Earticle

Recently, the role of artificial intelligence techniques in games is becoming important. Game artificial intelligence technology in the past, the graphics and sound technology were more important. However, Artificial intelligent technology in current games is necessary technology to provide variety pleasure to users, and role of user's partner or helper. Learning ability of artificial intelligence technologies is attention getting technology. In this paper, we apply the learning system to the game agent to implement it, and a nalysis its performance.

Federated learning (FL) has emerged as a promising distributed machine learning technique. It has the potential to play a key role in future Internet of Things (IoT) networks by ensuring the security and privacy of user data combined with efficient utilization of communication resources. This paper addresses the challenge of maximizing energy efficiency in FL systems. We employed simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) techniques. Also, we jointly optimized power allocation and central processing unit (CPU) resource allocation to minimize latency-constrained energy consumption. We formulated an optimization problem using a Markov decision process (MDP) and utilized a deep deterministic policy gradient (DDPG) reinforcement learning algorithm to solve our MDP problem. We tested the proposed algorithm through extensive simulations and confirmed it converges in a stable manner and provides enhanced energy efficiency compared to conventional schemes.

Wireless sensor networks are susceptible to a variety of network attacks. Due to the limited energy of nodes and selfish nodes in the network, the packet delivery rate is lower. To address these issues, we innovatively propose an energy-harvesting Q-learning secure routing algorithm with authenticated-encryption. The algorithm uses physical unclonable functions and optimized Q-learning to ensure that the transmission path is reliable. Meanwhile, we combine the LSTM-based prediction model to predict the energy value that the nodes replenish. In addition, simulations are performed to compare the performances of the proposed algorithm with other algorithms under different attacks. The proposed algorithm has greater improvements in the packet delivery rate, filtering selfish nodes, and reducing node energy consumption.

This investigation explores the use of mixed-reality in collaborative diagnosis by sharing medical data in real-time between multiple physicians using Head-Mounted Display (HMD) devices. Object detection and alignment of the digitized data with the object are the backbone in any mixed-reality application. In this paper, deep-learning networks are used in detecting the patient’s face in the physical world and the medical data is aligned to the patient via the Region-Enhanced-Weight-and-Perturb Iterative-Closest-Point (RE-WAPICP) algorithm. Experiments were performed by sharing a 3D digital model of intracerebral vascular with multi-viewers in a mix-reality environment and the results show that this approach is feasible.

This study presents a systematic review of deep learning applications in dermatological diagnosis, analyzing 212 peer-reviewed studies published from 2019 to 2025. A structured search across major scientific databases identified methods, performance trends, and clinical relevance. Advanced architectures such as Vision Transformers, multimodal fusion models, and few-shot learning demonstrate substantial improvements in detecting both common and rare skin conditions. Despite these advances, challenges related to dataset imbalance, clinical integration, and fairness remain significant. The review outlines key research priorities to guide the development of equitable and clinically reliable AI-based dermatological solutions.

본 연구의 목적은 한국의료패널 2012년 자료를 이 용하여 고지혈증 유병에 영향을 미치는 변수를 확인 하고 이를 예측하는 분류모형을 개발하는데 있다. 분류모형에 투입되는 변수 선정을 위해 로지스틱 회 귀분석, 의사결정트리 c4.5, 유전자 알고리즘을 각 각 적용하여 선정하였다. 고지혈증 유병을 예측하기 위해 SVM과 meta learning 알고리즘을 이용하였다. 먼저 SVM의 경우 변수를 6개만 투입하였을 때 정확도가 가장 높았으 며, meta learning의 경우 메타분류기를 SVM으로 하 여 변수 6개를 투입한 경우가 가장 높았다. 본 연구는 기존 연구에서 많이 다루지 않은 고지 혈증을 예측하는 모형을 개발했다는 점과 여러 변수 기법을 적용하여 모델 정확도를 기여하였다. 그러나 메타러닝 성과가 크게 향상되지 않은 점은 본 연구 의 한계이자 추후 관련 연구에서 보완되어야 할 부분이다.

