Face Recognition System Based on Kernel Principle Component Analysis and Fuzzy-Support Vector Machine

Hussein, Harith A.

doi:10.25130/tjps.23.2018.020

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Radhika et al have used the SVM algorithm to detect damage to buildings by wind and tropical cyclone [23]. KNN and SVM algorithms are implemented successfully in many different areas such as health [24], indoor location detection [25], social networks [26] and image processing [27]. Classification methods have shown that they are useful in the areas where applied.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Fresh Performance of Steel Fiber Reinforced Self-Compacting Concrete Using Quadratic SVM and Weighted KNN Models

2020

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Steel fiber reinforced self-compacting concrete (SFRSCC) is a special type of concrete that is widely researched in literature due to its superior properties. As it is difficult to provide its high workability qualities, SFRSCC is thought to be in need of an economic and quick design process. In this study, it is aimed to predict the fresh properties of SFRSCC mixtures following with the standards at the preliminary design stage. With this aim, two different classification methods were applied successfully to a comprehensive dataset collected from international publications. The models used to classify the fresh performance of SFRSCC were Weighted K-Nearest Neighbors (W-KNN) and Quadratic Support Vector Machine (Q-SVM). Consequently, acceptable success rates were obtained from the models. For the prediction of slump-flow, the accuracy values were 0.76 and 0.84 for the W-KNN and Q-SVM models, respectively. For the V-funnel time, the accuracy values were 0.90 and 0.92 for the W-KNN and Q-SVM models, respectively. Owing to the recommended methods, it is expected to reduce the number of trial mixtures in the preliminary design stage of SFRSCC. INDEX TERMS Fresh properties, self-compacting concrete, steel fiber, quadratic support vector machine, weighted k-nearest neighbor.

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