• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.127-135
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• 2011

Efficient ordering of decision variables is one of the methods that find solutions quickly in the depth first search using backtracking. At this time, development of variables ordering algorithms considering dynamic and static properties of the problems is very important. However, to exploit optimal variable ordering algorithms appropriate to the problems. In this paper, we propose a problem classifying rule which provides problem type based on variables' properties, and use this rule to predict optimal type of variable ordering algorithms. We choose frequency allocation problem as a DS-type whose decision variables have dynamic and static properties, and estimate optimal variable ordering algorithm. We also show the usefulness of problem classifying rule by applying base station problem as a special case whose problem type is not generated from the presented rule.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.102-108
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• 2012

The paper proposes an algorithm which finds a nonlinear cut path for a printed grayscale touching character image. The conventional algorithms were observed to fail in situations of complicated touching. We analyzed those situations, and based on the analysis results we identified problematic issues of the conventional algorithms. We modified the conventional algorithms in two aspects. First we propose a new penalizing term which is probable to guide correctly the cut path for touching situations difficult to separate. Second the preposed algorithm adopts a strategy of producing both the downward and upward paths and selecting better one. The experimental results on actual touching character images showed that the proposed algorithm was superior th conventional algorithms by 3~4% in terms of success ratio of separation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.167-170
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• 2005

In the mobile environments for the vector map services, a map simplification work through the map generalization steps helps improve the readability of the map on a large scale. The generalization operations are various such as selection, aggregation, simplification, displacement, and so on, the formal operation algorithms have not been built yet. Because the algorithms require deep special knowledge and heuristic, which make it hard to automate the processes. This thesis proposes some map generalization algorithms specialized in mobile vector map services, based on previous works. We will show the detail to adapt the approaches on the mobile environment, to display complex spatial objects efficiently on the mobile devices which have restriction on the resources

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.35-42
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• 2021

In the current context of digitalization of education, the use of modern methods and techniques of data analysis and processing in order to improve students' school results has a very important role. In our paper, we aimed to perform a comparative study of the classification performances of AdaBoost, SVM, Naive Bayes, Neural Network and kNN algorithms to classify the results obtained at the Baccalaureate by students from a college in Suceava, during 2012-2019. To evaluate the results we used the metrics: AUC, CA, F₁, Precision and Recall. The AdaBoost algorithm achieves incredible performance for classifying the results into two categories: promoted / rejected. Next in terms of performance is Naive Bayes with a score of 0.999 for the AUC metric. The Neural Network and kNN algorithms obtain scores of 0.998 and 0.996 for AUC, respectively. SVM shows poorer performance with the score 0.987 for AUC. With the help of the HeatMap and DataTable visualization tools we identified possible correlations between classification results and some characteristics of data.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• Journal of the Korean Society of Radiology
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• v.82 no.1
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• pp.12-28
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• 2021

Mammography is the primary imaging modality for breast cancer detection; however, a high level of expertise is needed for its interpretation. To overcome this difficulty, artificial intelligence (AI) algorithms for breast cancer detection have recently been investigated. In this review, we describe the characteristics of AI algorithms compared to conventional computer-aided diagnosis software and share our thoughts on the best methods to develop and validate the algorithms. Additionally, several AI algorithms have introduced for triaging screening mammograms, breast density assessment, and prediction of breast cancer risk have been introduced. Finally, we emphasize the need for interest and guidance from radiologists regarding AI research in mammography, considering the possibility that AI will be introduced shortly into clinical practice.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.

• Journal of Internet Technology
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• v.19 no.6
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• pp.1691-1698
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• 2018

In this paper, we are to address the problem of item recommendations to users in shopping malls selling several different kinds of items, e.g., daily necessities such as cosmetics, detergent, and food ingredients. Most of current recommendation algorithms are developed for sites selling only one kind of items, e.g., music or movies. To devise efficient recommendation algorithms suitable for repetitively purchasing items, we give a method to implicitly assign ratings for these items by making use of repetitive purchase counts, and then use these ratings for the purpose of recommendation prediction with the help of user-based collaborative filtering and item-based collaborative filtering algorithms. We also propose associate item-based recommendation algorithm. Items are called associate items if they are frequently bought by users at the same time. If a user is to buy some item, it is reasonable to recommend some of its associate items. We implement user-based (item-based) collaborative filtering algorithm and associate item-based algorithm, and compare these three algorithms in view of the recommendation hit ratio, prediction performance, and recommendation coverage, along with computation time.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.75-75
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• 2004

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.240-243
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• 2000

In this paper, new Multi-Modulus blind Equalizer Algorithms for QAM signal set is propsed and analyzed and its performance is evaluated. The MMA algorithm combines the benifits of RCA and CMA. A new Dual-mode blind Algorithms for QAM signal set is derived. The concept of this algorithms is based on the Dual-Mode algorithm and the MMA algorithm. In order to analyze and evaluate the performance of new MMA algorithms, computer simulation are performed for the nonsquare QAM signal constellations. Form the simulation results, we can verify that new MMA algorithms converges very fast comparing to conventional MMA algorithm.