2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)最新文献_第6页

Comparison of Performance of Machine Learning Algorithms for Cervical Cancer Classification 宫颈癌分类的机器学习算法性能比较

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807849

Hamza Karani, Ashish Gangurde, G. Dhumal, Waidehi Gautam, Samiksha Hiran, Abha Marathe

{"title":"Comparison of Performance of Machine Learning Algorithms for Cervical Cancer Classification","authors":"Hamza Karani, Ashish Gangurde, G. Dhumal, Waidehi Gautam, Samiksha Hiran, Abha Marathe","doi":"10.1109/ICAECT54875.2022.9807849","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807849","url":null,"abstract":"Cervical cancer, which is the fourth leading cause of mortality among women, displays no symptoms in its early stages. Cervical cancer is currently diagnosed using only a few approaches using Machine Learning techniques. Certain approaches such as PAP Test, HPV Test, Colposcopy and Biopsy require medical staff intervention and cancer is not detected until a certain stage is reached. These procedures are also too costly in developing countries. Detection of Cervical Cancer using Machine Learning and Deep Learning techniques come into play to solve this issue. A few to name are: CervDetect[1], a hybridized model using a combination of Random Forest and Shallow Neural Networks, ResNet50 – A Convolutional Neural Network’s pre-trained model works effectively on classification of cervical cancer cells using images. This research paper experiments and analyses two Support Vector Machine (SVM) techniques as well as K-Nearest Neighbor (KNN), Random Forest(RF), Logistic Regression and Gaussian Naïve Bayes (GNB) algorithms for cervical cancer diagnosis. The dataset used is Cervical cancer (Risk Factors) Data Set from UCI Repository[2] . There are 32 risk factors and four target variables in cervical cancer dataset: Citology, Hinselmann, Schiller and Biopsy. The two SVM-based techniques namely SVM Linear and SVM Radial, KNN, RF, Logistic Regression and GNB have diagnosed and categorized all four targets respectively. Following that, a comparison between these six methods is done and inferences are drawn on which algorithm performs better on each of the targets.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123961881","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: 4

Comparative analysis of SRF based Shunt Active Filter using Grey Wolf and Eagle Perching Optimization 基于SRF的灰狼和鹰栖优化并联有源滤波器的比较分析

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807876

B. Rao, Pushpa Sahu, Rajkumar Jhapte

引用次数: 0

An Autonomous Internet of Things (IoT) pipeline network for Image acquisition in Agricultural Applications 农业应用中图像采集的自主物联网(IoT)管道网络

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807923

Amarjeet Singh, N. Jeebaratnam, U. Sesadri, K. S. Rao, Nellore Manoj Kumar

{"title":"An Autonomous Internet of Things (IoT) pipeline network for Image acquisition in Agricultural Applications","authors":"Amarjeet Singh, N. Jeebaratnam, U. Sesadri, K. S. Rao, Nellore Manoj Kumar","doi":"10.1109/ICAECT54875.2022.9807923","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807923","url":null,"abstract":"Agriculture is considered to be a self-sufficient component of the country's economy. In terms of agricultural output, India is second only to the United States in terms of production. Irrigation, fertiliser, and crop rotation are three of the most significant elements in agricultural production. The usage of the Internet of Things can make crop production predictions and other components that contribute to high yielding crops more accurate and efficient (IoT). In the past, a pipeline for making predictions was created. A solution based on the Internet of Things (IoT) is being developed and designed to address this problem (IoT). When operating in a Wireless Sensor Network (WSN), sensor nodes are required to perform tasks such as observation, data collection, and transaction processing in order to exhaust their energy. Farmers saving time and money by collecting this information on their own is a win-win situation. To make advantage of this network, you must have a wireless sensor network (WSN), which must be capable of transmitting large amounts of data at a low rate while also consuming little power and communicating over short distances.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128228439","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

LSTM Based Approach for Timely Detection of Gradual Development of Electrical Fault in Power System 基于LSTM的电力系统逐步发展的电气故障及时检测方法

