Earticle

USNs, NFC, M2M에서 확장된 사물인터넷은 새로운 컨버전스 기술로써 주요 이슈가 되고 있다. 사물인터넷은 인간의 개입 없이 센싱 네트워크와 프로세싱 간에 공동으로 지능형 연결을 구축할 수 있는네트워킹으로 정의된다. 본 연구의 목적은 사물인터넷이 물류 산업에 기여하는 것과 미래 사회에 대한변화를 예측하는 것이다. 실제 세계 시장의 리더가 되기 위해서는 물류 산업의 사물인터넷 개발을 더많이 요구하고 새로운 서비스 창조와 차별화된 시장을 위한 전략이 필요하다. 그러므로 물류 산업의 세계 리더가 되기 위해서는 다른 것들 보다 사물인터넷 표준화, 사생활 보호, 보안문제 등이 중요하다는것을 알 수 있다. 따라서, 본 연구에서는 물류 산업의 사물인터넷 개발을 위해서 고객편의를 높이도록노력하고 소비자의 수요를 잘 고려하여 차별화된 사물인터넷 물류 서비스를 제공할 수 있게 하여야 한다는 것을 보여주고자 함이 본 논문의 목적이며, 이를 위해 국내외 사례를 분석하고자 한다.

IoT(Internet of Things) has become a major issue as new type of convergence technology, expending existing of USNs(Ubiquitous Sensor Networks), NFC(Near Field Communication), and M2M(Machine to Machine). The IoT technology defines as a networking for things, which can establish intelligent links collaboratively for sensing networking and processing between each other without human intervention. The purpose of this study is to investigate to forecast the future distribution changes and orientation of contribution of distribution industry on IoT and to provide the implication of distribution changes. To become a global market leader, IoT requires much more development of core technology of IoT for distribution industry, new service creation and try to use a market-based demand side strategy to create markets. So, to become a global leader in distribution industry, this study results show that first of all establishment of standardization of IoT, privacy safeguards, security issues, stability and value were more important than others. The research findings suggest that the development goals of IoT should strive to boost the creation of a global leader in distribution industry and convenience to consider consumers’ demands as the most important thing.

IT의 발전으로 기기 간 통신을 이용하는 M2M 시장이 급성장하고 있으며, 많은 기업들이 M2M 사업에 참여하고 있다. 본 논문에서는 텔레매틱스의 개념 및 차량 네트워크 보안의 취약성을 알아보았다. 차량 및 IT 기술 의 융합과 이동통신망 기술의 발전은 사용자에게 제공되는 서비스의 질은 향상 시켰지만, 이로 인한 보안 위협은 다양해졌다. 텔레매틱스 사업에서 이동통신사업자의 참여로 생성될 수 있는 새로운 비즈니스 모델을 제시하였으며, 이러한 환경에서 발생 될 수 있는 차량 이동통신망 보안 취약성을 분석하였다. 이 중 발생할 수 있는 취약성을 해결 하기 위한 방법으로 M2M 기기와 스마트폰 및 차량 상호 인증 기법을 제시하였다.

As the developing of the information technology, M2M market that is using communication between devices is growing rapidly and many companies are involved in M2M business. In this paper, the concept of telematics and vulnerabilities of vehicle network security are discussed. The convergence of vehicle and information technology, the development of mobile communication technology have improved quality of service that provided to user but as a result security threats has diverse. We proposed new business model that be occurred to the participation of mobile carriers in telematics business and we analyzed mobile radio communication network security vulnerabilities. We proposed smart phone and Vehicle authentication scheme with M2M device as a way to solve vulnerabilities.

정보통신기술 발전에 따라 많은 장치들간의 통신과 네트워킹의 수용이 이뤄지고 있다. 장치간의 통신을 위한 융합 사업이 빠르게 발전되어지고 있다. IT 융합 통신은 무선통신에서 차후 개척분야의 하나로 여겨지고 있다. 본 논문에서는 IT 융합 구조에서 M2M, 지능형 자동차, 스마트그리드, U-헬스케어에 대한 보안 위협요소를 분석하 였다. 임베디드 시스템 보안, 포렌식 보안, 사용자 인증과 키관리 기법에 대한 IT 융합 보안의 방향을 제안하였다.

