2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)最新文献_第8页

Design and Optimization of Transformer by Combining Finite Element Approach and Improved Genetic Algorithm 结合有限元法和改进遗传算法的变压器设计与优化

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807885

Uma Bharathi, Kaaviya Vharshiny, Shreshth Verma, Asmita Ajay, B. Sreekeessoon, R. C. Naidu

{"title":"Design and Optimization of Transformer by Combining Finite Element Approach and Improved Genetic Algorithm","authors":"Uma Bharathi, Kaaviya Vharshiny, Shreshth Verma, Asmita Ajay, B. Sreekeessoon, R. C. Naidu","doi":"10.1109/ICAECT54875.2022.9807885","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807885","url":null,"abstract":"The electrical transformer is a crucial component for altering voltage levels in the electricity system. Electrical transformers are normally constructed by trial and error, but some obstacles, such as expensive prices or unexpected performance, may occur from time to time. Often, transformer optimization design aims to reduce manufacturing costs or boost transformer efficiency. Several literatures have lately highlighted the finite element approach and artificial intelligence (AI) methodologies for enhancing transformer performance. For example, artificial neural networks(ANNs) may be used to forecast the function of core design parameters when employing AI to analyse transformer loss . Georgilakis and colleagues likewise employed artificial neural networks to minimize core loss in constructed transformers, and the Taguchi technique was used to improve individual core manufacturing process losses. A multiple technique is an effective solution even if the objective functions of transformer design are relatively complicated. For transformer optimization, one of the versatile approaches, which combines the finite element method (FEM) with the genetic algorithm (GA), is advantageous. The objective of the study is to provide the results of a multi-method investigation into transformer design optimization. The genetic algorithm (GA) and the finite element approach(FEM) are combined in this multiple methodology .","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":"131824964","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

An Enhanced Energy Efficient Protocol for Wireless Body Area Network 一种增强的无线体域网节能协议

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807836

Smita Sagar Gupta, N. Gupta, B. Verma

引用次数: 3

Self Risk Assessment Model Embedded with Conversational User interface for Selection of Health Insurance Product 嵌入会话用户界面的健康保险产品选择的自我风险评估模型

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9808023

V. Ilango, V. Uma, Shamitha S Kotekani, Anand Shankar Raja M

{"title":"Self Risk Assessment Model Embedded with Conversational User interface for Selection of Health Insurance Product","authors":"V. Ilango, V. Uma, Shamitha S Kotekani, Anand Shankar Raja M","doi":"10.1109/ICAECT54875.2022.9808023","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9808023","url":null,"abstract":"In this research, we propose a dynamic model that works through Human-Computer Interaction to facilitate a smooth customer experience for health insurance prospects. The model facilitates the prospects to self assess their health risks. The integration of Conversational User interface, such as Mobile User Interface, Graphic User Interface and Bots with transcoder permits seamless use of the model by any category of prospects, irrespective of their language. Moreover, the model also helps the visually impaired person to interact without any hassle with the presence of a transcoder that permits conversion of text into speech and vice versa. The learner model comprises of the Prospects’ detail module and Risk Assessment modules. The Prospects’ detail module collects data from the predefined list. The risk assessment module profiles and assesses the risk based on the data inputted in the Prospects’ detail module. The risk assessment level module categorizes the level of risk as low, moderate or high for each prospect depending on the total risk exposure level. The total risk exposure level is computed based on the pre-defined threshold. This model aids the prospect in determining the risk level and thereby facilitates self-selection of health insurance policy, thus reducing over reliance on the insurer. This model helps the prospect to take an independent purchase decision.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"51 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":"122834516","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

Analysis of classification based predicted disease using machine learning and medical things model 利用机器学习和医学事物模型分析基于分类的预测疾病

