• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.38-59
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• 2022

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.7
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• pp.1252-1273
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• 2011

In Web Service Composition (WSC) area, services selection aims at selecting an appropriate candidate from a set of functionally-equivalent services to execute the function of each task in an abstract WSC according to their different QoS values. In despite of many related works, few of previous studies consider transactional constraints in QoS-aware WSC, which guarantee reliable execution of Composite Web Service (CWS) that is composed by a number of unpredictable web services. In this paper, we propose a novel global selection-optimal approach in WSC by considering both transactional constraints and end-to-end QoS constraints. With this approach, we firstly identify building rules and the reduction method to build layer-based Directed Acyclic Graph (DAG) model which can model transactional relationships among candidate services. As such, the problem of solving global optimal QoS utility with transactional constraints in WSC can be regarded as a problem of solving single-source shortest path in DAG. After that, we present Graph-building algorithms and an optimal selection algorithm to explain the specific execution procedures. Finally, comprehensive experiments are conducted based on a real-world web service QoS dataset. The experimental results show that our approach has better performance over other competing selection approaches on success ratio and efficiency.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.66-73
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• 2017

This study was performed to verify the relationships among selection attribute, restaurant trust, experiential value, and recommendation focusing on sport center consumers. The data were collected from visitors who registered more than three months in the sport center in South Korea. Total 500 survey was distributed and 330 participants were used for further statistical analysis. SPSS 23.0 and AMOS 21.0 for Windows were used for statistical analysis. Five factors of selection attribute (menu, interior, exterior, staff, convenience) were extracted, and measured by using 15 questions. According to the results of this study, interior, exterior, and staff factors have positive effects on restaurant trust, and interior and menu were significant predictors of the experiential value. In addition, present study confirmed the theoretical relationship among trust, experiential value, and recommend intention as perceived by sport center visitors. Although there are many studies which demonstrated the relationships among selection attribute and other outcome variables, little research explained the relationships among the variables from sport center consumers. Therefore, this study will contribute to provide meaningful results and some practical implications for both academia and the related foodservice industry.

• East Asian Journal of Business Economics (EAJBE)
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• v.4 no.2
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• pp.48-60
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• 2016

This study analyzes what effects does restaurant's well-being attribute selection have on word-of-mouth intention. Based on the result, this study aims to provide basic data for establishing Korean restaurant's service strategy and marketing strategy. The researchers surveyed 350 customers who visited a Korean restaurant located in Kangbook, Seoul. We encoded gathered data and analyzed them using SPSS 17.0 statistics package program. Following are the analyzed results. First, under hypothesis 1 - Korean restaurant's well-being attribute selection will have a positive influence on re-visitation intention - it is shown that sufficiency, healthiness, and steadiness have similar influence on re-visitation intention. Second, under hypothesis 2 - Korean restaurant's well-being attribute selection will have a positive influence on word-of-mouth intention - it is shown that sufficiency, healthiness, environment, and steadiness have similar influence on word -of-mouth intention. Third, under hypothesis 3 - Korean restaurant's re-visitation intention will have a positive influence on word -of-mouth intention - it is considered that eliciting customer's re-visitation intention also has influence on word-of-mouth intention. We will be necessary to consult how to derive customer's re-visitation intention or word-of-mouth intention by considering factors which customers of traditional Korean restaurant value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1140-1155
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• 2021

Up to now, many state-of-arts have been proposed for two-way relaying system with linear modulations. The performances of antenna selection (AS) at both transmit and relay nodes need to be investigated in some two-way relaying multiple-input multiple-output (TWRM) systems. In this paper, the goal is focused on the study of nonlinear modulations, i.e., continuous phase modulation (CPM) in TWRM systems. Firstly, the joint phase trellis are simplified by reversed Rimoldi processing so as to reduce the systems' complexity. Then the performances of joint transmit and receive antenna selection (JTRAS) with CPM modulations in two-way relaying MIMO systems are analyzed. More exactly, the pair wise probability (PEP) is used to evaluate the error performance based on the CPM signal matrix, which is calculated in terms of Laurent expression. Since the channels subject to two terminal nodes share common antennas at relay node R in multiple-access scheme, we revise the JTRAS algorithm and compare it to existing algorithm via simulation. Finally, the error performances for various schemes of antenna selection are simulated and compared to the analysis in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3008-3028
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• 2022

In this paper, we investigate traffic scheduling for a delay-sensitive multi-hop relay network, and aim to minimize the priority-based end-to-end delay of different data packet via joint relay selection, subcarrier assignment, and power allocation. We first derive the priority-based end-to-end delay based on queueing theory, and then propose a two-step method to decompose the original optimization problem into two sub-problems. For the joint subcarrier assignment and power control problem, we utilize an efficient particle swarm optimization method to solve it. For the relay selection problem, we prove its convexity and use the standard Lagrange method to deal with it. The joint relay selection, subcarriers assignment and transmission power allocation problem for each hop can also be solved by an exhaustive search over a finite set defined by the relay sensor set and available subcarrier set. Simulation results show that both the proposed routing scheme and the resource allocation scheme can reduce the average end-to-end delay.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.1-8
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• 2008

This paper introduces three efficient frame selection schemes to reduce the computation complexity for the multi-reference and variable block size Motion Estimation (ME). The proposed RSP (Reference Selection Pass) scheme can minimize the overhead of frame selection. The MFS (Modified Frame Selection) scheme can reduce the number of search points about 18% compared with existing schemes considering the motion of image during the reference frame selection process. In addition, the TPRFS (Two Pass Reference frame Selection) scheme can minimize the frame selection operation for the variable block size ME in H.264/AVC using the character of selected reference frame according to the block size. The simulation results show the proposed schemes can save up to 50% of the ME computation without degradation of image Qualify. Because the proposed schemes can be separated from the block matching process, they can be used with any existing single reference fast search algorithms.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.739-747
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• 2018

With a large feature space data, feature selection has become an extremely important procedure in the Data Mining process. But the traditional feature selection methods with single process may no longer fit for this procedure. In this paper, we proposed a hybrid efficient feature selection model for high dimensional data. We have applied our model on KNHNAES data set, the result shows that our model outperforms many existing methods in terms of accuracy over than at least 5%.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.453-458
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• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.