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Novel Image Classification Method Based on Few-Shot Learning in Monkey Species
Wang, Guangxing,Lee, Kwang-Chan,Shin, Seong-Yoon The Korea Institute of Information and Commucation 2021 Journal of information and communication convergen Vol.19 No.2
This paper proposes a novel image classification method based on few-shot learning, which is mainly used to solve model overfitting and non-convergence in image classification tasks of small datasets and improve the accuracy of classification. This method uses model structure optimization to extend the basic convolutional neural network (CNN) model and extracts more image features by adding convolutional layers, thereby improving the classification accuracy. We incorporated certain measures to improve the performance of the model. First, we used general methods such as setting a lower learning rate and shuffling to promote the rapid convergence of the model. Second, we used the data expansion technology to preprocess small datasets to increase the number of training data sets and suppress over-fitting. We applied the model to 10 monkey species and achieved outstanding performances. Experiments indicated that our proposed method achieved an accuracy of 87.92%, which is 26.1% higher than that of the traditional CNN method and 1.1% higher than that of the deep convolutional neural network ResNet50.
An Improved Text Classification Method for Sentiment Classification
Wang, Guangxing,Shin, Seong Yoon The Korea Institute of Information and Commucation 2019 Journal of information and communication convergen Vol.17 No.1
In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.
A Text Sentiment Classification Method Based on LSTM-CNN
Guangxing Wang,Seong-Yoon Shin(신성윤),Won Joo Lee(이원주) 한국컴퓨터정보학회 2019 韓國컴퓨터情報學會論文誌 Vol.24 No.12
머신 러닝의 심층 개발로 딥 러닝 방법은 특히 CNN(Convolution Neural Network)에서 큰 진전을 이루었다. 전통적인 텍스트 정서 분류 방법과 비교할 때 딥 러닝 기반 CNN은 복잡한 다중 레이블 및 다중 분류 실험의 텍스트 분류 및 처리에서 크게 발전하였다. 그러나 텍스트 정서 분류를 위한 신경망에도 문제가 있다. 이 논문에서는 LSTM (Long-Short Term Memory network) 및 CNN 딥 러닝 방법에 기반 한 융합 모델을 제안하고, 다중 카테고리 뉴스 데이터 세트에 적용하여 좋은 결과를 얻었다. 실험에 따르면 딥 러닝을 기반으로 한 융합 모델이 텍스트 정서 분류의 예측성과 정확성을 크게 개선하였다. 본 논문에서 제안한 방법은 모델을 최적화하고 그 모델의 성능을 개선하는 중요한 방법이 될 것이다. With the in-depth development of machine learning, the deep learning method has made great progress, especially with the Convolution Neural Network(CNN). Compared with traditional text sentiment classification methods, deep learning based CNNs have made great progress in text classification and processing of complex multi-label and multi-classification experiments. However, there are also problems with the neural network for text sentiment classification. In this paper, we propose a fusion model based on Long-Short Term Memory networks(LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.
A New Image Classification Method
Guangxing Wang,Kwang-Chan Lee,Seong-Yoon Shin 한국정보통신학회 2021 2016 INTERNATIONAL CONFERENCE Vol.12 No.1
In this paper, we propose a new image classification method based on several trainings, which is mainly used to solve model overfitting and non-convergence in image classification tasks of small data sets and to improve classification accuracy. This method, based on prime number learning, uses model structure optimization to extend the underlying convolutional neural network (CNN) model and adds a convolutional layer to extract more image features to improve classification accuracy.
ResNet-Based Simulations for a Heat-Transfer Model Involving an Imperfect Contact
Guangxing Wang,조광현,신성윤 한국정보통신학회 2022 Journal of information and communication convergen Vol.20 No.4
Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.
Mingcui Wang,Guangxing Li,Jiechao Yin,Xiaofeng Ren 대한수의학회 2009 JOURNAL OF VETERINARY SCIENCE Vol.10 No.4
A porcine reproductive and respiratory syndrome virus (PRRSV) was obtained from clinic samples. Genes 5 and 6 encoding for the viral glycoprotein 5 and a membrane protein of the PRRSV designated as HH08 were amplified by reverse transcription-PCR. These sequences were compared with reference sequences derived from different geographical locations. The results indicated that the virus belongs to the North American type rather than European. Comparative analyses of the genetic diversity between the PRRSV isolate HH08 and other Chinese as well as foreign reference strains of PRRSV were discussed based on the sequence comparison and the topology of phylogenetic trees constructed in this study.