• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.59-65
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• 2019

There are various subtypes of cells in cancer tissues, but it is hard to confirm their composition experimentally. Here, we estimated the cell composition of each sample from gene expression data by using statistical machine learning approaches, two different regression models and investigated whether the cell composition was different between cancer and normal tissue. As a result, we found that CD8 T cell and Neutrophil were increased in oral cancer tissues compared to normal tissues. In addition, we applied t-SNE, which is one of the unsupervised learning, to verify whether normal tissue and oral cancer tissue can be clustered by the derived cell composition. Moreover, we showed that it is possible to predict oral cancer and normal tissue by several supervised classification algorithms. The study would help to improve the understanding of the immune cell infiltration at oral cancer.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.6
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• pp.125-134
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• 2004

When cryptosystem designers implement devices that computing power or memory is limited such as smart cards, PDAs and so on, not only he/she has to be careful side channel attacks(SCA) but also the cryptographic algorithms within the device has to be efficient using small memory. For this purpose, countermeasures such as Moiler's method, Okeya-Takagi's one and overlapping window method, based on window method to prevent SCA were proposed. However, Moiler's method and Okeya-Talngi's one require additional cost to prevent other SCA such as DPA, Second-Order DPA, Address-DPA, and so on since they are immune to only SPA. Also, overlapping window method has a drawback that requires big memory. In this paper, we analyze existing countermeasures and propose an efficient and secure countermeasure that is immune to all existing SCA using advantages of each countermeasure. Moreover, the proposed countermeasure can enhance the efficiency using mixed coordinate systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.903-909
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• 2019

Periodontal disease is observed in many adult persons. However we has not clear know the molecular mechanism and how to treat the disease at the molecular levels. Here, we investigated the molecular differences between periodontal disease and normal controls using gene expression data. In particular, we checked whether the periodontal disease and normal tissues would be classified by machine learning algorithms using gene expression data. Moreover, we revealed the differentially expression genes and their function. As a result, we revealed that the periodontal disease and normal control samples were clearly clustered. In addition, by applying several classification algorithms, such as decision trees, random forests, support vector machines, the two samples were classified well with high accuracy, sensitivity and specificity, even though the dataset was imbalanced. Finally, we found that the genes which were related to inflammation and immune response, were usually have distinct patterns between the two classes.

• IE interfaces
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• v.23 no.2
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• pp.147-155
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• 2010

Reliability allocation is defined as a problem of determination of the reliability for subsystems and components to achieve target system reliability. The determination of both optimal component reliability and the number of component redundancy allowing mixed components to maximize the system reliability under resource constraints is called reliability-redundancy allocation problem(RAP). The main objective of this study is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for reliability-redundancy allocation problem that decides both optimal component reliability and the number of component redundancy to maximize the system reliability under cost and weight constraints. The global optimal solutions of each example are obtained by using CPLEX 11.1. The component structure, reliability, cost, and weight were computed by using HPGA and compared the results of existing metaheuristic such as Genetic Algoritm(GA), Tabu Search(TS), Ant Colony Optimization(ACO), Immune Algorithm(IA) and also evaluated performance of HPGA. The result of suggested algorithm gives the same or better solutions when compared with existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improve solution through swap, 2-opt, and interchange processes. In order to calculate the improvement of reliability for existing studies and suggested algorithm, a maximum possible improvement(MPI) was applied in this study.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2222-2229
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• 2023

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.410-427
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• 2023

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

• Fisheries and Aquatic Sciences
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• v.22 no.12
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• pp.28.1-28.8
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• 2019

Background: In the present study, we evaluated four commonly used housekeeping genes, viz., actin-β, elongation factor-1α (EF1α), acidic ribosomal protein (ARP), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as internal references for quantitative analysis of immune genes in nervous necrosis virus (NNV)-infected seven-band grouper, Hyporthodus septemfasciatus. Methods: Expression profiles of the four genes were estimated in 12 tissues of healthy and infected seven-band grouper. Expression stability of the genes was calculated using the delta Ct method, BestKeeper, NormFinder, and geNorm algorithms. Consensus ranking was performed using RefFinder, and statistical analysis was done using GraphpadPrism 5.0. Results: Tissue-specific variations were observed in the four tested housekeeping genes of healthy and NNV-infected seven-band grouper. Fold change calculation for interferon-1 and Mx expression using the four housekeeping genes as internal references presented varied profiles for each tissue. EF1α and actin-β was the most stable expressed gene in tissues of healthy and NNV-infected seven-band grouper, respectively. Consensus ranking using RefFinder suggested EF1α as the least variable and highly stable gene in the healthy and infected animals. Conclusions: These results suggest that EF1α can be a fairly better internal reference in comparison to other tested genes in this study during the NNV infection process. This forms the pilot study on the validation of reference genes in Hyporthodus septemfasciatus, in the context of NNV infection.

• BMB Reports
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• v.53 no.4
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• pp.191-205
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• 2020

The unexpected pandemic set off by the novel coronavirus 2019 (COVID-19) has caused severe panic among people worldwide. COVID-19 has created havoc, and scientists and physicians are urged to test the efficiency and safety of drugs used to treat this disease. In such a pandemic situation, various steps have been taken by the government to control and prevent the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2). This pandemic situation has forced scientists to rework strategies to combat infectious diseases through drugs, treatment, and control measures. COVID-19 treatment requires both limiting viral multiplication and neutralizing tissue damage induced by an inappropriate immune reaction. Currently, various diagnostic kits to test for COVID-19 are available, and repurposing therapeutics for COVID-19 has shown to be clinically effective. As the global demand for diagnostics and therapeutics continues to rise, it is essential to rapidly develop various algorithms to successfully identify and contain the virus. This review discusses the updates on specimens/samples, recent efficient diagnostics, and therapeutic approaches to control the disease and repurposed drugs mainly focusing on chloroquine/hydroxychloroquine and convalescent plasma (CP). More research is required for further understanding of the influence of diagnostics and therapeutic approaches to develop vaccines and drugs for COVID-19.

• Journal of Power Electronics
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• v.18 no.3
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• pp.931-943
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• 2018

Wireless power transfer (WPT) technology has been the focus of a lot of research due to its safety and convenience. The Z-source inverter (ZSI) was introduced into WPT systems to realize improved system performance. The ZSI regulates the dc-rail voltage in WPT systems without front-end converters and makes the inverter bridge immune to shoot-through states. However, when the WPT system is combined with a ZSI, the system parameters must be configured to prevent the ZSI from entering an "accidental shoot-through" (AST) state. This state can increase the THD and decrease system power and efficiency. This paper presents a mathematical analysis for the characteristics of a WPT system and a ZSI while addressing the causes of the AST state. To deal with this issue, the impact of the system parameters on the output are analyzed under two control algorithms and the primary compensation capacitance range is derived in detail. To validate the analysis, both simulations and experiments are carried out and the obtained results are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1317-1341
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• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.