• Journal of KIISE
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• v.43 no.12
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• pp.1356-1364
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• 2016

This paper proposes a novel aesthetic photo recomposition method using a deep convolutional neural network (DCNN). Previous recomposition approaches define the aesthetic score of photo composition based on the distribution of salient objects, and enhance the photo composition by maximizing the score. These methods suffer from heavy computational overheads, and often fail to enhance the composition because their optimization depends on the performance of existing salient object detection algorithms. Unlike previous approaches, we address the photo recomposition problem by utilizing DCNN, which shows remarkable performance in object detection and recognition. DCNN is used to iteratively predict cropping directions for a given photo, thus generating an aesthetically enhanced photo in terms of composition. Experimental results and user study show that the proposed framework can automatically crop the photo to follow specific composition guidelines, such as the rule of thirds.

• Journal of Distribution Science
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• v.11 no.6
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• pp.51-58
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• 2013

Purpose - Nowadays, more than ever before, fierce competition, deep market segmentation, short product life cycles, and intensifying customer needs are putting increasing pressure on franchise's organizations to satisfy their customers by creating market-oriented relationships with and enhancing their market knowledge of them. One way that this might be achieved is by establishing deep ties (i.e., job commitment and job satisfaction) with their employees. Therefore, the purpose of this study is to examine how two important constructs of franchises' strategic efforts, LO (learning orientation) and RMO (relationship marketing orientation), affect job satisfaction, given the mediating role of job commitment. A franchise system comprises a set of contractual arrangements by which mutual obligations are performed. An organizational learning goal motivates employees to improve their abilities and master the tasks they perform. Relationship marketing, in addition, is to identify, establish, maintain, and enhance relationships with customers and other stakeholders to ensure that the objectives of all parties are met and this is done through the mutual exchange of promises. In a relationship marketing orientation, then, a firm creates, maintains, and enhances a strong relationship with its customers by sustaining long-term ties. This study was designed to examine the evolution of various theoretical approaches to franchise systems in order to determine whether theories about firms have significantly affected the franchise system. To this end, the authors developed a structural model consisting of several constructs. Previous studies have suggested that franchises' learning and relationship marketing orientations are important occupational immersion dimensions driving job satisfaction. Research design, data, methodology - We empirically tested a process of how the learning orientation and the relationship marketing orientation influence job commitment and job satisfaction using survey data drawn from 150 responding franchisees who were interviewed about their individual tendencies. Results - The results of this study provide empirical evidence that learning orientation, relationship marketing orientation, and job commitment all influence franchisees' job satisfaction. The results of this study indicate that, first, learning orientation had a significant effect on job satisfaction; second, relationship marketing orientation was positively related to job commitment; third, job commitment had a significant effect on job satisfaction. We also found that relationship marketing orientation and job satisfaction were mediated by job commitment. Conclusions - The findings of this study confirm the importance of learning orientation and relationship marketing orientation in maintaining a positive marketing relationship between franchiser and franchisee from to the perspective of the market. This indicates that franchiser support such as educational programs provided by the franchiser will help franchisees attain higher business management achievement and satisfaction. Moreover, a positive relationship between franchisees and consumers can be maintained through tie effects. Our findings also suggest that learning orientation plays a critical role in job satisfaction within the franchise system.

• Genomics & Informatics
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• v.17 no.2
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• pp.17.1-17.3
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• 2019

Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• Journal of the Korean School Mathematics Society
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• v.17 no.1
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• pp.73-92
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• 2014

This study was performed in part with the task to find measures to improve the defining characteristics of feelings, value, interest, self-efficacy, and others aspects in regards to learning math among elementary and middle school students. For this study, it was essential to understand the appropriate questions that are needed to be asked during a consultation at a math clinic, for students that are having a hard time learning math. As a method for performing this study, the content of scheduled counseling over 2 years from a math clinic were collected and the questions that were given and taken were analyzed in order to figure out the types of questions needed in order to effectively examine students that are facing difficulty with learning math. The analysis was performed using Grounded theory analysis by Strauss & Corbin(1998) and went through the process of open coding, axial coding, and selective coding. For the paradigm in the categorical analysis stage, 'attitude towards learning math' was set as the casual condition, 'feelings towards learning math' was set as the contextual condition, 'confidence in one's ability to learn math' was set as the phenomenon, 'individual tendencies when learning math' was set as the intervening condition, 'self-management of learning math' was set as the action/interaction strategy, and 'method of learning' was set as the consequence. Through this, the questions that appeared during counseling were linked into categories and subcategories. Through this process, 81 concepts were deducted, which were grouped into 31 categories. I believe that this data can be used as grounded theory for standardization of consultation in clinics.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.453-462
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• 2020

The research postulates the conceptualization of talent in the Vietnamese banking sector via examining the factors pertaining to the concept of talent and talent management (TM) in the sector. This study applied qualitative research methods. A total of 20 managers and directors of ten banks (three public, four private and three foreign banks) were recruited for semi-structured interviews. The findings revealed that a combination of interconnected soft skills, learning ability, flexibility, technology adaptability, integrity and risk management skills contributes to talent identification. Managers in some private banks construed talent to be commensurate with high performance and high potential, whereas managers in public banks and foreign banks mainly relied on performance results in talent recognition. Moreover, talented employees holding sales-related jobs are given the most attention by management in the studied banks. Regarding practical implications, the banking community and practitioners' focus should be imparted to soft skills development and integrity control in order to foster employee performance and attitudes. Attention should be paid not only to sales positions, but also to other positions within the bank. This study is one of a few which explores talent concepts and TM approaches in the banking sector in general and Vietnamese banking field in particular.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.147-153
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• 2023

In recent times, an absence of effective crowd management has led to numerous stampede incidents in crowded places. A crucial component for enhancing on-site crowd management effectiveness is the utilization of crowd counting technology. Current approaches to analyzing congested scenes have evolved beyond simple crowd counting, which outputs the number of people in the targeted image to a density map. This development aligns with the demands of real-life applications, as the same number of people can exhibit vastly different crowd distributions. Therefore, solely counting the number of crowds is no longer sufficient. CSRNet stands out as one representative method within this advanced category of approaches. In this paper, we propose a crowd counting network which is adaptive to the change in the density of people in the scene, addressing the performance degradation issue observed in the existing CSRNet(Congested Scene Recognition Network) when there are changes in density. To overcome the weakness of the CSRNet, we introduce a system that takes input from the image's information and adjusts the output of CSRNet based on the features extracted from the image. This aims to improve the algorithm's adaptability to changes in density, supplementing the shortcomings identified in the original CSRNet.

• Journal of the Korean Society for information Management
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• v.25 no.3
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• pp.27-39
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• 2008

In bibliographic data, the use of personal names to indicate authors makes it difficult to specify a particular author since there are numerous authors whose personal names are the same. Resolving same-name author instances into different individuals is called author resolution, which consists of two steps: calculating author similarities and then clustering same-name author instances into different person groups. Author similarities are computed from similarities of author-related bibliographic features such as coauthors, titles of papers, publication information, using supervised or unsupervised methods. Supervised approaches employ machine learning techniques to automatically learn the author similarity function from author-resolved training samples. So far however, a few machine learning methods have been investigated for author resolution. This paper provides a comparative evaluation of a variety of recent high-performing machine learning techniques on author disambiguation, and compares several methods of processing author disambiguation features such as coauthors and titles of papers.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.