2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)最新文献_第5页

Fault Detection In Bottle Caps And Label Alignment Using Convolutional Neural Network 基于卷积神经网络的瓶盖故障检测与标签对齐

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10118182

Sapana Indarchand Tatu, S. Singh, S. Bansod, Prashant Pal

引用次数: 0

Comparison of Performance of Machine Learning Algorithms for Diabetes Detection 机器学习算法在糖尿病检测中的性能比较

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10118315

Pranav Dalve, Dyuti Bobby, Abha Marathe, Atharva Dusane, S. Daga

{"title":"Comparison of Performance of Machine Learning Algorithms for Diabetes Detection","authors":"Pranav Dalve, Dyuti Bobby, Abha Marathe, Atharva Dusane, S. Daga","doi":"10.1109/ICAECT57570.2023.10118315","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118315","url":null,"abstract":"In today's society, Diabetes Mellitus affects a large portion of the population. The purpose of this research is to examine a few Machine Learning (ML) algorithms to assist in prediction of Type 2 diabetes, a disorder that alters the body's blood sugar processing. For the diabetes prediction proposed, the prominent PIMA Indian Diabetes Dataset was utilized. Support Vector Machine (SVM), K-Nearest Neighbours (KNN), XGBoost (Extreme gradient boosting), and Random Forest Regression are the techniques that were investigated. SVM yields an accuracy of 84.5%, sensitivity of 88.00% and specificity of 81.00%. KNN yields an accuracy of 80.5% sensitivity of 83.00% and specificity of 78.00%. XGBoost yields an accuracy of 83.41 %, sensitivity of 80.18% and specificity of 86.61%. Random Forest Regression yields an accuracy of 91.26%, sensitivity of 82.76% and specificity of 98.96%. Diabetes may be detected early and treated promptly, slowing the course of the disease. Using an ML model to reliably foresee Type 2 diabetes might prove beneficial. As a result, machine learning and artificial intelligence have found a home in the healthcare industry. The Random Forest method should be used for a far more accurate and specific identification, according to a comparison of the models.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123838946","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

Development of Hut Shaped Solar Photo Voltaic System with Rotating Convex Lens And Concave Mirror Mechanism 带有旋转凸透镜和凹面镜机构的棚屋型太阳能光伏系统的研制

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10117700

B. Pakkiraiah, G. Durgasukumar, P. S. Devi, E. Satyanarayana, M. Rekha, P. P. Kumar

{"title":"Development of Hut Shaped Solar Photo Voltaic System with Rotating Convex Lens And Concave Mirror Mechanism","authors":"B. Pakkiraiah, G. Durgasukumar, P. S. Devi, E. Satyanarayana, M. Rekha, P. P. Kumar","doi":"10.1109/ICAECT57570.2023.10117700","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10117700","url":null,"abstract":"A novel method has been proposed to design and develop a Triangular Hut-shaped photovoltaic panel with rotating mechanism using modified maximum power point tracking (MPPT). In this, irradiance for the Primary panel can be provided by the nature itself with the available Sun and Secondary panels are to be provided by reflection with the rotating convex lens and concave mirror rotating gear mechanism. Here that the rotation of the convex lens and concave mirror will be depending upon the position of the Sun. To locate the Sun an electro-optical sensor will be used. It consists of light-dependent resistor (LDR) sensors to track sun position. Modified MPPT based controller detects the difference of voltage signals sent by the LDRs. Based on the outcome of LDRs, signals will be transferred to the Optocoupler for the activation of the servo mechanism. The servo mechanism of the convex lens and concave mirror rotates either in clockwise or anticlockwise direction based on signals reached to the driver circuit. The inputs to this controller will be solar irradiation and temperature. The experimental results of Triangular Hut-shaped PV panels will be compared with the conventional PV system results.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124810477","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

Violence Detection in Real Life Videos using Deep Learning 使用深度学习的现实生活视频中的暴力检测

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10117775

Bhaktram Jain, Aniket Paul, P. Supraja

{"title":"Violence Detection in Real Life Videos using Deep Learning","authors":"Bhaktram Jain, Aniket Paul, P. Supraja","doi":"10.1109/ICAECT57570.2023.10117775","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10117775","url":null,"abstract":"These days, it is essential to avoid or identify violence as soon as possible because it is spreading in an unpredictable way. Violence must be identified on real-time films taken by numerous surveillance cameras at all times and in all locations, which makes it difficult to perform. It might be time-consuming to continuously monitor CCTV surveillance cameras, thus it is essential to automatically spot any unusual activity. As soon as violent behaviours take place, it should be able to make a trustworthy real-time detection and notify the appropriate authorities. The dataset contains both violence and non-violence videos from real life situations. A methodology for detecting violence has been presented by us that uses a network similar to the U-NET with the encoder mobilenetv2 to extract spatial features before moving on to an LSTM block for the extraction of temporal features and binary classification. The results of the trial revealed that the precision is 95% and the accuracy is 94% utilising a dataset based on real life situations. The recommended model uses minimal computer resources while yet producing useful results.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121670179","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

