• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.101-106
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• 2021

Breast cancer is the number one cause of deaths from cancer in women, knowing the type of breast cancer in the early stages can help us to prevent the dangers of the next stage. The performance of the deep learning depends on large number of labeled data, this paper presented convolutional neural network for classification breast cancer from images to benign or malignant. our network contains 11 layers and ends with softmax for the output, the experiments result using public BreakHis dataset, and the proposed methods outperformed the state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.67-74
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• 2022

One type of network security breach is the availability breach, which deprives legitimate users of their right to access services. The Denial of Service (DoS) attack is one way to have this breach, whereas using the Intrusion Detection System (IDS) is the trending way to detect a DoS attack. However, building IDS has two challenges: reducing the false alert and picking up the right dataset to train the IDS model. The survey concluded, in the end, that using a real dataset such as MAWILab or some tools like ID2T that give the researcher the ability to create a custom dataset may enhance the IDS model to handle the network threats, including DoS attacks. In addition to minimizing the rate of the false alert.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1558-1562
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• 2005

This paper describes the design and implementation of a wireless home network system using Home Network Control Protocol (HNCP) called the wireless HNCP home network system. For wireless interfaces of HNCP, IEEE 802.11b and IEEE 802.15.4 standard protocols are considered. With the implementation of the wireless HNCP home network system, a simple analysis about coexistence between IEEE 802.11b and IEEE 802.15.4 is achieved. Through the implemented wireless HNCP home network system and the analytical results about the coexistence between both two different wireless protocols, the feasibility of the wireless HNCP home network system is shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4902-4932
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• 2016

The popularity of network virtualization has recently regained considerable momentum because of the emergence of OpenFlow technology. It is essentially decouples a data plane from a control plane and promotes hardware programmability. Subsequently, OpenFlow facilitates the implementation of network virtualization. This study aims to provide an overview of different approaches to create a virtual network using OpenFlow technology. The paper also presents the OpenFlow components to compare conventional network architecture with OpenFlow network architecture, particularly in terms of the virtualization. A thematic OpenFlow network virtualization taxonomy is devised to categorize network virtualization approaches. Several testbeds that support OpenFlow network virtualization are discussed with case studies to show the capabilities of OpenFlow virtualization. Moreover, the advantages of popular OpenFlow controllers that are designed to enhance network virtualization is compared and analyzed. Finally, we present key research challenges that mainly focus on security, scalability, reliability, isolation, and monitoring in the OpenFlow virtual environment. Numerous potential directions to tackle the problems related to OpenFlow network virtualization are likewise discussed.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.25-32
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• 2017

We are living in networks which are regarded as a society. However, it is difficult to designate a specific position or the impact over sociological relationships and virtual links. In this paper, we conceptualize two themes of the network as Physical Network and Virtual Network, and observe their cross-network effects. New concept called Networked Community (NC) is then introduced to walk through both PN and VN by using the element of connections say connectivity feature. Through modeling NC by the theme of network transposition and egocentric network, we try to comprehend all possible networks for detecting the problems and solutions by using both sides' idea. Experimental results show that we can model real-world problems and then analyze them through NC by measurable and structural manner.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.132-140
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• 2021

Under the mobile ad-hoc network system, the main reason for causing congestion is because of the limited availability of resources. On the other hand, the standardised TCP based congestion controlling mechanism is unable to control and handle the major properties associated with the shared system of wireless channels. It creates an effect on the design associated with suitable protocols along with protocol stacks through the process of determining the mechanisms of congestion on a complete basis. Moreover, when bringing a comparison with standard TCP systems the major environment associated with mobile ad hoc network is regraded to be more problematic on a complete basis. On the other hand, an agent-based mobile technique for congestion is designed and developed for the part of avoiding any mode of congestion under the ad-hoc network systems.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.796-804
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• 2021

In computer vision, visual tracking method addresses the problem of localizing an specific object in video sequence according to the bounding box. In this paper, we propose a tracking method by introducing the feature correlation comparison into the siamese network to increase its matching identification. We propose a way to compute location of object to improve matching performance by a correlation operation, which locates parts for solving the searching problem. The higher layer in the network can extract a lot of object information. The lower layer has many location information. To reduce error rate of the object center point, we built a siamese network that extracts the distribution and location information of target objects. As a result of the experiment, the average center error rate was less than 25%.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.67-78
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• 2024

Software-Defined Networking (SDN) is a new emerging networking paradigm that has adopted a logically centralized architecture to increase overall network performance agility and programmability. Combining network virtualization with SDN will guarantees for combined advantages of improved flexibility and network performance. Combining SDN with hypervisors divides the network physical resources into several logical transparent and isolated virtual SDN network (vSDN), where each has its virtual controller. However, SDN hypervisors bring several advantages as well as several challenges to its network operators as for the virtual appliances, their efficient placement, assurance of network performance is mandatory, and their dynamic instantiation with their migration. In this article, we provide a brief and concise review of network virtualization along with its implementation in the SDN network. SDN hypervisors types are discussed, and taxonomy is provided to demonstrate the importance of hypervisors in SDN. A comparison of SDN hypervisors is performed to elaborate on the vital hypervisor software along with their features, and different challenges are discussed faced by the SDN network. A framework is proposed to add combined functionalities of hypervisors to create a more effective and efficient virtual system. The purpose of the framework is to increase network performance through proper configuration of resources, software, control plane isolation functions with defined rules and policies.