• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.83-90
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• 2022

Traditional Cloud Computing would be unable to safely host IoT data due to its high latency as the number of IoT sensors and physical devices accommodated on the Internet grows by the day. Because of the difficulty of processing all IoT large data on Cloud facilities, there hasn't been enough research done on automating the security of all components in the IoT-Cloud ecosystem that deal with big data and real-time jobs. It's difficult, for example, to build an automatic, secure data transfer from the IoT layer to the cloud layer, which incorporates a large number of scattered devices. Addressing this issue this article presents an intelligent algorithm that deals with enhancing security aspects in IoT cloud ecosystem using butterfly optimization algorithm.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.79-82
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• 2019

This study aims to design and implement automated SEO tools that has applied the artificial intelligence techniques for search engine optimization (SEO; Search Engine Optimization). Traditional Search Engine Optimization (SEO) on-page optimization show limitations that rely only on knowledge of webpage administrators. Thereby, this paper proposes the metadata generation system. It introduces three approaches for recommending metadata; i) Downloading the metadata which is the top of webpage ii) Generating terms which is high relevance by using bi-directional Long Short Term Memory (LSTM) based on attention; iii) Learning through the Generative Adversarial Network (GAN) to enhance overall performance. It is expected to be useful as an optimizing tool that can be evaluated and improve the online marketing processes.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.112-117
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• 2003

This paper deals with the problem of synthesis of the 3-dimensional Grasp Planning. In previous studies the genetic algorithm has been used to find optimal grasping points, but it had a limitation such as the determination time of grasping points was so long. To overcome this limitation we proposed a new algorithm which employs the Neural Network. In the Neural network we chose input parameters based on the shape of the object and output parameters resulted from optimization with the GA method. In this study the GRNN method is employed, it has been trained by the result value of optimization method and it has been tested by known object. The algorithm is verified by computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4451-4466
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• 2014

We present joint radio selection and relay scheme that delivers data from a source to a sink in heterogeneous stationary sensor networks consisting of various radio interfaces. The proposed scheme finds the optimal relay nodes and their corresponding radio interfaces that minimize energy consumption throughout the network while satisfying the end-to-end packet deadline requirement. We formulate the problem of routing through radio interface selection into binary integer programs, and obtain the optimal solution by solving with an optimization solver. We examine a trade-off relationship between energy consumption and packet delay based on network level simulations. We show that given the end-to-end deadline requirement, our routing algorithm finds the most energy-efficient routing path and radio interface across mesh hops. We demonstrate that the proposed routing scheme exploits the given packet delivery time to turn into network benefit of reducing energy consumption compared to routing based on single radio interface.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.119-131
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• 2009

Existing proposals on route optimization for nested Network Mobility(NEMO) have a problem that it is difficult to optimize a route promptly in an environment where a MR moves frequently. Also, they have L3 handoff latency as well as route optimization latency until an optimized route is formed. In this paper, we propose a L3 handoff scheme that supports fast route optimization for nested NEMO without any additional optimization procedure. To achieve this, our proposed scheme is designed to include a procedure that an AR acquires address informations of a MR. After receiving binding update message from the MR, the AR performs the binding update procedure with the MR's HA on behalf of the MR. Packets are delivered to the AR only passing by the MR's HA after a bi-directional tunnel is formed between the AR and the HA. The result of our performance evaluation has shown that the proposed scheme could provide excellent performance compared with the RRH and the ONEMO.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.49-57
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• 2012

In P2P streaming management, peer's churning and finding efficient topology architecture optimization algorithm that reduces streaming delay is important. This paper studies a topology optimization algorithm based on the P2P streaming using peer's link information. The proposed algorithm is based on the estimation of peer's upload bandwidth using peer's link information on mesh-network. The existing algorithm that uses the information of connected links is efficient to reduce message overload in the point of resource management. But it has a risk of making unreliable topology not considering upload bandwidth. And when some network error occurs in a server-closer-peer, it may make the topology worse. In this paper we propose an algorithm that makes up for the weak point of the existing algorithm. We compare the existing algorithm with the proposed algorithm using test data and analyze each simulation result.

• International Journal of Contents
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• v.12 no.3
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• pp.22-28
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• 2016

Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.5
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• pp.551-561
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• 1992

A neural optimization network and fuzzy rules are proposed to control the redundant robot manipulators in an environment with obstacle. A neural optimization network is employed to solve the optimization problem for resolved motion control of redundant robot manipulators in an environment with obstacle. The fuzzy rules are proposed to determine the weights of neural optimization networks to avoid the collision between robot manipulators and obstacle. The inputs of fuzzy rules are the resultant distance and change of the distance and sum of the changes by differential motion of each joint. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision aboidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planar type with three degrees of freedom.