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

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718495

Yuting Zhao, M. Mohammadian, Julio Romero, Hamed Sarbazhosseini

引用次数: 0

Social Media Rumour Detection Through Graph Attention Networks 基于图形关注网络的社交媒体谣言检测

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718466

Xinpeng Zhang, Shuzhi Gong, R. Sinnott

引用次数: 1

A new Dataset of Wideband Radar Signals for Training Deep Neural Networks on Classification and Detection Tasks 一种新的用于训练深度神经网络分类和检测任务的宽带雷达信号数据集

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718398

M. A. Ammar, M. Abdel-Latif, K. Badran, H. A. Hassan

{"title":"A new Dataset of Wideband Radar Signals for Training Deep Neural Networks on Classification and Detection Tasks","authors":"M. A. Ammar, M. Abdel-Latif, K. Badran, H. A. Hassan","doi":"10.1109/CSDE53843.2021.9718398","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718398","url":null,"abstract":"in the deep learning field, the availability of datasets is a very important requirement for developing deep neural network models and benchmarking. This paper introduces a new dataset of wideband radar signals (WBR-DS-1) that is essential for training, developing, and benchmarking deep neural network models for the classification and detection of wideband radar signals. Typical ESM receiver parameters, propagation channels, and environmental parameters are simulated to guarantee the dataset’s usability. The Electronic Support Measures (ESM) systems are responsible for the interception and characterization of the different radar signals. In this paper, the ESM sensor is assumed to be a ground-based one. Two scenarios are proposed to describe the geometric relationship between the ground-based ESM sensor and both the airborne and ground-based radar systems. The air-to-ground scenario is corresponding to airborne radars in front of the ground-based ESM sensor while the ground-to-ground scenario is corresponding to ground radars in front of ground-based ESM. One of the most important impairments that signals are subjected to during propagation is multipath fading. The multipath fading causes random variance in features and parameters of the radar signals. Both Rayleigh and Rician multipath channels with typical path losses and Doppler shifts are applied to simulate the environment. To verify that the dataset is suitable for training deep neural network models, a convolutional neural network (CNN) model has been trained and tested for classification of radar signals and detection of frequency modulated continuous wave (FMCW) radars.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"61 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":"121019642","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

Mobility Assistance for Visually Impaired Using LiDAR 使用激光雷达为视障人士提供行动辅助

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9744605

Vikrant Gurav, Abhinav Parameshwaran, Kevin Sherla

引用次数: 0

Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation 胎儿健康评价的心图生物医学信号分类与解释

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718415

Ggaliwango Marvin, Md. Golam Rabiul Alam

引用次数: 3

An Efficient Approach for Skin Disease Detection using Deep Learning 一种基于深度学习的皮肤病检测方法

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718427

Jihan Alam

引用次数: 2

Cyber-Physical Systems and Digital Twins in Practice – A Real-Life Application Example 网络物理系统和数字孪生在实践中-一个现实生活中的应用实例

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718481

Ulrich Dahmen, Marc Priggemeyer, Juergen Rossmann

引用次数: 2

Machine Learning Solutions for Investigating Streams Data using Distributed Frameworks: Literature Review 使用分布式框架调查流数据的机器学习解决方案:文献综述

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718391

Kunal Kumar, Neeraj Anand Sharma, A. B. M. S. Ali

引用次数: 0

Face Image Scanning for Differentiation of Child/Adult images using a CNN-Based Model 基于cnn的人脸图像扫描儿童/成人图像区分模型

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718484

Mirza Jamal Ahmed, Nurul Aza Abdullah

引用次数: 0

IoT Cybersecurity: On the Use of Machine Learning Approaches for Unbalanced Datasets 物联网网络安全:在不平衡数据集上使用机器学习方法

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718426

S. Azad, Syeda Salma Naqvi, F. Sabrina, S. Sohail, S. Thakur

{"title":"IoT Cybersecurity: On the Use of Machine Learning Approaches for Unbalanced Datasets","authors":"S. Azad, Syeda Salma Naqvi, F. Sabrina, S. Sohail, S. Thakur","doi":"10.1109/CSDE53843.2021.9718426","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718426","url":null,"abstract":"Machine learning can effectively be used to detect cyberattacks in IoT networks by learning patterns from previous attack datasets. Unfortunately, datasets used for training machine learning models to detect cyberattacks are almost always unbalanced. As training methods usually try to minimise the loss function by correctly classifying the instances of the majority class, the minority class instances are more likely to be misclassified. This paper aims to develop an insight into the effectiveness of two different approaches for handling unbalanced datasets – weighted loss function, and synthetic minority oversampling technique (SMOTE) in enhancing the capacity of two machine learning algorithms – artificial neural network (ANN) and light gradient boosting model (LGBM) to correctly classify minority class instances. The results suggest that both SMOTE and weighted loss function enhance the recall rate for minority classes significantly, however, it comes at the cost of slightly reduced precision. Moreover, it is found that LGBM, being an ensemble classifier, has an inherent capacity of learning from unbalanced data and hence, outperforms ANN in detecting minority class instances.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"175 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":"124305557","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