South Afr. Comput. J.最新文献_第9页

Crime Type Prediction in Saudi Arabia Based on Intelligence Gathering 基于情报收集的沙特阿拉伯犯罪类型预测

South Afr. Comput. J. Pub Date : 2023-01-01 DOI: 10.1093/comjnl/bxac053

Saleh Albahli, Waleed Albattah

引用次数: 1

Practical Attacks on Reduced-Round 3D and Saturnin 对reduce - round 3D和Saturnin的实际攻击

South Afr. Comput. J. Pub Date : 2023-01-01 DOI: 10.1093/comjnl/bxab174

Tao Hou, Ting Cui, Jiyan Zhang

引用次数: 0

Interopera: An Efficient Cross-Chain Trading Protocol Interopera:一个高效的跨链交易协议

South Afr. Comput. J. Pub Date : 2023-01-01 DOI: 10.1093/comjnl/bxac030

Lingyuan Yin, Jing Xu, Zhenfeng Zhang

引用次数: 0

Comparative Analysis of Overlap Community Detection Techniques on Social Media Platform 社交媒体平台上重叠社区检测技术的比较分析

South Afr. Comput. J. Pub Date : 2023-01-01 DOI: 10.1093/comjnl/bxac050

Pawan Meena, M. Pawar, Anjana Pandey

引用次数: 0

Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers 基于fpga的LSTM神经网络在实时雷达告警接收机脉冲信号检测中的性能评价

South Afr. Comput. J. Pub Date : 2022-12-16 DOI: 10.1093/comjnl/bxac167

Erdogan Berkay Tekincan, Tülin Erçelebİ Ayyildiz, Nizam Ayyildiz

{"title":"Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers","authors":"Erdogan Berkay Tekincan, Tülin Erçelebİ Ayyildiz, Nizam Ayyildiz","doi":"10.1093/comjnl/bxac167","DOIUrl":"https://doi.org/10.1093/comjnl/bxac167","url":null,"abstract":"\u0000 Radar warning receivers are real-time systems used to detect emitted signals by the enemy targets. The conventional method of detecting the signal is to determine the noise floor and differentiate the signals above the noise floor by setting a threshold value. The common methodology for detecting signals in noisy environment is Constant False Alarm Rate (CFAR) detection. In CFAR methodology, threshold level is determined for a specified probability of false alarm. CFAR dictates the signal power to be detected is higher than the noise floor, i.e. signal-to-noise ratio (SNR) should be positive. To detect radar signals for negative SNR values machine learning techniques can be used. It is possible to detect radar signals for negative SNR values by Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). In this study, we evaluated whether LSTM ANN can replace the CFAR algorithm for signal detection in real-time radar receiver systems. We implemented a Field Programmable Gate Array (FPGA) based LSTM ANN architecture, where pulse signal detection could be performed with 94% success rate at -5 dB SNR level. To the best of our knowledge our study is the first where LSTM ANN is implemented on FPGA for radar warning receiver signal detection.","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"66 1","pages":"1040-1052"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86823694","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

Resistance Distances in Simplicial Networks 简单网络中的阻力距离

South Afr. Comput. J. Pub Date : 2022-12-12 DOI: 10.48550/arXiv.2212.05759

Ming-Zhang Zhu, Wanyue Xu, Zhongzhi Zhang, Haibin Kan, Guanrong Chen

引用次数: 1

Analysis Performance Of Image Processing Technique Its Application by Decision Support Systems On Covid-19 Disease Prediction Using Convolution Neural Network 图像处理技术及其在决策支持系统中应用卷积神经网络进行Covid-19疾病预测的性能分析

