Vietnam. J. Comput. Sci.最新文献_第2页

Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks 基于深度卷积神经网络集成框架的胸部x线异常定位与分类

Vietnam. J. Comput. Sci. Pub Date : 2022-07-29 DOI: 10.1142/s2196888822500348

Vu-Thu-Nguyet Pham, Quang-Chung Nguyen, Quang-Vu Nguyen

引用次数: 3

Dynamic Spatial Transformer WaveNet Network for Traffic Forecasting 用于交通预测的动态空间变压器波网

Vietnam. J. Comput. Sci. Pub Date : 2022-06-17 DOI: 10.1142/s2196888822500324

Khac-Hoai Nam Bui, Ngoc Dung Nguyen, Hongsuk Yi

引用次数: 1

An Efficient Temporal Inter-Object Association Rule Mining Algorithm on Time Series 一种有效的时间序列对象间关联规则挖掘算法

Vietnam. J. Comput. Sci. Pub Date : 2022-06-03 DOI: 10.1142/s2196888822500294

N. Vu, Vo Thi Ngoc Chau

引用次数: 0

Automatic Classification of Bird Sounds: Using MFCC and Mel Spectrogram Features with Deep Learning 鸟叫声的自动分类:基于深度学习的MFCC和Mel谱图特征

Vietnam. J. Comput. Sci. Pub Date : 2022-06-03 DOI: 10.1142/s2196888822500300

S. Carvalho, E. F. Gomes

引用次数: 4

Toward Improving the Quality of Mutation Operator and Test Case Effectiveness in Higher-Order Mutation Testing 提高高阶突变检测中突变算子的质量和测试用例的有效性

Vietnam. J. Comput. Sci. Pub Date : 2022-05-23 DOI: 10.1142/s2196888822500282

Van-Nho Do, Q. Nguyen, Thanh-Binh Nguyen

引用次数: 0

Tweets Topic Classification and Sentiment Analysis Based on Transformer-Based Language Models 基于转换语言模型的推文主题分类和情感分析

Vietnam. J. Comput. Sci. Pub Date : 2022-05-13 DOI: 10.1142/s2196888822500269

Ranju Mandal, Jinyan Chen, S. Becken, Bela Stantic

引用次数: 2

An Improving Approach for Top-Rank-k Frequent Pattern Mining 一种改进的顶级频繁模式挖掘方法

Vietnam. J. Comput. Sci. Pub Date : 2022-04-20 DOI: 10.1142/s2196888822500221

Ham Nguyen

引用次数: 0

The Quest for Customer Intelligence to Support Marketing Decisions: A Knowledge-Based Framework 寻求支持营销决策的客户智能:一个基于知识的框架

Vietnam. J. Comput. Sci. Pub Date : 2022-04-11 DOI: 10.1142/s2196888822500208

Nguyen Anh Khoa Dam, T. Dinh, W. Menvielle

{"title":"The Quest for Customer Intelligence to Support Marketing Decisions: A Knowledge-Based Framework","authors":"Nguyen Anh Khoa Dam, T. Dinh, W. Menvielle","doi":"10.1142/s2196888822500208","DOIUrl":"https://doi.org/10.1142/s2196888822500208","url":null,"abstract":"The quest for customer intelligence to create value in marketing has highlighted the significance of the research focus of this paper. Customer intelligence, which is defined as understandings or insights resulting from the application of analytic techniques, plays a significant role in the survival and prosperity of enterprises in the knowledge-based economy. In this light, the paper has developed a framework of customer intelligence to support marketing decisions through the lens of knowledge-based theory. The proposed framework aims at supporting enterprises to identify the right customer data for the right customer intelligence corresponding with the right marketing decisions. In this light, four types of customer intelligence are clarified including product-aware intelligence, customer DNA intelligence, customer experience intelligence, and customer value intelligence. The applications of customer intelligence are also elucidated with relevant marketing decisions to maximize value creation. To illustrate the framework, an example is presented. The importance and originality of this study are that it responds to changes in customer intelligence in the age of massive data and covers multifaced aspects of marketing decisions.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114937826","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