Smart Controller IoT based for Electric Vehicles 基于电动汽车的智能控制器物联网

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10118325

Goteti Sai Navyasri, B. N. Krishna, Pamidi Sai Manideep, V. Devi

引用次数: 0

Data Quality Assessment Using Benford's Law and Excel 使用Benford定律和Excel进行数据质量评估

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10118030

T. Shalini, T. S. Shanker Rao, T. Shyamili, T. S. Rao

引用次数: 0

Analysis of Optimization Techniques for Spectral Efficiency and Power Control in Massive MIMO Networks 大规模MIMO网络频谱效率和功率控制优化技术分析

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10118038

Jyotsna Palavalasa, Madhu Ramarakula

引用次数: 0

Trace Clustering Techniques for Process Mining 过程挖掘的跟踪聚类技术

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10117842

Lakshmi Narayana N, Jagadishwari V

引用次数: 0

Inception Nesterov Momentum Adam L2 Regularized Learning Rate CNN for Sugarcane Disease Classification Inception Nesterov动量Adam L2正则化学习率CNN用于甘蔗病害分类

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10117792

R. Aruna, M. Devi, Ashish Anand, Utkarsh Dutta, Chilakapati Narasimha Shruti Sagar

{"title":"Inception Nesterov Momentum Adam L2 Regularized Learning Rate CNN for Sugarcane Disease Classification","authors":"R. Aruna, M. Devi, Ashish Anand, Utkarsh Dutta, Chilakapati Narasimha Shruti Sagar","doi":"10.1109/ICAECT57570.2023.10117792","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10117792","url":null,"abstract":"The primary source of sugar extractor is sugarcane, which is regarded as a revenue source crop globally. Sugarcane is a cash crop that greatly benefits the economy. Therefore, it is crucial for farmers to identify and cure diseases in the sugarcane production at the appropriate time. To address this issue, deep learning techniques are employed. The disease in the sugarcane crop could affect the overall yield of the crop. With this view, this paper recommends Inception Nadam L2 Regularized Gradient Descent (NLRGD) CNN model to classify the sugar cane disease like red rot, smut, mosaic, yellow leaf, red-orange rust using the Sugarcane Disease Dataset from KAGGLE. The proposed NLRGD model uses Sugarcane Disease Dataset from KAGGLE with 3000 sugarcane plant leaflets having 1000 Bacterial Blight, 1000 Red Dot and 1000 healthy sugarcane plant images. The CXAGD model was designed using 3 X 3 convolutional layers and maximum pooling layer followed by 1 X 1 Dropout and Dense layer for each 3 X 3 convolution and trained with learning rate of L2 regularized Gradient Descent and NADAM optimizer. The NLRGD built with Inception network with L2 regularized Gradient Descent and NADAM optimizer with 0.5 dropout, weight decay of Gradient Descent L2 regularization. The dataset for sugarcane disease was partitioned into training and testing data, and the training data were fitted to both the NLRGD model and other CNN models in order to compare the effectiveness of the proposed NLRGD model. Python was coded on a Gtx Geforce Tesla V100 Nvidia graphics card server with a batch size of 64 and 40 training epochs. Results of execution indicate that the suggested NLRGD model showcase the accuracy of 96.75%, Precision of 96.62%, Recall of 96.25% and FScore of 96.76% when compared with existing CNN models.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130951254","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

Directional over Current Relay Coordination in presence of Distributed Generation Using Harris' Hawks Optimization for Adaptive Protection 基于哈里斯鹰优化自适应保护的分布式发电定向超电流继电器协调

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2023-01-05 DOI: 10.1109/ICAECT57570.2023.10118258

Simardeep Kaur, Shimpy Ralhan

{"title":"Directional over Current Relay Coordination in presence of Distributed Generation Using Harris' Hawks Optimization for Adaptive Protection","authors":"Simardeep Kaur, Shimpy Ralhan","doi":"10.1109/ICAECT57570.2023.10118258","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118258","url":null,"abstract":"One of the major issues that arises when a DG forms an integrated part of an existing distribution system is the protection of the grid system. In order to achieve the best coordination of DOCRs in connected power networks under the influence of DG, an adaptive protection coordination approach is presented in this study. The Harris Hawks Optimization (HHO) coordination technique is employed. It was chosen because HHO can handle search challenges such local optimal solutions, multi-modality, and misleading optima. With the use of adaptive relays which take into account the protection schemes that adjust settings of the relay based on the existing conditions of the system relay miss-operation can be prevented. MATLAB code using HHO technique is developed for the optimal coordination of DOCR and DigSILENT/Power Factory is used to develop the model for the adaptive protection scheme. The results support the validity of the proposed scheme to account for the DGs' uncertainty and choose the best protection settings while ensuring a minimal operating time.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"403 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124307476","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