• Journal of KIISE:Databases
• /
• v.37 no.5
• /
• pp.258-266
• /
• 2010

In this paper, we introduce a novel method to predict next update of blogs. The number of RSS feeds registered on meta-blogs is on the order of several million. Checking for updates is very time consuming and imposes a heavy burden on network resources. Since blog search engine has limited resources, there is a fix number of blogs that it can visit on a day. Nevertheless we need to maximize chances of getting new data, and the proposed method which predicts update probability on blogs could bring better chances for it. Also this work is important to avoid distributed denial-of-service attack for the owners of blogs. Furthermore, for the internet as whole this work is important, too, because our approach could minimize traffic. In this study, we assumed that there is a specific pattern to when a blogger is actively posting, in terms of days of the week and, more specifically, hours of the day. We analyzed 15,119 blogs to determine a blogger's posting preference. This paper proposes a method to predict the update probability based on a blogger's posting history and preferred days of the week. We applied proposed method to 12,115 blogs to check the precision of our predictions. The evaluation shows that the model has a precision of 0.5 for over 93.06% of the blogs examined.

• Applied Chemistry for Engineering
• /
• v.18 no.6
• /
• pp.643-649
• /
• 2007

Lipase-catalyzed esterification of structural butanol isomers and n-butyric acid was investigated in supercritical carbon dioxide. The experiments were performed in a high pressure cell for 5 hrs with a stirring rate of 150 rpm at 323.15 K and 130 bar. The Candida Antarctica lipase B (CALB) was used in whole system as a catalyst. The experimental results were analyzed by GC-FID using a INNOWax capillary column. The conversion yield and the tendency of the esterification in supercritical carbon dioxide were compared with estimated results by molecular dynamics simulation. Based on the Ping-Pong Bi-Bi mechanism with competitive inhibition, each step of the reaction was optimized; using this result the transition state was predicted. Conformational preference of isomers was also analyzed using molecular dynamics simulations. This kind of approach will be further extended to the prediction of enzyme-catalyzed reactions using computers.

• Journal of KIISE:Software and Applications
• /
• v.31 no.4
• /
• pp.467-477
• /
• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Ecology and Resilient Infrastructure
• /
• v.10 no.1
• /
• pp.11-19
• /
• 2023

Alien species are known to threaten regional biodiversity globally, which has increased global interest regarding introduction of alien species. The Ministry of Environment of Korea designated species that have not yet been introduced into the country with potential threat as alert alien species to prevent damage to the ecosystem. In this study, potential habitats of Esox lucius and Maccullochella peelii, which are predatory and designated as alert alien fish, were predicted on a national basis. Habitat suitability was evaluated using EHSM (Ecological Habitat Suitability Model), and water temperature data were input to calculate Physiological Habitat Suitability (PHS). The prediction results have shown that PHS of the two fishes were mainly controlled by heat or cold stress, which resulted in biased habitat distribution. E. lucius was predicted to prefer the basins at high latitudes (Han and Geum River), while M. peelii preferred metropolitan areas. Through these differences, it was expected that the invasion pattern of each alien fish can be different due to thermal preference. Further studies are required to enhance the model's predictive power, and future predictions under climate change scenarios are required to aid establishing sustainable management plans.

• Tuberculosis and Respiratory Diseases
• /
• v.86 no.4
• /
• pp.284-293
• /
• 2023

Background: Attitudes towards smoking, lung cancer screening, and perceived risk of lung cancer have not been widely studied in Malaysia. The primary objective of this study was to describe the factors affecting the willingness of high-risk current smokers and ex-smokers to undergo low-dose computed tomography (LDCT) screening for lung cancer. Methods: A prospective, cross-sectional questionnaire study was conducted in current smokers or ex-smokers aged between 55 and 80 years at three hospitals in Kota Kinabalu, Sabah, Malaysia. The questionnaire recorded the following parameters: perceived lung cancer risk; Prostate Lung Colon Ovarian Cancer 2012 risk prediction model excluding race and ethnicity predictor (PLCO_m2012norace); demographic characteristics; psychosocial characteristics; and attitudes towards lung cancer and lung cancer screening. Results: A vast majority of the 95 respondents (94.7%) indicated their willingness to undergo screening. Stigma of lung cancer, low levels of knowledge about lung cancer symptoms, concerns about financial constraints, and a preference for traditional medication were still prevalent among the respondents, and they may represent potential barriers to lung cancer screening uptake. A desire to have an early diagnosis (odds ratio [OR], 11.33; 95% confidence interval [CI], 1.53 to 84.05; p=0.02), perceived time constraints (OR, 3.94; 95% CI, 1.32 to 11.73; p=0.01), and proximity of LDCT screening facilities (OR, 14.33; 95% CI, 1.84 to 111.4; p=0.01) had significantly higher odds of willingness to undergo screening. Conclusion: Although high-risk current smokers and ex-smokers are likely to undergo screening for lung cancer, several psychosocial barriers persist. The results of this study may guide the policymakers and clinicians regarding the need to improve lung cancer awareness in our population.

