2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)最新文献_第5页

Improving Project Control by Utilizing Predictive Data Analytic Models 利用预测数据分析模型改进项目控制

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718399

Kamal Jaafar, Ahmad Aloran, Mohamad Watfa

引用次数: 0

Design Data Literacy – Impact of Data Literacy in Virtual Product Development 设计数据素养——数据素养在虚拟产品开发中的影响

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718430

Tim G. Giese, R. Anderl

{"title":"Design Data Literacy – Impact of Data Literacy in Virtual Product Development","authors":"Tim G. Giese, R. Anderl","doi":"10.1109/CSDE53843.2021.9718430","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718430","url":null,"abstract":"In today’s information age, due to the massive amounts of data generated, an understanding about the interaction with data is becoming increasingly significant. While in virtual product development of technical components, a lot of research regarding the collection and storage of data exists, we identified missing key competencies for the interaction with design data. In the collaborative and multidisciplinary design process of technical products, designers are missing knowledge about: the origin of a data piece, an understanding about the reliability of a source, as well as transparency to whom their data is distributed and how it is further used. Approaching this problem, we identified the necessity of providing designers with relevant skills and information. Therefore, we developed a novel concept for the introduction and application of Data Literacy to product development in order to create transparency and awareness for a responsible generation and use of design data. The concept is called Design Data Literacy and consists of the 3 major pillars: Data Provenance, Data Tracking and Tracing, as well as Data Sovereignty. To validate the concept, we conducted a prototypical implementation and evaluation. We came to the conclusion that the presented concept can be used effectively to introduce and apply Data Literacy to product development.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114459919","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

Run or Pat: Using Deep Learning to Classify the Species Type and Emotion of Pets 跑还是拍:使用深度学习对宠物的物种类型和情感进行分类

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718465

R. Sinnott, U. Aickelin, Yu Jia, Elizabeth R.J. Sinnott, Pei-Yun Sun, Rio Susanto

引用次数: 1

Three-dimensional hybrid mesh generation method for the ejector used in proton exchange membrane fuel cells 质子交换膜燃料电池喷射器三维混合网格生成方法

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718382

Denghao Zhang, Xuebin Yang, Zhongxuan Du

{"title":"Three-dimensional hybrid mesh generation method for the ejector used in proton exchange membrane fuel cells","authors":"Denghao Zhang, Xuebin Yang, Zhongxuan Du","doi":"10.1109/CSDE53843.2021.9718382","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718382","url":null,"abstract":"As a key component of the proton exchange membrane fuel cell (PEMFC) anode hydrogen circulation system, the flow performance in the ejector largely determines the efficiency of the anode hydrogen circulation system. Therefore, it is important to predict the flow characteristics in the ejector and to supply design analysis by using a numerical simulation. This study presents a three-dimensional hybrid mesh generation method for numerical simulation using ICEM CFD software. Firstly, the ejector geometry model is processed by creating an auxiliary surface, which serves as a hybrid interface. The mesh generation method is highlighted on this hybrid interface. The computational domains are then divided into both structured and unstructured meshes. The causes of low-quality mesh might be the wrong arrangement of nodes or unreasonable maximum size of mesh, and thus a quality improvement method for mesh-smoothing is proposed according to different types of meshes. Finally, the generation method of the hybrid mesh is evaluated by ICEM CFD software quality metrics and the simulation result of Fluent software. The numerical simulation results show good agreement with the experimental data with a maximum error of only 4.62%. The proposed method can reduce the difficulty of mesh generation and ensure a certain mesh quality. The method is also applicable to mesh generation for other complex geometric models.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123614464","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

Statistical Methods for Data mining Mathematics students' online presence 数据挖掘数学学生在线表现的统计方法

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718397

M. Naseem, E. Reddy, Ravneil Nand

{"title":"Statistical Methods for Data mining Mathematics students' online presence","authors":"M. Naseem, E. Reddy, Ravneil Nand","doi":"10.1109/CSDE53843.2021.9718397","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718397","url":null,"abstract":"COVID-19 has caused major changes in every aspect of human endeavor, including efforts in the higher education sector. A sudden shift from face-to-face and blended settings to a completely online delivery mode has introduced changes to conventional teaching methods, and made learning rely heavily on technology and the Internet. Hence, students' online engagement with these tools has become even more important for their academic success. Therefore, there is a need to investigate the effects of various indicators of students' online presence on their academic performance. This paper explores the effectiveness of online presence in Higher Education Institutes, where COVID-19 has shifted the deliveries to online mode. The chosen indicator is frequency that will be adequately used to quantify the effectiveness of online presence on student performance in online mathematics courses. Statistical methods are used to measure the correlation and association between students' online presence indicators and their performance. As such, it would allow to build models to predict future outcomes or occurrences and student performances, with a major focus on mathematics and statistics courses. The results show that there is an increase in student online interaction in courses during COVID-19 era; however, it is consistent with the Online Measureable Presence Model (OMPM) model where frequency was the dominant indicator of student performance.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121606945","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

