2019 International Conference on Computational Science and Computational Intelligence (CSCI)最新文献_第7页

Converting Neural Networks to Rule Foam 将神经网络转换为规则泡沫

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00100

A. K. Panda, B. Kosko

引用次数: 5

Teaching Cyber Security Topics Effectively in a College or University with Limited Resources 在资源有限的学院或大学有效地教授网络安全主题

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00158

C. V. Gonzalez, Gwang Jung

引用次数: 1

Maintaining Data Integrity in Fog Computing Based Critical Infrastructure Systems 基于雾计算的关键基础设施系统数据完整性维护

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00014

Abdulwahab Alazeb, B. Panda

引用次数: 11

Agnostic Approach for Microservices Autoscaling in Cloud Applications 云应用中微服务自动扩展的不可知方法

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00264

Abeer Abdel Khaleq, Ilkyeun Ra

引用次数: 23

University Online Courses: Correlation between Students' Participation Rate and Academic Performance 大学网络课程:学生参与率与学习成绩的关系

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00147

Sahar Voghoei, Navid Hashemi Tonekaboni, D. Yazdansepas, H. Arabnia

{"title":"University Online Courses: Correlation between Students' Participation Rate and Academic Performance","authors":"Sahar Voghoei, Navid Hashemi Tonekaboni, D. Yazdansepas, H. Arabnia","doi":"10.1109/CSCI49370.2019.00147","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00147","url":null,"abstract":"It has been generally believed that higher participation in discussion forums in online classes would result in better student performance. To better understand this correlation on a large scale, we have studied 291 distinct online courses offered during Summer 2019 at Georgia Gwinnett College. Several studies in the literature have focused on analyzing the data from the Massive Open Online Courses (MOOCs). However, in this research, we have focused on University-based Online Courses (UOCs) for undergraduate students, where the curriculum enforces students to take these courses. Although a higher participation rate in online forums has a direct correlation with a higher grade in MOOCs, in OUCs, students with top grades are not necessarily the most active students. Our analysis shows a consistent pattern in UOCs where during the first two-thirds of the semester, students who belong to the GPA range of ~70 to ~80 percentile of the class have the highest rate of participation, while during the last one-third of the semester, the ones who belong to the GPA range of ~87 to ~93 percentile, contribute the most. On the other hand, we found out that the common characteristic of top students in all classes, is their consistency in participation throughout the semester, regardless of the number of their posts.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117260599","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: 10

Trade-Offs between Early Software Defect Prediction versus Prediction Accuracy 早期软件缺陷预测与预测准确性之间的权衡

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00216

L. Alhazzaa, Anneliese Amschler Andrews

引用次数: 0

On the Relevance of IT Security in TDL 论TDL中IT安全的相关性

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00044

Tobias Eggendorfer, Volker Eiseler

引用次数: 0

Economic Dispatch for Power System with Short-Term Solar Power Forecast 太阳能发电短期预测下的电力系统经济调度

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00096

E. Espinosa-Juárez, Jorge Luis Solano-Gallegos, F. Ornelas‐Tellez

引用次数: 3

Incorporating Association Patterns into Manifold Clustering for Enabling Predictive Analytics 将关联模式整合到流形聚类中以实现预测分析

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00243

B. Sy, Jin Chen, Rebecca Horowitz

引用次数: 3

Traffic Sign Identification Using Deep Learning 使用深度学习识别交通标志

2019 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2019-12-01 DOI: 10.1109/CSCI49370.2019.00063

Ratheesh Ravindran, M. Santora, M. Faied, Mohammad Fanaei

{"title":"Traffic Sign Identification Using Deep Learning","authors":"Ratheesh Ravindran, M. Santora, M. Faied, Mohammad Fanaei","doi":"10.1109/CSCI49370.2019.00063","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00063","url":null,"abstract":"One of the most crucial enabling technologies for automated driving systems is the ability to reliably detect and classify a wide range of traffic signs in various driving conditions at different distances. Due to the complexity and dynamic nature of driving environments, it is difficult to reliably detect traffic signs with conventional image processing methods. Artificial intelligence in combination with image processing has proven to be a great success to address this problem in recent studies. This paper focuses on the selection of Deep Neural Networks (DNN) based on the application-oriented performance by taking into consideration the mean Average Precision (mAP) and Frames Per Second (FPS) as the major evaluation criteria. Faster Region-based Convolutional Neural Network (Faster R-CNN) is a newly proposed DNN in the literature that has proven to exhibit a balanced tradeoff between mAP and FPS performance measures. This paper starts with a DNN transfer learning and then implements the Faster R-CNN algorithm for the real-time detection and classification of traffic signs using the Robot Operating System (ROS). To reduce the errors due to DNN inaccurate detection, Tesseract\" is added to detect the text in the identified traffic signs. The German Traffic Sign Detection Benchmark (GTSDB) dataset is used in this paper, and additional dataset are created to solve the lack of certain traffic signs in the GTSDB dataset. Simulation with ROS-Gazebo and real-time trials using the Polaris Gem e2 equipped with NVIDIA Drive PX2 demonstrate the efficiency of the proposed integration of DNN with Tesseract in detecting and classifying a wide range of traffic signs.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115574183","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: 10