South Afr. Comput. J. Pub Date : 2022-11-29 DOI: 10.1093/comjnl/bxac154

K. Ravishankar, C. Jothikumar

{"title":"Analysis Performance Of Image Processing Technique Its Application by Decision Support Systems On Covid-19 Disease Prediction Using Convolution Neural Network","authors":"K. Ravishankar, C. Jothikumar","doi":"10.1093/comjnl/bxac154","DOIUrl":"https://doi.org/10.1093/comjnl/bxac154","url":null,"abstract":"\u0000 The Covid-19 pandemic has been identified as a key issue for human society, in recent times. The presence of the infection on any human is identified according to different symptoms like cough, fever, headache, breathless and so on. However, most of the symptoms are shared by various other diseases, which makes it challenging for the medical practitioners to identify the infection. To aid the medical practitioners, there are a number of approaches designed which use different features like blood report, lung and cardiac features to detect the disease. The method captures the lung image using magnetic resonance imaging scan device and records the cardiac features. Using the image, the lung features are extracted and from the cardiac graph, the cardiac features are extracted. Similarly, from the blood samples, the features are extracted. By extracting such features from the person, the method estimates different weight measures to predict the disease. Different methods estimate the similarity of the samples in different ways to classify the input sample. However, the image processing techniques are used for different problems in medical domain; the same has been used in the detection of the disease. Also, the presence of Covid-19 is detected using different set of features by various approaches.","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"238 1","pages":"1030-1039"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77152223","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

Lung Lobe Segmentation and Feature Extraction-Based Hierarchical Attention Network for COVID-19 Prediction from Chest X-Ray Images 基于肺叶分割和特征提取的分层关注网络用于胸部x射线图像的COVID-19预测

South Afr. Comput. J. Pub Date : 2022-10-19 DOI: 10.1093/comjnl/bxac136

S. C. Magneta, C. Sundar, M. S. Thanabal

{"title":"Lung Lobe Segmentation and Feature Extraction-Based Hierarchical Attention Network for COVID-19 Prediction from Chest X-Ray Images","authors":"S. C. Magneta, C. Sundar, M. S. Thanabal","doi":"10.1093/comjnl/bxac136","DOIUrl":"https://doi.org/10.1093/comjnl/bxac136","url":null,"abstract":"\u0000 Coronavirus disease 2019 (COVID-19) is a rising respiratory sickness. It causes harsh pneumonia and is considered to cover higher collisions in the healthcare domain. The diagnosis at an early stage is more complex to get accurate treatment for reducing the stress in the clinical sector. Chest X-ray scan is the standard imaging diagnosis test employed for pneumonia disease. Automatic detection of COVID-19 helps to control the community outbreak but tracing this viral infection through X-ray results in a challenging task in the medical community. To automatically detect the viral disease in order to reduce the mortality rate, an effective COVID-19 detection method is modelled in this research by the proposed manta-ray multi-verse optimization-based hierarchical attention network (MRMVO-based HAN) classifier. Accordingly, the MRMVO is the incorporation of manta-ray foraging optimization and multi-verse optimizer. Based on the segmented lung lobes, the features are acquired from segmented regions in such a way that the process of COVID-19 detection mechanism is carried out with the features acquired from interested lobe regions. The proposed method has good performance with the measures, such as accuracy, true positive rate and true negative rate with the values of 93.367, 89.921 and 95.071%.","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"39 1","pages":"508-522"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79854480","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 Efficient Deep Learning Approach To IoT Intrusion Detection 一种高效的物联网入侵检测深度学习方法

South Afr. Comput. J. Pub Date : 2022-09-30 DOI: 10.1093/comjnl/bxac119

Jinkun Cao, Liwei Lin, Ruhui Ma, Haibing Guan, Mengke Tian, Y. Wang

引用次数: 2

Correction to: Learning Disjunctive Multiplicity Expressions and Disjunctive Generalize Multiplicity Expressions From Both Positive and Negative Examples 更正:从正反两个例子中学习析取多重表达和析取泛化多重表达

South Afr. Comput. J. Pub Date : 2022-09-17 DOI: 10.1093/comjnl/bxac117

引用次数: 0