• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1291-1298
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• 2017

Optical coherence tomography (OCT) is the most common medical imaging device with the ability to image the retina in eyes at micrometer resolution and to visualize the pathological indicators of many retinal diseases such as Age-Related Macular Degeneration (AMD) and diabetic retinopathy. Accordingly, there have been research activities to analyze and process OCT images to identify those indicators and make medical decisions based on the findings. In this paper, we use a deep convolutional neural network for analysis of OCT volume data to distinguish AMD from normal patients. We propose a novel approach where images in each OCT volume are grouped together into sub-volumes and the network is trained by those sub-volumes instead of individual images. We conducted an experiment using public data set to evaluate the performance of the proposed approach. The experiment confirmed performance improvement of our approach over the traditional image-by-image training approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.164-169
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• 2003

In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory networks from genome expression data. The proposed model overcomes the dimensionality problem of multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene Ontology, pathway, and protein protein interaction information for extracting other related genes. With these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray data itself but also well-proved biological knowledge. This integrated approach can improve there liability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S. Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].

• International Journal of Contents
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• 제13권1호
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• pp.45-52
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• 2017

Carbon Capture and Storage (CCS) technologies involve three major stages, i.e., capture, transport, and storage. The transportation stage of CCS technologies has received relatively little attention because the requirements for $CO_2$ transport differ based on the industry-related conditions, geological, and demographical characteristics of each country. In this study, we analyzed the properties of $CO_2$ transport using a pipeline. This study has important implications for ensuring the stability of a long-term CCS as well as the large cost savings, as compared to the small cost ratio as a percentage of the entire CCS system. The state of $CO_2$, network topologies, and node distribution are among the major factors that influence $CO_2$ transport via pipelines. For the analysis of the properties of $CO_2$ transport using a pipeline, the $CO_2$ pipeline connections were visualized by the simulator developed by Lee [11] based on the network topologies in $CO_2$ transport. The case of Korean CCS technologies was applied to the simulation.

• 한국경영과학회지
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• 제31권2호
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• pp.141-155
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• 2006

In this paper, we address an extended hub-and-spoke transportation network design problem (EHSNP). In the existing hub location problems, the location and number of spokes, and shipments on spokes are given as input data. These may, however, be viewed as the variables according to the areas which they cover. Also, the vehicle routing in each spoke needs to be considered to estimate the network cost more correctly. The EHSNP is a problem of finding the location of hubs and spokes, and pickup/delivery routes from each spoke, while minimizing the total related transportation cost in the network. The EHSNP is an integrated problem that consists of several interrelated sub-problems. To solve EHSNP, we present an approach based on a symbiotic evolutionary algorithm (symbiotic EA), which are known as an efficient tool to solve complex integrated optimization problems. First, we propose a framework of symbiotic EA for EHSNP and its genetic elements suitable for each sub-problem. To analyze the proposed algorithm, the extensive experiments are performed with various test-bed problems. The results show that the proposed algorithm is promising in solving the EHSNP.

• Industrial Engineering and Management Systems
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• 제14권4호
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• pp.347-371
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• 2015

A hybrid procedure based on multi-Criteria Satisfaction Analysis (MUSA) and a Network Data Envelopment Analysis (NDEA) is proposed to evaluate the relative efficiency of customer services in bank branches. First, a three-stage process including sub-processes such as customer expectations, customer satisfaction, and customer loyalty, is defined to model the banking customer services. Then, fulfillment of customer expectations, customer loyalty level, and the customer satisfaction degree are measured and quantified through a multi-dimensional questionnaire based on customers' perceptions analysis and MUSA method, respectively. The customer services scores and the other criteria such as mean of employee evaluation score, operation costs, assets, deposits, loans, number of accounts are considered in network three-stage DEA model. The proposed NDEA model is formed based on multipliers perspective, output-oriented, and constant return to scale assumptions. The proposed NDEA model quantifies and assesses the total efficiency of main process and assigns the efficiency to customer expectations, customer satisfactions, and customer loyalties sub-processes in bank branches. The whole procedure is applied on 30 bank branches in IRAN. The proposed approach can be used in other organizations such as airports, airline agencies, urban transportation systems, railway organizations, chain stores, chain restaurants, public libraries, and entertainment centers.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.35-42
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• 2021

In the current context of digitalization of education, the use of modern methods and techniques of data analysis and processing in order to improve students' school results has a very important role. In our paper, we aimed to perform a comparative study of the classification performances of AdaBoost, SVM, Naive Bayes, Neural Network and kNN algorithms to classify the results obtained at the Baccalaureate by students from a college in Suceava, during 2012-2019. To evaluate the results we used the metrics: AUC, CA, F₁, Precision and Recall. The AdaBoost algorithm achieves incredible performance for classifying the results into two categories: promoted / rejected. Next in terms of performance is Naive Bayes with a score of 0.999 for the AUC metric. The Neural Network and kNN algorithms obtain scores of 0.998 and 0.996 for AUC, respectively. SVM shows poorer performance with the score 0.987 for AUC. With the help of the HeatMap and DataTable visualization tools we identified possible correlations between classification results and some characteristics of data.

• IMMUNE NETWORK
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• 제23권1호
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• pp.11.1-11.11
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• 2023

Memory T cells that mediate fast and effective protection against reinfections are usually generated upon recognition on foreign Ags. However, a "memory-like" T-cell population, termed virtual memory T (T_VM) cells that acquire a memory phenotype in the absence of foreign Ag, has been reported. Although, like innate cells, T_VM cells reportedly play a role in first-line defense to bacterial or viral infections, their protective or pathological roles in immune-related diseases are largely unknown. In this review, we discuss the current understanding of T_VM cells, focusing on their distinct characteristics, immunological properties, and roles in various immune-related diseases, such as infections and cancers.

• Journal of applied mathematics & informatics
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• 제42권1호
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• pp.77-83
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• 2024

Chung defined a pebbling move on a graph G as the removal of two pebbles from one vertex and the addition of one pebble to an adjacent vertex. The monophonic pebbling number guarantees that a pebble can be shifted in the chordless and the longest path possible if there are any hurdles in the process of the supply chain. For a connected graph G a monophonic path between any two vertices x and y contains no chords. The monophonic pebbling number, µ(G), is the least positive integer n such that for any distribution of µ(G) pebbles it is possible to move on G allowing one pebble to be carried to any specified but arbitrary vertex using monophonic a path by a sequence of pebbling operations. The aim of this study is to find out the monophonic pebbling numbers of the sun graphs, (C_n × P₂) + K₁ graph, the spherical graph, the anti-prism graphs, and an n-crossed prism graph.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.161-172
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• 2017

In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.