• Journal of Information Processing Systems
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• 제13권2호
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• pp.215-235
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• 2017

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

• 전자통신동향분석
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• 제33권4호
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• pp.33-42
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• 2018

Object classification and detection are fundamental technologies in computer vision and its applications. Recently, a deep-learning based approach has shown significant improvement in terms of object classification and detection. This report reviews the progress of deep-learning based object classification and detection in views of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and analyzes recent trends of object classification and detection technology and its applications.

• 정보관리연구
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• 제27권2호
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• pp.33-64
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• 1996

본 연구는 데이터베이스 기술분류의 표준시안을 제시하기 위하여 1차년도(1994년) 연구 결과에 대한 관점을 체계화하고 구체화시켜 수정, 보완하는 형식으로 이루어졌다. 분류관점을 정보와 이를 지원하는 시스템 측면으로 크게 나누어, 데이터베이스 일반, 정보유통, 정보검색, 데이터베이스 시스템, 주변 관련주제를 분류기준으로 하는 표준 시안의 모형이 제시되었다.

• ETRI Journal
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• 제32권1호
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• pp.22-32
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• 2010

The pervasive game environments have activated explosive growth of the Internet over recent decades. Thus, understanding Internet traffic characteristics and precise classification have become important issues in network management, resource provisioning, and game application development. Naturally, much attention has been given to analyzing and modeling game traffic. Little research, however, has been undertaken on the classification of game traffic. In this paper, we perform an interpretive traffic analysis of popular game applications at the transport layer and propose a new classification method based on a simple decision tree, called an alternative decision tree (ADT), which utilizes the statistical traffic characteristics of game applications. Experimental results show that ADT precisely classifies game traffic from other application traffic types with limited traffic features and a small number of packets, while maintaining low complexity by utilizing a simple decision tree.

• 반도체디스플레이기술학회지
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• 제15권3호
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• pp.6-11
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• 2016

Silicon wafer is one of main materials in solar cell. Micro-cracks in silicon wafer are one of reasons to decrease efficiency of energy transformation. They couldn't be observed by human eye. Also, their shape is not only various but also complicated. Accordingly, their shape classification is absolutely needed for manufacturing process quality and its feedback. The performance of typical classification techniques which is principal component analysis(PCA), neural network, fusion model to integrate PCA with neural network, and support vector machine(SVM), are evaluated using pattern features of micro-cracks. As a result, it has been confirmed that the SVM gives good results in micro-crack classification.

• Industrial Engineering and Management Systems
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• 제14권2호
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• pp.210-219
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• 2015

A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using well-known classifiers.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.111-120
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• 2020

Generative adversarial networks (GANs) achieved impressive performance on image generation and visual classification applications. However, adversarial networks meet difficulties in combining the generative model and unstable training process. To overcome the problem, we combined the deep residual network with upsampling convolutional layers to construct the generative network. Moreover, the study shows that image generation and classification performance become more prominent when the residual layers include on the generator. The proposed network empirically shows that the ability to generate images with higher visual accuracy provided certain amounts of additional complexity using proper regularization techniques. Experimental evaluation shows that the proposed method is superior to image generation and classification tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3658-3679
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• 2022

Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic. In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed. The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset. On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%. To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique. The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers.

• 전자통신동향분석
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• 제37권4호
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• pp.28-35
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• 2022

To solve the challenge of waste plastics, this study investigated the related technologies and company trends along the plastic life cycle, and primarily describes ICT technologies to improve efficiency in the process of sorting and sorting waste plastics. Waste plastic discharge caused by the explosive increase in parcel traffic because of COVID-19 is also growing exponentially. Hence, waste treatment is emerging as a social challenge. Most of the domestic waste classification depends on the manual process according to the waste pollution level. The plastic material classification approach using the spectroscopy approach reveals a high error in the contaminated waste plastic classification, but if the Artificial Intelligence-based image classification technology is employed together, the classification precision can be enhanced because of the type of waste plastic product and the contaminated part can be differentiated.