• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.77-82
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• 2019

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.417-426
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• 2023

To improve the effect of image restoration and solve the image detail loss, an image dehazing enhancement algorithm based on mean guided filtering is proposed. The superpixel calculation method is used to pre-segment the original foggy image to obtain different sub-regions. The Ncut algorithm is used to segment the original image, and it outputs the segmented image until there is no more region merging in the image. By means of the mean-guided filtering method, the minimum value is selected as the value of the current pixel point in the local small block of the dark image, and the dark primary color image is obtained, and its transmittance is calculated to obtain the image edge detection result. According to the prior law of dark channel, a classic image dehazing enhancement model is established, and the model is combined with a median filter with low computational complexity to denoise the image in real time and maintain the jump of the mutation area to achieve image dehazing enhancement. The experimental results show that the image dehazing and enhancement effect of the proposed algorithm has obvious advantages, can retain a large amount of image detail information, and the values of information entropy, peak signal-to-noise ratio, and structural similarity are high. The research innovatively combines a variety of methods to achieve image dehazing and improve the quality effect. Through segmentation, filtering, denoising and other operations, the image quality is effectively improved, which provides an important reference for the improvement of image processing technology.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.629-638
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• 2005

In this paper, we consider two issues in collaborative filtering, which are closely related with the number of ratings of a user. First issue is the relationship between the number of ratings of a user and the performance of collaborative filtering. The relationship is investigated with two datasets, EachMovie and Movielens datasets. The number of ratings of a user is critical when the number of ratings is small, but after the number is over a certain threshold, its influence on recommendation performance becomes smaller. We also provide an explanation on the relationship between the number of ratings of a user and the performance in terms of neighborhood formations in collaborative filtering. The second issue is how to select an initial product list for new users for gaining user responses. We suggest and analyze 14 selection strategies which include popularity, favorite, clustering, genre, and entropy methods. Popularity methods are adequate for getting higher number of ratings from users, and favorite methods are good for higher average preference ratings of users.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.183-187
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• 2005

In this paper, we propose a heuristic method to select features using a Two-Phase Markov Blanket-based (TPMB) algorithm. The first phase, filtering phase, of TPMB algorithm works by filtering the obviously redundant features. A non-linear correlation method based on Information theory is used as a metric to measure the redundancy of a feature [1]. In second phase, approximating phase, the Markov Blanket (MB) of a system is estimated by employing the concept of cross entropy to identify the MB. We perform experiments on microarray data and report two popular dataset, AML-ALL [3] and colon tumor [4], in this paper. The experimental results show that the TPMB algorithm can significantly reduce the number of features while maintaining the accuracy of the classifiers.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.253-257
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• 2012

This paper presents an efficient filtering system for a metastable state-based true random number generator. To output a result with high randomness, we use loop-storage for storing the value of metastability. During the metastable state, the output value is accumulated to the storage. When the non-metastable state arises, the stored metastable value will be used for output instead of the result of the non-metastable state. As a result, we can maintain high entropy together with the original throughput.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.1-8
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• 2010

The researches about gaze recognition which current user gazes and finds the location have increasingly developed to have many application. The gaze recognition of existence all about researches have got problems because of using equipment that Infrared(IR) LED, IR camera and head-mounted of high price. This study propose and implement the gaze recognition system based on SVM using a single PC Web camera. The proposed system that divide the gaze location of 36 per 9 and 4 to recognize gaze location of 4 direction and 9 direction recognize user's gaze. Also, the proposed system had apply on image filtering method using difference image entropy to improve performance of gaze recognition. The propose system was implements experiments on the comparison of proposed difference image entropy gaze recognition system, gaze recognition system using eye corner and eye's center and gaze recognition system based on PCA to evaluate performance of proposed system. The experimental results, recognition rate of 4 direction was 94.42% and 9 direction was 81.33% for the gaze recognition system based on proposed SVM. 4 direction was 95.37% and 9 direction was 82.25%, when image filtering method using difference image entropy implemented. The experimental results proved the high performance better than existed gaze recognition system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.74-80
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• 2013

In this study, a novel and flexible recommender system was developed, based on product taxonomy and usage patterns of users. The proposed system consists of the following four steps : (i) estimation of the product-preference matrix, (ii) construction of the product-preference matrix, (iii) estimation of the popularity and similarity levels for sought-after products, and (iv) recommendation of a products for the user. The product-preference matrix for each user is estimated through a linear combination of clicks, basket placements, and purchase statuses. Then the preference matrix of a particular genre is constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product with respect to the total. The popularity and similarity levels of a user's clicked product are estimated with an entropy index. Based on this information, collaborative and content-based filtering is used to recommend a product to the user. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site. Our results clearly showed that the proposed hybrid method is superior to conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.520-529
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• 2007

In this paper, we study the characteristics of parameters which are related to performance of MCTF which is a key technique for wavelet-based scalable video coding, and propose an improved MCTF method. The proposed MCTF method adopts the motion estimation of which motion vector field is distributed more uniformly using variable block sizes. By using the proposed method, the decomposition performance of temporal filter is improved, and the energy in high-frequency frames is reduced. It can help the entropy coder to generate lower bitrate. From simulation results, we verify the decomposed energy on high-frequency frame using the proposed method is reduced by 25.86% at the most in terms of variance of the high-frequency frame.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.