• Journal of Satellite, Information and Communications
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• v.11 no.1
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• pp.55-57
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• 2016

In this paper, we present an algorithm to determine the abnormal behavior through a CCTV-based behavioral recognition and a pattern of hand using ConvexHull. In the existing way that using CCTV for crime prevention, facial recognition is mainly used. Facial recognition is the way that compares the faces that are seen on the screen and faces of criminals for determining how dangerous targets are, however, this way is hard to predict future criminal behavior. Therefore, to predict more various situations, abnormal behaviours are determined with targets' incline of arms, legs and bodys and patterns of hand movements. it can forecast crimes when an acting has been getting within common normality out, comparing whose acting patterns with the crime patterns.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1113-1122
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• 2020

Recently, as violent crimes of crime without reason (Korea : Do not ask), women and the elderly are getting serious. In the existing system, many CCTVs are installed, but it is difficult to prevent crime due to only follow-up measures after a crime occurs. This device prevents crime through this device for incidents in shaded areas and closed spaces such as apartments and buildings. To do this, we research this technology to develop products and software. It sends an alarm signal using communication technology to a specific place where you want to receive an event of an alarm or a CCTV device operated using image analysis big data technology and convergence sensor technology for a specific target of the behavior expected to be a crime or movement. Develop the device. This development device researches and develops this device and supplies low-cost devices to consumers, which is used as a device that predicts the occurrence of crime in advance, processes it as an alarm signal in real time, and transmits it, and constitutes a standalone device and a server. Will provide the device to be connected.

• Convergence Security Journal
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• v.24 no.2
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• pp.151-160
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• 2024

In recent years with high interest rates and inflations, which worsen people's lives, voice phishing crimes also increase along with damage. Voice phishing that becomes more evolved by technology developments causes serious financial and mental damage to victims. This work aims to study time series models for its accurate prediction. ARIMA, SARIMA and SARIMAX models are compared. As exogenous variables, the amount of damages and the numbers of arrests and criminals are adopted. Forecasting performances are evaluated. Prediction intervals are constructed along with empirical coverages, which justify the superiority of the model. Finally, the numbers of voice phishing up to December 2024 are predicted, through which we expect the establishment of future prevention strategies for voice phishing.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Korean Journal of Forensic Psychology
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• v.13 no.2
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• pp.99-119
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• 2022

Research on sex offense has shown that sex offenders are very heterogeneous. Sex offenders are heterogeneous in their probability of risk of recidivism. Some sex offenders are known to be much higher in their tendencies to reactivate than others. The study examined the predictive and explanatory power of static and dynamic risk factors in STATIC-99 and HAGSOR-Dynamic which have been used in Korean correctional facilities since 2014. STATIC-99 and HAGSOR-Dynamic showed moderate predictive accuracy for all crimes(AUC = .737, AUC = .597, respectively, ps < .001). However, when examining sex crime alone, only STATIC-99 predicted recidivism significantly(AUC = .743, p < .001). The incremental predictive power of HAGSOR-Dynamic was confirmed; the explanatory power of Model 2 comprising both static and dynamic risk factors were significant beyond Model 1 comprising only static factors(∆χ²= 12.721, p < .001), but this tendency was only applied to the model of all crimes. These findings were discussed with implications of practicing the sex offender assessment and treatment.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• Anatomy & Biological Anthropology
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• v.31 no.4
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• pp.121-131
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• 2018

Stature is one of the unique biological properties of a person, which can be used for identification of the individual. In this regard, statures are estimated for the unknown victims from crimes and disasters. However, the accuracy of estimates may be compromised by inappropriate methodologies and/or practices of stature estimation. Discussed in this study are the methodological issues related to the current practices of forensic anthropological stature estimation in Korea, followed by suggestions to enhance the accuracy of the stature estimates. Summaries of forensic anthropological examinations for 560 skeletal remains, which were conducted at the National Forensic Service (NFS), were reviewed. Mr. Yoo Byung-eun's case is utilized as an example of the NFS's practices. To estimate Mr. Yoo's stature, Trotter's (1970) femur equation was applied even though the fibula equation of a lower standard error was available. In his case report, the standard error associated with the equation (${\pm}3.8cm$) was interpreted as an 'error range', which gave a hasty impression that the prediction interval is that narrow. Also, stature shrinkage by aging was not considered, so the estimated stature in Mr. Yoo's case report should be regarded as his maximum living stature, rather than his stature-at-death. Lastly, applying Trotter's (1970) White female equations to Korean female remains is likely to underestimate their statures. The anatomical method will enhance the accuracy of stature estimates. However, in cases that the anatomical method is not feasible, the mathematical method based on Korean samples should be considered. Since 1980's, effort has been made to generate stature estimation equations using Korean samples. Applying the equations based on Korean samples to Korean skeletal remains will enhance the accuracy of the stature estimates, which will eventually increase the likelihood of successful identification of the unknowns.

• Korean Journal of Culture and Social Issue
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• v.29 no.4
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• pp.455-476
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• 2023

This study focused on the characteristics of motiveless crimes that mainly originated from interpersonal problems and were acts of revenge against innocent third parties. This study confirmed the relationship between the experience of social exclusion and displaced aggression and examined the relationship between the two variables. We sought to confirm the role of related factors such as stress and social support. For this purpose, we established and tested hypotheses about the mediatingon effect of stress and the moderated mediatingon effect of social support on the effect of social exclusion experience on displaced aggression among 353 adult males aged between 19 and 49 years. The main results are that, first, social exclusion had a positive effect on displaced aggression. Second, stress was found to partially mediate the relationship between social exclusion and displaced aggression. Third, the hypothesis that social support would moderate the mediating effect of stress was not provedvaild, but the conditional direct effect of social support was confirmed in the mediation model. In other words, social support did not affect the indirect effect mediated by stress, but appeared to moderate the direct effect between social exclusion and displaced aggression. Social exclusion's prediction of displaced aggression was significant only in the average social support group (mean) and the high group (M+1SD), and appeared to increase as the group increased. This means that in groups with high social support, displaced aggression is used as a stress control strategy, which is a different result from previous studies that found that social support plays a role in lowerings aggression. People with low levels of social support showed unexpected results in that they used displaced aggression less frequently despite their experiencinge of social exclusion. In the discussion, the social implications of these results were interpreted, and additional research ideas were proposed to specify the relationship between social exclusion and displaced aggression.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.