• Journal of Information Processing Systems
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• 제13권4호
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• pp.893-913
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• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• 의정연구
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• 제23권2호
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• pp.109-146
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• 2017

이 연구의 목적은 2016년-2017년 박근혜 대통령 퇴진 촛불집회 참여의 결정요인을 분석하는 것이다. 구체적으로 사회경제적 요인, 정치적 정향, 감정적 요인, 정치적 태도 및 행동방식이 촛불집회 참여에 미친 영향력을 규명하였다. 이러한 작업은 2008년 촛불집회 참여에 영향을 미쳤던 주요한 변수의 유효성과 비제도적 정치참여의 한 유형인 항의집회 참여에 영향을 미치는 일반적 요인의 유효성을 검증하는 과정을 통해서 수행되었다. 자료수집은 촛불집회의 참여자(2016년 10월 29일-2017년 3월 11일, 20차 촛불집회)와 비참여자를 대상으로 온라인 설문조사를 통해 이루어졌고, 참여자의 경우 참여 횟수를 포함하였다. 분석 결과, 성별(남자), 진보 이념, 진보 정당 지지,'국정농단'에 대한 분노, 대통령 직무수행에 대한 불만, 정치적 결사체 참여 활동, 비투표적정치참여 활동, TV와 종이신문 이용의 영향력이 확인되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3962-3980
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• 2019

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to $[1,1,{\cdots},1]^T$. The solutions of all the mapping matrices are integrated into an overall objective function, which uses ${\ell}_{2,1}$-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.

• 대한원격탐사학회지
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• 제35권1호
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• ETRI Journal
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• 제41권4호
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• 전기전자학회논문지
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• 제26권2호
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• pp.169-175
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• 2022

본 논문에서는 k-최근접 이웃 (KNN) 알고리즘을 이용하여 초음파 센서 간 간섭을 줄이고 정확한 거리값을 예측하는 기법을 제안한다. 기존 기법에서는 이전 측정값과 현재 측정값을 비교하여 그 차이가 한계값을 벗어나면 간섭 신호로 인식하고 배제하지만 부정확한 예측이 자주 발생한다. KNN 알고리즘은 다수의 초음파 센서에서 입력되는 측정값을 분류하여 정확도 높은 예측이 가능하다. 간섭이 잘 발생하는 환경을 만들기 위해 다수의 동종 초음파 센서로 간섭 신호를 발생시킨 상태에서 거리 측정 실험을 진행하였고, 간섭으로 인해 발생하는 오류를 KNN 알고리즘을 통해 크게 줄일 수 있음을 확인하였다. 또한 기존 보팅 기법과 제안하는 기법의 결과를 비교하여 제안하는 기법의 성능이 우수한 것을 확인하였다.

• 한국IT서비스학회지
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• 제21권4호
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• pp.75-90
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• 2022

From the past to the present, reviews have had much influence on consumers' purchasing decisions. Companies are making various efforts, such as introducing a review incentive system to increase the number of reviews. Recently, as various types of reviews can be left, reviews have begun to be recognized as interesting new content. This way, reviews have become essential in creating loyal customers. Therefore, research and utilization of reviews are being actively conducted. Some studies analyze reviews to discover customers' needs, studies that upgrade recommendation systems using reviews, and studies that analyze consumers' emotions and attitudes through reviews. However, research that predicts the future using reviews is insufficient. This study used a dataset consisting of two reviews written in pairs with differences in usage periods. In this study, the direction of consumer product evaluation is predicted using KoBERT, which shows excellent performance in Text Deep Learning. We used 7,233 reviews collected to demonstrate the excellence of the proposed model. As a result, the proposed model using the review text and the star rating showed excellent performance compared to the baseline that follows the majority voting.

• 한국콘텐츠학회논문지
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• 제22권10호
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• pp.563-574
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• 2022

본 연구는 정치정보원이 다양화되면서 그동안 설득커뮤니케이션 영역에서 주목받지 못했던 정보원의 속성을 정치 콘텐츠를 노출시키는 정치 유튜버에 적용해 그 영향력을 분석하였다. 이를 위해 전국의 20세 이상 성인남녀 326명을 대상으로 국내 여론조사 기관을 통해 온라인 설문조사를 실시했다. 연구결과 정치 유튜버의 유사성, 카리스마, 전문성의 순으로 해당 콘텐츠에 대한 시청자의 태도에 긍정적인 영향을 끼쳤다. 콘텐츠를 통해 노출되는 정치인/정당에 대한 태도에는 정치 유튜버의 친숙성, 카리스마, 유사성, 매력성, 신뢰성 순으로 긍정적인 영향을 미쳤다. 유튜브를 통해 노출되는 정치콘텐츠의 경우 콘텐츠에 대한 태도와 그 콘텐츠에 등장하는 정치인/정당에 대한 태도에 영향을 미치는 속성이 다르게 나타났으며, 콘텐츠에 대한 태도는 정치인/정당에 대한 태도와 이들에 대한 지지에 긍정적인 영향을 미쳤다.

• The Journal of Asian Finance, Economics and Business
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• 제9권8호
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• pp.89-97
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• 2022

The e-commerce market faces significant credit risks due to the complexity of the industry and information asymmetries. Therefore, credit risk has started to stymie the growth of e-commerce. However, there is no reliable system for evaluating the creditworthiness of e-commerce companies. Therefore, this paper constructs a credit risk evaluation index system that comprehensively considers the online and offline behavior of online retail enterprises, including 15 indicators that reflect online credit risk and 15 indicators that reflect offline credit risk. This paper establishes an integration method based on a fuzzy integral support vector machine, which takes the factor analysis results of the credit risk evaluation index system of online retail enterprises as the input and the credit risk evaluation results of online retail enterprises as the output. The classification results of each sub-classifier and the importance of each sub-classifier decision to the final decision have been taken into account in this method. Select the sample data of 1500 online retail loan customers from a bank to test the model. The empirical results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy, which provides a basis for banks to establish a reliable evaluation system.