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A Feature-Rich Vietnamese Named Entity Recognition Model 一个特征丰富的越南语命名实体识别模型
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4353
Pham Quang Nhat Minh
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引用次数: 0
Constructing Vietnamese WordNet: A Case Study 构建越南语词网:个案研究
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4352
Khang Nhut Lam, J. Kalita
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引用次数: 1
Topics Identification in a Corpus based on Transformers 基于变换的语料库主题识别
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4187
J. Trejo, Rodrigo Cadena Martínez
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引用次数: 0
Tunisian Dialect Agglutination Processing with Finite Transducers 突尼斯方言有限换能器的凝集处理
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4344
Roua Torjmen, K. Haddar
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引用次数: 0
Cybersecurity and Internet of Things. Outlook for this decade 网络安全和物联网。未来十年展望
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-3925
Jairo Eduardo Márquez Díaz
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引用次数: 0
Development of a Normalized Hadith Narrator Encyclopedia with TEI 用TEI编写规范的圣训叙述者百科全书
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4349
Hajer Maraoui, K. Haddar, Laurent Romary
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引用次数: 0
Towards an Automatic Mark-up of Rhetorical Structure in Student Essays 论学生作文修辞结构的自动标注
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4355
Eckhard Bick
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引用次数: 0
Distributional Word Vectors as Semantic Maps Framework 分布式词向量语义映射框架
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4356
Amir Bakarov
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引用次数: 0
Comprehensive Performance Analysis based on Classical Machine Learning and Deep Learning Methods for Predicting the COVID-19 Infections 基于经典机器学习和深度学习方法的COVID-19感染预测综合性能分析
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-3782
Prabhat Kumar
The COVID-19 (coronavirus disease) has been declared a pandemic throughout the world by the WHO (World Health Organization). The number of active COVID-19 cases is increasing day by day and clinical laboratory findings consume more time while interpreting the COVID-19 infected result. There are limited treatment facilities and proper guidelines for reducing infection rates. To overcome these limitations, the requirement of clinical decision support systems embedded with prediction algorithms is raised. In our study, we have architected the clinical prediction system using classical machine learning, deep learning algorithms, and experimental laboratory data. Our model estimated which patients were likely infected with COVID-19 disease. The prediction performances of our models are evaluated based on the accuracy score. The experimental dataset has been provided by Hospital Israelita Albert Einstein at Sao Paulo, Brazil, which included the records of 600 patients from 18 laboratory findings with 10% COVID-19 disease infected patients. Our model has been validated with a train-test split approach, 10-fold cross-validation, and AUC-ROC curve score. The experimental results show that the infected patients with COVID-19 disease are identified at an accuracy of 91.88% through the deep learning method (Convolutional Neural Network (CNN)) and 89.79 % through classical machine learning (Logistic Regression) respectively. This high accuracy is evidence that our prediction model could be readily used for predicting the COVID-19 infections and assisting the health experts in better diagnosis and clinical studies. © 2022 Instituto Politecnico Nacional. All rights reserved.
世界卫生组织(WHO)宣布新型冠状病毒感染症(COVID-19)为全球大流行。新冠肺炎活跃病例日益增多，临床检验结果在解释新冠肺炎感染结果时花费的时间更长。治疗设施和降低感染率的适当指导方针有限。为了克服这些限制，提出了嵌入预测算法的临床决策支持系统的要求。在我们的研究中，我们使用经典的机器学习、深度学习算法和实验实验室数据构建了临床预测系统。我们的模型估计了哪些患者可能感染了COVID-19疾病。我们的模型的预测性能是基于准确度评分来评估的。实验数据集由巴西圣保罗的以色列阿尔伯特·爱因斯坦医院提供，其中包括来自18个实验室发现的600名患者的记录，其中10%的患者感染了COVID-19。我们的模型已通过训练检验分离方法、10倍交叉验证和AUC-ROC曲线评分进行验证。实验结果表明，深度学习方法(卷积神经网络(CNN))和经典机器学习方法(Logistic回归)对COVID-19感染患者的识别准确率分别为91.88%和89.79%。这证明我们的预测模型可以很容易地用于预测COVID-19感染，并协助卫生专家更好地进行诊断和临床研究。©2022国立理工大学版权所有。
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引用次数: 0
Luminescence Properties of Nanomaterials 纳米材料的发光特性
Computación Y Sistemas
Pub Date : 2022-09-05 DOI: 10.13053/cys-26-3-4148
M. Aguilar-Jáuregui, Cuahutémoc Peredo-Macías, S. D. O. Jiménez, Paulina Alejandra Flores de los Rios, E. S. M. Martínez
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引用次数: 0
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