Vietnam. J. Comput. Sci.最新文献_第3页

ViLO - A Core Ontology of Vietnamese Legal Documents ViLO——越南法律文件的核心本体

Vietnam. J. Comput. Sci. Pub Date : 2022-03-09 DOI: 10.1142/s2196888822500178

Minh Duc Nguyen, Giang H. Nguyen-Thi, Cuong H. Nguyen-Dinh

引用次数: 1

Solving Robust Production Planning Problem with Interval Budgeted Uncertainty in Cumulative Demands 考虑累积需求区间预算不确定性的鲁棒生产计划问题求解

Vietnam. J. Comput. Sci. Pub Date : 2022-02-14 DOI: 10.1142/s2196888822500166

A. Kasperski, P. Zieliński

引用次数: 0

Heterogeneous Fault Prediction Using Feature Selection and Supervised Learning Algorithms 基于特征选择和监督学习算法的异构故障预测

Vietnam. J. Comput. Sci. Pub Date : 2022-01-24 DOI: 10.1142/s2196888822500142

R. Arora, Arvinder Kaur

{"title":"Heterogeneous Fault Prediction Using Feature Selection and Supervised Learning Algorithms","authors":"R. Arora, Arvinder Kaur","doi":"10.1142/s2196888822500142","DOIUrl":"https://doi.org/10.1142/s2196888822500142","url":null,"abstract":"Software Fault Prediction (SFP) is the most persuasive research area of software engineering. Software Fault Prediction which is carried out within the same software project is known as With-In Fault Prediction. However, local data repositories are not enough to build the model of With-in software Fault prediction. The idea of cross-project fault prediction (CPFP) has been suggested in recent years, which aims to construct a prediction model on one project, and use that model to predict the other project. However, CPFP requires that both the training and testing datasets use the same set of metrics. As a consequence, traditional CPFP approaches are challenging to implement through projects with diverse metric sets. The specific case of CPFP is Heterogeneous Fault Prediction (HFP), which allows the program to predict faults among projects with diverse metrics. The proposed framework aims to achieve an HFP model by implementing Feature Selection on both the source and target datasets to build an efficient prediction model using supervised machine learning techniques. Our approach is applied on two open-source projects, Linux and MySQL, and prediction is evaluated based on Area Under Curve (AUC) performance measure. The key results of the proposed approach are as follows: It significantly gives better results of prediction performance for heterogeneous projects as compared with cross projects. Also, it demonstrates that feature selection with feature mapping has a significant effect on HFP models. Non-parametric statistical analyses, such as the Friedman and Nemenyi Post-hoc Tests, are applied, demonstrating that Logistic Regression performed significantly better than other supervised learning algorithms in HFP models.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116940848","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: 0

Forecasting the Coffee Consumption Demand in Vietnam Based on Grey Forecasting Model 基于灰色预测模型的越南咖啡消费需求预测

Vietnam. J. Comput. Sci. Pub Date : 2022-01-20 DOI: 10.1142/s2196888822500129

Ngoc Thang Nguyen, Van-Thanh Phan, Van Dat Nguyen, Thanh Ha Le, Thao Vy Pham

引用次数: 0

Using Ensemble and TOPSIS with AHP for Classification and Selection of Web Services 利用集成和TOPSIS结合AHP对Web服务进行分类和选择

Vietnam. J. Comput. Sci. Pub Date : 2021-12-31 DOI: 10.1142/s2196888822500130

Mithilesh Pandey, Sunita Jalal, Chetan S. Negi, D. Yadav

引用次数: 0

In Your Face: Person Identification Through Ratios and Distances Between Facial Features 在你的脸上:通过面部特征之间的比例和距离来识别人

Vietnam. J. Comput. Sci. Pub Date : 2021-11-17 DOI: 10.1142/s2196888822500105

Mohammad Alsawwaf, Z. Chaczko, Marek Kulbacki, Nikhil Sarathy

{"title":"In Your Face: Person Identification Through Ratios and Distances Between Facial Features","authors":"Mohammad Alsawwaf, Z. Chaczko, Marek Kulbacki, Nikhil Sarathy","doi":"10.1142/s2196888822500105","DOIUrl":"https://doi.org/10.1142/s2196888822500105","url":null,"abstract":"These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114408437","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: 8