Identifying Fallers Based on Functional Parameters: A Machine Learning Approach 基于功能参数的落差识别:一种机器学习方法

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718435

F. Fahimi, W. Taylor, R. Dietzel, G. Armbrecht, N. Singh

{"title":"Identifying Fallers Based on Functional Parameters: A Machine Learning Approach","authors":"F. Fahimi, W. Taylor, R. Dietzel, G. Armbrecht, N. Singh","doi":"10.1109/CSDE53843.2021.9718435","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718435","url":null,"abstract":"Falls are a leading cause of fracture and mortality in older adults, and hence represent a considerable socioeconomic burden in aging societies. Detection of individuals at a high risk of falls and evaluation of associated factors enable implementation of targeted therapies and timely intervention. The most common indicator for fall prediction is history of falling, but this is a subjective predictor and fails to detect first-time fallers simply because it is absent in such cases. In this study, we used functional variables extracted from multiple functional domains, and implemented several machine learning (ML) methods to classify fallers vs non-fallers retrospectively. We also performed feature importance analysis to provide an insight into the underlying features. Performed within a cross-validation setting, we identified the ML algorithm that best maps individuals’ functional measures to their fall status. In addition, we applied this algorithm for prospective identification of fall risk. In retrospective classification, k-nearest neighbours (KNN) model achieved a sensitivity of 74% and a specificity of 75%. In prospective evaluation, it achieved sensitivity and specificity of 80%. These results reflect the superior capability of machine learning in fallers identification even with a very small dataset.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131282414","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: 1

Confusion detection using neural networks 基于神经网络的混淆检测

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718422

Chaitali Samani, Madhu Goyal

引用次数: 1

Novel AI based pre-silicon Performance estimation and validation of complex System-on-Chip 基于人工智能的复杂片上系统预硅性能评估与验证

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718369

Manoj Kumar Munigala, Surinder Sood, K.N Madhusudhan.

{"title":"Novel AI based pre-silicon Performance estimation and validation of complex System-on-Chip","authors":"Manoj Kumar Munigala, Surinder Sood, K.N Madhusudhan.","doi":"10.1109/CSDE53843.2021.9718369","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718369","url":null,"abstract":"With the advancement of Very Large Scale Integration (VLSI) technology, the demand for integrating heterogeneous components (multi-core, graphics, high-bandwidth peripherals etc.) is increasing exponentially which is leading to the complex System-on-Chip (SOC) design. In this paradigm, the performance of complex SOC is the key matrix that defines various product portfolios across laptops, desktops, and servers market segments. The overall SOC performance depends on the numerous design and architectural parameters(frequency, cores, etc.) of the heterogeneous components integrated into the design. This leads to the necessity for performance estimation based validation of the SOC under different design and configuration parameters. Existing traditional standard methods incorporate time-consuming and non-exhaustive cycle-level simulations, which are slow and lead to incompleteness in achieving performance targets at pre-silicon level. The proposed novel AI based performance estimation based technique is used to obtain fast and accurate performance estimates for a complex SOC, which explores the design under multiple configurations without running simulation test content and aids in evaluating design Hardware (HW) bottlenecks and enhancing debug capabilities.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116560847","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

Churn Prediction in Telecom Industry using Machine Learning Ensembles with Class Balancing 基于类平衡的机器学习集成的电信行业流失预测

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718498

Abdullahi Chowdhury, Shahriar Kaisar, M. Rashid, Sakib Shahriar Shafin, J. Kamruzzaman

{"title":"Churn Prediction in Telecom Industry using Machine Learning Ensembles with Class Balancing","authors":"Abdullahi Chowdhury, Shahriar Kaisar, M. Rashid, Sakib Shahriar Shafin, J. Kamruzzaman","doi":"10.1109/CSDE53843.2021.9718498","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718498","url":null,"abstract":"Telecommunication service providers are going through a very competitive and challenging time to retain existing customers by offering new and attractive services (e.g., unlimited local and international calls, high-speed internet, new phones). It is therefore imperative to analyse and predict customer churn behaviour more accurately. One of the major challenges to analyse churn data and build better prediction model is the imbalance nature of the data. Customer behaviour for churn and non-churn scenarios may contain resembling features. Using a single classifier or simple oversampling method to handle data imbalance often struggles to identify the minority (churn) class data. To overcome the issue, we introduce a model that uses sophisticated oversampling technique in conjunction with ensemble methods, namely Random Forest, Gradient Boost, Extreme Gradient Boost, and AdaBoost. The hyperparameters of the baseline ensemble methods and the oversampling methods were tuned in several ways to investigate their impact on prediction performances. Using a widely used publicly available customer churn dataset, prediction performance of the proposed model was evaluated in term of various metrics, namely, accuracy, precision, recall, F-1 score, AUC under ROC curve. Our model outperformed the existing models and significantly reduced both false positive and false negative prediction.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134289548","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

Herding Predators Using Swarm Intelligence 使用群体智能放牧掠食者

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718476

S. Kumar, Kunal Chand, Lata I. Paea, Imanuel Thakur, Maria Vatikani

引用次数: 6