Pub Date : 2022-01-01DOI: 10.1109/KSE56063.2022.9953800
Thi Van Vu, T. Luong, Van Quan Hoang
{"title":"An efficient Privacy-Preserving Recommender System","authors":"Thi Van Vu, T. Luong, Van Quan Hoang","doi":"10.1109/KSE56063.2022.9953800","DOIUrl":"https://doi.org/10.1109/KSE56063.2022.9953800","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80294912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A comprehensive and bias-free evaluation of genomic variant clinical interpretation tools","authors":"Nguyen Minh Trang, Anh-Vu Mai-Nguyen, Tran Hoang Anh, Do Nguyet Minh, Nguyen Thanh Nguyen","doi":"10.1109/KSE53942.2021.9648755","DOIUrl":"https://doi.org/10.1109/KSE53942.2021.9648755","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74144913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1109/KSE53942.2021.9648655
Nguyen Ha Thanh, Binh Dang, Minh Q. Bui, Le-Minh Nguyen
{"title":"Evaluate and Visualize Legal Embeddings for Explanation Purpose","authors":"Nguyen Ha Thanh, Binh Dang, Minh Q. Bui, Le-Minh Nguyen","doi":"10.1109/KSE53942.2021.9648655","DOIUrl":"https://doi.org/10.1109/KSE53942.2021.9648655","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82836028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01Epub Date: 2020-12-16DOI: 10.1109/kse50997.2020.9287797
Tien T T Tran, Sy V Nghiem, Van T Le, Tho T Quan, Vinh Nguyen, Hong Yung Yip, Olivier Bodenreider
The Unified Medical Language System, or UMLS, is a repository of medical terminology developed by the U.S. National Library of Medicine for improving the computer system's ability of understanding the biomedical and health languages. The UMLS Metathesaurus is one of the three UMLS knowledge sources, containing medical terms and their relationships. Due to the rapid increase in the number of medical terms recently, the current construction of UMLS Metathesaurus, which heavily depends on lexical tools and human editors, is error-prone and time-consuming. This paper takes advantages of the emerging deep learning models for learning to predict the synonyms and non-synonyms between the pairs of biomedical terms in the Metathesaurus. Our learning approach focuses a subset of specific terms instead of the whole Metathesaurus corpus. Particularly, we train the models with biomedical terms from the Disorders semantic group. To strengthen the models, we enrich the inputs with different strategies, including synonyms and hierarchical relationships from source vocabularies. Our deep learning model adopts the Siamese KG-LSTM (Siamese Knowledge Graph - Long Short-Term Memory) in the architecture. The experimental results show that this approach yields excellent performance when handling the task of synonym detection for Disorders semantic group in the Metathesaurus. This shows the potential of applying machine learning techniques in the UMLS Metathesaurus construction process. Although the work in this paper focuses only on specific semantic group of Disorders, we believe that the proposed method can be applied to other semantic groups in the UMLS Metathesaurus.
统一医学语言系统(Unified Medical Language System，简称UMLS)是一个医学术语库，由美国国家医学图书馆开发，用于提高计算机系统理解生物医学和健康语言的能力。UMLS元辞典是三个UMLS知识库之一，包含医学术语及其关系。由于近年来医学术语数量的迅速增加，目前的UMLS元词典的构建严重依赖于词汇工具和人工编辑，容易出错且耗时。本文利用新兴的深度学习模型来学习预测元词库中生物医学术语对之间的同义词和非同义词。我们的学习方法侧重于特定术语的子集，而不是整个元词库。特别地，我们用来自障碍语义组的生物医学术语训练模型。为了增强模型，我们使用不同的策略来丰富输入，包括来自源词汇表的同义词和层次关系。我们的深度学习模型在架构上采用了Siamese Knowledge Graph - lstm (Siamese Knowledge Graph - Long - short - Memory)。实验结果表明，该方法在处理元词库中紊乱语义组的同义词检测任务时取得了很好的效果。这显示了在UMLS元辞典构建过程中应用机器学习技术的潜力。虽然本文的工作只关注特定的语义组，但我们相信该方法可以应用于UMLS元词典中的其他语义组。
{"title":"Siamese KG-LSTM: A deep learning model for enriching UMLS Metathesaurus synonymy.","authors":"Tien T T Tran, Sy V Nghiem, Van T Le, Tho T Quan, Vinh Nguyen, Hong Yung Yip, Olivier Bodenreider","doi":"10.1109/kse50997.2020.9287797","DOIUrl":"https://doi.org/10.1109/kse50997.2020.9287797","url":null,"abstract":"<p><p>The Unified Medical Language System, or UMLS, is a repository of medical terminology developed by the U.S. National Library of Medicine for improving the computer system's ability of understanding the biomedical and health languages. The UMLS Metathesaurus is one of the three UMLS knowledge sources, containing medical terms and their relationships. Due to the rapid increase in the number of medical terms recently, the current construction of UMLS Metathesaurus, which heavily depends on lexical tools and human editors, is error-prone and time-consuming. This paper takes advantages of the emerging deep learning models for learning to predict the synonyms and non-synonyms between the pairs of biomedical terms in the Metathesaurus. Our learning approach focuses a subset of specific terms instead of the whole Metathesaurus corpus. Particularly, we train the models with biomedical terms from the Disorders semantic group. To strengthen the models, we enrich the inputs with different strategies, including synonyms and hierarchical relationships from source vocabularies. Our deep learning model adopts the Siamese KG-LSTM (Siamese Knowledge Graph - Long Short-Term Memory) in the architecture. The experimental results show that this approach yields excellent performance when handling the task of synonym detection for Disorders semantic group in the Metathesaurus. This shows the potential of applying machine learning techniques in the UMLS Metathesaurus construction process. Although the work in this paper focuses only on specific semantic group of Disorders, we believe that the proposed method can be applied to other semantic groups in the UMLS Metathesaurus.</p>","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/kse50997.2020.9287797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40583474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-14DOI: 10.1007/978-3-319-11680-8_29
