• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2815-2817
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• 2002

Classification method of cancers using cDNA microarrays data was developed using genetic algorithms and neural networks. For gene selection, 2308 genes were ranked using genetic algorithms, and selected by frequency number of selection from 1000 of genetic iterative runs. To calculate fitness values, artificial neural networks are used as classifier. The small, round blue cell tumors (SRBCTs) which is difficult to distinguish via pathological single test was used as test diseases for classification, and the test results showed the 96% of exact classification capability for 25 test samples.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.234-238
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• 1996

In this paper, Plasma-Enhanced Chemical Vapor Deposition (PECVD) modeling using Polynomial Neural Networks (PNN) has been introduced. The deposition of SiO2 was characterized via a 25-1 fractional factorial experiment, was used to train PNNs using predicted squared error (PSE). The optimal neural network structure and learning parameters were determined by means of a second fractional factorial experiment. The optimized networks minimized both learning and prediction error. From these PNN process models, the effect of deposition conditions on film properties has been studied. The deposition experiments were carried out in a Plasma Therm 700 series PECVD system. The models obtained will ultimately be used for several other manufacturing applications, including recipe synthesis and process control.

• 한국컴퓨터정보학회논문지
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• 제20권8호
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• pp.85-92
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• 2015

Due to the advancements in the capabilities of smart devices and home networks, we are able to easily access multimedia contents stored in a home server. In this paper, we present a wireless media content sharing mechanism for home networks that utilizes UPnP-based DLNA technology. We also present a novel Peer-to-Peer content sharing system that is able to operate on the client as well as server. Our system supports multiple push/pull services simultaneously via a multi-thread technique, and our intuitive user interface facilitates ease of use. Future studies would explore the feasibility of implementing our system in a multi-hop environment or providing a community-wide service.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.231-243
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• 2022

The expansion of new applications and business models is being significantly fueled by the development of Fifth Generation (5G) networks, which are becoming more widely accessible. The creation of the newest intelligent vehicular net- works and applications is made possible by the use of Vehicular Ad hoc Networks (VANETs) and Software Defined Networking (SDN). Researchers have been concentrating on the integration of SDN and VANET in recent years, and they have examined a variety of issues connected to the architecture, the advantages of software defined VANET services, and the new features that can be added to them. However, the overall architecture's security and robustness are still in doubt and have received little attention. Furthermore, new security threats and vulnerabilities are brought about by the deployment and integration of novel entities and several architectural components. In this study, we comprehensively examine the good and negative effects of the most recent SDN-enabled vehicular network topologies, focusing on security and privacy. We examine various security flaws and attacks based on the existing SDVN architecture. Finally, a thorough discussion of the unresolved concerns and potential future study directions is provided.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• International Journal of Computer Science & Network Security
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• 제24권9호
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• pp.195-201
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• 2024

Vehicular Ad-hoc Networks (VANETs) became very popular in few years and it has been widely used in research and industry communities. VANET is a collection of wireless vehicle nodes forming a temporary network without using any centralized Road Side Unit (RSU). VANET is a subset of Mobile Adhoc Networks (MANET). It improves the safety of vehicles. It also supports Intelligent Transportation Systems.Routing is the major component of communication protocols in VANETs. Packets are to be routed from the source node to destination node. Because of frequent topology changes and routing overhead, selection of routing protocol in VANET is a great challenge. There are various routing protocols available for VANET. This paper involves study of Temporally Ordered Routing protocol (TORA) and performance metrics are analyzed with the help of NS2 Simulator.

• Journal of Internet Technology
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• 제20권1호
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• pp.293-300
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• 2019

The Internet of Things (IoT) enables embedded devices to connect to the internet either through IP or the web in a physical environment. The increase in performance of wireless access services, adaptive load balancing, and interference routing metric becomes the key challenges in Wireless Mesh Networks (WMN). However, in the case of IoT over WMN, a large number of users generate abundant net flows, which can result in network traffic jam. Therefore, in this paper, we propose a Load Balancing and Interference Delay Aware routing metric algorithm to efficiently address the issues present in the current work. The proposed scheme efficiently utilizes the available mesh station queue information and the number of mesh stations suffering from channel interference in the available path. The simulations results show that the proposed scheme performed superior to the existing routing metrics present in the current literature for similar purposes.

• Journal of Internet Technology
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• 제22권2호
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• pp.297-309
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• 2021

Mobile sinks are being exploited for various purposes for solving the hotspot problem. Thus, the mobility support technique of mobile sinks is important, and it has been studied steadily. A survey was conducted of studies of mobile sinks in sensor networks having different movement patterns depending on the applications. The related techniques were divided into three main categories according to mobility pattern: predefined, random, and control. In addition, communications for mobile sinks are strongly affected by whether there is a single mobile sink or multiple ones. Based on this two-level categorization, an overview is presented of some state-of-the-art mobility support techniques researched during the past three years, and then the technique are analyzed according to various criteria. Finally, this survey concludes with a summary and a discussion of some future research challenges.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.833-852
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• 2019

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1145-1157
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• 2020

A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.