Digital Image Evolution of Artwork Without Human Evaluation Using the Example of the Evolving Mona Lisa Problem 没有人类评价的艺术品的数字图像演变——以《蒙娜丽莎》问题演变为例

Vietnam. J. Comput. Sci. Pub Date : 2021-11-17 DOI: 10.1142/s2196888822500075

Julia Garbaruk, D. Logofătu, C. Bǎdicǎ, Florin Leon

引用次数: 2

Classification of Low-Grade and High-Grade Glioma from MR Brain Images Using Multiple-Instance Learning with Combined Feature Set 结合特征集的多实例学习方法在MR脑图像中分类低级别和高级别胶质瘤

Vietnam. J. Comput. Sci. Pub Date : 2021-10-23 DOI: 10.1142/s2196888822500117

C. C. Benson, V. Lajish, K. Rajamani

{"title":"Classification of Low-Grade and High-Grade Glioma from MR Brain Images Using Multiple-Instance Learning with Combined Feature Set","authors":"C. C. Benson, V. Lajish, K. Rajamani","doi":"10.1142/s2196888822500117","DOIUrl":"https://doi.org/10.1142/s2196888822500117","url":null,"abstract":"Fully automatic brain image classification of MR brain images is of great importance for research and clinical studies, since the precise detection may lead to a better treatment. In this work, an efficient method based on Multiple-Instance Learning (MIL) is proposed for the automatic classification of low-grade and high-grade MR brain tumor images. The main advantage of MIL-based approach over other classification methods is that MIL considers an image as a group of instances rather than a single instance, thus facilitating an effective learning process. The mi-Graph-based MIL approach is proposed for this classification. Two different implementations of MIL-based classification, viz. Patch-based MIL (PBMIL) and Superpixel-based MIL (SPBMIL), are made in this study. The combined feature set of LBP, SIFT and FD is used for the classification. The accuracies of low-grade–high-grade tumor image classification algorithm using SPBMIL method performed on [Formula: see text], [Formula: see text] and FLAIR images read 99.2765%, 99.4195% and 99.2265%, respectively. The error rate of the proposed classification system was noted to be insignificant and hence this automated classification system could be used for the classification of images with different pathological conditions, types and disease statuses.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131119603","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: 0

Empirical Analysis of Phrase-Based Statistical Machine Translation System for English to Hindi Language 基于短语的英语到印地语统计机器翻译系统实证分析

Vietnam. J. Comput. Sci. Pub Date : 2021-10-06 DOI: 10.1142/s219688882250004x

A. Babhulgaonkar, S. Sonavane

引用次数: 2

An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh 基于机器视觉的孟加拉三种水果病害识别的探索性分析

Vietnam. J. Comput. Sci. Pub Date : 2021-09-18 DOI: 10.1142/s2196888822500087

Md. Tarek Habib, Md. Jueal Mia, Mohammad Shorif Uddin, F. Ahmed

{"title":"An Explorative Analysis on the Machine-Vision-Based Disease Recognition of Three Available Fruits of Bangladesh","authors":"Md. Tarek Habib, Md. Jueal Mia, Mohammad Shorif Uddin, F. Ahmed","doi":"10.1142/s2196888822500087","DOIUrl":"https://doi.org/10.1142/s2196888822500087","url":null,"abstract":"Bangladesh, being a densely populated country, hinges on agriculture for the security of finance and food to a large extent. Hence, both the fruits’ quantity and quality turn out to be very important, which can be degraded due to the attacks of various diseases. Automated fruit disease recognition can help fruit farmers, especially remote farmers, for whom adequate cultivation support is required. Two daunting problems, namely disease detection, and disease classification are raised by automated fruit disease recognition. In this research, we conduct an intense investigation of the applicability of automated recognition of the diseases of three available Bangladeshi local fruits, viz. guava, jackfruit, and papaya. After exerting four notable segmentation algorithms, [Formula: see text]-means clustering segmentation algorithm is selected to segregate the disease-contaminated parts from a fruit image. Then some discriminatory features are extracted from these disease-contaminated parts. Nine noteworthy classification algorithms are applied for disease classification to thoroughly get the measure of their merits. It is observed that random forest outperforms the eight other classifiers by disclosing an accuracy of 96.8% and 89.59% for guava and jackfruit, respectively, whereas support vector machine attains an accuracy of 94.9% for papaya, which can be claimed good as well as attractive for forthcoming research.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557410","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: 2