• The KIPS Transactions:PartD
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• v.8D no.3
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• pp.233-240
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• 2001

As software technology is in progress and software quantification is getting more important, many metrics have been proposed to quantify a variety of system entities. These metrics can be classified into two different forms : scalar metric and metric vector. Though some recent studies pointed out the composition problem of the scalar metric form, many scalar metrics are successfully used in software development organizations due to their practical applications. In this paper, it is concluded that hybrid metric form weighting external complexity is most suitable for scalar metric form. With this concept, a general framework for hybrid metrics construction independent of the development methodologies and target system type is proposed. This framework was successfully used in two projects that quantify the analysis phase of the structured methodology and the design phase of the object oriented real-time system, respectively. Any organization can quantify system entities in a short time using this framework.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10B
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• pp.630-638
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• 2005

As real-time media applications based on IP multicast networks spread widely, the end-to-end QoS (quality of service) provisioning for these applications have become very important. To guarantee the end-to-end QoS of multi-party media applications, it is essential to monitor the time-varying status of both network metrics (i.e., delay, jitter and loss) and system metrics (i.e., CPU and memory utilization). In this paper, targeting the multicast-enabled AG (Access Grid) group collaboration tool based on multi-Party real-time media services, a hybrid monitoring scheme that can monitor the status of both multicast network and node system is investigated. It combines active monitoring and passive monitoring approaches to measure multicast network. The active monitoring measures network-layer metrics (i.e., network condition) with probe packets while the passive monitoring checks application-layer metrics (i.e., user traffic condition by analyzing RTCP packets). In addition, it measures node system metrics from system API. By comparing these hybrid results, we attempt to pinpoint the causes of performance degradation and explore corresponding reactions to improve the end-to-end performance. The experimental results show that the proposed hybrid monitoring can provide useful information to coordinate the performance improvement of multi-party real-time media applications.

• Journal of Information Science Theory and Practice
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• v.7 no.2
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• pp.23-31
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• 2019

We report results of selection-bias-free approaches to the analysis of the impact of open access (OA) models on citation metrics. We studied reference groups of Gold and Green OA articles and the group of non-OA (Paywall) articles with the new functionality of the Web of Science Core Collection database, the InCites platform of Clarivate Analytics, and the Dimensions database of Digital Science. For each reference group we obtained the values of the percent of cited articles and citation impact and their dependence on the depth of the citation period. Different research fields were analyzed in two schemas of the InCites platform. We report the higher values and growth rates of the citation metrics: citation impact and %Cited, in the OA reference groups over the Paywall group. The Green OA articles demonstrate the highest values of citation metrics among all the OA models. Dependence of the value of citation impact on citation period follows linear law with R2 values close to 0.9-1.0. The overall annual growth rates of citation impact of the Green OA, Gold OA, and the Paywall articles, k equal, respectively, 3.6, 2.4, and 1.4 in Dimensions and 4.6, 3.6, and 2.3 in the Web of Science Core Collection. We suppose that earlier results reported for the articles in pure OA journals vs. articles in Paywall journals were affected by the high citation impact of the Green and Hybrid OA articles that could not be elucidated in the Paywall journals at that time.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.129-137
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• 2005

Criticality prediction models that identify fault-prone spots using system design specifications play an important role in reducing development costs of large systems such as telecommunication systems. Many criticality prediction models using complexity metrics have been suggested. But most of them need training data set for model training. And they are classification models that can only classify design entities into fault-prone group and non fault-prone group. To solve this problem, this paper builds a new prediction model, HMM, using two styled hybrid metrics. HMM has strong point that it does not need training data and it enables comparison between design entities by criticality. HMM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering internal characteristics and accuracy of prediction.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.1-18
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• 2007

In the early stage of weapon system acquisition process, acquisition policy is necessarily established to acquire weapon system in a faster, better, cheaper way. Several alternatives, such as "buy", "domestic research and development", and "technical corporative production", can be considered for the best acquisition policy making. However, the comparison factors for those alternatives have different metrics and values. Therefore, the aim of this paper is to suggest KAAM(Knowledge-based Alternatives Analysis Model) as a scientific method to compare the alternatives having such different metrics and values and giving a weighted and normalized single measurement for the easy comparison. KAAM is a hybrid model incorporating Satty technique, Delphi/Shang method, Consensus method, and SAW method. KAAM is implemented on Microsoft Excel environment and provided tabular form user interface. Finally, an illustrative example is shown using KAAM.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.23-32
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• 2024

