• Genomics & Informatics
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• v.17 no.3
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• pp.30.1-30.4
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• 2019

Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.426-432
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• 2022

HVAC systems play a critical role in reducing energy consumption in buildings. Integrating occupants' thermal comfort evaluation into HVAC control strategies is believed to reduce building energy consumption while minimizing their thermal discomfort. Advanced technologies, such as visual sensors and deep learning, enable the recognition of occupants' discomfort-related actions, thus making it possible to estimate their thermal discomfort. Unfortunately, it remains unclear how accurate a deep learning-based classifier is to recognize occupants' discomfort-related actions in a working environment. Therefore, this research evaluates the classification performance of occupants' discomfort-related actions while sitting at a computer desk. To achieve this objective, this study collected RGB video data on nine college students' cold discomfort-related actions and then trained a deep learning-based classifier using the collected data. The classification results are threefold. First, the trained classifier has an average accuracy of 93.9% for classifying six cold discomfort-related actions. Second, each discomfort-related action is recognized with more than 85% accuracy. Third, classification errors are mostly observed among similar discomfort-related actions. These results indicate that using human action data will enable facility managers to estimate occupants' thermal discomfort and, in turn, adjust the operational settings of HVAC systems to improve the energy efficiency of buildings in conjunction with their thermal comfort levels.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.470-480
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• 2006

The longer the input sentences, the worse the parsing results. To improve the parsing performance, many methods about long sentence segmentation have been reserarched. As an isolating language, Chinese sentence has fewer cues for sentence segmentation. However, the average frequency of comma usage in Chinese is higher than that of other languages. The syntactic information that the comma conveys can play an important role in long sentence segmentation of Chinese languages. This paper proposes a method for classifying commas in Chinese sentences according to the context where the comma occurs. Then, sentences are segmented using the classification result. The experimental results show that the accuracy of the comma classification reaches 87.1%, and with our segmentation model, the dependency parsing accuracy of our parser is improved by 5.6%.

• Journal of Multimedia Information System
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• v.2 no.4
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• pp.295-302
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• 2015

This article discusses the features and trends of development of the process of implementation of multimedia systems in various fields, research substantiate the basic concepts of multimedia systems, information flow, describes the classification and characterization of information flows and systems. Described container TIAV, which is designed with all the modern features and is aimed at future trends in the field of play.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2375-2382
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• 2016

Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• Korean Security Journal
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• no.8
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• pp.155-196
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• 2004

In the 1970th, Security industry in Korea based auxiliary measures for confrontation about increase of a crime by Industrialization and Urbanization. However, This based growth of 1980th - 1990th Security industry, On the Study consider expansion step of Security industry in Korea with classification policing conditions in the 1970th and Security Law in the 1976th. In the 1976th, Security industry in Korea play an important part by maintenance of social order and inspire 'Security of one's own accord' into the hearts of the people.

• Journal of Korean Society of Rural Planning
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• v.17 no.3
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• pp.27-41
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• 2011

This research The main focus of this research is to provide basic data for concrete recreation planning of future site by selecting Gwangmyeong-Siheung housing district, large residential development district focused on rural areas, by evaluation of recreation value and detailed biotope type classification. The main results of analysis are as follows. As a result of basic survey of the research area, total 79 family and 307 taxonomic groups are identified and also naturalization index and urbanization index were estimated 16.6 % and 17.6% respectively. Also, as a result of biotope type classification, it is divide into 12 biotope type gorups including forest biotope type group and its subordinate 53 biotop types. As a result of first value evaluation, there are total 13 biotope types such as vegetation-full artificial rivers in I grade. In addition it is analyzed as 9 types of II grade, 5 types of III grade, 8 types of IV grade, 18 types of V grade. Lastly, as a result of second evauation, it is analyzed that there are 21 special meaningful areas for recreation and natural experience(1a, 1b), and 50 meaningful areas for recreation and natural experience(2a, 2b, 2c). It is regarded that the results of biotope types classification and recreation value from this research play roles of analyzing the Suitable site for recreation area before development in terms of large residential development district, and then these results provide important basic data to secure recreational and natural experience area in development planning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.876-883
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• 2008

A clearly defined e-catalog (or product) information is a key foundation for an e-commerce system. A classification (or categorization) is a core information to build clear e-catalogs, can play an important role in quality of e-commerce systems using e-catalogs. However, as the wide use of online business transactions, the volume of e-catalog information that needs to be managed in a system has become drastically large, and the classification task of such data has become highly complex. In this paper, we present an e-catalog classifier system, and report on our effort to improve an e-catalog management process and to standardize e-catalogs for enterprises by use of automated approach for e-catalog classifier systems. Also we introduce some of the issues that we have experienced in the projects, so that our work may help those who do a similar project in the future.