• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.318-326
• /
• 2022

Marketing stress testing is a new method of identifying the company's strengths and weaknesses in a turbulent environment. Technically, this is a complex procedure, so it involves artificial intelligence and machine learning. The main problem is currently the development of methodological approaches to the development of the company's digital model, which will provide a framework for machine learning. The aim of the study was to identify and develop an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. This aim provided the company's activities to be considered as a set of elements (business processes, products) and factors that affect them (marketing environment). The article proposes an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. The proposed approach includes four main elements that are subject to parameterization: elements of the company's internal environment, factors of the marketing environment, the company' core competency and factors impacting the company. Matrices for evaluating the results of the work of expert groups to determine the degree of influence of the marketing environment factors were developed. It is proposed to distinguish between mega-level, macro-level, meso-level and micro-level factors depending on the degree of impact on the company. The methodological limitation of the study is that it involves the modelling method as the only one possible at this stage of the study. The implementation limitation is that the proposed approach can only be used if the company plans to use machine learning for marketing stress testing.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.134-141
• /
• 2023

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

• Journal of Information Technology Services
• /
• v.7 no.1
• /
• pp.255-274
• /
• 2008

The purpose of this research is to develop an information technology (IT) usage level assessment model for service industry. It is necessary to develop an assessment model for service industry's IT usage to improve service productivity. However, it is not easy to develop assessment models due to service industry's diversity. In this paper a generic IT usage assessment model for service industry has been developed and validated through a descriptive approach. Key factors affecting service productivity have been identified and analysed. A pilot test on IT usage level has been performed to investigate the relevance and importance of IT usage indicators (factors). As a result, a set of effective IT usage indicators for service industry have been found. A short-cut model and a full scale model have been proposed for efficient and effective usage. The results of this study can be used for enhancement of service industry productivity through the increase of IT usage level.

• Journal of Korea Technology Innovation Society
• /
• v.14 no.spc
• /
• pp.1187-1208
• /
• 2011

This research aims to propose a systematic approach to identify emerging technology-product portfolio for small and medium enterprises (SMEs). Firstly, operational definition of emerging technology for SMEs is presented. Secondly, research framework is suggested and case study to show usefulness of the newly proposed framwork is analyzed. In detail, reference patent set which represent company's capabilities and business area are constructed. The research constructs patent data set for bibliometric analysis using reference patent set and citing patents to 2nd level. Clustering (expert judgement) and keyword based bibliometric approach are used. Then, cluster activity index (AI) and relevance index (RI) comparing with reference patent set are estimated. With emerging technology-product portfolio using AI and RI, a firm can identify emerging technology-product area and monitoring area.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.382-387
• /
• 2002

In this paper, we propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. Traditional spectral based classification cannot account for the spatial information and may result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of classes on the basis of surrounding observations is incorporated into the Bayesian framework. This approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data set was carried out.

• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.153-162
• /
• 2010

The finite buffer GI/G/1/K system is set up by using an unconventional arrangement of the state space, in which the remaining interarrival time or service time is chosen as the level. The stationary distributions of resulting Markov chain can be explicitly determined, and the chain is positive recurrent without any restriction. This is an advantage of this method, compared with that using the elapsed time approach [2].

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.4
• /
• pp.2093-2108
• /
• 2017

In this paper, we describe a novel method for recognizing human actions from different views via view knowledge transfer. Our approach is characterized by two aspects: 1) We propose a unsupervised topic transfer model (TTM) to model two view-dependent vocabularies, where the original bag of visual words (BoVW) representation can be transferred into a bag of topics (BoT) representation. The higher-level BoT features, which can be shared across views, can connect action models for different views. 2) Our features make it possible to obtain a discriminative model of action under one view and categorize actions in another view. We tested our approach on the IXMAS data set, and the results are promising, given such a simple approach. In addition, we also demonstrate a supervised topic transfer model (STTM), which can combine transfer feature learning and discriminative classifier learning into one framework.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.5
• /
• pp.571-575
• /
• 2010

Mutation testing is known as a very useful technique for measuring the effectiveness of a test data set and finding weak points of the test set. An equivalent mutant degrades the effectiveness of mutation testing. Elimination of equivalent mutants is a very important problem in mutation testing.In this paper, we proposed kinds of methods for detecting class-level equivalent mutants. These methods judge the equivalency of mutants through structural informations and behavioral information of the original program and mutants using static analysis. We found that our approach can detect not a few of equivalent mutants and expected that the cost of mutation testing can be saved considerably.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.2
• /
• pp.221-228
• /
• 2020

This study suggests an alternative to the conventional collaborative filtering method for predicting consumer choice, using case-based reasoning. The algorithm of case-based reasoning determines the similarity between the alternative sets that each subject chooses. Case-based reasoning uses the inverse of the normalized Euclidian distance as a similarity measurement. This normalized distance is calculated by the ratio of difference between each attribute level relative to the maximum range between the lowest and highest level. The alternative case-based reasoning based on similarity predicts a target subject's choice by applying the utility values of the subjects most similar to the target subject to calculate the utility of the profiles that the target subject chooses. This approach assumes that subjects who deliberate in a similar alternative set may have similar preferences for each attribute level in decision making. The result shows the similarity between comparable alternatives the consumers consider buying is a significant factor to predict the consumer choice. Also the interaction effect has a positive influence on the predictive accuracy. This implies the consumers who looked into the same alternatives can probably pick up the same product at the end. The suggested alternative requires fewer predictors than conjoint analysis for predicting customer choices.

• KIEE International Transactions on Power Engineering
• /
• v.5A no.1
• /
• pp.46-55
• /
• 2005

This paper proposes a new method for choice of the best transmission system expansion plan on the side of highest satisfaction level of decision maker using fuzzy integer programming. The proposed method considers the permissibility and ambiguity of the investment budget (economics) for constructing the new transmission lines and the delivery marginal rate (reliability criteria) of the system by modeling the transmission expansion problem as a fuzzy integer programming one. It solves the optimal strategy (reasonable as well as flexible) using a fuzzy set theory-based on branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. Under no or only a very small database for the evaluation of reliability indices, the proposed technique provides the decision maker with a valuable and practical tool to solve the transmission expansion problem considering problem uncertainties. Test results on the 63-bus test system show that the proposed method is practical and efficiently applicable to transmission expansion planning.