Climate change and invasive alien plant species (IAPs) are having a significant impact on mountain ecosystems. The combination of climate change and socio-economic development is exacerbating the invasion of IAPs, which are a major threat to biodiversity loss and ecosystem functioning. Species distribution modelling has become an important tool in predicting the invasion or suitability probability under climate change based on occurrence data and environmental variables. MaxEnt modelling was applied to predict the current suitable distribution of most noxious weed A. adenophora (Spreng) R. King and H. Robinson and analysed the changes in distribution with the use of current (year 2000) environmental variables and future (year 2050) climatic scenarios consisting of 3 representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5) in Bhutan. Species occurrence data was collected from the region of interest along the road side using GPS handset. The model performance of both current and future climatic scenario was moderate in performance with mean temperature of wettest quarter being the most important variable that contributed in model fit. The study shows that current climatic condition favours the A. adenophora for its invasion and RCP 2.6 climatic scenario would promote aggression of invasion as compared to RCP 4.5 and RCP 8.5 climatic scenarios. This can lead to characterization of the species as preferring moderate change in climatic conditions to be invasive, while extreme conditions can inhibit its invasiveness. This study can serve as reference point for the conservation and management strategies in control of this species and further research.

최근 인공지능의 딥러닝과 머신러닝을 이용한 예측시스템에 관한 연구가 활발히 진행되고 있다. 인공지능의 발전으로 인해 투자관리자의 역할을 인공지능을 대신하고 있으며, 투자관리자보다 높은 수익률로 인해 점차 인공지능 으로 거래를 하는 알고리즘 거래가 보편화하고 있다. 알고리즘 매매는 인간의 감정을 배제하고 조건에 따라 기계적으로 매매를 진행하기 때문에 장기적으로 접근했을 때 인간의 매매 수익률보다 높게 나온다. 인공지능의 딥러닝 기법은 과거 의 시계열 데이터를 학습하고 미래를 예측하여 인간처럼 학습하게 되고, 변화하는 전략에 대응할 수 있어 활용도가 증가하고 있다. 특히 LSTM기법은 과거의 데이터 일부를 기억하거나 잊어버리는 형태로 최근의 데이터의 비중으로 높 여 미래 예측에 사용하고 있다. 최근 facebook에서 개발한 인공지능 알고리즘인 fbprophet은 높은 예측 정확도를 자랑하며 주가나 암호화폐 시세 예측에 사용되고 있다. 따라서 본 연구는 fbprophet을 활용하여 실제 값과 차이를 분석하고 정확한 예측을 위한 조건들을 제시하여 암호화폐 자동매매를 하기 위한 새로운 알고리즘을 제공하여 건전한 투자 문화를 정착시키는 데 이바지하고자 한다.

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

In this study, AAHT was estimated using machine learning algorithms of ensemble techniques to improve the limitations of existing studies. Among the machine learning algorithms, a random forest algorithm was used, and the traffic volume estimation accuracy was analyzed as MAPE 20.7%. It was analyzed that the higher the traffic volume level, the higher the traffic volume estimation accuracy, and the traffic volume level of 3,000 vehicles/hour or more was analyzed to be 8% or less of MAPE. It was analyzed that the accuracy secured at the current level was very high compared to the existing model.