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807893

A. R., T. Rajeev

{"title":"LSTM Based Approach for Timely Detection of Gradual Development of Electrical Fault in Power System","authors":"A. R., T. Rajeev","doi":"10.1109/ICAECT54875.2022.9807893","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807893","url":null,"abstract":"Power system reliability and efficiency are becoming a primary concern with the increase in load and expansion of power grids. Electrical faults in the power system should be detected and cleared immediately due to their critical impact on the reliability and stability of the system. This paper proposes an approach to predict the faults in the power system using machine learning techniques like Long Short-Term Memory (LSTM). The LSTM model is used to predict gradual faults in the system before their actual occurrence. Three-phase measurements of voltages, currents, and active power during faults and normal operating conditions are taken as data inputs to train the models. The robustness of the method is verified by simulating the fault with different parameters. The proposed method can be expanded to the distribution network of the power system. A modified IEEE 9 bus system is modelled in MATLAB/Simulink and is used to get the data for the experiment. The results from the experiment prove the feasibility of using LSTM networks for predicting the faults in the power system.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129841132","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

Implementation of Z-Source Resonant Converter for Power Factor Correction & Wireless Charging Applications 用于功率因数校正和无线充电应用的z源谐振变换器的实现

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807975

Ayush Srivastava, Narendra Kumar

引用次数: 0

Comparative Analysis of Optimal Capacitor Placement and D-STATCOM towards Power Consumption and Power Loss Minimization 优化电容器布局与D-STATCOM对功耗和功耗最小化的比较分析

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807895

R. Sahu, Baidyanath Bag, Neha Smitha Lakra

引用次数: 1

Modelling Investigations on Efficiency of Miniaturized Piezoelectric Energy Harvester 小型压电能量采集器效率的建模研究

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9808035

Satyanarayana Talam, S. L. S. Iswarya Vinjamuri, Siva Keerthi Rayuru Akkala, Padmaja Nirmala Gudipati, Gokul Phala Chandra Lankireddy

{"title":"Modelling Investigations on Efficiency of Miniaturized Piezoelectric Energy Harvester","authors":"Satyanarayana Talam, S. L. S. Iswarya Vinjamuri, Siva Keerthi Rayuru Akkala, Padmaja Nirmala Gudipati, Gokul Phala Chandra Lankireddy","doi":"10.1109/ICAECT54875.2022.9808035","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9808035","url":null,"abstract":"Currently, energy harvesting is an interesting area of research, and the world is looking for green energy as an alternative source. Energy harvesters take advantage of ambient sources including light, heat, and motion. The present investigation focuses on modelling a piezoelectric unimorph cantilever to evaluate the static analysis of the electric potential by optimizing the dimensions, materials (PZT-4, ZnO, and PVDF), and shapes to realize optimized efficiency. The unimorph cantilever was modelled with L-and H-shapes by exploring its electric potential through boundary loads ranging from 10 to 100N and the simulation was performed with and without the steps included. Among all three materials, PVDF material has shown the maximum electric potential than that of other materials under 100N boundary load when steps are inserted compared to without insertion of steps. The maximum electric potential observed for L and H shapes are 14.6mV and 69.4mV respectively. From the analysis of these results, the H-shaped piezoelectric energy harvester with PVDF under a 100N boundary load exhibited the highest conversion efficiency. COMSOL Multiphysics software version 5.2 is used.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126826722","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

A Convolutional Artificial Intelligence for Disease Classification in Fruits and Vegetables 卷积人工智能在果蔬疾病分类中的应用

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807847

Murali Krishna Boddepalli, Karishma Shaik, Phalgun Taraka Chaitanya Pantakota, Sai Krishna Goriparthi, Siva Reddy Vanga

引用次数: 0

Automatic Spectrum Sensing Techniques Using Support Vector Machine In Cognitive Radio Network 认知无线电网络中基于支持向量机的自动频谱感知技术

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807922

Mustafa Arkwazee, M. Ilyas, Ammar Dawood Jasim

引用次数: 1

Renal Calculi Detection using Image Processing 利用图像处理技术检测肾结石

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807961

Govind Raj R, Riya Cyriac, D. Pamela, P. Manimegalai, Prawin Angel Michael

引用次数: 0