Estimating the Properties of Ground-Waste-Brick Mortars Using DNN and ANN

Article Properties
Cite
Karaci, Abdulkadir, et al. “Estimating the Properties of Ground-Waste-Brick Mortars Using DNN and ANN”. Computer Modeling in Engineering &Amp; Sciences, vol. 118, no. 1, 2019, pp. 207-28, https://doi.org/10.31614/cmes.2019.04216.
Karaci, A., Yaprak, H., Ozkaraca, O., Demir, I., & Simsek, O. (2019). Estimating the Properties of Ground-Waste-Brick Mortars Using DNN and ANN. Computer Modeling in Engineering &Amp; Sciences, 118(1), 207-228. https://doi.org/10.31614/cmes.2019.04216
Karaci A, Yaprak H, Ozkaraca O, Demir I, Simsek O. Estimating the Properties of Ground-Waste-Brick Mortars Using DNN and ANN. Computer Modeling in Engineering & Sciences. 2019;118(1):207-28.
Citations
Title Journal Journal Categories Citations Publication Date
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  • Technology: Engineering (General). Civil engineering (General)
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  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
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Predicting COVID-19 Cases on a Large Chest X-Ray Dataset Using Modified Pre-trained CNN Architectures

 Applied Computer Systems
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Application of an interpretable artificial neural network to predict the interface strength of a near-surface mounted fiber-reinforced polymer to concrete joint Journal of Zhejiang University-SCIENCE A
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Citations Analysis
Category Category Repetition
Technology: Engineering (General). Civil engineering (General)3
Technology: Building construction: Architectural engineering. Structural engineering of buildings1
Science: Chemistry1
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials1
Science: Physics1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
The category Technology: Engineering (General). Civil engineering (General) 3 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled X-ışını görüntülerinden omuz implantlarının tespiti ve sınıflandırılması: YOLO ve önceden eğitilmiş evrişimsel sinir ağı tabanlı bir yaklaşım and was published in 2021. The most recent citation comes from a 2023 study titled Predicting COVID-19 Cases on a Large Chest X-Ray Dataset Using Modified Pre-trained CNN Architectures. This article reached its peak citation in 2021, with 4 citations. It has been cited in 5 different journals, 20% of which are open access. Among related journals, the Computer Modeling in Engineering & Sciences cited this research the most, with 4 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year