• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.303-309
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• 2022

Software Defined Networking (SDN) is a novel approach that have accelerated the development of numerous technologies such as policy-based access control, network virtualization, and others. It allows to boost network architectural flexibility and expedite the return on investment. However, this increases the system's complexity, necessitating the expenditure of dollars to assure the system's security. Network Function Virtualization (NFV) opens up new possibilities for network engineers, but it also raises security concerns. A number of Internet service providers and network equipment manufacturers are grappling with the difficulty of developing and characterizing NFVs and related technologies. Through Moodle's efforts to maintain security, this paper presents a detailed review of security-related challenges in software-defined networks and network virtualization services.

• IMMUNE NETWORK
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• v.11 no.5
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• pp.268-280
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• 2011

Background: Dengue virus, which belongs to the Flavivirus genus of the Flaviviridae family, causes fatal dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS) with infection risk of 2.5 billion people worldwide. However, approved vaccines are still not available. Here, we explored the immune responses induced by alternating prime-boost vaccination using DNA vaccine, adenovirus, and vaccinia virus expressing E protein of dengue virus type 2 (DenV2). Methods: Following immunization with DNA vaccine (pDE), adenovirus (rAd-E), and/or vaccinia virus (VV-E) expressing E protein, E protein-specific IgG and its isotypes were determined by conventional ELISA. Intracellular CD154 and cytokine staining was used for enumerating CD4+ T cells specific for E protein. E protein-specific CD8+ T cell responses were evaluated by in vivo CTL killing activity and intracellular IFN-${\gamma}$ staining. Results: Among three constructs, VV-E induced the most potent IgG responses, Th1-type cytokine production by stimulated CD4+ T cells, and the CD8+ T cell response. Furthermore, when the three constructs were used for alternating prime-boost vaccination, the results revealed a different pattern of CD4+ and CD8+ T cell responses. i) Priming with VV-E induced higher E-specific IgG level but it was decreased rapidly. ii) Strong CD8+ T cell responses specific for E protein were induced when VV-E was used for the priming step, and such CD8+ T cell responses were significantly boosted with pDE. iii) Priming with rAd-E induced stronger CD4+ T cell responses which subsequently boosted with pDE to a greater extent than VV-E and rAd-E. Conclusion: These results indicate that priming with live viral vector vaccines could induce different patterns of E protein-specific CD4+ and CD8+ T cell responses which were significantly enhanced by booster vaccination with the DNA vaccine. Therefore, our observation will provide valuable information for the establishment of optimal prime-boost vaccination against DenV.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.125-135
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• 2022

This study aims to explore variables using machine learning and provide analysis techniques suitable for predicting pharmacy sales whether government statistical indicators built to create an industrial ecosystem based on data, network, and artificial intelligence affect pharmacy sales. Therefore, this study explored predictive variables and performance through machine learning techniques such as Random Forest, XGBoost, LightGBM, and CatBoost using analysis data from January 2016 to December 2021 for 28 government statistical indicators and pharmacies in the retail sector. As a result of the analysis, economic sentiment index, economic accompanying index circulation change, and consumer sentiment index, which are economic indicators, were found to be important variables affecting pharmacy sales. As a result of examining the indicators MAE, MSE, and RMSE for regression performance, random forests showed the best performance than XGBoost, LightGBM, and CatBoost. Therefore, this study presented variables and optimal machine learning techniques that affect pharmacy sales based on machine learning results, and proposed several implications and follow-up studies.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.394-400
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• 2022

