2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4)最新文献

Wireless Electronic Notice Board and Attendance Monitoring System 无线电子布告栏及考勤监控系统

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/C2I456876.2022.10051245

D. Ram Nivas, M. Kathirvelu, M. Ishwarya Niranjana, R. Krishnaraj, J. Dhanasekar

{"title":"Wireless Electronic Notice Board and Attendance Monitoring System","authors":"D. Ram Nivas, M. Kathirvelu, M. Ishwarya Niranjana, R. Krishnaraj, J. Dhanasekar","doi":"10.1109/C2I456876.2022.10051245","DOIUrl":"https://doi.org/10.1109/C2I456876.2022.10051245","url":null,"abstract":"Educational Technology is growing field and it is playing an effective vital role in current pandemic situation there by providing assistance by making learning happier and interesting. Several researches are being carried out to improve the teaching and learning practices. In addition to this care should be taken in continuous monitoring and information sharing for ensuring effective participation of the students. Institutions and public utility places use notice boards for sticking various notices for daily information dissemination and data clarification. Day to day updating is difficult and time consuming. Also, a separate personal assistance is required to take care of these notices. These notice boards need to be taken care to prevent it from false data publication and prevention of damage. A raspberry pi based wireless electronic notice is implemented with a projector-based display and voice output. In addition to this, telegram application is used for sending authorized information to the user. An attendance monitoring system is implemented by adapting the technology of RFID. The result of this attendance monitoring system is compared with that of the object detector-based attendance monitoring system implemented with the help of MATLAB. The results seem to be satisfactory there by being a pathway for adapting new technologies in educational institution.","PeriodicalId":165055,"journal":{"name":"2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116942245","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

IEEE Copyright and Consent Form IEEE版权及同意表格

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/c2i456876.2022.10051417

引用次数: 0

Hardware Implementation of Z-Source Inverter for Commercial Applications 商用z源逆变器的硬件实现

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/C2I456876.2022.10051367

K. Chitra, V. K, Siddharth Shendge, V. T. Reddy, Tarun Pati, Sourav Kumar

引用次数: 0

Performance analysis of Incremental boosting based Transfer Learning in Deep CNN 基于增量增强的深度CNN迁移学习性能分析

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/C2I456876.2022.10051386

G. S, H. R

{"title":"Performance analysis of Incremental boosting based Transfer Learning in Deep CNN","authors":"G. S, H. R","doi":"10.1109/C2I456876.2022.10051386","DOIUrl":"https://doi.org/10.1109/C2I456876.2022.10051386","url":null,"abstract":"Imbalanced datasets are one of the important research constraints available in image classification. Due to which the classifier performance is greatly reduced leading to overfitting and under fitting problems. However, this is greatly applicable for better well-balanced datasets. Adaboost classifier model is one such technique proven for its accuracy both in terms of margin theory and in terms of statistical point of view. Many novel approaches use boosting and bagging methods to improve the performance of classifier models. In this research, we are focusing on the effectiveness of boosting procedures in deep Convolution neural network (deep CNN) for classification and modification of ensemble approaches are done with transfer learning techniques. The Computational Complexity of the classifier affects the performance accuracy of the same. Based on the above idea, the input data for training the model is subsampled and reweighted for better efficiency and less complexity. Performance metrics used to analyze the performance of simple AdaBoost classifier, boosted GMM, boosted SVM, incremental boosting based transfer learning approaches using GMM and SVM with and without subsampling procedures are the accuracy, the training time, the predicting time of testing, the volume of the model, and the loss function. Along with the above-said metrics, three more essential parameters, namely the Jaccard index, Dice coefficient, and Mathews correlation coefficients, are used. Based on the experiments carried over the benign and malignant melanoma images from the ISIC database, the boosting based transfer learning approach in deep CNN gives an accuracy of 99.19% and confusion matrix created over the classifier has given the sensitivity of 98.46%.","PeriodicalId":165055,"journal":{"name":"2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115984991","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

IEEE Copyright and Consent Form IEEE版权及同意表格

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/c2i456876.2022.10051437

引用次数: 0

IEEE Copyright and Consent Form IEEE版权及同意表格

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/c2i456876.2022.10051617

引用次数: 0

Automated Detection of Diabetic Retinopathy using Deep Learning in Retinal Fundus Images: Analysis 在视网膜眼底图像中使用深度学习自动检测糖尿病视网膜病变:分析

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/C2I456876.2022.10051419

Manjushree R, Bhoomika D, Rekha R. Nair, T. Babu

{"title":"Automated Detection of Diabetic Retinopathy using Deep Learning in Retinal Fundus Images: Analysis","authors":"Manjushree R, Bhoomika D, Rekha R. Nair, T. Babu","doi":"10.1109/C2I456876.2022.10051419","DOIUrl":"https://doi.org/10.1109/C2I456876.2022.10051419","url":null,"abstract":"Diabetes frequently results in diabetic retinopathy (DR), which develops retinal lesions that impair vision. It can cause blindness if not caught in time. Treatments simply maintain vision because DR is an irreversible process. The risk of blindness can be considerably decreased with early DR detection and treat- ment. The traditional diagnosis of DR retinal fundus pictures by an ophthalmologist is time-consuming, labor-intensive, expensive, and prone to error in comparison to computer-assisted diagnostic techniques Recent advances in deep learning have propelled them to the top of the list of the most widely used methods. Deep learning is particularly effective at classifying and analysing medical images.Convolutional neural networks, a more common and effective deep learning technique, handle medical images very effectively. The proposed model makes use of Inception V3which is Convolutional Neural Network that provides accuracy of 93% which is the highest accuracy when compared to AlexNet, DenseNet121, RestNet50 and EfficientnetBO in detecting diabetic retinopathy.","PeriodicalId":165055,"journal":{"name":"2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124766122","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

Cryptanalysis of Biometric Based Secure User Authentication Protocol for IoT Applications 物联网应用中基于生物识别的安全用户认证协议的密码分析

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/C2I456876.2022.10051341

P. Ramalingam, P. Pabitha

引用次数: 5

IEEE Copyright and Consent Form IEEE版权及同意表格

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/c2i456876.2022.10051270

引用次数: 0

An Insight to High Gain DC-DC Power Converters for Low Voltage PV System 低压光伏系统中高增益DC-DC电源变换器的研究

2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2022-12-15 DOI: 10.1109/C2I456876.2022.10051222

V. Devi, K. Kumar

引用次数: 0