Intruder Detection by Using Faster R-CNN in Power Substation

Srijakkot, Krit; Kanjanasurat, Isoon; Wiriyakrieng, Nuttakan; Lartwatechakul, Mayulee; Benjangkaprasert, C.

doi:10.1007/978-3-030-44044-2_16

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Machine learning (ML) is a useful tool for data visualization and management that can quickly and accurately solve a variety of problems. In previous ML research, K. Srijakkot, et al demonstrated the good performance of intruder detection in power substations with different environments and models, including a short calculation time and high accuracy [4,5]. Not only does ML has advantages in detecting intruders, but also in the medical field, where pre-processing and the IterNet model have demonstrated high accuracy in extracting retinal blood vessels [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison of logistic regression and random forest algorithms for airport’s runway assignment

Kanjanasurat

Jungsuwadee

Lasakul

et al. 2023

J. Phys.: Conf. Ser.

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Various automation systems are currently developed using machine learning techniques. It is used to predict and decide on numerous complex tasks in order to reduce the likelihood of human error. Logistic regression is one of the most widely employed machine learning (ML) algorithm. In this study, the accuracy of logistic regression was compared to that of random forest for the assignment of Suvarnabhumi Airport runways to arriving aircraft. The accuracy of the logistic regression model was determined to be 82%, while the accuracy of the random forest model was 77%. Logistic regression was found to be more precise for predicting the appropriate runway to assign to arriving aircraft.

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Section: Introductionmentioning

confidence: 99%