2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)最新文献_第10页

Analysis of Tissue Engineering for the Scaffolds in Dentistry 组织工程技术在牙科支架中的应用分析

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10059628

Shalabh Mehrotra, Ashwini Mathur

引用次数: 0

Software Effort Estimation Using Hard Limiting Techniques with Special Reference to Small Size Technical &Analytical Projects 使用硬限制技术的软件工作量估算，特别适用于小型技术和分析项目

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10060696

T. Kumar, M. Jayaram

{"title":"Software Effort Estimation Using Hard Limiting Techniques with Special Reference to Small Size Technical &Analytical Projects","authors":"T. Kumar, M. Jayaram","doi":"10.1109/ICERECT56837.2022.10060696","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10060696","url":null,"abstract":"Effort estimation is the process of predicting a software's likely cost and development time. Estimating software development effort is still a complex problem that receives a lot of research attention. The accuracy with which the effort necessary for software development is estimated, and it is a significant success element in software project management. The estimated effort should be roughly equal to the actual effort in a good software estimation model. Accurate estimation enables managers to allocate resources for the planning and coordination of all activities. In this paper the effort estimation for small size projects involving technical and analytical aspects of engineering intent. The estimation of time resources is done in three steps firstly Identification of seven novel traits of software (LOC, N&C, CGPA, R, CC, AC, FP). Secondly PCA was implemented to decide the significant parameters among the software features. Lastly linear regression and polynomial regression models were developed using the significant features predicted by the PCA. The result of evaluation of the two models are encouraging with the minimum RMSE of 32-204 minutes and maximum regression coefficients(r2) of 0.95-0.98","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123032239","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

Anomaly Detection in Cardiac Related Datasets 心脏相关数据集的异常检测

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10059654

K. Nayana, S. Vinay, S. Ashwini

{"title":"Anomaly Detection in Cardiac Related Datasets","authors":"K. Nayana, S. Vinay, S. Ashwini","doi":"10.1109/ICERECT56837.2022.10059654","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10059654","url":null,"abstract":"cardiovascular disease is one of the most common diseases in the modern world. If recognized early, then it can significantly reduce the damage to the patient. This work describes the detection of anomalies in electrocardiogram (ECG) readings. Anomaly detection in data mining finds instances, occurrences, and observations that differ from a dataset's regular pattern of activity. Using the ECG dataset as input, the initial step in this method is signal pre-processing. High pass, low pass, and notch filters are used to de-noise ECG signals as part of the signal pre-processing. ECG signal de-noising is a significant pre-processing step that highlights the characteristic waves in ECG data while attenuating the disturbances. The emergence of the ECG signal coefficients from signal pre-processing is trained and tested in the second stage. The classification of the ECG signal using LSTM RNN Model is the final stage. Recurrent neural networks are artificial neural networks that use sequential data or time series data (RNN). The LSTM RNN Model effectively separates out extraneous data, prevents signal information loss, lowers computational complexity, and classifies the ECG signal.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115762005","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

The Practical Application of Mapping Yangtze River Waterway by UAV 无人机在长江航道测绘中的实际应用

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10060676

Wei Cheng, Xiaotang Xia

引用次数: 0

Construction of Domain Ontology Considering Organic Fertilizers for A Sustainable Agriculture 面向可持续农业的有机肥料领域本体构建

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10060598

Kushala V M, Supriya M C, Divakar H R

引用次数: 0

Accident Prediction Using KNN Algorithm 基于KNN算法的事故预测

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10059746

A. M, A. K, Amrutha K, A. M, Chandanashree K R

引用次数: 0

Mining and Recognition of English Learning Patterns of Mobile Users Based on Intelligent Algorithm 基于智能算法的移动用户英语学习模式挖掘与识别

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10060032

Jingtai Li

引用次数: 0

A Proposed Microstrip Patch Antenna Design for Wi-Max, WLAN and Application for Satellite 一种用于Wi-Max、WLAN的微带贴片天线设计及其在卫星上的应用

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10060553

Nabila Sultana Anonnya, Md. Mostafizur Rahman

引用次数: 0

Charging Station for E-Vehicle Using Solar with IoT 使用太阳能和物联网的电动汽车充电站

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10060451

K. N. Kumar, P. Badrinath, S. Vickraman, G. Satheesan

引用次数: 0

Design of NBW-MHO with BERT model for prediction of Breast Cancer in IoT Healthcare System 基于BERT模型的NBW-MHO物联网医疗系统乳腺癌预测设计

2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) Pub Date : 2022-12-26 DOI: 10.1109/ICERECT56837.2022.10059743

Rajlakshmi Ghatkamble, P. Pareek, P. D.

{"title":"Design of NBW-MHO with BERT model for prediction of Breast Cancer in IoT Healthcare System","authors":"Rajlakshmi Ghatkamble, P. Pareek, P. D.","doi":"10.1109/ICERECT56837.2022.10059743","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10059743","url":null,"abstract":"The key to successful early recovery and treatment of breast cancer in today's healthcare system is an accurate and prompt diagnosis. Over the last several years, the IoT has undergone a transition that makes it possible to analyse both real-time techniques. Medical diagnostics are aided by the Internet of Medical Things, which connects various medical equipment and artificial intelligence applications with the healthcare network. Most women with breast cancer don't make it because the disease isn't detected early enough using today's best methods. Therefore, doctors and scientists are confronted with a significant challenge in recognizing breast cancer at an primary stage. We present a medical IoT-based diagnostic system that can distinguish between patients with cancer and those without it in an Internet of Things setting. Malignant vs benign categorization is performed using an unique transfer learning technique called BERT, which is based on a previously learned language model. In particular, this research looks at how well novel fine-tuning approaches based on transfer learning might improve BERT's capacity to capture significant context. This research improves the BERT model's classification accuracy by using a Black Widow-meta-heuristic Optimization (NBW-MHO) feature selection strategy to refine feature selection from the breast cancer dataset. The WDBC dataset served as a testbed for the suggested method. The suggested model's classification accuracy using the BERT model and NBW-MHO was 95.20 percent.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122655761","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