• Journal of Science Education
• /
• v.33 no.1
• /
• pp.12-30
• /
• 2009

The purpose of this study was to analyze recognition characteristics of science gifted students on the earth system based on their thinking style. The subjects were 24 science gifted students at the Science Institute for Gifted Students of a university located in metropolitan city in Korea. The students' thinking styles were firstly examined on the basis of the Sternberg's theory of mental self-government. And then, the students were divided into two groups: Type I group(legislative, judicial, global, liberal) and Type II group(executive, local, conservative) based on Sternberg's theory. Data was collected from three different type of questionnaires(A, B, C types), interview, word association method, drawing analyses, concept map, hidden dimension inventory, and in-depth interviews. The findings of analysis indicated that their thinking styles were characterized by 'Legislative', 'Executive', 'Anarchic', 'Global', 'External', 'Liberal' styles. Their preference were conducting new projects and using creative problem solving processes. The results of students' recognition characteristics on earth system were as follows: First, though the two groups' quantitative value on 'System Understanding' was very similar, there were considerable distinctions in details. Second, 'Understanding the Relationship in the System' was closely connected to thinking styles. Type I group was more advantageous with multiple, dynamic, and recursive approach. Third, in the relation to 'System Generalization' both of the groups had similar simple interpretational ability of the system, but Type I group was better on generalization when 'hidden dimension inventory' factor was added. On the system prediction factor, however, students' ability was weak regardless of the type. Consequently, more specific development strategies on various objects are needed for the development and application of the system learning program. Furthermore, it is expected that this study could be practically and effectively used on various fields related to system recognition.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.157-173
• /
• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Radiation Oncology Journal
• /
• v.23 no.3
• /
• pp.143-156
• /
• 2005

Background: The best dose-fractionation regimen of the definitive radiotherapy for cervix cancer remains to be clearly determined. It seems to be partially attributed to the complexity of the affecting factors and the lack of detailed information on external and intra-cavitary fractionation. To find optimal practice guidelines, our experiences of the combination of external beam radiotherapy (EBRT) and high-dose-rate intracavitary brachytherapy (HDR-ICBT) were reviewed with detailed information of the various treatment parameters obtained from a large cohort of women treated homogeneously at a single institute. Materials and Methods: The subjects were 743 cervical cancer patients (Stage IB 198, IIA 77, IIB 364, IIIA 7, IIIB 89 and IVA 8) treated by radiotherapy alone, between 1990 and 1996. A total external beam radiotherapy (EBRT) dose of $23.4\~59.4$ Gy (Median 45.0) was delivered to the whole pelvis. High-dose-rate intracavitary brachytherapy (HDR-IBT) was also peformed using various fractionation schemes. A Midline block (MLB) was initiated after the delivery of $14.4\~43.2$ Gy (Median 36.0) of EBRT in 495 patients, while In the other 248 patients EBRT could not be used due to slow tumor regression or the huge initial bulk of tumor. The point A, actual bladder & rectal doses were individually assessed in all patients. The biologically effective dose (BED) to the tumor ($\alpha/\beta$=10) and late-responding tissues ($\alpha/\beta$=3) for both EBRT and HDR-ICBT were calculated. The total BED values to point A, the actual bladder and rectal reference points were the summation of the EBRT and HDR-ICBT. In addition to all the details on dose-fractionation, the other factors (i.e. the overall treatment time, physicians preference) that can affect the schedule of the definitive radiotherapy were also thoroughly analyzed. The association between MD-BED $Gy_3$ and the risk of complication was assessed using serial multiple logistic regression models. The associations between R-BED $Gy_3$ and rectal complications and between V-BED $Gy_3$ and bladder complications were assessed using multiple logistic regression models after adjustment for age, stage, tumor size and treatment duration. Serial Coxs proportional hazard regression models were used to estimate the relative risks of recurrence due to MD-BED $Gy_{10}$, and the treatment duration. Results: The overall complication rate for RTOG Grades $1\~4$ toxicities was $33.1\%$. The 5-year actuarial pelvic control rate for ail 743 patients was $83\%$. The midline cumulative BED dose, which is the sum of external midline BED and HDR-ICBT point A BED, ranged from 62.0 to 121.9 $Gy_{10}$ (median 93.0) for tumors and from 93.6 to 187.3 $Gy_3$ (median 137.6) for late responding tissues. The median cumulative values of actual rectal (R-BED $Gy_3$) and bladder Point BED (V-BED $Gy_3$) were 118.7 $Gy_3$ (range $48.8\~265.2$) and 126.1 $Gy_3$ (range: $54.9\~267.5$), respectively. MD-BED $Gy_3$ showed a good correlation with rectal (p=0.003), but not with bladder complications (p=0.095). R-BED $Gy_3$ had a very strong association (p=<0.0001), and was more predictive of rectal complications than A-BED $Gy_3$. B-BED $Gy_3$ also showed significance in the prediction of bladder complications in a trend test (p=0.0298). No statistically significant dose-response relationship for pelvic control was observed. The Sandwich and Continuous techniques, which differ according to when the ICR was inserted during the EBRT and due to the physicians preference, showed no differences in the local control and complication rates; there were also no differences in the 3 vs. 5 Gy fraction size of HDR-ICBT. Conclusion: The main reasons optimal dose-fractionation guidelines are not easily established is due to the absence of a dose-response relationship for tumor control as a result of the high-dose gradient of HDR-ICBT, individual differences In tumor responses to radiation therapy and the complexity of affecting factors. Therefore, in our opinion, there is a necessity for individualized tailored therapy, along with general guidelines, in the definitive radiation treatment for cervix cancer. This study also demonstrated the strong predictive value of actual rectal and bladder reference dosing therefore, vaginal gauze packing might be very Important. To maintain the BED dose to less than the threshold resulting in complication, early midline shielding, the HDR-ICBT total dose and fractional dose reduction should be considered.

• Journal of Intelligence and Information Systems
• /
• v.19 no.1
• /
• pp.57-77
• /
• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.