The radio resources available in a wireless network system are limited. Therefor, job of managing resources is not easy task. Because the resources are shared among the UEs that are connected, the process of assigning resources must be carefully controlled. The packet scheduler in an LTE network is in charge of allocating resources to the user equipment (UE). Femtocells networks are being considered as a promising solution for poor channel performance for mulitple environments. The implementation of femtocells into a macrocell (traditional base station) would boost the capacities of the cellular network. To increase femtocells network capacity, a reliable Packet Scheduler mechanism should be implemented. The Packet Scheduler technique is introduced in this paper to maximize capacity of the network while maintaining fairness among UEs. The proposed solution operates in a manner consistent with this principle. An analysis of the proposed scheme's performance is conducted using a computer simulation. The results reveal that it outperforms the well-known PF scheduler in terms of cell throughput and average throughput of UEs.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.417-419
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• 2022

This paper undertakes educational games and gamification, their features, importance, and integration into the educational process. Besides outlining features, benefits, and difficulties, it highlights the difference between gaming, gamification, and game-based learning. The article contends that game-based learning and gamification elements such as reward, completion, and cooperation develop students' positive attitudes toward the curriculum and boost their learning motivation.

• Journal of the Korean Geosynthetics Society
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• v.23 no.3
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• pp.15-22
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• 2024

Predicting ground settlement during the improvement of soft ground and the construction of a structure is an crucial factor. Numerous studies have been conducted, and many prediction equations have been proposed to estimate settlement. Settlement can be calculated using the compression index of clay. In this study, data on water content, void ratio, liquid limit, plastic limit, and compression index from the Busan New Port area were collected to construct a dataset. Correlation analysis was conducted among the collected data. Machine learning algorithms, including Random Forest, Neural Network, Linear Regression, Ada Boost, and Gradient Boosting, were applied using the Orange mining program to propose compression index prediction models. The models' results were evaluated by comparing RMSE and MAPE values, which indicate error rates, and R² values, which signify the models' significance. As a result, water content showed the highest correlation, while the plastic limit showed a somewhat lower correlation than other characteristics. Among the compared models, the AdaBoost model demonstrated the best performance. As a result of comparing each model, the AdaBoost model had the lowest error rate and a large coefficient of determination.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.115-122
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• 2017

This paper proposes a deep learning algorithm based sign detection and recognition system for the blind. The proposed system is composed of sign detection stage and sign recognition stage. In the sign detection stage, aggregated channel features are extracted and AdaBoost classifier is applied to detect regions of interest of the sign. In the sign recognition stage, convolutional neural network is applied to recognize the regions of interest of the sign. In this paper, the AdaBoost classifier is designed to decrease the number of undetected signs, and deep learning algorithm is used to increase recognition accuracy and which leads to removing false positives which occur in the sign detection stage. Based on our experiments, proposed method efficiently decreases the number of false positives compared with other methods.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.4
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• pp.298-305
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• 2012

This paper proposes a soft switching boost converter with an auxiliary resonant circuit. The auxiliary resonant circuit is added to a general boost converter and that is composed of one switch, one diode, one inductor and two capacitors. The resonant network helps the main switch to operate with a zero voltage switching(ZVS) and auxiliary switch also operates under the zero voltage and zero current conditions. The soft switching range is extended by the auxiliary switch and it is possible to control the proposed converter with a pulse width modulation(PWM). The ZVS and ZCS techniques make switching losses decreased and efficiency of the system improved. A theoretical analysis is verified through the simulation and experiment.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.271-282
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• 2021

This study was conducted to develop a model for predicting the length of stay for premature infants through machine learning. For the development of this model, 6,149 cases of premature infants discharged from the hospital from 2011 to 2016 of the discharge injury in-depth survey data collected by the Korea Centers for Disease Control and Prevention were used. The neural network model of the initial hospitalization was superior to other models with an explanatory power (R²) of 0.75. In the model added by converting the clinical diagnosis to CCS(Clinical class ification software), the explanatory power (R²) of the cubist model was 0.81, which was superior to the random forest, gradient boost, neural network, and penalty regression models. In this study, using national data, a model for predicting the length of stay for premature infants was presented through machine learning and its applicability was confirmed. However, due to the lack of clinical information and parental information, additional research is needed to improve future performance.