Cybernetics and Systems最新文献_第9页

A Fuzzy Based Routing Approach for Improving Online Conferencing Services in Software Defined Networking 基于模糊路由的软件定义网络在线会议服务改进方法

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-27 DOI: 10.1080/01969722.2022.2148919

Jianwen Cheng, Xiaoyan Zhu, Simin Abedi

引用次数: 1

Autonomous Forecasting of Traffic in Cellular Networks Based on Long-Short Term Memory Recurrent Neural Network 基于长短期记忆递归神经网络的蜂窝网络流量自主预测

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-24 DOI: 10.1080/01969722.2023.2166245

Rohini G, G. C., Nagendra Singh, Vishal Ratansing Patil

引用次数: 0

Blockchain Mechanism-Based Attack Detection in IoT with Hybrid Classification and Proposed Feature Selection 基于区块链机制的物联网攻击检测与混合分类和建议特征选择

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-24 DOI: 10.1080/01969722.2023.2166266

Rekha H., S. M

引用次数: 0

A Performance Analysis of an Enhanced Graded Precision Localization Algorithm for Wireless Sensor Networks 一种增强的无线传感器网络分级精度定位算法的性能分析

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-23 DOI: 10.1080/01969722.2023.2166709

Mani Yuvarasu, A. Balaram, Subramanian Chandramohan, Dilip Kumar Sharma

引用次数: 4

IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & Adafruit.io on Defined Area 基于Arduino ESP8266和Adafruit的基于物联网的RFID学生考勤监控系统。界定区域

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-23 DOI: 10.1080/01969722.2023.2166243

Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal, A. Chaturvedi

引用次数: 20

A Deep Learning Approach to Detecting Objects in Underwater Images 一种水下图像中目标检测的深度学习方法

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-23 DOI: 10.1080/01969722.2023.2166246

Kalaiarasi G, Ashok J, Saritha B, Manoj Prabu M

引用次数: 1

SR Motor T-I Characteristics Performance Simulation Validation through Experimental Results SR电机T-I特性仿真实验验证

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-20 DOI: 10.1080/01969722.2023.2166242

Sunil V. Patil, R. Saxena

引用次数: 0

Mayfly Optimization with Deep Belief Network-Based Automated COVID-19 Cough Classification Using Biological Audio Signals 基于生物音频信号的基于深度信念网络的新型冠状病毒咳嗽自动分类的蜉蝣优化

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-20 DOI: 10.1080/01969722.2023.2166244

G. Ayappan, S. Anila

{"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}

引用次数: 0

An Energy-Aware Agent-Based Resource Allocation Using Targeted Load Balancer for Improving Quality of Service in Cloud Environment 一种基于能量感知代理的资源分配，使用目标负载均衡器提高云环境中的服务质量

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-20 DOI: 10.1080/01969722.2023.2166247

Umamageswaran Jambulingam, K. Balasubadra

{"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}

引用次数: 0

Asymptotically Tracking Control of Structural Balance for Discrete-Time Links System Associated with External Stimulations and State Observer 具有外部刺激和状态观测器的离散连杆系统结构平衡的渐近跟踪控制

IF 1.7 4区计算机科学

Cybernetics and Systems Pub Date : 2023-01-20 DOI: 10.1080/01969722.2023.2166688

Yi Peng, Peitao Gao, Yinhe Wang, Juanxia Zhao

引用次数: 0