• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.409-410
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• 2022

This paper proposes 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.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.113-118
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• 2024

Pursuance Sentiment Analysis on Twitter is difficult then performance it's used for great review. The present be for the reason to the tweet is extremely small with mostly contain slang, emoticon, and hash tag with other tweet words. A feature extraction stands every technique concerning structure and aspect point beginning particular tweets. The subdivision in a aspect vector is an integer that has a commitment on ascribing a supposition class to a tweet. The cycle of feature extraction is to eradicate the exact quality to get better the accurateness of the classifications models. In this manuscript we proposed Term Frequency-Inverse Document Frequency (TF-IDF) method is to secure Principal Component Analysis (PCA) with Naïve Bayes Classifiers. As the classifications process, the work proposed can produce different aspects from wildly valued feature commencing a Twitter dataset.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.268-273
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• 2017

Recently, deep learning encoder-based approach has been actively applied in the field of sentiment classification. However, Long Short-Term Memory network deep learning encoder, the commonly used architecture, lacks the quality of vector representation when the length of the documents is prolonged. In this study, for effective classification of the sentiment documents, we suggest the use of attention method-based deep learning encoder that generates document vector representation by weighted sum of the outputs of Long Short-Term Memory network based on importance. In addition, we propose methods to modify the attention method-based deep learning encoder to suit the sentiment classification field, which consist of a part that is to applied to window attention method and an attention weight adjustment part. In the window attention method part, the weights are obtained in the window units to effectively recognize feeling features that consist of more than one word. In the attention weight adjustment part, the learned weights are smoothened. Experimental results revealed that the performance of the proposed method outperformed Long Short-Term Memory network encoder, showing 89.67% in accuracy criteria.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.689-701
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• 2017

In recent offshore industries, various ambiguous terms have been used without clear definition or classification, causing difficulties in legal, technical, and educational understanding and usage. For an example, the commonly used term of 'Offshore Plant' in Korea is not an universal word technically. There has been no clear technical or legal definition about the 'Offshore Plant' and its classification is also very ambiguous; sometimes it is used to refer offshore oil and gas production platform or it is used to mean offshore renewable power generation plant in some cases. To build a conceptual framework, therefore, this paper suggests a classification of offshore units (1) using internationally agreed terms, (2) agreed with the technical classification used by the ship classification society and (3) being able to include not only the current but also future concepts of offshore units.

• Journal of the Korean Society of Food Culture
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• v.29 no.2
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• pp.178-186
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• 2014

This study sought to prove the validity of Hansik Yangnyomjang classification Korean culinary education. survey was conducted among Korean Cuisine professionals, culinary instructors, culinary professionals and potential students from various backgrounds. ata were collected by self-administered questionnaires and analyzed by reliability analysis, frequency analysis and t-test. any differences in terms of the validity of Hansik Yangnyomjang classification between groups based on their majors, teaching experiences, and knowledge of sauce classification. First, the result showed that fermented Jang is core element Korean cuisine. Second, Hansik Yangnyomjang classification needs to be organized around Balhyojang. Third, Hansik Yangnyomjang classification for beginners and foreigners who want to learn Korean Cuisine relatively easily. Finally, the term 'sauce' is not suitable for replacing Yangnyomjang.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.31-41
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• 2018

Text classification has been studied for a long time in the Natural Language Processing field. In this paper, we propose an article- and paragraph-level genre classification system using Word2Vec-based LSTM, GRU, and CNN models for large-scale English corpora. Both article- and paragraph-level classification performed best in accuracy with LSTM, which was followed by GRU and CNN in accuracy performance. Thus, it is to be confirmed that in evaluating the classification performance of LSTM, GRU, and CNN, the word sequential information for articles is better than the word feature extraction for paragraphs when the pre-trained Word2Vec-based word embeddings are used in both deep learning-based article- and paragraph-level classification tasks.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.1-7
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• 2019

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.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.403-416
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• 2001

In this paper, we are mainly concerned with the classification of nonscillatory solutions for the higher order difference equation and some existence results for some kinds of nonoscillatory solutions.

• Journal of Korean Academy of Rural Health Nursing
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• v.15 no.2
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• pp.66-73
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• 2020

Purpose: This study was done to report nursing case for ADL improvement of elders who have CVA(Cerebrovascular Accident) sequelae. Methods: The client had registered in the C visiting nursing center after being decided a long-term care Grade 2. Data were collected through consultation logs for recipients, Activities of Daily Living (ADL) records, fall risk assessment (Huhn) sheets, decubitus ulcer risk assessment (Braden Scale) sheets, cognition assessment (K-MMSE) sheets, long-term care benefit provision records, and interviews with visiting nurse. Data were collected and analyzed according to the Omaha System problem classification. The intervention scheme and the problem rating scale for performance were applied to present the case for home-visit nursing. Results: The client registered in August, 2018, was provided home-visit nursing care once a week as of September 2020. ADL, cognitive levels and decubitus ulcer risks were found to have improved. Conclusion: This case report presents the value of classifying nursing problems and checking nursing intervention provided to patients with problems of ADL. The presentation of home-visit nursing cases applying a standardized nursing problem classification scheme for clients with various problems showed that a high quality level of care is guaranteed and evidence-based nursing can be provided by visiting nurses.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.681-692
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• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.