• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Journal of Periodontal and Implant Science
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• v.53 no.5
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• pp.336-346
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• 2023

Purpose: The Korea Oral Biobank Network (KOBN) was established in 2021 as a branch of the Korea Biobank Network under the Korea Centers for Disease Control and Prevention to provide infrastructure for the collection, management, storage, and utilization of human bioresources from the oral cavity and associated clinical data for basic research and clinical studies. Methods: To address the need for the unification of the biobanking process, the KOBN organized the concept review for all the processes. Results: The KOBN established standard operating procedures for the collection, processing, and storage of oral samples. Conclusions: The importance of collecting high-quality bioresources to generate accurate and reproducible research results has always been emphasized. A standardized procedure is a basic prerequisite for implementing comprehensive quality management of biological resources and accurate data production.

• Korean Circulation Journal
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• v.52 no.3
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• pp.233-242
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• 2022

Background and Objectives: For adult men with congenital heart disease (ACHD), data on erectile dysfunction (ED) is limited. We aimed to assess the frequency of ED, its role in patient-physician communication and to identify parameters predicting ED. Methods: Male ACHD ≥18 years registered at the German National Register for Congenital Heart Defects were invited to participate in an online questionnaire about sexual health. Participants with presumed ED according to International Index of Erectile Function Score were compared to patients without ED. Results: The 371 patients responded to the questionnaire (83% with moderate to highly complex ACHD). The 43% presented with more than mild ED. When ED was present, patients complained about general anxiety to be sexually active more often (p<0.05) and underwent sexual activity less frequently compared to those without ED (p<0.05). Age ≥40 years (odds ratio [OR], 3.04; p=0.002), being single (OR, 6.82; p<0.0001), anxiety to be sexually active (OR, 2.64; p=0.0002) and psychiatric disease (OR, 4.33; p<0.0007) emerged as independent predictors for ED. Overall, patients sought medical advice in 6.7% of cases, whilst 29.6% would appreciate an active approach by the physician to address this sensitive topic. Conclusions: ED is affecting one third to one half of male ACHD according to a questionnaire-based analysis. Older age, being single, fear of sexual activity due to ACHD and psychiatric disorder emerged as independent predictors for ED. These parameters can easily be assessed to identify patients at risk. ED should be addressed proactively by health professionals.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.263-266
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• 2005

We made a stepping stone for asthma study by analyzing an asthma-specific protein-protein interaction network. It follows the power-law degree distribution and its hub nodes and skeleton frame of the network agreed with the prior knowledge about asthma pathway. This study is providing a systematic approach to analyze the complex effect of genes or to represent the frame of their relations associated with specific disease.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.281-282
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• 2016

The electrical current generated by heart creates not only electric potential but also a magnetic field. In this study, the signals obtained magnetocardiogram (MCG) using 61 channel superconducting quantum interference device(SQUID) system the clinical significance of various parameters has been developed MCG. Neural network algorithm was used to perform the analysis of heart disease.

• Korean Journal of Biological Psychiatry
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• v.4 no.2
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• pp.194-197
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• 1997

Neural network models, also known as connectionist models or PDP models, simulate some functions of the brain and may promise to give insight in understanding the cognitive brain functions. The models composed of neuron-like elements that are linked into circuits can learn and adapt to its environment in a trial and error fashion. In this article, the history and principles of the neural network modeling are briefly reviewed, and its applications to psychiatry are discussed.

• The Journal of the Korea Contents Association
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• v.1 no.1
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• pp.8-14
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• 2001

In this paper, we suggests a algorithms of recognizing the disease region by extracting particular organ from medical image. This method can extract liver region in spite of input image including many organs and charged format by using multi-threshold of feed-back-structure for segmentation liver region, and suggest the recognition of disease region in extracted liver, using multi-neural network structured by RBF and BP, overcoming the defect of single-neural network. The algorithm in this paper is proficient in adaptation for a multi form change of input medical image. This algorithm can be used at tole-medicine through automatic recognition after recognizing of the disease region by real-tire medical Image.

• Journal of radiological science and technology
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• v.43 no.3
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• pp.195-208
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• 2020

Artificial intelligence(AI) is a field of computer science that is defined as allowing computers to imitate human intellectual behavior, even though AI's performance is to imitate humans. It is grafted across software-based fields with the advantages of high accuracy and speed of processing that surpasses humans. Indeed, the AI based technology has become a key technology in the medical field that will lead the development of medical image analysis. Therefore, this article introduces and discusses the concept of deep learning-based medical imaging analysis using the principle of algorithms for convolutional neural network(CNN) and back propagation. The research cases application of the AI based medical imaging analysis is used to classify the various disease(such as chest disease, coronary artery disease, and cerebrovascular disease), and the performance estimation comparing between AI based medical imaging classifier and human experts.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.