본 연구에서는 머신러닝 알고리즘을 적용한 모델 별 급성관상동맥증후군 진단 정확도를 비교 평가하고자 한다. 급성 관상동맥증후군 총 857명의 데이터를 학습데이터와 검증데이터로 7 : 3 비율로 구분하고 10 fold로 설정하여 학습에 진행하였다. 학습에 사용된 모델은 random forest, gradient boosting, ada boosting로 학습 후 정확도, AUC, recall, precision, F1 score, kappa로 평가하고, 최종 생성된 모델을 통해 AUC-ROC 곡선과 혼동행렬, 정확도를 비교 평가 하였다. 실험 결과 random forest 모델의 성능이 가장 우수 하였으며, AUC-ROC곡선과 혼동행렬 검증 결과 STEMI 예측성능은 gradient boosting, NSTEMI와 불안정성협심증은 random forest가 가장 예측을 잘 하였다. 검증데이터를 이용한 정확도는 random forest가 100%로 가장 우수한 결과를 도출하였다. 머신러닝 알고리즘을 적용 한 모델에 따른 급성관상동맥증후군 예측은 random forest가 가장 우수하였다. 본 연구결과를 통해 머신러닝 알고리즘 의 보건의료분야 적용에 대한 기초 및 근거 자료로 활용 될 수 있을 것으로 사료되며, 추후 앙상블 기법을 이용한 추가 연구를 통해 효율성을 높일 수 있을 것으로 사료된다.

In this study, we aimed to compare and evaluate the diagnostic accuracy of acute coronary syndrome for each model to which machine learning algorithm was applied. The data of a total of 857 patients with acute coronary syndrome were divided into train dataset and test dataset at a ratio of 7 : 3, and learning was performed by setting 10 folds. The model used for learning is evaluated by accuracy, AUC, recall, precision, F1 score, and kappa after training with random forest, gradient boosting, and ada boosting, and the AUC-ROC curve, confusion matrix, and accuracy are compared through the final model generated. evaluated. As a result of the experiment, the performance of the random forest model was the best, and as a result of the AUC-ROC curve and confusion matrix verification, the prediction performance of STEMI was gradient boosting, and the random forest predicted NSTEMI and unstable angina the best. As for the accuracy using verification data, the random forest produced the best results with 100%. The prediction of acute coronary syndrome according to the model applying the machine learning algorithm was the best in the random forest. It is believed that the results of this study can be used as basic and evidence data for the application of machine learning algorithms to the health care field, and further research using ensemble techniques can improve efficiency.

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.

Background/Objectives Deep learning which main technology in AI has high growth with being applied to field of speech recognition and Image classification. Especially, Deep learning technology in the field of Image classification is being applied as a core technology to Self-driving and crime prevention monitoring system that is recently emerging as the future industry. Methods/Statistical analysis: Various algorithm which is improved and developed CNN being able to do image process is suggested as Deep learning model in image recognition field. In this paper, we introduce various object detection algorithm including CNN. And explore most representative algorithms just R-CNN, Fast R-CNN, Faster R-CNN and difference between versions of YOLO devised to detect and track in real time. Findings: This paper evaluates deep learning algorithm’s performance by comparative analysis about mAP (mean average precision) and FPS (frames per second). In result of performance evaluation, YOLO algorithm is confirmed as that It shows excellent result in speed that detects and recognizes object and accuracy in real time system environment. Finally, we search cases in field of autonomous driving and access control system and home anti-crime system. Improvements/Applications: In this research, we can understand object detection algorithm among speech recognition technologies and proper field in each algorithm, apply security service based on image, recommend proper algorithm in various environment just like autonomous driving and security work, etc.

The duration of an inpatient stay affects hospital administration and improves hospital effectiveness in terms of controlling expenses and raising patient standards. It also assists in identifying the correlations among illnesses requiring hospitalization. For our study, we took 24,150 records from of the Open Data database pertaining to inpatient admissions in 2023. We used a number of methods, including Neural Networks, Deep Learning, Linear Regression, and Support Vector Machines, to predict the Length of Stay (LOS). We converted the data to numerical form for predictive purposes, dividing the dataset into 70% for training and 30% for testing. We assessed the model's performance using Root Mean Squared Error (RMSE) and split the forecast into four LOS categories: 0-2, 3-4, 5-7, and 8 days or more. The study also employed the Apriori algorithm to identify illness association rules that could impact LOS estimates. The results showed that identifying illness correlations is one element that might aid in enhancing the capacity to predict LOS.

