• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.1000-1013
• /
• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• Journal of Information Processing Systems
• /
• v.16 no.3
• /
• pp.718-732
• /
• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• Journal of Radiation Industry
• /
• v.5 no.2
• /
• pp.151-157
• /
• 2011

Crosslinked SPEEK/PVDF membrane were prepared by EB radiation method with various contents of PVDF. The prepared membranes were subjected to a comparative study of proton exchange membranes for fuel cell appreciations. The crosslinked SPEEK/PVDF membranes were characterized by using DMA, DSC and SAXS. The DMA data indicate that the ionic modulus values and cluster $T_g$ decrease with increasing PVDF content. Thus, it was suggested that the number of clustering in the crosslinked membranes can be reduced with increasing PVDF content. The DSC results were shown that the degree of crystalline of the membrane increased with increasing PVDF content. The morphology of the crosslinkied membranes was shown that with increasing PVDF content, the number of crystalline domain of the SPEEK/PVDF membranes increased but ionic aggregation of the membranes decreased. The water uptake behavior, ionic exchange capacity (IEC) and proton conductivity were decreased with increasing PVDF content. The overall findings suggest that the crosslinked membranes offer the possibility for improving the performance of PEMFC, provided that the membranes have thermal and hydration stability.

• Journal of Climate Change Research
• /
• v.7 no.4
• /
• pp.507-512
• /
• 2016

This study was conducted to classify agroclimatic zones in South Korea. To classify the agroclimatic zones, such climatic factors as amount of rainfall from April to May, amount of rainfall in October, monthly average air temperature in January, monthly average air temperature from April to May, monthly average air temperature from April to September, monthly average air temperature from December to March, monthly minimum air temperature in January, monthly minimum air temperature from April to May, Warmth Index were considered as major influencing factors on the crop growth. Climatic factors were computed from monthly air temperature and precipitation of climatological normal year (1981~2010) at 1 km grid cell estimated from a geospatial climate interpolation method. The agroclimatic zones using k-means cluster analysis method were classified into 6 zones.

• International Journal of Advanced Culture Technology
• /
• v.7 no.2
• /
• pp.238-243
• /
• 2019

Recently, the importance of big data continues to be emphasized, and it is applied in various fields based on data mining techniques, which has a great influence on the health care industry. There are many healthcare industries, but only home health care is considered here. However, applying this to real problems does not always give perfect results, which is a problem. Therefore, data mining techniques are used to solve these problems, and the algorithms that affect performance are evaluated. This paper focuses on the effects of healthcare services on patient satisfaction and satisfaction. In order to use the CVParameterSelectin algorithm and the SMOreg algorithm of the classify method of data mining, it was evaluated based on the experiment and the verification of the results. In this paper, we analyzed the services of home health care institutions and the patient satisfaction analysis based on the name, address, service provided by the institution, mood of the patients, etc. In particular, we evaluated the results based on the results of cross validation using these two algorithms. However, the existence of variables that affect the outcome does not give a perfect result. We used the cluster analysis method of weka system to conduct the research of this paper.

• Journal of dental hygiene science
• /
• v.19 no.2
• /
• pp.86-95
• /
• 2019

The modern era of microbial genome analysis began in earnest in the 2000s with the generalization of metagenomics and gene sequencing techniques. Studying complex microbial community such as oral cavity and colon by a pure culture is considerably ineffective in terms of cost and time. Therefore, various techniques for genomic analysis have been developed to overcome the limitation of the culture method and to explore microbial communities existing in the natural environment at the gene level. Among these, DNA fingerprinting analysis and microarray chip have been used extensively; however, the most recent method of analysis is metagenomics. The study summarily examined the overview of metagenomics analysis techniques, as well as domestic and foreign studies on disease genomics and cluster analysis related to oral metagenome. The composition of oral bacteria also varies across different individuals, and it would become possible to analyze what change occurs in the human body depending on the activity of bacteria living in the oral cavity and what causality it has with diseases. Identification, isolation, metabolism, and presence of functional genes of microorganisms are being identified for correlation analysis based on oral microbial genome sequencing. For precise diagnosis and treatment of diseases based on microbiome, greater effort is needed for finding not only the causative microorganisms, but also indicators at gene level. Up to now, oral microbial studies have mostly involved metagenomics, but if metatranscriptomic, metaproteomic, and metabolomic approaches can be taken together for assessment of microbial genes and proteins that are expressed under specific conditions, then doing so can be more helpful for gaining comprehensive understanding.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.93-95
• /
• 2014

In this paper, we proposed efficient forwarding path computing method using Context-Awareness Mobility Prediction Model. Context-Awareness Mobility Prediction Model is storing and classifying node's previous velocity and direction according to time in the hierarchical cluster structure. To overcome environment which node-to-node connection is broken off easily, the proposed algorithm calculate the connectivity formed matrix structure by comparing predicted velocity and direction, and use masking operation for selecting relay moving to destination. The proposed algorithm identified to show short delay by utilizing forwarding path which is continue node-to-node connection in the unstable situation.

• International Journal of Contents
• /
• v.15 no.3
• /
• pp.32-38
• /
• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.3
• /
• pp.865-873
• /
• 2021

COVID-19 pandemic has brought the world into economic recession. This study aims to present an analysis of the readiness of creative e-commerce in Indonesia. Data was collected from various locations that may represent the Creative Industry in Indonesia. The quantitative method has been applied as a research approach by gathering questionnaires from 383 business owners selected using the cluster random sampling method. Based on the results of the analysis and discussion, it is concluded that E-Commerce Readiness is very important in times of uncertainty such as the COVID-19 pandemic. The level of readiness will determine the continuity and sustainability of a company or business in a volatile business environment. E-Commerce Readiness can be evaluated based on Technology Readiness, Organizational Readiness, and Environmental Readiness. Not all perspectives are taken into consideration in making decisions about the implementation or improvement of E-Commerce in the pandemic period. Technology Readiness is seen as the most significant impact on a company's ability to cope with volatility, while Environmental Constraints encourage businesses to adopt E-Commerce and take it to the next level. On the other hand, Organizational Readiness has no effect on the E-Commerce readiness of the company because the company or organization does not consider this aspect.

• Journal of the Korean Society of Industry Convergence
• /
• v.24 no.4_2
• /
• pp.481-490
• /
• 2021

In the ultrasonic welding machine, when the load fluctuates, the L and C of the piezo element in the oscillation part change. As a result, the resonant frequency is changed, so it is necessary to match the operating frequency of the ultrasonic welding machine to the new resonant frequency. That is, in order to maximize the output of the oscillation unit of the ultrasonic welding machine, it is inevitable to follow the resonance frequency. Accordingly, many methods for following the resonant frequency are being actively studied. In addition, in order to check the effect of external inductance on the operation of the ultrasonic welding machine, The equivalent circuit of the piezo element was analyzed by including the external inductance for resonance in the equivalent circuit of the piezo element, and the method of selecting an appropriate inductance was described. In this paper, we propose a new system that allows the switching frequency of the inverter to tracking the resonance frequency even if the resonance frequency is changed due to the load of the ultrasonic welding machine.