23 Temmuz 2024 Dergi makalesi Açık Erişim

Gülmez, Burak

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    <subfield code="x">Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications, 227, 120346.</subfield>
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    <subfield code="x">Gülmez, B. (2023). A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images. Annals of Operations Research, 328(1), 617-641.</subfield>
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    <subfield code="x">Gülmez, B. (2022). MonkeypoxHybridNet: A hybrid deep convolutional neural network model for monkeypox disease detection. International research in engineering sciences, 3, 49-64.</subfield>
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    <subfield code="x">Gülmez, B. (2023). A novel deep learning model with the Grey Wolf Optimization algorithm for cotton disease detection. Journal of Universal Computer Science, 29(6), 595.</subfield>
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    <subfield code="x">Gülmez, B., Emmerich, M., &amp; Fan, Y. (2024). Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows. Applied Artificial Intelligence, 38(1), 2325302.</subfield>
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    <subfield code="x">Gülmez, B., &amp; Kulluk, S. (2023). Analysis and price prediction of secondhand vehicles in Türkiye with big data and machine learning techniques. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(4), 2279-2290.</subfield>
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    <subfield code="x">Gülmez, B. (2023). Market zinciri ürün dağıtımı probleminin farklı genetik algoritma versiyonları ile çözümü ve karşılaştırması. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 180-196.</subfield>
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    <subfield code="x">Gülmez, B., &amp; Kulluk, S. (2019). Social spider algorithm for training artificial neural networks. International Journal of Business Analytics (IJBAN), 6(4), 32-49.</subfield>
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    <subfield code="x">Gülmez, B. (2022). Zaman serisi analizi ile talep tahmini ve bir fabrikadaki üretim planlama. Mühendislik Alanında Uluslararası Araştırmalar, 2, 57-74.</subfield>
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    <subfield code="x">Gülmez, B. (2024). Advancements in rice disease detection through convolutional neural networks: a comprehensive review. Heliyon.</subfield>
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    <subfield code="x">Gülmez, B. (2023). Optimizing and comparison of market chain product distribution problem with different genetic algorithm versions.</subfield>
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    <subfield code="x">Gülmez, B., &amp; Kulluk, S. (2023). Türkiye'de ikinci el araçların büyük veri ve makine öğrenme teknikleriyle analizi ve fiyat tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(4), 2279-2290.</subfield>
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    <subfield code="x">Gülmez, B. (2022). Demand forecasting and production planning in a factory with time series analysis. International Research in Engineering Sciences, 2, 57-74.</subfield>
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    <subfield code="a">A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images</subfield>
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    <subfield code="a">&lt;p&gt;The coronavirus occurred in Wuhan (China) first and it was declared a global pandemic. To detect coronavirus X-ray images can be used. Convolutional neural networks (CNNs) are used commonly to detect illness from images. There can be lots of different alternative deep CNN models or architectures. To find the best architecture, hyper-parameter optimization can be used. In this study, the problem is modeled as a multi-objective optimization (MOO) problem. Objective functions are multi-class cross entropy, error ratio, and complexity of the CNN network. For the best solutions to the objective functions, multi-objective hyper-parameter optimization is made by NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, MOEA/D, and proposed Swarm Genetic Algorithms (SGA). SGA is a swarm-based algorithm with a cross-over process. All six algorithms are run and give Pareto optimal solution sets. When the figures obtained from the algorithms are analyzed and algorithm hypervolume values are compared, SGA outperforms the NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, and MOEA/D algorithms. It can be concluded that SGA is better than others for multi-objective hyper-parameter optimization algorithms for COVID-19 detection from X-ray images. Also, a sensitivity analysis has been made to understand the effect of the number of the parameters of CNN on model success.&lt;/p&gt;</subfield>
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