이 연구는 정확도 높은 유·가스 저류층 특성화를 위하여 원시 물리검층 자료로부터 암석 코어자료와 부합하는 고해상도의 합성 물리검층 자료를 생성하는 딥러닝 기반 방법을 제안한다. 제안한 방법의 신뢰도는 노르웨이 Volve 유전을 대상으로 세 가지 유형의 딥러닝 알고리듬(심층신경망, 합성곱신경망, 장단기메모리)을 적용하여 평가하였다. 위 알고리듬들은 타 물리검층 자료로부터 음파 검층 자료를 고해상도로 추정하였다. 전반적으로 각 딥러닝 알고리듬의 성능이 우수하였다. 특히, 장단기메모리 알고리듬의 예측 성능은 원시해상도 대비 2배, 5배, 10배의 고해상도 자료를 생성한 경우에 대해 결정계수가 0.9 이상으로 우수하게 나타났다. 제안한 모델은 향후 코어 기반 저류층인자와 부합하는 물리검층 기반 저류층 인자의 도출에 활용할 수 있을 것으로 기대한다.

This study proposed a deep-learning-based approach that generates synthetic high-resolution log data from original-resolution log data for accurate reservoir characterization, where the resolution of the synthetic data is comparable to that of core data. The reliability of the proposed approach was tested with application to the Volve oil field in Norway using three deep-learning algorithms (i.e., deep neural network, convolutional neural network, and long short-term memory). These deep-learning algorithms were employed to generate high-resolution sonic log data from other log-type data. The overall performance of each algorithm was acceptable. In particular, the long short-term memory algorithm yields a coefficient of determination greater than 0.9 when the high-to-original-resolution ratios are two, five, and ten. We anticipate that the proposed model can be used to derive logging-based reservoir parameters with a resolution that is comparable to that of core-based reservoir parameters.

본 연구에서는 청소년 비행이 지속적인 사회문제로 대두됨에 따라 청소년의 온․오프라인 비행을 예측하는 주요 요인들을 탐색하고 전통적 비행이론(사회학습이론, 일반긴장이론, 사회통제이론, 일상활동이론, 낙인이론)의 적용 가능성을 살펴보았다. 분석에 활용된 데이터는 한국아동․청소년패널조사 2010(KCYPS 2010)의 초1, 초4, 중1 패널 6차년도 데이터이다(N=4,137). 예측 모형을 구축함에 있어 전통적 통계기반의 회귀모형 대신 랜덤포레스트 머신러닝 기법을 활용함으로써 예측 성능 향상과 더불어 보다 많은 예측요인의 고려 가능성에 초점을 두었다. 랜덤포레스트 분석 결과, 청소년의 온․오프라인 비행을 설명하는 데에 전통적인 비행이론은 여전히 유효하였으며, 온라인 비행은 주로 개인적 요인(일상활동이론, 낙인이론)과, 오프라인 비행은 사회적 요인(사회학습이론, 사회통제이론)과 관련이 있는 것으로 나타났다. 또한 일반긴장이론은 온라인 비행과 오프라인 비행 모두를 예측하는 중요한 이론적 기반임을 확인할 수 있었다. 본 연구는 머신러닝 기법을 통해 청소년 비행에 영향을 주는 주요 요인을 도출하고, 기존 비행이론의 활용 가능성도 함께 고려했다는 점에서 의의가 있으며, 청소년 온․오프라인비행에 대한 예방 및 개입 방향성을 재고하는 기반을 제공할 것이라 기대된다.

