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Language: search.filters.language.EnglishSubject: search.filters.subject.Simulated annealing

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    Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis
    (IEEE, 2024)
    Franco-Gaona, Erick
    ;
    Avila-Garcia, Maria-Susana
    ;
    Cruz-Aceves, Ivan
    ;
    Orocio-Garcia, Hiram-Efrain
    ;
    Escobedo-Gordillo, Andrés
    Coronary stenosis is a disease that claims millions of lives each year. Early detection of this condition is crucial for patient survival. Currently, physicians perform detection by x-ray angiograms, however, the variability of diagnoses and the difficulty of access to expertise has led to the need for automated, computer-assisted diagnosis. In this work explores the use of deep learning to classify stenosis or non-stenosis in angiogram images using convolutional neural networks from scratch. A methodology to fine-tuning network architectures automatically using metaheuristic optimization techniques is proposed, demonstrating superior performance to fine-tuning empirically and proposing a new architecture in the literature to classify coronary stenosis. The proposed architectures achieved 86.02% and 95.67% F1-score with simulated annealing and iterated local search techniques, respectively. ©The authors ©IEEE
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    Multi-threaded Spotted Hyena Optimizer with thread-crossing techniques
    (2021)
    Martínez Ríos, Félix Orlando  
    ;
    Murillo-Suarez, Alfonso
    This paper presents a Multi-threaded version of the Spotted Hyena Optimizer algorithm with thread-crossing techniques (MT-SHO) to improve the ability of the algorithm to explore the search space. The original algorithm is inspired by the hunting behavior of the spotted hyena. Along the different sections of the work, we explain in detail how the original algorithm simulates the spotted hyena's behavior to optimize highly complex mathematical functions and how we handle the procedures and results of the multi-threaded version, with thread-crossing techniques that improve the ability to explore and exploit the search space by letting threads learn between them. We present the experiments used to determine the best value of the parameters used in the parallel version of the algorithm and to prove that our proposal obtains significantly good results we compare the results obtained by evaluating 24 benchmark functions with the results published for the original algorithm as well as other metaheuristic algorithms. © 2021 Elsevier B.V.. All rights reserved.
    Scopus© Citations 3  33  1