• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3725-3748
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• 2018

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.306-320
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• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.

• ETRI Journal
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• v.33 no.6
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• pp.904-913
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• 2011

Three-dimensional (3D) memories using through-silicon vias (TSVs) as vertical buses across memory layers will likely be the first commercial application of 3D integrated circuit technology. The memory dies to stack together in a 3D memory are selected by a die-selection method. The conventional die-selection methods do not result in a high-enough yields of 3D memories because 3D memories are typically composed of known-good-dies (KGDs), which are repaired using self-contained redundancies. In 3D memory, redundancy sharing between neighboring vertical memory dies using TSVs is an effective strategy for yield enhancement. With the redundancy sharing strategy, a known-bad-die (KBD) possibly becomes a KGD after bonding. In this paper, we propose a novel die-selection method using KBDs as well as KGDs for yield enhancement in 3D memory. The proposed die-selection method uses three search-space conditions, which can reduce the search space for selecting memory dies to manufacture 3D memories. Simulation results show that the proposed die-selection method can significantly improve the yield of 3D memories in various fault distributions.

• 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.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1564-1569
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• 2009

Project managers play a key role in cost, time, and quality of a project. Selection of an appropriate project manager, therefore, is considered as one of the most important decisions in any construction project. It should be noted that most important decision makings are carried out by the project manager throughout the project. Traditionally, project manager selection in construction companies in Iran is through organizing an interview with candidates and selecting the most appropriate choice in accordance with the capabilities, potentials and individual specifications coupled with the requirements of the project. In the same direction, organizing interview on selection of appropriate candidate is usually carried out by senior managers of companies. Determination of the most important criteria for selection of project managers and also identification of significance coefficient of each criterion can highly help senior managers of companies to make sound selection decisions. In this paper, a numerical model has been considered for determination of significance of each criterion, details of which are submitted for selection of project manager in Iranian petrochemical, oil and gas sector companies. For this reason, all criteria- considered by senior managers of the companies under study- are first determined. Then, information obtained through 38 interviews, conducted by senior managers of the mentioned companies while selecting project manager, is analyzed. Significant coefficient of each criterion is calculated through the accumulated data using fuzzy curves method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2587-2600
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• 2012

It has been demonstrated that choosing an appropriate relay node can improve the transmission rate for the system. However, such system improvement brought by the relay selection may be degraded with the presence of the malicious relay nodes, which are selected but refuse to cooperate for transmissions deliberately. In this paper, we formulate the relay selection issue as a restless bandit problem with the objective to maximize the average rate, while considering the credibility of each relay node, which may be different at each time instant. Then the optimization problem is solved by using the priority-index heuristic method effectively. Furthermore, a low complexity algorithm is offered in order to facilitate the practical implementations. Simulation results are conducted to demonstrate the effectiveness of the proposed trust-based relay selection scheme.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.682-686
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• 2016

A fully differential RC calibrator for accurate cut-off frequency of a programmable channel selection filter is proposed. The proposed RC calibrator consists of an RC timer, clock generator, synchronous counter, digital comparator, and control block. To verify the proposed RC calibrator, a six-order Chebyshev programmable low-pass filter with adjustable 3 dB cut-off frequency, which is controlled by the proposed RC calibrator, was implemented in a $0.18-{\mu}m$ CMOS technology. The channel selection filter with the proposed RC calibrator draws 1.8 mA from a 1.8 V supply voltage and the measured 3 dB cut-off frequencies of the channel selection LPF is controlled accurately by the RC calibrator.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• The Research Journal of the Costume Culture
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• v.28 no.2
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• pp.181-198
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• 2020

Using the developed wedding planner selection criteria scale, this study examined whether wedding planner selection criteria differ according to consumer characteristics such as demographic characteristics and wedding preparation behaviors. The main survey for this study was conducted via the Internet with 295 consumers aged 20-30 living in the Seoul metropolitan area. The data collected from the survey processed and analyzed using the statistical programs SPSS 21.0 t-test. Analyzing how wedding planner selection criteria differ according to consumers' demographic characteristics and wedding preparation behaviors, results shown for the wedding planner selection criteria were all four points on average except for individual characteristics and important sub-factors regardless of the consumers' characteristics, and various results were derived depending on the consumers' characteristics. This study has various practical implications in that it verified the difference in wedding planner selection criteria according to consumer characteristics and determined how much money consumers were willing to play for wedding planners. It is recommended that future studies take various approaches to investigate how wedding planner users are satisfied with or place importance on wedding planner services and conduct empirical using the selection criteria developed in this study to compare influential variables that affect behavior intention and willingness to pay according to consumer type.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.