Abu Zitar, Raed

    Published 2023
    “…An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lévy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. …”
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    Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms by Humna Khan (17541972)

    Published 2022
    “…The outcomes revealed that these ML algorithms can be useful in predicting ground losses during wild blueberry harvesting in the selected fields.…”
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    Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect by Habeeb, Abdallah

    Published 2022
    “…The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). …”
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    Real-Time Selective Harmonic Mitigation Technique for Power Converters Based on the Exchange Market Algorithm by Abraham Marquez Alcaide (18582451)

    Published 2020
    “…The Exchange Market Algorithm (EMA), initially focused on optimizing financial transactions, is considered and executed to achieve the SHM targets. …”
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    Logarithmic spiral search based arithmetic optimization algorithm with selective mechanism and its application to functional electrical stimulation system control by Abu Zitar, Raed

    Published 2022
    “…A biomedical application of a novel metaheuristic optimizer is proposed in this paper by constructing an enhanced arithmetic optimization algorithm (AOA). The latter algorithm was constructed using the logarithmic spiral (Ls) search mechanism from the whale optimization algorithm and the greedy selection scheme from the differential evolution algorithm. …”
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    Using Machine Learning Algorithms to Forecast Solar Energy Power Output by Ali Jassim Lari (22597940)

    Published 2025
    “…The data forecasting horizon used was a 24-h window in steps of 30 min. We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. …”
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    Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment by Sachi Gupta (19560268)

    Published 2022
    “…Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. …”
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    An Optimized Feature Selection Technique in Diversified Natural Scene Text for Classification Using Genetic Algorithm by Ghulam Jillani Ansari (16896342)

    Published 2021
    “…The Support Vector Machine (SVM) algorithm is used for classification and the average F-Score is used as fitness function and target condition. …”
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    Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm by De Rochechouart, Maxence

    Published 2023
    “…The results show an optimum resource allocation with an information-based cost function that is optimized by an Auction Algorithm.…”
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    TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection by Zina Chkirbene (16869987)

    Published 2020
    “…TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. Initially, the features are grouped randomly to increase the probability of making them participating in the generation of different groups, and sorted based on their accuracy scores. …”
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