2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)最新文献_第9页

Deep Learning based Breast Image Classification Study for Cancer Detection 基于深度学习的乳腺癌图像分类研究

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100206

C. Sarada, V. Dattatreya, K. Lakshmi

引用次数: 0

Internet of Things Based Tired Detection using Deep Learning Techniques 基于深度学习技术的物联网疲劳检测

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099783

Nijaguna G S, Sharanya S. Kumar, Devika Sv, Bechoo Lal, P. S

{"title":"Internet of Things Based Tired Detection using Deep Learning Techniques","authors":"Nijaguna G S, Sharanya S. Kumar, Devika Sv, Bechoo Lal, P. S","doi":"10.1109/ICICACS57338.2023.10099783","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099783","url":null,"abstract":"Sleep is essential for human survival since it helps to restore and maintain our bodies' immune systems and other essential processes. One-third of a person's life is devoted to sleeping, although few are aware of the many positive aspects of this activity. Two distinct types of sleep, REM and NREM, have been identified. A good night's rest is achieved when REM and NREM sleep alternate in a regular pattern. Disruptions to this cycle, whether they originate physiologically or psychologically, have been linked to a variety of health problems. Polysomnography (PSG) equipment is often used in sleep labs inside hospitals to perform sleep studies. A polysomnogram is an in-depth medical technique that records a patient's vital signs while they sleep and necessitates a hospital stay. Clinically, sleep apnea is defined as a breathing disease in which there are periodic pauses in breathing lasting 10 seconds or more that occur more than five times during the night. Sleep apnea may be classified as either Obstructive, Central, or Mixed. The prevalent sleep problem known as obstructive sleep apnea (OSA) is caused by the relaxation of muscles in the upper airway during sleep. The purpose of this study is to provide a technique for screening for Obstructive Sleep Apnea by analysing Heart Rate Variability of Electrocardiogram (ECG) data while the subject is asleep. The goals of this study are to create computational approaches for identifying OSA based on characteristics extracted from Heart Rate Variability (HRV) signals derived from sleep electrocardiograms (ECGs). Physio Net's Apnea-ECG recordings serve as the source for the ECG data.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132510718","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

Research on Application of Computer Big Data Technology in 5G Communication Network 计算机大数据技术在5G通信网络中的应用研究

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100070

Zhenlin Huang, Jianxin Yang

{"title":"Research on Application of Computer Big Data Technology in 5G Communication Network","authors":"Zhenlin Huang, Jianxin Yang","doi":"10.1109/ICICACS57338.2023.10100070","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100070","url":null,"abstract":"5G mobile communication as one of the important tools of national life, need to ensure network stability and security, network optimization is an important means to accomplish this task, and network prediction is the premise of network optimization. In view of the two chronic problems of traditional network prediction methods, which are lack of user data and based on hypothetical network scenarios, this paper proposes to apply big data technology to network core performance index (KPI) prediction in the form of time series. Meanwhile, support vector machine and association rule technology are used to further optimize the prediction process and prediction results. So that it has a faster prediction rate and higher prediction accuracy. In the process of research and application of this technology, it is necessary to constantly improve the technical system, strengthen technical investigation and research, combine with the demand of mobile communication network, fundamentally improve the effective application of data mining technology. Aiming at the application of data mining technology in mobile communication network optimization to discuss, understanding and analysis of the characteristics of the data mining technology, combined with the construction of the mobile communication network demand, with perfect technology processes, from an objective point of view, analysis the characteristics of the RED algorithm, improve the application of data mining technology in the mobile communication network.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128106341","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

Estimation of Transmission Bandwidth for VoIP Signals over IP Packet Transmission Network using Capacity Computing Method 基于容量计算的IP分组传输网络中VoIP信号传输带宽估计

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100177

Rajesh Singh, A. Joshi

{"title":"Estimation of Transmission Bandwidth for VoIP Signals over IP Packet Transmission Network using Capacity Computing Method","authors":"Rajesh Singh, A. Joshi","doi":"10.1109/ICICACS57338.2023.10100177","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100177","url":null,"abstract":"The traditional telephone network is the required transmission bandwidth of 64kbit/s. The so-called VoIP is an IP packet transmission network as a transmission platform, simulated voice signal compression, packaging and continuous special processing, which can use the connectionless UDP protocol for transmission. Sending voice signals over an IP network requires several components and functions. The simplest form of a network consists of two or more devices with VoIP capabilities connected through an IP network. In this paper, a smart estimation of transmission bandwidth for VoIP signals over IP packet transmission network using capacity computing method. A speech network establishes a physical connection between communication endpoints (a line) and transmits encoded signals between the endpoints. Unlike circuit switching networks, IP networks do not create connections. It requires data to be placed into variable long data reports or packets, and then address and control information must be sent for each datagram and sent over the network to the destination.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654243","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

Photovoltaic Panel Hot Spot Recognition Based on Lightweight SSD 基于轻量化SSD的光伏板热点识别

