• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.187-198
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• 2019

Extensive literature exists on how to estimate optimal thresholds based on various accuracy measures using receiver operating characteristic (ROC) and cumulative accuracy profile (CAP) curves. This paper now proposes an alternative measure to represented the specific partial area under the ROC and CAP curves. The relationship between ROC and CAP functions is examined using differential equations of the new defined partial area under curves. In addition, the relationship with the optimal thresholds under conditions of various accuracy measures for the ROC and CAP functions is also derived. We assume there are two kinds of distribution functions composing the mixed distribution as various normal distributions before finding the optimal thresholds. Corresponding type 1 and 2 errors are also explored and discussed under various conditions for accuracy measures.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.4
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• pp.266-273
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• 2019

The efficiency and effectiveness of mission performance can be greatly changed according to the operating conditions such as the number of manned aircraft, flight altitude, and so on, in performing search and reconnaissance missions using a large number of small reconnaissance unmanned aerial vehicles. However, it is not easy to determine which operating conditions are most reasonable. Therefore, in this study, we developed an unmanned airplane flight simulation that can detect and identify the target while avoiding collision according to autonomous flight, suggesting a way to derive operating conditions when operating a large number of unmanned aerial vehicles.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.807-822
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• 2021

Various accuracy measures that can be explained on the odds curve are discussed, and an alternative accuracy measure, the maximum square, is proposed based on the characteristics of the odds curve. Thresholds corresponding to these accuracy measures are obtained by considering various probability distribution functions and an illustrative example. Their characteristics are discussed while comparing many kinds of statistics measuring thresholds. Therefore, we can conclude that optimal thresholds could be explored from the odds curve, similar to the ROC curve, and that the maximum square measure can be used as a good accuracy measure that can improve the performance of the binary classification model.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.461-467
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• 2020

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.275-281
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• 2022

Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.462-475
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• 2022

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.359-359
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• 2023

최근 기후변화로 인한 홍수 및 가뭄과 같은 자연재해가 증가함에 따라 이를 선제적으로 탐지 및 예방할 수 있는 해결책에 대한 필요성이 증가하고 있다. 이러한 수재해를 예방하기 위해서 하천, 저수지 등 가용수자원의 지속적인 모니터링은 필수적이다. SAR 위성 영상의 경우 주야간 및 기상상황에 상관없이 지속적인 수체 탐지가 가능하다. 일반적으로 SAR 기반 수체 탐지 시 송수신 방향이 동일한 편파(co-polarized) 영상을 사용한다. 하지만 co-polarized 영상의 경우 바람 및 강우에 민감하게 반응하여 수체 미탐지의 가능성이 존재한다. 한편 송수신 방향이 서로 다른 편파(cross-polarized) 영상은 강우 및 바람의 영향에 민감하지 않지만 식생에 민감하게 반응하여 수체의 오탐지율이 높다는 단점이 존재한다. 이에 SAR 영상의 편파 특성에 따라 수체 탐지의 정확도 차이가 발생하여 최적의 편파 영상 조합을 구성하는 것이 중요하다. 본 연구에서는 Sentinel-1 SAR 위성의 VV, VH, VV+VH 편파 영상과 머신러닝 알고리즘 중 하나인 SVM (support vector machine)을 활용하여 수체탐지를 수행하였다. 편파 영상 조합별 수체 탐지 결과의 검증을 위하여 혼동행렬 (confusion matrix) 기반 평가지수를 사용하였다. 각각의 수체탐지 결과의 비교 및 분석을 통하여 SAR 기반 수체 탐지를 위한 최적의 밴드 조합을 도출하였다. 본 연구결과를 바탕으로 차후 높은 시공간 해상도를 가진 SAR 영상의 활용이 가능하다면 수재해 및 수자원 관리의 효율성을 높일 수 있을 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.95-99
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• 2023

Recently Machine Learning has been considered as one of the active research areas of Computer Science. The various Artificial Intelligence techniques are used to solve the classification problems of environmental sciences, biological sciences, and medical sciences etc. Due to the heterogynous and malfunctioning weather sensors a considerable amount of noisy data with missing is generated, which is alarming situation for weather prediction stockholders. Filling of these missing values with proper method is really one of the significant problems. The data must be cleaned before applying prediction model to collect more precise & accurate results. In order to solve all above stated problems, this research proposes a novel weather forecasting system which consists upon two steps. The first step will prepare data by reducing the noise; whereas a decision model is constructed at second step using regression algorithm. The Confusion Matrix will be used to evaluation the proposed classifier.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.59-66
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• 2023

The Golden Cross is commonly seen as a buy signal in financial markets, but its reliability for predicting stock price movements is limited due to market volatility. This paper introduces a time-invariant approach that considers the Golden Cross as a singular event. Utilizing LSTM neural networks, we forecast significant stock price changes following a Golden Cross occurrence. By comparing our approach with traditional time series analysis and using a confusion matrix for classification, we demonstrate its effectiveness in predicting post-event stock price trends. To conclude, this study proposes a model with a precision of 83%. By utilizing the model, investors can alleviate potential losses, rather than making buy decisions under all circumstances following a Golden Cross event.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.57-66
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• 2023

Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.