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Diabetes is a disease that has no permanent cure; hence early detection is required. Data mining, machine learning (ML) algorithms, and Neural Network (NN) methods are used in diabetes prediction in our research. We used the Pima Indian Diabetes (PID) dataset for our research, collected from the UCI Machine Learning Repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. We used seven ML algorithms on the dataset to predict diabetes. We found that the model with Logistic Regression (LR) and Support Vector Machine (SVM) works well on diabetes prediction. We built the NN model with a different hidden layer with various epochs and observed the NN with two hidden layers provided 88.6% accuracy.

Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. RR-interval signals known as heart rate variability (HRV) signals (derived from electrocardiogram (ECG) signals) can be effectively used for the non-invasive detection of diabetes. This research paper presents a methodology for classification of diabetic and normal HRV signals using deep learning architectures. We employ long short-term memory (LSTM), convolutional neural network (CNN) and its combinations for extracting complex temporal dynamic features of the input HRV data. These features are passed into support vector machine (SVM) for classification. We have obtained the performance improvement of 0.03% and 0.06% in CNN and CNN-LSTM architecture respectively compared to our earlier work without using SVM. The classification system proposed can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%.

In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method of multipliers (R-A-ADMM). We introduce learnable parameters to optimize the algorithm with given data sets. The derived algorithm is an accelerated version of LISTA-AT that controls the threshold for each entry according to the previously recovered estimate. Numerical results show that the proposed Accel-LISTA-AT algorithm converges much faster and recovers the sparse signals with lower mean squared errors than the other learning-based sparse recovery algorithms.

In heterogeneous landscapes, high-resolution land cover classification is vital for planning, ecological monitoring, and green infrastructure management. This study evaluates the performance of five machine learning algorithms: Decision Tree (DT), Naïve Bayes, Support Vector Machine (SVM), Random Tree (RT), and K-Nearest Neighbors (KNN), using UAV multispectral imagery and object-based image analysis (OBIA). Five scenarios were designed to compare algorithm accuracy. Additionally, a sixth scenario applied the best-performing algorithm to a feature subset selected through Recursive Feature Elimination (RFE), to examine the effect of feature optimization. RT achieved the highest overall classification accuracy (76.75%) and Kappa coefficient (0.7066), while SVM showed limited performance in complex environments. Height features contributed most to accuracy improvements, followed by spectral and geometric features. In class-specific analysis, the Naïve Bayes algorithm yielded the highest Producer’s Accuracy (90.86%) for forest-type land cover but had a lower User’s Accuracy (70.41%), indicating overclassification. In contrast, RT showed more balanced performance (PA = 87.09%, UA = 85.71%), suggesting greater reliability. The results demonstrate the benefits of integrating algorithm selection with feature optimization to improve classification accuracy in complex settings. This approach provides methodological insights for fine-scale mapping of vegetated areas and supports future applications in landscape monitoring and urban green space assessment.

According to new WHO statistics, heart disease is the top reason of death worldwide, killing 17.9 million people each year. This is a growing number. One of the most wellknown issues in clinical offices is that no two professionals have the same knowledge and talent when serving their patients. Researchers are utilizing data mining and machine learning techniques to overcome these difficulties by using predictive analytics to anticipate the risk of heart problems. This study examines the accuracy of various machine learning methods, including Logistic Regression, Naive Bayes, Decision Trees, Support Vector Machines, Neural Networks, and Stochastic Gradient Descent in the prediction of heart disease based on various factors and symptoms such as gender, age, chest pain, and blood sugar using appropriate data. The research entails applying a typical data mining approach to accurately uncover relationships between numerous data sources to predict heart disease. These machine learning algorithms take less time and are more accurate at predicting heart illness, which will lower the global convergence of essential life.

Asthma is an important global health problem affecting nearly 300 million individuals and responsible for 250,000 deaths each year. Asthma is defined by obstruction to the airways, and difficult testing of lung function via spirometry or body plethysmography require complete cooperation by patients in all populations, including the elderly and those who are otherwise ill. Knees even more problematic is that smoking tobacco or respiratory changes in patients with asthma are only largely detectable when it is too late, meaning their respiratory performance has been compromised.

