Pub Date : 2023-01-27DOI: 10.1080/01969722.2022.2148919
Jianwen Cheng, Xiaoyan Zhu, Simin Abedi
{"title":"A Fuzzy Based Routing Approach for Improving Online Conferencing Services in Software Defined Networking","authors":"Jianwen Cheng, Xiaoyan Zhu, Simin Abedi","doi":"10.1080/01969722.2022.2148919","DOIUrl":"https://doi.org/10.1080/01969722.2022.2148919","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43871837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-24DOI: 10.1080/01969722.2023.2166245
Rohini G, G. C., Nagendra Singh, Vishal Ratansing Patil
{"title":"Autonomous Forecasting of Traffic in Cellular Networks Based on Long-Short Term Memory Recurrent Neural Network","authors":"Rohini G, G. C., Nagendra Singh, Vishal Ratansing Patil","doi":"10.1080/01969722.2023.2166245","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166245","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48093289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-24DOI: 10.1080/01969722.2023.2166266
Rekha H., S. M
{"title":"Blockchain Mechanism-Based Attack Detection in IoT with Hybrid Classification and Proposed Feature Selection","authors":"Rekha H., S. M","doi":"10.1080/01969722.2023.2166266","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166266","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43785552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1080/01969722.2023.2166243
Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal, A. Chaturvedi
{"title":"IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & Adafruit.io on Defined Area","authors":"Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal, A. Chaturvedi","doi":"10.1080/01969722.2023.2166243","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166243","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44477449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1080/01969722.2023.2166246
Kalaiarasi G, Ashok J, Saritha B, Manoj Prabu M
{"title":"A Deep Learning Approach to Detecting Objects in Underwater Images","authors":"Kalaiarasi G, Ashok J, Saritha B, Manoj Prabu M","doi":"10.1080/01969722.2023.2166246","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166246","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46164755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1080/01969722.2023.2166242
Sunil V. Patil, R. Saxena
{"title":"SR Motor T-I Characteristics Performance Simulation Validation through Experimental Results","authors":"Sunil V. Patil, R. Saxena","doi":"10.1080/01969722.2023.2166242","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166242","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48164588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1080/01969722.2023.2166244
G. Ayappan, S. Anila
Abstract The outbreak of the COVID-19 pandemic has made widespread testing a need for controlling the disease. Numerous recent investigations have shown that many people with COVID-19 show no outward signs of illness. As a result, these patients are more likely to unwittingly spread the virus because they will not take a COVID-19 test. In order to get tested, patients will need to travel to a lab, putting others at risk of exposure. Recent studies have shown that people with COVID-19 who are asymptomatic have distinctive coughs and breathing patterns compared to the general population. This prompted the study of cough and breath sounds in COVID-19 patients as a means of differentiating them from those of non-COVID lung infection patients and the general population. In this article, we present a robust, efficient, and extensible method for identifying symptomatic patterns in biological audio signals. Cough digitized audio files are subjected to spectral analysis via a stationary wavelet transform (SWT). The proposed model employs ADASYN technique to handle the class imbalance problem. Also, features like Mel-frequency cepstral coefficients (MFCCs), log frame energies, zero crossing rate (ZCR), and kurtosis are extracted. For classification process, deep belief network (DBN) model is utilized. Finally, mayfly optimization (MFO) algorithm is exploited for optimal hyper-parameter tuning of the DBN model. The experimental validation of the proposed model takes place using open access dataset. Proposed method is compared with other methods in terms accuracy, specificity, sensitivity, F1-Score, precision and recall. The experimental outcomes demonstrated the betterment of the proposed model over other recent state of art approaches.
{"title":"Mayfly Optimization with Deep Belief Network-Based Automated COVID-19 Cough Classification Using Biological Audio Signals","authors":"G. Ayappan, S. Anila","doi":"10.1080/01969722.2023.2166244","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166244","url":null,"abstract":"Abstract The outbreak of the COVID-19 pandemic has made widespread testing a need for controlling the disease. Numerous recent investigations have shown that many people with COVID-19 show no outward signs of illness. As a result, these patients are more likely to unwittingly spread the virus because they will not take a COVID-19 test. In order to get tested, patients will need to travel to a lab, putting others at risk of exposure. Recent studies have shown that people with COVID-19 who are asymptomatic have distinctive coughs and breathing patterns compared to the general population. This prompted the study of cough and breath sounds in COVID-19 patients as a means of differentiating them from those of non-COVID lung infection patients and the general population. In this article, we present a robust, efficient, and extensible method for identifying symptomatic patterns in biological audio signals. Cough digitized audio files are subjected to spectral analysis via a stationary wavelet transform (SWT). The proposed model employs ADASYN technique to handle the class imbalance problem. Also, features like Mel-frequency cepstral coefficients (MFCCs), log frame energies, zero crossing rate (ZCR), and kurtosis are extracted. For classification process, deep belief network (DBN) model is utilized. Finally, mayfly optimization (MFO) algorithm is exploited for optimal hyper-parameter tuning of the DBN model. The experimental validation of the proposed model takes place using open access dataset. Proposed method is compared with other methods in terms accuracy, specificity, sensitivity, F1-Score, precision and recall. The experimental outcomes demonstrated the betterment of the proposed model over other recent state of art approaches.","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":"54 1","pages":"767 - 786"},"PeriodicalIF":1.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48339280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1080/01969722.2023.2166247
Umamageswaran Jambulingam, K. Balasubadra
Abstract In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity.
{"title":"An Energy-Aware Agent-Based Resource Allocation Using Targeted Load Balancer for Improving Quality of Service in Cloud Environment","authors":"Umamageswaran Jambulingam, K. Balasubadra","doi":"10.1080/01969722.2023.2166247","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166247","url":null,"abstract":"Abstract In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity.","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":"54 1","pages":"1111 - 1131"},"PeriodicalIF":1.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47807878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1080/01969722.2023.2166688
Yi Peng, Peitao Gao, Yinhe Wang, Juanxia Zhao
{"title":"Asymptotically Tracking Control of Structural Balance for Discrete-Time Links System Associated with External Stimulations and State Observer","authors":"Yi Peng, Peitao Gao, Yinhe Wang, Juanxia Zhao","doi":"10.1080/01969722.2023.2166688","DOIUrl":"https://doi.org/10.1080/01969722.2023.2166688","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48566651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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