The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering最新文献

An efficient Privacy-Preserving Recommender System 一种高效的隐私保护推荐系统

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2022-01-01 DOI: 10.1109/KSE56063.2022.9953800

Thi Van Vu, T. Luong, Van Quan Hoang

引用次数: 0

A comprehensive and bias-free evaluation of genomic variant clinical interpretation tools 对基因组变异临床解释工具进行全面、无偏见的评估

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2021-01-01 DOI: 10.1109/KSE53942.2021.9648755

Nguyen Minh Trang, Anh-Vu Mai-Nguyen, Tran Hoang Anh, Do Nguyet Minh, Nguyen Thanh Nguyen

引用次数: 0

Evaluate and Visualize Legal Embeddings for Explanation Purpose 为了解释目的，评估和可视化法律嵌入

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2021-01-01 DOI: 10.1109/KSE53942.2021.9648655

Nguyen Ha Thanh, Binh Dang, Minh Q. Bui, Le-Minh Nguyen

引用次数: 0

Siamese KG-LSTM: A deep learning model for enriching UMLS Metathesaurus synonymy. Siamese KG-LSTM:一个用于丰富UMLS元同义词的深度学习模型。

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2020-11-01 Epub Date: 2020-12-16 DOI: 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

{"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":"2020 ","pages":"281-286"},"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}

引用次数: 3

New Mechanism of Combination Crossover Operators in Genetic Algorithm for Solving the Traveling Salesman Problem 遗传算法中组合交叉算子求解旅行商问题的新机制

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2020-01-14 DOI: 10.1007/978-3-319-11680-8_29

Pham Dinh Thanh, Huynh Thi Thanh Binh, L. Bui

引用次数: 23

A data-driven approach to evaluate the social media post and its influences on customers 一种数据驱动的方法来评估社交媒体帖子及其对客户的影响

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2020-01-01 DOI: 10.1109/KSE50997.2020.9287648

Pham Thi Viet Huong, Tran Anh Vu

引用次数: 0

Airline Stock Performance: PRASM, RASM or Profit? 航空股表现:是盈利还是盈利?

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2020-01-01 DOI: 10.1109/KSE50997.2020.9287787

Le Duc Thinh, N. Lam

引用次数: 0

Reducing Blocking Artifacts in CNN-Based Image Steganography by Additional Loss Functions 利用附加损失函数减少cnn图像隐写中的块伪影

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2020-01-01 DOI: 10.1109/KSE50997.2020.9287408

T. Pham, Viet-Cuong Ta, Thi Thanh Thuy Pham, T. Lê

引用次数: 0

Experimental Study on Software Fault Prediction Using Machine Learning Model 基于机器学习模型的软件故障预测实验研究

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919429

Thi Minh Phuong Ha, Duy Hung Tran, L. Hạnh, N. Binh

{"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":"547 1","pages":"1-5"},"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}

引用次数: 3

Building a Specific Amino Acid Substitution Model for Dengue Viruses 构建登革病毒特异性氨基酸取代模型

The ... International Conference on Knowledge and Systems Engineering. International Conference on Knowledge and Systems Engineering Pub Date : 2018-01-01 DOI: 10.1109/KSE.2018.8573341

Thu Le Kim, C. C. Dang, L. Vinh

引用次数: 0