• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.17-29
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• 2021

Purpose: During 2015-2019, the average amount of political donation to the national assembly members in Korea was 1,000 won per person. Despite its benefits such as receiving tax credits, the donation system has not been actively practiced. This paper aims to promote political donations by suggesting a recommendation system of national assembly members by analysing the bills they proposed. Methods: In this paper, we propose a recommendation system based on two aspects: how similar the newly proposed or ammended bills are to the sponsors' interest (similarity index) and how much effort national assembly members put into those bills (intensity index). More than 25,000 bills were used to measure the recommendation quality index consisted with both the similarity and the intensity indices. Word2vec was used to calculate the similarity index of the bills proposed by the national assembly member to the sponsor's interest. The intensity index is calculated by diving the number of newly proposed or entirely revised bills with the number of senators who took part in those bills. Subsequently, we multiply the similarity index by the intensity index to obtain the recommendation quality index that can assist sponsors to identify potential assembly members for their donation. Results: We apply the proposed recommendation system to personas for illustration. The recommendation system showed an average f1 score about 0.69. The analysis results provide insights in recommendation for donation. Conclusion: n this study, the recommendation system was proposed to promote a political donation for national assembly members by creating the recommendation quality index based on the similarity and the intensity indices. We expect that the system presented in this paper will lower user barriers to political information, thereby boosting political sponsorship and increasing political participation.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.834-851
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• 2017

The amount of sources of information available on the web using ontologies as support continues to increase and is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combining structure-based and reasoning-based approaches in order to discover new semantic correspondences between entities of different ontologies. We used the biblio test of the benchmark series and anatomy series of the Ontology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance of our approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzed the contribution of our method compared to structural and semantic methods. The results obtained show that our performance provides good performance. Indeed, these results are better than those of the LogMap system in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevant than YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbased methods.