간세포암 환자에서 TACE 치료 반응은 환자의 개별적인 특성에 따라 달라지므로, 치료 효과를 정확히 예측하는 것은 여전히 중요한 도전 과제로 남아 있다. 이에 본 연구에서는 다양한 머신러닝 알고리즘(Logistic Regression, Linear Support Vector Machine, Random Forest, XGBoost)을 활용하여 TACE 치료 반응을 예측하는 모델을 개발하고, 이들의 성능을 비교하여 가장 우수한 모델을 도출하고자 하였다. 나이, 성별, BMI, 간염 바이러스, 간경변증, 문맥 고혈 압, 생체표지자, CBCT, DAP을 독립변수로, 치료 완전반응을 종속변수로 설정하여 예측 모델을 개발하였다. 성능 평가 결과, 선형 모델 중에서는 Logistic Regression이 가장 높은 성능을 보였으며, 비선형 모델에서는 Random Forest가 재현율(82.12%)에서 높은 값을 나타냈다. 그러나 XGBoost는 정확도(78.57%), 정밀도(81.43%), F1 점수(81.71%)에서 더 높은 성능을 보여 종합적으로 가장 우수한 예측력을 보였다. 이러한 결과는 XGBoost 모델이 복잡한 임상 데이터를 효과적으로 처리할 수 있으며, 향후 TACE 치료 계획 수립에 유용하게 활용될 것으로 사료된다.

Treatment response to TACE (Transarterial chemoembolization) in patients with HCC (Hepatocellular carcinoma) varies depending on individual patient variables, making accurate prediction of treatment outcomes a significant challenge. Therefore, this study aimed to develop Prediction models for treatment response using various machine learning algorithms, including Logistic Regression, Linear Support Vector Machine, Random Forest, and XGBoost, and to compare their performance to identify the most effective model. The Prediction models were developed using independent variables such as age, sex, BMI (Body Mass Index), hepatitis virus, liver cirrhosis, portal hypertension, biomarker, CBCT (Cone Beam Computed Tomography), and DAP (Dose Area Product), with complete treatment response set as the dependent variable. Performance evaluation showed that Logistic Regression had the highest performance among linear models, while Random Forest demonstrated superior recall (82.12%) among nonlinear models. However, XGBoost outperformed other models in terms of accuracy (78.57%), precision (81.43%), and F1 score (81.71%), demonstrating the best overall predictive performance. These results suggest that the XGBoost model can effectively handle complex clinical data and may be useful in supporting future TACE treatment planning.

본 연구는 2023년 충청남도 홍성군 산불피해지 대상으로 Maximum Likelihood Classification(MLC), Random Forest(RF), Support Vector Machine(SVM), K-Nearest Neighbors(KNN) 이상 4가지 머신러닝 알고리 즘 모델의 산불피해 강도 분류 성능을 파악하기 위해 실시되었다. 검증데이터와 비교한 결과, 모델 총 성능은 Random Forest(RF)의 Overall Accuacy(OA)와 Cohen’s Kappa 계수가 97.2%와 0.95로 가장 높은 성능을 보였다. RF는 여러 개의 의사결정 나무들을 결합하여 최종 예측을 하므로 데이터의 불균형을 해소할 수 있다. 또한, 산불피해 강도에 따른 성능은 모든 머신러닝 알고리즘 모델에서 열해 피해지가 수관화 및 지표화 피해지에 비해 낮은 탐지 성능을 나타냈다. 이는 수관화ㆍ열해 피해지의 분광 반사율 패턴이 유사하고, 열해·지표화 피해지의 경우 복잡한 수관층 구조로 인해 탐지 성능이 낮은 것으로 판단된다. 이와 같은 머신러닝 알고리즘 모델을 통한 산불피해 강도별 면적을 산출은 신속한 복구계획 수립뿐만 아니라 산불로 인한 온실가스 배출량 산정에 기초자료를 제공할 수 있을 것이다.

