{"title":"Crime Type Prediction in Saudi Arabia Based on Intelligence Gathering","authors":"Saleh Albahli, Waleed Albattah","doi":"10.1093/comjnl/bxac053","DOIUrl":"https://doi.org/10.1093/comjnl/bxac053","url":null,"abstract":"","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"29 1","pages":"1936-1948"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87360544","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}
{"title":"Comparative Analysis of Overlap Community Detection Techniques on Social Media Platform","authors":"Pawan Meena, M. Pawar, Anjana Pandey","doi":"10.1093/comjnl/bxac050","DOIUrl":"https://doi.org/10.1093/comjnl/bxac050","url":null,"abstract":"","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"24 1","pages":"1893-1912"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86361741","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}
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.
{"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}
It is well known that in many real networks, such as brain networks and scientific collaboration networks, there exist higher-order nonpairwise relations among nodes, i.e., interactions between among than two nodes at a time. This simplicial structure can be described by simplicial complexes and has an important effect on topological and dynamical properties of networks involving such group interactions. In this paper, we study analytically resistance distances in iteratively growing networks with higher-order interactions characterized by the simplicial structure that is controlled by a parameter q. We derive exact formulas for interesting quantities about resistance distances, including Kirchhoff index, additive degree-Kirchhoff index, multiplicative degree-Kirchhoff index, as well as average resistance distance, which have found applications in various areas elsewhere. We show that the average resistance distance tends to a q-dependent constant, indicating the impact of simplicial organization on the structural robustness measured by average resistance distance.
{"title":"Resistance Distances in Simplicial Networks","authors":"Ming-Zhang Zhu, Wanyue Xu, Zhongzhi Zhang, Haibin Kan, Guanrong Chen","doi":"10.48550/arXiv.2212.05759","DOIUrl":"https://doi.org/10.48550/arXiv.2212.05759","url":null,"abstract":"It is well known that in many real networks, such as brain networks and scientific collaboration networks, there exist higher-order nonpairwise relations among nodes, i.e., interactions between among than two nodes at a time. This simplicial structure can be described by simplicial complexes and has an important effect on topological and dynamical properties of networks involving such group interactions. In this paper, we study analytically resistance distances in iteratively growing networks with higher-order interactions characterized by the simplicial structure that is controlled by a parameter q. We derive exact formulas for interesting quantities about resistance distances, including Kirchhoff index, additive degree-Kirchhoff index, multiplicative degree-Kirchhoff index, as well as average resistance distance, which have found applications in various areas elsewhere. We show that the average resistance distance tends to a q-dependent constant, indicating the impact of simplicial organization on the structural robustness measured by average resistance distance.","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"26 1","pages":"1922-1935"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79116240","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}
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.
{"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}
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%.
{"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}
Jinkun Cao, Liwei Lin, Ruhui Ma, Haibing Guan, Mengke Tian, Y. Wang
With the rapid development of the Internet of Things (IoT), network security challenges are becoming more and more complex, and the scale of intrusion attacks against the network is gradually increasing. Therefore, researchers have proposed Intrusion Detection Systems and constantly designed more effective systems to defend against attacks. One issue to consider is using limited computing power to process complex network data efficiently. In this paper, we take the AWID dataset as an example, propose an efficient data processing method to mitigate the interference caused by redundant data and design a lightweight deep learning-based model to analyze and predict the data category. Finally, we achieve an overall accuracy of 99.77% and an accuracy of 97.95% for attacks on the AWID dataset, with a detection rate of 99.98% for the injection attack. Our model has low computational overhead and a fast response time after training, ensuring the feasibility of applying to edge nodes with weak computational power in the IoT.
{"title":"An Efficient Deep Learning Approach To IoT Intrusion Detection","authors":"Jinkun Cao, Liwei Lin, Ruhui Ma, Haibing Guan, Mengke Tian, Y. Wang","doi":"10.1093/comjnl/bxac119","DOIUrl":"https://doi.org/10.1093/comjnl/bxac119","url":null,"abstract":"\u0000 With the rapid development of the Internet of Things (IoT), network security challenges are becoming more and more complex, and the scale of intrusion attacks against the network is gradually increasing. Therefore, researchers have proposed Intrusion Detection Systems and constantly designed more effective systems to defend against attacks. One issue to consider is using limited computing power to process complex network data efficiently. In this paper, we take the AWID dataset as an example, propose an efficient data processing method to mitigate the interference caused by redundant data and design a lightweight deep learning-based model to analyze and predict the data category. Finally, we achieve an overall accuracy of 99.77% and an accuracy of 97.95% for attacks on the AWID dataset, with a detection rate of 99.98% for the injection attack. Our model has low computational overhead and a fast response time after training, ensuring the feasibility of applying to edge nodes with weak computational power in the IoT.","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"4 1","pages":"2870-2879"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79822997","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}
{"title":"Correction to: Learning Disjunctive Multiplicity Expressions and Disjunctive Generalize Multiplicity Expressions From Both Positive and Negative Examples","authors":"","doi":"10.1093/comjnl/bxac117","DOIUrl":"https://doi.org/10.1093/comjnl/bxac117","url":null,"abstract":"","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"1 1","pages":"2329"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91155064","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}
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