As the developing of the information communication technology, more and more devices are with the capacity of communication and networking. The convergence businesses which communicate with the devices have been developing rapidly. The IT convergence communication is viewed as one of the next frontiers in wireless communications. In this paper, we analyze detailed security threats against M2M(Machine to Machine), intelligent vehicle, smart grid and u-Healthcare in IT convergence architecture. We proposed a direction of the IT convergence security that imbedded system security, forensic security, user authentication and key management scheme.

As we enter the digital age, new methods of personality testing-namely, machine learning-based personality assessment scales-are quickly gaining attraction. Because machine learning-based personality assessments are made based on algorithms that analyze digital footprints of people’s online behaviors, they are supposedly less prone to human biases or cognitive fallacies that are often cited as limitations of traditional personality tests. As a result, machine learning-based assessment tools are becoming increasingly popular in operational settings across the globe with the anticipation that they can effectively overcome the limitations of traditional personality testing. However, the provision of scientific evidence regarding the psychometric soundness and the fairness of machine learning-based assessment tools have lagged behind their use in practice. The current paper provides a brief review of empirical studies that have examined the validity of machine learning-based personality assessment, focusing primarily on social media text mining method. Based on this review, we offer some suggestions about future research directions, particularly regarding the important and immediate need to examine the machine learning-based personality assessment tools’ compliance with the practical and legal standards for use in practice (such as inter-algorithm reliability, test-retest reliability, and differential prediction across demographic groups). Additionally, we emphasize that the goal of machine learning-based personality assessment tools should not be to simply maximize the prediction of personality ratings. Rather, we should explore ways to use this new technology to further develop our fundamental understanding of human personality and to contribute to the development of personality theory.

Purpose: This study aimed to develop and compare machine learning models for predicting prediabetes in young adults in Korea using dietary intake data and to identify the most effective model. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey were used, with 823 participants aged 19-35 years selected after excluding those with missing data. Logistic regression, k-nearest neighbors, and random forest models were applied to predict prediabetes, and the analysis was conducted using the Orange 3.5 program. Five-fold cross-validation was performed to reduce performance variability, and test data were used for final model validation. Results: In the dataset, 14%-15% of participants were classified as having prediabetes. The random forest model showed the highest performance in terms of classification accuracy, harmonic mean of precision and recall, and precision. Logistic regression had the highest performance regarding the model’s ability to distinguish between individuals with and without prediabetes. Age, thiamine intake, and water intake emerged as the most important predictors. Conclusion: This study demonstrated the utility of using dietary intake data to predict prediabetes in young adults. The random forest model provided the highest prediction accuracy, supporting early detection and intervention, which could help to reduce unnecessary treatment. This highlights nurses’ important role in educating patients about lifestyle changes and implementing preventive care. Future studies should incorporate additional factors, such as psychological and lifestyle variables, to improve the model's performance.

Due to the complex functioning of Smart Healthcare Systems (SHS), many security concerns have been raised in the past. It provisions the attackers to hamper the working of SHS in a variety of ways, e.g., injection of false data to replace vital signs, tampering of medical devices to prevent informing critical situations, etc. In this work, a novel ML-based framework, i.e., SmartHealth is proposed to secure IoMT devices in SHS. SmartHealth watches the vital signs gathered through different IoMT to analyze the change in various body activities to differentiate between normal activities and dangerous security attacks. The performance of the SmartHealth is also analyzed for three different dangerous attacks. During performance analysis, it has been observed that SmartHealth can identify wicked activities in IoMT 92% times accurately with an F1-score of 90%.

Regression analysis is one of the most widely utilized methods because of its adaptability and simplicity. Recently, the machine learning (ML) approach, which is one aspect of regression methods, has been gaining attention from researchers, including social science, but there are only a few studies that compared the traditional approaches with the ML approach. This study was conducted to explore the usefulness of the ML approach by comparing the ordinary least square estimate (OLS), the stochastic gradient descent algorithm (SGD), and the support vector regression (SVR) with a model predicting and explaining the tuberculosis screening intention. The optimized models were evaluated by four aspects: computational speed, effect and importance of individual predictor, and model performance. The result demonstrated that each model yielded a similar direction of effect and importance in each predictor, and the SVR with the radial kernel had the finest model performance compared to its computational speed. Finally, this study discussed the usefulness and attentive points of the ML approach when a researcher utilizes it in the field of communication.