This study aims to evaluate the wildfire severity classification performance of four machine learning algorithm models, Maximum Likelihood Classification(MLC), Random Forest(RF), Support Vector Machine(SVM) and K-Nearest Neighbors(KNN), in the wildfire areas of Hongseong-gun, Chungcheongnam-do, in 2023. Compared with the verification dataset, the overall performance of the models indicated that the Random Forest(RF) showed the highest accuracy. The highest performance of the Random Forest(RF) model can be explained by its ability to combine multiple decision trees for a final prediction. Furthermore, the performance based on wildfire severity showed that all machine learning algorithm models had lower detection performance in heat damage areas compared with crown and surface fire areas. Crown and heat damage areas have similar spectral reflectance patterns, and heat damage and surface fire areas are considered to have shown lower performance due to the complex canopy structure. Machine learning based burn severity estimation provide critical baselines for rapid recovery planning and greenhouse gas emissions calculations.

The knowledge consolidation model (KCM) combines the rules extracted using KBANN (NeuroRule), Frequency Matrix (which is similar to the Naïve Bayesian technique), and C5.0 algorithms. The KCM can effectively integrate multiple rule sets into one centralized knowledge base. The cumulative rules from other single models can improve overall performance as it can reduce error-term and increase R-square. The architecture of KCM also called an ensemble approach. The key idea in the KCM is to combine a number of classifiers such that the resulting combined system achieves higher classification accuracy and efficiency than the original single models (classifiers). The aim of KCM is to design a composite system that outperforms any individual classifier by pooling together the decisions of all classifiers. Compared to single models, this KCM is better in its performance as it consolidates knowledge from single models. In order to verify the feasibility and effectiveness of KCM, personal credit rating dataset provided by a local bank in Seoul, Republic of Korea is used in this study. The results from the tests show that the performance of KCM is superior to that of the other single models such as multiple discriminant analysis, logistic regression, frequency matrix, neural networks, decision trees, and NeuroRule. Moreover, our model is superior to a previous algorithm for the extraction of rules from general neural networks.

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R2 value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m3 lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

We developed a deep learning-based algorithm with plant fruit images to predict the quantitative traits, fruit size, and weight. Highbush blueberry was selected as a model plant because of its commercial importance. Mask R-CNN was adopted for a deep learning guidance model to predict fruits' width, length, and weight. The deep learning algorithm had a high performance on object detection and image segmentation with more than 90% accuracy and detection rate.

본 연구는 머신러닝을 이용해 이미지로그의 압축파쇄대 깊이를 검출하는 분류모델을 개발하여 전문가 분석효율을 높이는 것이 목적이다. 경주지역 내 시추공에서 취득된 약 1 km 이미지로그에 대해 케이싱 구간 및 결측치를 제거하고 이미지로그 자료간 해상도(수직 및 수평 해상도가 각각 0.01 m와 2.5°)를 일치시키는 전처리과정을 수행하였다. 이를 통해 얻어진 총 99,090개 자료를 8:2 비율로 학습과 테스트 자료로 설정한 후, 의사결정나무 기반의 랜덤포레스트와 XGBoost를 적용하였다. 모델평가는 테스트자료에 대한 혼동행렬의 false negative(FN)를 통해 이뤄졌으며, XGBoost 모델은 FN이 87개로 랜덤포레스트 모델의 FN 162개에 비해 향상된 예측성능을 보였다. 이렇게 개발된 모델은 전문가의 압축파쇄대 상세 검출을 도와 분석 효율성을 제고할 수 있을 것으로 기대된다.

This study developed a classification model for the detection of borehole breakout depths in image logs using machine learning algorithms. Approximately 1 km of image log data acquired in Gyeongju was preprocessed to make it suitable for applying machine learning algorithms. This included removing missing values and data within casing intervals and aligning the resolution among image log data ? that is, vertical and horizontal resolutions were set at 0.01 m and 2.5°, respectively. Decision tree-based algorithms, random forest, and XGBoost were then applied to the preprocessed 99,090 data entries, which was divided into training (80%) and testing (20%) groups. The trained models were evaluated based on the false negative (FN) of the confusion matrix. The XGBoost model showed a superior predictive performance with 87 FNs when compared to the random forest model’s 162 FNs. The developed model can enable experts to perform detailed reviews of detected depths via the model.

