• 한국인공지능학회지
• /
• 제10권1호
• /
• pp.21-25
• /
• 2022

Korea has relatively less crime than other countries. However, the crime rate is steadily increasing. Many people think the crime rate is decreasing, but the crime arrest rate has increased. The goal is to check the relationship between CCTV and the crime rate as a way to lower the crime rate, and to identify the correlation between areas without CCTV and areas without CCTV. If you see a crime that can happen at any time, I think you should use a random forest algorithm. We also plan to use machine learning random forest algorithms to reduce the risk of overfitting, reduce the required training time, and verify high-level accuracy. The goal is to identify the relationship between CCTV and crime occurrence by creating a crime prevention algorithm using machine learning random forest techniques. Assuming that no crime occurs without CCTV, it compares the crime rate between the areas where the most crimes occur and the areas where there are no crimes, and predicts areas where there are many crimes. The impact of CCTV on crime prevention and arrest can be interpreted as a comprehensive effect in part, and the purpose isto identify areas and frequency of frequent crimes by comparing the time and time without CCTV.

• International journal of advanced smart convergence
• /
• 제13권2호
• /
• pp.61-68
• /
• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• 대한원격탐사학회지
• /
• 제37권6_3호
• /
• pp.1965-1974
• /
• 2021

위성 원격탐사 기법은 산림 모니터링에 적극적으로 활용될 수 있으며 우리나라 독자 운영 위성인 다목적실용위성을 활용하였을 때 특히 의미 깊다. 최근 들어 위성 원격탐사 자료에 머신러닝 기법을 적용함으로써 산림 모니터링을 수행하는 연구가 다수 이루어지고 있다. 머신러닝 기법을 통하여 제작된 산림모니터링 정보는 기존 산림 모니터링 방법의 효율성을 향상시키는 데에 활용할 수 있다. 머신러닝 기법의 경우 관심 지역과 활용 데이터의 특징에 따라 분류 정확도가 크게 달라지므로 다양한 모델을 적용함으로써 가장 효과적인 분류 결과를 도출하는 것이 매우 중요하다. 본 연구에서는 우리나라 삼척 지역에 대해 심층신경망을 적용함으로써 인공림과 자연림의 분류 성능을 확인하였다. 그 결과 픽셀 정확도가 약 0.857, F1 Score가 자연림과 인공림에 대해 각각 약 0.917과 0.433로 확인되었다. F1 score를 보았을 때 인공림의 분류 성능이 절대적으로는 낮은 수준을 나타냈다. 하지만 기존의 인공림과 자연림 분류 성능에 대해 F1 score를 기준으로 약 0.06, 그리고 0.10 향상된 성능을 확인할 수 있었다. 이러한 결과를 바탕으로 볼 때에 합성곱신경망 기반의 추가적인 모델을 적용함으로써 보다 적절한 모델을 분석할 필요가 있다.

• 한국산림과학회지
• /
• 제111권4호
• /
• pp.583-593
• /
• 2022

우리나라의 임목수확작업은 작업자의 고령화 및 고임금의 문제에 직면하고 있으며, 위기 개선을 위해 작업체계 개선 및 고성능 임업기계를 이용한 안정성 확보 등이 필요하다. 따라서 본 연구는 경사 35% 이상의 지역에서 타워야더와 프로세서를 기반으로 한 목재수확시스템을 적용할 때 다공정작업과 단공정작업의 생산성을 비교하며, 작업시스템의 비용절감 효과를 분석하고자 하였다. 국립산림과학원 광릉시험림 15임반 모두베기 지역을 대상으로 전목(가선)집재작업을 실시하였으며, 다공정과 단공정시스템의 생산성 및 비용을 분석하였다. 다목적집재장비를 이용한 단공정시스템은 타워야더와 프로세서를 이용한 다공정시스템에 비해 cycle당 집재본수가 1본/cycle 더 많아 집재작업 생산성은 약 1.5 m³/PMH (Productive Machine Hour; PMH)이, 조재작업은 약 1.6 m³/PMH이 더 높게 산출되었다. 다목적집재장비를 이용한 단공정(36,113원/m³)시스템은 다공정작업(41,065원/m³)시스템 보다 약 12.1%의 비용이 절감되었다. 또한 유휴시간(Idle time) 감소에 따라 단공정 및 다공정시스템 비용은 각각 최대 22.6%와 15.9%가 절감되었다.

• International Journal of Computer Science & Network Security
• /
• 제21권11호
• /
• pp.294-300
• /
• 2021

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.

• International Journal of Internet, Broadcasting and Communication
• /
• 제14권3호
• /
• pp.285-291
• /
• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• Journal of Korea Artificial Intelligence Association
• /
• 제1권1호
• /
• pp.1-6
• /
• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• 한국농림기상학회:학술대회논문집
• /
• 한국농림기상학회 2019년도 하계 학술발표초록집
• /
• pp.292-292
• /
• 2019

• 대한원격탐사학회지
• /
• 제39권5_3호
• /
• pp.891-907
• /
• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• 한국수자원학회논문집
• /
• 제56권9호
• /
• pp.563-574
• /
• 2023

최근 기후변화로 인해 유역의 기상자료에 대한 반응이 달라지고 있어 강우-유출 모의에 대한 연구는 중요해지고 있다. 아울러 최근 기계학습 기법에 대한 높은 관심으로 이를 통한 강우-유출 모의 역시 활발하게 증가하고 있으나 기계학습 모형이 전통적으로 사용되어온 개념적 모형에 비해 활용성이 높은지는 아직 확실치 않다. 본 연구에서는 개념적 모형인 GR6J와 기계학습 모형인 Random Forest 성능을 한국 전역의 38개 계측 유역에 대해 계측 유역 예측기법과 미계측 유역 예측기법을 이용해 평가하였다. 먼저 계측 유역 적용기법 평가를 위해 각 모형을 관측 일 유량자료에 학습시키고 분리된 평가기간에 대한 모의성능을 비교하였다. 이후 미계측 유역 모의성능 평가를 위해 인접성 기반 지역화 방법을 Leave-One-Out Cross-Validation (LOOCV)을 이용해 평가하였다. 그 결과 계측 유역 평가에서는 Random Forest 기법이 GR6J 모형보다 일관되게 높은 성능을 보였다. 학습된 데이터를 출력 값으로 재생산하도록 구조화되어 있는 기계학습 기법이 개념적 이론을 통한 모형보다 높은 재현성을 갖기 때문으로 판단된다. 하지만 Random Forest 모형의 성능은 미계측 유역의 예측기법으로는 재현되지 않았고 GR6J 모형보다 성능이 더 낮은 것이 확인되었다. 본 연구는 기계학습 모형은 계측 유역의 유출예측에는 적용성이 높을 수 있으나 미계측 유역에 대한 적용가능성은 전통적인 개념적 모형보다 낮을 수 있음을 제시한다.