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.

Since the government issued ICT priority policy for the last decade, Vietnam was reported to have impressed development of ICT infrastructure and Internet users. Until May of 2015, Vietnam has 39.7 millions of Internet users and 31 millions of social network user accounts. Facebook is the dominant website with more than 22 million Vietnamese users and 70% of those accesses Facebook via mobile phone. Several companies have utilized Facebook as the most effective interaction channels. The increasing of big text data such as posts and comments on Facebook that embed customer opinions requires method to mine sentiment text in Vietnamese language. This research applies machine learning with several algorithms such Naive-Bayes, decision trees and Support Vector Machine (SVM) for Vietnamese text data collected from fast-food industry on Facebook. The experiment results show that machine learning methods are able to classify Vietnamese sentiment text with the accuracy over 70%. Thus we proposed several recommendations for mining Vietnamese social text data.

Unlike other games such as chess, draughts and backgammon, computers are currently quite weak at the game of go ( baduk). Brute force is difficult due to the higher branching factor and game length. Human made algorithms become very complex before even approaching human strength on a subproblem of the game. One possible approach to this challenging problem is to use machine learning to let the program learn and improve without increased human effort. Machine learning has been successful in other games (e.g. draughts, backgammon). In this paper we give an overview of existing techniques. We discuss different aspects of learning, and propose some directions of research. In particular we believe that a first order representation language combined with a multistrategy learning system can achieve much more than what currently exists.

A Chinese complement construction composed of a head and its following complement contains distinctive linguistic features, while most other languages like Korean, English, etc. have no such features. The general classification of a complement construction by Chinese linguists is helpful but not enough to be completely transferred to a target language such as Korean. In this paper, we propose an approach where complement constructions are analyzed and classified based on the contrastive and computational viewpoint of the target language in Chinese‐to‐Korean machine translation. Further, the translation ambiguities of a head‐ndcomplement dependent case, which is the most complex type of complement construction in terms of translation, are resolved by k‐means clustering using mutual information. Some experimental results of such resolved ambiguities are provided from random samples. The proposed method improved the accuracy of disambiguation by 17.3 % with respect to the baseline.

This study aims to explore the limitations of Neural Machine Translation (NMT) in processing spoken text. Following literature review, an analysis of government press briefing transcriptions that have been processed by Google Translate will be analyzed based on the framework of the features of spoken language. The concept of cognitive complements will be employed to attempt to explain any identified pattern of errors made by the NMT. Specifically, the role of cognitive complements that exist outside of the utterances will be discussed, drawing on the theories of Lederer(1989) and Gutt(2000) among other scholars. A sample text analysis of spoken text translation will be conducted to investigate whether this inherently human mechanism works in the latest neural based machine translation engine, ‘Google Translate’. Analysis will show that while Google Translate demonstrated quite impressively on some of the well-crafted segments that resemble written language, it performed poorly on segments laden with features of spoken language. The findings of the study suggest that the need for human interpreters’ in mediating highly professional inter-lingual communication will likely persist in the future. As long as artificial intelligence does not acquire the ability to infer meaning from the cognitive complements as human beings to, the application of automated inter-lingual interpreting devices may need to be limited to certain market segments such as for tourism, entertainment and socializing. leisure.

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.

Mental health problems leading to depression have become a critical concern due to the growing engagement of people on social media platforms. Several past approaches have been implemented by analyzing the pattern and behaviour of the posts by users on social networking sites. This research study proposed a system for predicting users who may be depressed, based on the characteristics of users who is already affected. A combination of both the tweet-level and the user-level architecture was used to generate a more robust and reliable system where semantic embeddings trained from advanced neural networks were adopted under the tweet-level. SVM with Word2Vec and TF-IDF has been used and yielded an accuracy of 98.14% and recall of 95.63%.