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099712

Hongbin Li, Peng Li

{"title":"Photovoltaic Panel Hot Spot Recognition Based on Lightweight SSD","authors":"Hongbin Li, Peng Li","doi":"10.1109/ICICACS57338.2023.10099712","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10099712","url":null,"abstract":"An intelligent recognition technique of photovoltaic panel hot spot based on UAV and target detection algorithm is proposed in order to address the issues of low efficiency and high cost of manual operation and maintenance of photovoltaic panels. When the light intensity distribution is uneven, the photovoltaic panel may produce hot spot effect, damage the photovoltaic cell module, and cause system failure. In this research, a lightweight SSD-based photovoltaic panel hot spot recognition method is proposed, which addresses the issues of high target detection complexity and difficulties in implementing the algorithm on edge devices. The complexity of the model is decreased by using Resnet50 as the backbone network in place of VGG16. To increase the model's accuracy and complete the identification of solar module hot spots, the pyramid module and the Coordinate Attention module are introduced. Tested on the aerial photovoltaic panel dataset, the mAP of the proposed model reaches 86.28%, which is 2.58% higher than the original SSD target detection algorithm, and the proposed model compresses 35.90% of parameters and 48.47% of the calculation, which can meet the requirements of carrying the model to the UAV.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132223449","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

Research on Computer Network Security Situation Awareness Platform 计算机网络安全态势感知平台的研究

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099484

Siliang Wu, Yuhua Wei

引用次数: 0

Differences Analysis and Development Strategies of Regional Economies Based on Intelligent Hybrid Algorithm 基于智能混合算法的区域经济差异分析与发展策略

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10099899

Meng Du

引用次数: 0

The Performance Optimization of a Heating System to obtain the Maximum Efficiency in Different Weather Conditions 供热系统在不同天气条件下获得最大效率的性能优化

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100271

Y. Bisht, Jageshwar Ray

{"title":"The Performance Optimization of a Heating System to obtain the Maximum Efficiency in Different Weather Conditions","authors":"Y. Bisht, Jageshwar Ray","doi":"10.1109/ICICACS57338.2023.10100271","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100271","url":null,"abstract":"The efficiency of the heating system primarily depends on the efficient selection of the heating battery connection scheme. an even larger amplitude, the air flow is also saturated at the other extreme deflection point when the jet fully enters the tube. The displacement of the lip gives the flow an asymmetric waveform whose crests have frequencies that are multiples of the frequency of the deflecting wave. In this case, the jet is almost completely ejected from the tube and re-inflated with each displacement cycle, and the amount of energy it imparts to the reflected wave in the tube is stopped depending on the further increase in amplitude. Correspondingly, the effectiveness of air strings in producing sound vibrations decreases. In this case, an increase in the jet deflection amplitude only leads to a decrease in the conduction helix. A decrease in jet efficiency with increase in deflection amplitude is accompanied by an increase in energy losses in the element tube. Fluctuations in the tube are quickly set to a low level where the jet energy properly compensates for energy losses in the tube.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132123298","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

Fault Detection in Power Transmission Lines Using AI Model 基于AI模型的输电线路故障检测

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100005

A. Firos, N. Prakash, Rajasekhar Gorthi, M. Soni, Sonu Kumar, V. Balaraju

{"title":"Fault Detection in Power Transmission Lines Using AI Model","authors":"A. Firos, N. Prakash, Rajasekhar Gorthi, M. Soni, Sonu Kumar, V. Balaraju","doi":"10.1109/ICICACS57338.2023.10100005","DOIUrl":"https://doi.org/10.1109/ICICACS57338.2023.10100005","url":null,"abstract":"Unexpected failures in the electrical power transmission line can occur for several different, unpredictable reasons. Power failures on transmission lines can destroy the present power grid if faults aren't quickly detected and corrected. For consistent performance, it is essential to have a system in place for identifying and categorizing power system faults. Several academics have developed automated approaches for fault identification and classification; however, typical fault detection techniques depend on human feature extraction with previous understanding. It is crucial to detect transmission line faults to guarantee safety. Preventing costly damage to the network is one of the key advantages of earlier fault detection in a transmission line. Autonomous and efficient fault diagnosis in the power system remains a major problem in the area of intelligent fault diagnosis. Recent years have seen a surge in interest in the development of intelligent fault diagnosis techniques that make use of Machine Learning (ML). Different ML techniques for fault classification are presented in this research. Kaggle data is used after being cleaned and integrated. Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) are the ML models used. Using the metrics of evaluation, the optimal model is found. Results from experiments demonstrate that the NB will outperform other methods for fault detection in power transmission lines, with an accuracy rate of 97.77%, recall of 97.09%, the precision of 98.64%, and Fl-score of 97.86%.","PeriodicalId":274807,"journal":{"name":"2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133956860","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

Learning Future Terrorist Targets using Attention Based Hybrid CNN and BiLSTM Model 基于注意力的CNN和BiLSTM混合模型学习未来恐怖分子目标

2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) Pub Date : 2023-02-24 DOI: 10.1109/ICICACS57338.2023.10100298

Namitha Nayak, Manasa Rayachoti, Ananya Gupta, G. Prerna, Sreenath M V, D. Annapurna

引用次数: 2