이 연구는 다양한 물리검층자료를 학습한 딥러닝 알고리듬을 이용하여 저류층의 수포화도를 예측하는 대리 모델을 구축한다. 딥러닝 알고리듬으로 계산한 수포화도 추정치를 Archie 방정식 결과와비교함으로써 개발 모델의 성능 평가를 수행하였다. 이 연구는 4가지 물리검층자료(밀도, 공극률, 비저항, 감마선)를 심층신경망의 입력인자로 활용하여 수포화도를 평가하였다, 심층신경망 기법으로는 장단기메모리학습법을 사용하였으며 전형적인 다층 인공신경망과의 비교를 통해 성능을확인하였다. 장단기메모리학습법을 기반으로 수포화도를 예측한 결과, 결정계수의 값이 0.7 이상으로 우수한 성능을 보이는 것으로 확인하였다. 모델의 민감도 분석으로는 시퀀스 조정, 유정 역할전환, k-폴드 교차검증을 시행하였다. 모델의 적용 가능성은 북해 Volve 유전과 베트남 해상 유전에적용하여 성능을 검증하였다.

This study develops a surrogate model to predict water saturation from well log data using neural-network-based deep learning algorithms. The model performance is evaluated by comparing the water saturation estimates obtained using deep learning algorithms and Archie’s law. The surrogate model evaluates the water saturation of a target reservoir using four well-log data types (density, porosity, resistivity, and gamma ray). Long Short-Term Memory (LSTM) is employed as the deep neural network algorithm, and its performance is compared with that of a multi-layer artificial neural network. Prediction via the LSTM based model showed outstanding results with the coefficient of determination above 0.7. Sensitivity analysis is conducted through sequence tuning, switching of well type, and k-fold cross-validation. The applicability of the model has been validated for the Volve oilfield in the North Sea and an offshore oilfield in Vietnam.

인식 대상 학습 모델이 분류되어 있지 않거나 명확하게 분류되지 않은 경우 어휘 인식을 결정하지 못하여 인 식률이 저하되며 학습 모델 분류 형태가 변경되거나 새로운 학습 모델이 추가되면 인식 모델의 결정 트리 구조가 변 경되어야 하는 구조적 문제가 발생한다. 이러한 문제점을 해결하기 위하여 학습 모델 분류를 위한 결정 트리 학습 알 고리즘을 제안한다. 음운 현상이 충분히 반영된 음성 데이터베이스를 구성하고 학습 효과를 확보하기 위하여 학습 모 델 분류를 위한 결정 트리 방법을 사용하였다. 본 연구에서는 실내 환경에 대하여 어휘 종속 인식과 어휘 독립 인식 실험을 수행한 결과 실내 환경의 어휘 종속 실험에서는 98.3%의 인식 성능을 보였고, 어휘 독립 실험에서 98.4%의 인식 성능을 보였다.

Target learning model is not recognized in this category or not classified clearly failed to determine if the vocabulary recognition is reduced. Form of classification learning model is changed or a new learning model is added to the recognition decision tree structure of the model should be changed to a structural problem. In order to solve these problems, a decision tree learning model for classification learning algorithm is proposed. Phonological phenomenon reflected sound enough to configure the database to ensure learning a decision tree learning model for classifying method was used. In this study, the indoor environment-dependent recognition and vocabulary words for the experimental results independent recognition vocabulary of the indoor environment-dependent recognition performance of 98.3% in the experiment showed, vocabulary independent recognition performance of 98.4% in the experiment shown.

In this research, we have explored state-of-the-art deep learning algorithms for face emotion recognition. In this regard, we utilize a transfer learning technique for recognizing seven classes of emotions using image classifiers such as MobileNetv2, GoogleNet, ResNet101, VGGNet19. In addition, we also compared our results with a specially designed deep learning model for face emotion recognition such as “Deep Emotion”. We have utilized the dataset FER2013 exploited by many researchers. We have analyzed the accuracy, time of training, complexity in terms of layers, and other parameters. After training on FER2013, we have deployed each model on a Korean dataset obtained from Korean dramas videos containing images of celebrities. Our ultimate goal was to label all unknown Korean dataset after training on FER2013. We have presented three different programs, i.e. (i) automatic labeling, (ii) transfer learning-based models for unknown images, (iii) deep-emotion model for face emotion recognition. We have presented a detailed analysis of all models and our corresponding programs.

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.