An Estimation Approach to Optimize Energy Consumption in Wireless Sensor Network: A Health-Care Application 一种优化无线传感器网络能耗的估计方法:在医疗保健中的应用

Vietnam. J. Comput. Sci. Pub Date : 2022-04-11 DOI: 10.1142/s219688882250018x

Marwa Hachicha, Riadh Ben Halima, A. Jemal

{"title":"An Estimation Approach to Optimize Energy Consumption in Wireless Sensor Network: A Health-Care Application","authors":"Marwa Hachicha, Riadh Ben Halima, A. Jemal","doi":"10.1142/s219688882250018x","DOIUrl":"https://doi.org/10.1142/s219688882250018x","url":null,"abstract":"Wireless Sensor Network (WSN) is gaining popularity day by day in a large area of applications. However, the operation of WSN is facing a multitude of challenges, mainly in terms of energy consumption since WSN nodes operate with battery power and changing the batteries is a complicated task, as networks may include hundreds to thousands of nodes. In this context, it is very crucial to know the remaining energy value in the battery of the sensor node to take required actions before losing sensor’s function. Sending these measurements is very expensive in terms of energy and reduces the battery lifetime of the sensor and thus of the entire network. In this paper, we are interested in defining a probabilistic approach which aims to estimate these monitoring energy values and optimize energy consumption in WSN. Our approach is based on hidden Markov chains and includes two phases namely a learning phase and a prediction phase. Our approach is implemented as a web service. We illustrate our approach with a sensor-based health-care monitoring case study for COVID-19 patients. To evaluate our approach, we carry out experimentations based on the AvroraZ a simulator with a test for different types of applications and for different energy models: [Formula: see text]AMPS-specific model, Mica2-specific model, and Mica2-specific model with actual measurements. These experimentations demonstrate the accuracy and efficiency of our approach. Our results show that periodic WSN applications i.e. applications which send monitoring data periodically, tested with the [Formula: see text]AMPS-specific model perform an accuracy of 98.65%. In addition, our approach can perform a gain up to 75% of the battery charge of the sensor with an estimation of three-quarters of the remaining energy values.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123894177","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

Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks 基于生成对抗网络的综合交通标志图像生成

Vietnam. J. Comput. Sci. Pub Date : 2022-03-26 DOI: 10.1142/s2196888822500191

Christine Dewi, Rung-Ching Chen, Yan-Ting Liu

{"title":"Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks","authors":"Christine Dewi, Rung-Ching Chen, Yan-Ting Liu","doi":"10.1142/s2196888822500191","DOIUrl":"https://doi.org/10.1142/s2196888822500191","url":null,"abstract":"Recently, it was shown that convolutional neural networks (CNNs) with suitably annotated training data and results produce the best traffic sign detection (TSD) and recognition (TSR). The whole system’s efficiency is determined by the data collecting process based on neural networks. As a result, the datasets for traffic signs in most nations throughout the globe are difficult to recognize because of their diversity. To address this problem, we must create a synthetic image to enhance our dataset. We apply deep convolutional generative adversarial networks (DCGAN) and Wasserstein generative adversarial networks (Wasserstein GAN, WGAN) to generate realistic and diverse additional training images to compensate for the original image distribution’s data shortage. This study focuses on the consistency of DCGAN and WGAN images created with varied settings. We utilize an actual picture with various numbers and scales for training. Additionally, the Structural Similarity Index (SSIM) and the Mean Square Error (MSE) were used to determine the image’s quality. In our study, we computed the SSIM values between pictures and their corresponding real images. When more training images are used, the images created have a significant degree of similarity to the original image. The results of our experiment reveal that the most leading SSIM values are achieved when 200 total images of [Formula: see text] pixels are utilized as input and the epoch is 2000.","PeriodicalId":256649,"journal":{"name":"Vietnam. J. Comput. Sci.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128178270","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