Pham Dinh Thanh, Huynh Thi Thanh Binh, L. Bui
{"title":"New Mechanism of Combination Crossover Operators in Genetic Algorithm for Solving the Traveling Salesman Problem","authors":"Pham Dinh Thanh, Huynh Thi Thanh Binh, L. Bui","doi":"10.1007/978-3-319-11680-8_29","DOIUrl":"https://doi.org/10.1007/978-3-319-11680-8_29","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90848306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1109/KSE50997.2020.9287648
Pham Thi Viet Huong, Tran Anh Vu
{"title":"A data-driven approach to evaluate the social media post and its influences on customers","authors":"Pham Thi Viet Huong, Tran Anh Vu","doi":"10.1109/KSE50997.2020.9287648","DOIUrl":"https://doi.org/10.1109/KSE50997.2020.9287648","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75497965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1109/KSE50997.2020.9287787
Le Duc Thinh, N. Lam
{"title":"Airline Stock Performance: PRASM, RASM or Profit?","authors":"Le Duc Thinh, N. Lam","doi":"10.1109/KSE50997.2020.9287787","DOIUrl":"https://doi.org/10.1109/KSE50997.2020.9287787","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75943595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1109/KSE50997.2020.9287408
T. Pham, Viet-Cuong Ta, Thi Thanh Thuy Pham, T. Lê
{"title":"Reducing Blocking Artifacts in CNN-Based Image Steganography by Additional Loss Functions","authors":"T. Pham, Viet-Cuong Ta, Thi Thanh Thuy Pham, T. Lê","doi":"10.1109/KSE50997.2020.9287408","DOIUrl":"https://doi.org/10.1109/KSE50997.2020.9287408","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76412489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/KSE.2019.8919429
Thi Minh Phuong Ha, Duy Hung Tran, L. Hạnh, N. Binh
Faults are the leading cause of time consuming and cost wasting during software life cycle. Predicting faults in early stage improves the quality and reliability of the system and also reduces cost for software development. Many researches proved that software metrics are effective elements for software fault prediction. In addition, many machine learning techniques have been developed for software fault prediction. It is important to determine which set of metrics are effective for predicting fault by using machine learning techniques. In this paper, we conduct an experimental study to evaluate the performance of seven popular techniques including Logistic Regression, K-nearest Neighbors, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine and Multilayer Perceptron using software metrics from Promise repository dataset usage. Our experiment is performed on both method-level and class-level datasets. The experimental results show that Support Vector Machine archives a higher performance in class-level datasets and Multilayer Perception produces a better accuracy in method-level datasets among seven techniques above.
{"title":"Experimental Study on Software Fault Prediction Using Machine Learning Model","authors":"Thi Minh Phuong Ha, Duy Hung Tran, L. Hạnh, N. Binh","doi":"10.1109/KSE.2019.8919429","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919429","url":null,"abstract":"Faults are the leading cause of time consuming and cost wasting during software life cycle. Predicting faults in early stage improves the quality and reliability of the system and also reduces cost for software development. Many researches proved that software metrics are effective elements for software fault prediction. In addition, many machine learning techniques have been developed for software fault prediction. It is important to determine which set of metrics are effective for predicting fault by using machine learning techniques. In this paper, we conduct an experimental study to evaluate the performance of seven popular techniques including Logistic Regression, K-nearest Neighbors, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine and Multilayer Perceptron using software metrics from Promise repository dataset usage. Our experiment is performed on both method-level and class-level datasets. The experimental results show that Support Vector Machine archives a higher performance in class-level datasets and Multilayer Perception produces a better accuracy in method-level datasets among seven techniques above.","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77135269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.1109/KSE.2018.8573341
Thu Le Kim, C. C. Dang, L. Vinh
{"title":"Building a Specific Amino Acid Substitution Model for Dengue Viruses","authors":"Thu Le Kim, C. C. Dang, L. Vinh","doi":"10.1109/KSE.2018.8573341","DOIUrl":"https://doi.org/10.1109/KSE.2018.8573341","url":null,"abstract":"","PeriodicalId":93818,"journal":{"name":"The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83770224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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