Mobile Ad hoc Network is a network of multiple wireless nodes which communicate and exchange information together without any fixed and centralized infrastructure. The core objective for the development of MANET is to provide movability, portability and extensibility. Due to infrastructure less network topology of the network changes frequently this causes many challenges for designing routing algorithms. Many routing protocols for MANET have been suggested for last few years and research is still going on. In this paper we review three main routing protocols namely Proactive, Reactive and Hybrid, performance comparison of Proactive such as DSDV, Reactive as AODV, DSR, TORA and Hybrid as ZRP in different network scenarios including dynamic network size, changing number of nodes, changing movability of nodes, in high movability and denser network and low movability and low traffic. This paper analyzes these scenarios on the performance evaluation metrics e.g. Throughput, Packet Delivery Ratio (PDR), Normalized Routing Load(NRL) and End To-End delay(ETE).This paper also reviews various network layer security attacks challenge by routing protocols, detection mechanism proposes to detect these attacks and compare performance of these attacks on evaluation metrics such as Routing Overhead, Transmission Delay and packet drop rates.

• Journal of Digital Contents Society
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• v.9 no.4
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• pp.625-633
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• 2008

Recently, the participation, opening and joint ownership of the users are important issue. The users want professional and accurate information from web. But users often suffer from retrieving accurate information. Even though the users find information they want, it is not guaranteed that the information is reliable since there are too much information placed on the web. Thus, we propose the novel rank metric to promote reliability and efficiency in information retrieval. In order to verify our approach, we implement a web site based on the proposed rank metric for nonofficial medical science information. The proposed rank metric based on user's level. This is to give score of text through differential rate depending on the user's level. The proposed rank metric enhances the reliability of text which is reflecting the user's mental factor. Thus, this method can be used for enhancing the reliability of text.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1522-1545
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• 2012

Call admission control (CAC) plays an important role in mobile cellular network to guarantee the quality of service (QoS). In this paper, a dynamic hybrid CAC scheme with integrated cutoff priority and handoff queue for mobile cellular network is proposed and some performance metrics are derived. The unique characteristic of the proposed CAC scheme is that it can support any number of service types and that the cutoff thresholds for handoff calls are dynamically adjusted according to the number of service types and service priority index. Moreover, timeouts of handoff calls in queues are also considered in our scheme. By modeling the proposed CAC scheme with a one-dimensional Markov chain (1DMC), some performance metrics are derived, which include new call blocking probability ($P_{nb}$), forced termination probability (PF), average queue length, average waiting time in queue, offered traffic utilization, wireless channel utilization and system performance which is defined as the ratio of channel utilization to Grade of Service (GoS) cost function. In order to validate the correctness of the derived analytical performance metrics, simulation is performed. It is shown that simulation results match closely with the derived analytic results in terms of $P_{nb}$ and PF. And then, to show the advantage of 1DMC modeling for the performance analysis of our proposed CAC scheme, the computing complexity of multi-dimensional Markov chain (MDMC) modeling in performance analysis is analyzed in detail. It is indicated that state-space cardinality, which reflects the computing complexity of MDMC, increases exponentially with the number of service types and total channels in a cell. However, the state-space cardinality of our 1DMC model for performance analysis is unrelated to the number of service types and is determined by total number of channels and queue capacity of the highest priority service in a cell. At last, the performance comparison between our CAC scheme and Mahmoud ASH's scheme is carried out. The results show that our CAC scheme performs well to some extend.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.559-564
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• 2013

In this paper a new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately. In the field of wind energy research, accurate forecast of wind speed is a challenging task. This will influence the power system scheduling and the dynamic control of wind turbine. The wind data used here is measured at 15 minute time intervals. The performance is evaluated based on the metrics, namely, mean square error, mean absolute error, sum squared error of the proposed model and compared with the back propagation model. Simulation studies are carried out and it is reported that the proposed model outperforms the compared model based on the metrics used and conclusions were drawn appropriately.