• IE interfaces
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• v.25 no.1
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• pp.96-105
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• 2012

Recently, the interoperability has become more important to enhance the net-centric capabilities of the warfighter. DITA (Defense Information Technical stAndard) is the set of IT standards for improving interoperability, scalability, effectiveness, and efficiency. In this paper, we analyzed the standardizing process to derive the selection criteria and structurized the derived selection criteria using the KJ (Kawakita Jiro) method. Finally, we developed an evaluation model for selecting a standard for DITA using AHP (Analytic Hierarchy Process). As a result, we present eight selection criteria (maintainability, trend, stability, portability, effect of other standard, constraint of the network, and applicability to the systems). We also applied some examples that several IT standards to our selection model for validating the model. We expect our model to help to decide objectively whether the new standard can be listed in DITA.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.233-238
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• 2007

An approach to identify Chinese event types is proposed in this paper which combines a good feature selection policy and a Maximum Entropy (ME) model. The approach not only effectively alleviates the problem that classifier performs poorly on the small and difficult types, but improve overall performance. Experiments on the ACE2005 corpus show that performance is satisfying with the 83.5% macro - average F measure. The main characters and ideas of the approach are: (1) Optimal feature set is built for each type according to local feature selection, which fully ensures the performance of each type. (2) Positive and negative features are explicitly discriminated and combined by using one - sided metrics, which makes use of both features' advantages. (3) Wrapper methods are used to search new features and evaluate the various feature subsets to obtain the optimal feature subset.

• Knowledge Management Research
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• v.12 no.3
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• pp.97-109
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• 2011

The purpose of this paper is to analyze what are the key determinants in selection of mobile banking technology platform, main challenges in a given developments of technology as well as rate the importance of factors in regards to various criterions. In addition, this paper proposes comprehensive approach for solving mobile banking technology platform selection problem. To serve the purpose, this paper uses the Analytic Hierarchy Process (AHP) to select best mobile banking technology platform for not only in functionality aspect, but also in organizational aspect. We expect that the results of this study will be useful for banks who have intention to select mobile banking technology platform in the future.

• Journal of Information Processing Systems
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• v.6 no.2
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• pp.235-252
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• 2010

Regression testing is a very costly process performed primarily as a software maintenance activity. It is the process of retesting the modified parts of the software and ensuring that no new errors have been introduced into previously tested source code due to these modifications. A regression test selection technique selects an appropriate number of test cases from a test suite that might expose a fault in the modified program. In this paper, we propose both a regression test selection and prioritization technique. We implemented our regression test selection technique and demonstrated in two case studies that our technique is effective regarding selecting and prioritizing test cases. The results show that our technique may significantly reduce the number of test cases and thus the cost and resources for performing regression testing on modified software.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10539-10542
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• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.129-132
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• 2021

Two feature selection approaches are analyzed in this study. First Approach used in this paper is Filter Approach which comprises of correlation technique. It provides two reduced feature sets using positive and negative correlation. Secondly Approach used in this paper is the wrapper approach which comprises of Sequential Forward Selection technique. The reduced feature set obtained by positive correlation results comprises of Rate of Acceleration, Intensity and Formant. The reduced feature set obtained by positive correlation results comprises of Rasta PLP, Log energy, Log power and Zero Crossing Rate. Pitch, Rate of Acceleration, Log Power, MFCC, LPCC is the reduced feature set yield as a result of Sequential Forwarding Selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3268-3283
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• 2018

With the advent of the Information Age, the source identification of digital images, as a part of digital image forensics, has attracted increasing attention. Therefore, an effective technique to identify the source of digital images is urgently needed at this stage. In this paper, first, we study and implement some previous work on image source identification based on sensor pattern noise, such as the Lukas method, principal component analysis method and the random subspace method. Second, to extract a purer sensor pattern noise, we propose a sample selection method to improve the random subspace method. By analyzing the image texture feature, we select a patch with less complexity to extract more reliable sensor pattern noise, which improves the accuracy of identification. Finally, experiment results reveal that the proposed sample selection method can extract a purer sensor pattern noise, which further improves the accuracy of image source identification. At the same time, this approach is less complicated than the deep learning models and is close to the most advanced performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4269-4292
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• 2014

Currently, increasing numbers of access points (AP) are being deployed in enterprise offices, campuses and municipal downtowns for flexible Internet connectivity, but most of these access points are idle or redundant most of the time, which causes significant energy waste. Therefore, with respect to power conservation, applying energy efficient strategies in WIFI networks is strongly advocated. One feasible method is dynamically managing network resources, particularly APs, by powering devices on or off. However, when an AP is powered on, the device is initialized through a long boot time, during which period clients cannot be associated with it; therefore, the network performance would be greatly impacted. In this paper, based on a global view of an entire WLAN, we propose an AP selection technology, known as Temporary Access Selection (TAS). The criterion of TAS is a fusion metric consisting of two evaluation indexes which are based on throughput and battery life, respectively. TAS is both service and clients' preference specific through balancing the data rate, battery life and packet size. TAS also works well independently in traditional WLANs in which no energy efficient strategy is deployed. Moreover, this paper demonstrates the feasibility and performance of TAS through experiments and simulations with Network Simulator version 3 (NS3).

• Journal of Digital Convergence
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• v.6 no.3
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• pp.75-84
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• 2008

The purpose of our this research, based on the Media Selection Theory, the Technology Acceptance Model, and the Social Influence Theory, is to investigate the influential factors that affect media selection in banking transactions. Analyses showed that for location sensitive bank window's and ATMs (automatic teller machines), defined as offline-based transaction channels, convenience was the variable affecting media selection. However, in the case of online media not related to location, (phone banking, internet banking, and mobile banking) reliability was the significant variable influencing use. The findings show that banking organizations may benefit from identifying traits of media affecting use, and should differentiate customer services for competitive advantage.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.219-223
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• 2001

Within a mechanical system such as an automotive the number of standard machine parts is increasing, so that the parts selection becomes more important than ever before. Selection of appropriate bearings in the preliminary design phase of a machine is also important. In this paper, three decision-making approaches are compared to find out a model that is appropriate to bearing selection problem. An artificial neural network, which is trained with real design cases, is used to select a bearing mechanism at the first step. Then, the subtype of the bearing is selected by the weighting factor method. Finally, types of peripherals such as lubrication methods are determined by a rule-based expert system.