Adolescent delinquency is a substantial social problem that occurs in both offline and online domains. The current study utilized random forest algorithms to identify predictors of adolescents’ online and offline delinquency. Further, we explored the applicability of classic delinquency theories (social learning, strain, social control, routine activities, and labeling theory). We used the first-grade and fourth-grade elementary school panels as well as the first-grade middle school panel (N=4,137) among the sixth wave of the nationally-representative Korean Children and Youth Panel Survey 2010 for analysis. Random forest algorithms were used instead of the conventional regression analysis to improve the predictive performance of the model and possibly consider many predictors in the model. Random forest algorithm results showed that classic delinquency theories designed to explain offline delinquency were also applicable to online delinquency. Specifically, salient predictors of online delinquency were closely related to individual factors(routine activities and labeling theory). Social factors(social control and social learning theory) were particularly important for understanding offline delinquency. General strain theory was the commonly important theoretical framework that predicted both offline and online delinquency. Findings may provide evidence for more tailored prevention and intervention strategies against offline and online adolescent delinquency.

본 논문에서는 기계학습과 관련된 다양한 사례들에 대한 연구를 바탕으로 기계학습 응용 및 학습 알고리즘의 성능 개선 방안을 제시한다. 이를 위해 기계학습 기법을 적용하여 결과를 얻어낸 문헌을 자료로 수집하고 학문 분야로 나누어 각 분야에서 적합한 기계학습 기법을 선택 및 추천하였다. 공학에서는 SVM, 의학에서는 의사결정나무, 그 외 분야에서는 SVM이 빈번한 이용 사례와 분류/예측의 측면에서 그 효용성을 보였다. 기계학습의 적용 사례 분석을 통해 응용 방안의 일반적 특성화를 꾀할 수 있었다. 적용 단계는 크게 3단계로 이루어진다. 첫째, 데이터 수집, 둘째, 알고리즘을 통한 데이터 학습, 셋째, 알고리즘에 대한 유의미성 테스트 이며, 각 단계에서의 알고리즘의 결합을 통해 성능을 향상시킨다. 성능 개선 및 향상의 방법은 다중 기계학습 구조 모델링과 +α 기계학습 구조 모델링 등으로 분류한다.

This paper aims to present the way to bring about significant results through performance improvement of learning algorithm in the research applying to machine learning. Research papers showing the results from machine learning methods were collected as data for this case study. In addition, suitable machine learning methods for each field were selected and suggested in this paper. As a result, SVM for engineering, decision-making tree algorithm for medical science, and SVM for other fields showed their efficiency in terms of their frequent use cases and classification/prediction. By analyzing cases of machine learning application, general characterization of application plans is drawn. Machine learning application has three steps: (1) data collection; (2) data learning through algorithm; and (3) significance test on algorithm. Performance is improved in each step by combining algorithm. Ways of performance improvement are classified as multiple machine learning structure modeling, +α machine learning structure modeling, and so forth.

메타학습 알고리즘인 Adaboost는 단순한 분류기(classifier)인 Haar-like feature 필터와 결 합하여 빠른 속도와 높은 정확도를 나타내기 때문에 많은 객체 검출에 분야에 사용되고 있으며 그 중 특히 얼굴 인식 분야에서 뛰어난 성 능을 나타내고 있다. 그러나 Adaboost 알고리즘은 얼굴인식 시 정면 얼굴과 같은 단일 모델 검출에는 매우 효율적 이지만 측면 얼굴과 정면 얼굴이 혼합된 모델에서의 검출 시 성능이 저하되는 문제점을 가 지고 있다. 이 문제를 해 결하기 위해 본 논문에서는 혼합모델의 검출 성능을 높이기 위해 인스턴스 정보를 저장하여 학습하는 알고리즘인 IBL(Instance Based Learning)를 Haar-like feature 필터와 결합한 알고리즘을 제안한다.

Adaboost is a meta learning algorithm and it is used many area of object detection. In the field of face detection it is used in conjunction with Haar-like feature filtering because of its fast speed and high accuracy. Adaboost shows very effective performance for face detection in single distribution model such as front face but it shows reduced performance on multiple distribution models such as mixture of front face and side face. As a result of reduced performance this paper proposed to improve performance of multiple distribution models using IBL (Instance Based Learning) algorithm combined with the Haar-like feature.