• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.4
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• pp.366-375
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• 2009

In collaborative filtering, many neighbors are needed to improve the quality and stability of the recommendation. The quality may not be good mainly due to the high similarity between two users not guaranteeing the same preference for products considered for recommendation. This paper proposes a consistency definition, rather than similarity, based on information entropy between two users to improve the recommendation. This kind of consistency between two users is then employed as a trust metric in collaborative filtering methods that select neighbors based on the metric. Empirical studies show that such collaborative filtering reduces the number of neighbors required to make the recommendation quality stable. Recommendation quality is also significantly improved.

• Journal of the Korean Society for information Management
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• v.2 no.2
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• pp.3-37
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• 1985

This research deals with the problems encountered in designing systems for more efficient and effective information retrieval used in the proliferation of literature. This research was designed to develop and test 1) the partitioning a large bibliographic data base into quality oriented subsets (quality filtering), and 2) a system for effective and efficient information retrieval within subsets of data base (relevance). In order to accomplish this partitioning, the 'kernel' technique of graph theory was applied. In addition, a method of quality filtering utilizing the 'epidemic' theory and the 'obsolescence' of scientific literature was developed.

• Journal of the Korean Society for information Management
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• v.3 no.1
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• pp.3-16
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• 1986

This research deals with the problems encountered in designing systems for more efficient and effective information retrieval used in the proliferation of literature. This research was designed to develop and test 1) the partitioning a large bibliographic data base into quality oriented subsets (quality filtering), and 2) a system for effective and efficient Information retrieval within subsets of data base (relevance). In order to accomplish this partitioning, the 'kernel' technique of graph theory was applied. In addition, a method of quality filtering utilizing the 'epidemic' theory and the 'obsolescence' of scientific literature was developed.

• Journal of the Korean Society for Library and Information Science
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• v.14
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• pp.93-130
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• 1987

Scientific communication is an information exchange activity between scientists. Scientific communication is carried out in a variety of informal and formal ways. Basically, informal communication takes place by word of mouth, whereas formal communication occurs via the written word. Science is a highly interdependent activity in which each scientist builds upon the work of colleagues past and present. Consequently, science depends heavily on scientific communication. In this study, three mathematical models, namly Brillouin measure, logistic equation, and Markov chain are examined. These models provide one with a means of describing and predicting the behavior of scientific communication process. These mathematical models can be applied to construct quality filtering algorithms for subject literature which identify synthesized elements (authors, papers, and journals). Each suggests a different type of application. Quality filtering for authors can be useful to funding agencies in terms of identifying individuals doing the best work in a given area or subarea. Quality filtering with respect to papers can be useful in constructing information retrieval and dissemination systems for the community of scientists interested m the field. The quality filtering of journals can be a basis for the establishment of small quality libraries based on local interests in a variety of situations, ranging from the collection of an individual scientist or physician to research centers to developing countries. The objective of this study is to establish the theoretical framework for informetrics which is defined as the quantitative analysis of scientific communication, by investigating mathematical models of scientific communication.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.40 no.2
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• pp.73-78
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• 2007

The suitability of a flexible fiber filter for removing suspended solid (SS) in a recirculating aquaculture system was evaluated. This study focused on variation in the performance with a change in filtering time, influent water quality, and filtering mode duration. The particle distribution diagram of the filter effluent showed that the number of particles bigger than $5-8{\mu}m$ decreased dramatically, and the removal efficiency exceeded 80%. Although the removal efficiencies of SS and chemical oxygen demand (COD) were dependent on the quality of the influent, the SS and COD concentrations of the effluent were not affected by the influent concentrations. This was despite the deterioration if water quality after feeding in the rearing tank. The performance of the filter was not affected by the filtering mode duration, feeding conditions, or filtering time. The SS concentration and turbidity of the recirculating-type rearing tank were 30% and 50% lower, respectively, than of the a non-recirculating-type rearing tank under the same operating conditions. The flexible fiber filter was applicable to a recirculating aquaculture system that uses plenty of seawater, based on its low filtering resistance $(2kg_f/cm^2)$, high flux $(330m^3/m^2/hr)$, and high fine particle removal efficiency (80%, $5-8{\mu}m$).

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.313-320
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• 2017

The In-loop filtering methods such as de-blocking filter and SAO(Sample Adaptive Offset) applied to the HEVC standard achieves coding efficiency and subjective quality improvement by reducing the blocking artifacts and the ringing artifacts. However, despite the use of In-loop filtering methods, the artifacts called a corner outlier occurring at the corner points of block boundaries are not removed. In this paper, the corner outlier artifacts are reduced by the detection, determination, and filtering processes on the corner outlier pixels. Experimental results show that the proposed method improves the subjective picture quality and slightly increases the coding efficiency in Inter prediction.

• The Journal of Society for e-Business Studies
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• v.14 no.1
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• pp.1-11
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• 2009

Many neighbors are needed for making the recommendation quality better and stable in collaborative filtering. Furthermore, the quality is not so good mainly due to a reason that high similarity between two users does not guarantee the same preference to items considered for recommendation. Dissimilar users who have consistency in item selection can be useful for predicting preferences. This paper proposes a new collaborative filtering method, defining trust based on consistency for improving this phenomenon. Empirical studies show that such a method reduces the number of neighbors required to make the recommendation quality stable and the recommendation quality itself is also significantly improved.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.339-345
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• 2022

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.129-137
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• 2008

In this paper, we propose an improved Bayesian Filtering mechanism to reduce the False Positives that occurs in the existing Bayesian Filtering mechanism. In the existing Bayesian Filtering mechanism, the same Bayesian Filtering DB trained at the e-mail server is applied to each e-mail user. Also, the training method using receiving e-mails only could not provide the high quality of ham DB. Due to these problems, the existing Bayesian Filtering mechanism can produce the False Positives which misclassify the ham e-mails into the spam e-mails. In the proposed mechanism, the sending e-mails of the user are treated as the high quality of ham information, and are trained to the Bayesian ham DB automatically. In addition, by providing a different Bayesian DB to each e-mail user respectively, more efficient e-mail filtering service is possible. Our experiments show the improvement of filtering accuracy by 3.13%, compared to the existing Bayesian Filtering mechanism.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.123-139
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• 2011

Web spam has a significant influence on the ranking quality of web search results because it promotes unimportant web pages. Therefore, web search engines need to filter web spam. web spam filtering is a concept that identifies spam pages - web pages contributing to web spam. TrustRank, Anti-TrustRank, Spam Mass, and Link Farm Spam are well-known web spam filtering algorithms in the research literature. The output of these algorithms depends upon the input seed. Thus, refinement in the input seed may lead to improvement in the quality of web spam filtering. In this paper, we propose seed refinement techniques for the four well-known spam filtering algorithms. Then, we modify algorithms, which we call modified spam filtering algorithms, by applying these techniques to the original ones. In addition, we propose a strategy to achieve better quality for web spam filtering. In this strategy, we consider the possibility that the modified algorithms may support one another if placed in appropriate succession. In the experiments we show the effect of seed refinement. For this goal, we first show that our modified algorithms outperform the respective original algorithms in terms of the quality of web spam filtering. Then, we show that the best succession significantly outperforms the best known original and the best modified algorithms by up to 1.38 times within typical value ranges of parameters in terms of recall while preserving precision.