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807903

H. K. Bhuyan, T. Arun Sai, M. Charan, K. Vignesh Chowdary, Biswajit Brahma

{"title":"Analysis of classification based predicted disease using machine learning and medical things model","authors":"H. K. Bhuyan, T. Arun Sai, M. Charan, K. Vignesh Chowdary, Biswajit Brahma","doi":"10.1109/ICAECT54875.2022.9807903","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807903","url":null,"abstract":"Health diseases have been issued seriously harmful in human life due to different dehydrated food and disturbance of working environment in the organization. Precise prediction and diagnosis of disease become a more serious and challenging task for primary deterrence, recognition, and treatment. Thus, based on the above challenges, we proposed the Medical Things (MT) and machine learning models to solve the healthcare problems with appropriate services in disease supervising, forecast, and diagnosis. We developed a prediction framework with machine learning approaches to get different categories of classification for predicted disease. The framework is designed by the fuzzy model with a decision tree to lessen the data complexity. We considered heart disease for experiments and experimental evaluation determined the prediction for categories of classification. The number of decision trees (M) with samples (MS), leaf node (ML), and learning rate (I) is determined as MS=20, ML=3, I=0.1, then mean test score(m) is 20.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"68 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":"127767834","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

Plant Disease Prediction using Transfer Learning Techniques 利用迁移学习技术进行植物病害预测

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807956

A. Lakshmanarao, N. Supriya, A. Arulmurugan

{"title":"Plant Disease Prediction using Transfer Learning Techniques","authors":"A. Lakshmanarao, N. Supriya, A. Arulmurugan","doi":"10.1109/ICAECT54875.2022.9807956","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807956","url":null,"abstract":"Plant diseases are a significant hazard to feed a growing population, but due to a lack of infrastructure in many regions of the world, timely detection is challenging. Finding and detecting plant illness is essential in agricultural production. It takes a great deal of time and effort to find the disease. Agricultural sector can also reap the benefits of machine learning and deep learning. There has been a recent rise in the use of ML & DL techniques in plant disease identification. In this paper, we applied transfer learning technique for plant disease prediction. We used a ‘plantvillage’ dataset collected from Kaggle. Images of fifteen different types of plant leaves (Tomato, Potato, Pepper bell), from three distinct plants are included in this collection. We split the original dataset into three parts for three different plants and applied three transfer learning techniques VGG16, RESNET50, Inception and achieved accuracy of 98.7%, 98.6%, 99% respectively. The results of experiments shown that our proposed model achieved good accuracy when compared to traditional models.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"40 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":"127909203","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 Deep Transfer Learning-Based Approach to Detect Potato Leaf Disease at an Earlier Stage 基于深度迁移学习的马铃薯叶病早期检测方法

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807963

Md Rahmatul Kabir Rasel Sarker, Nasrin Akter Borsha, Md. Sefatullah, A. Khan, Somaiya Jannat, Hasmot Ali

{"title":"A Deep Transfer Learning-Based Approach to Detect Potato Leaf Disease at an Earlier Stage","authors":"Md Rahmatul Kabir Rasel Sarker, Nasrin Akter Borsha, Md. Sefatullah, A. Khan, Somaiya Jannat, Hasmot Ali","doi":"10.1109/ICAECT54875.2022.9807963","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807963","url":null,"abstract":"Bangladesh is an agricultural country and potato is the most cultivated crop here and even worldwide. But the production of potatoes is declining day by day due to various potato leaf diseases which can result in significant environmental and economic damage. It's difficult for farmers to find out which diseases damaging crops because they tend to use the traditional approach and the result is not accurate always. For that, it’s hard to take a decision on which fertilizers to apply. This traditional approach is a more time-consuming and slow process. To detect leaf diseases of potato at the early stage, this study present a deep learning-based approach using ResNet50. Using this technique, farmers can find out the actual diseases of potato in a feasible, efficient and time-saving way at their early stage and able to take fast decisions. That will help to grow more potatoes. It can be ensured that this model can bring many benefits in the agricultural field both economic and ecologic sides. This study works on the most two common diseases of potato leaves including late blight, early blight, and one healthy leaf. To find out the best model, this study has chosen 3 neural networks. After analyzing CNN, VGG19, and ResNet50 models get the accuracy according to 84%, 93%, and 97% for a collection of 2,152 images. In this paper, ResNet50 model achieves the highest accuracy.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"50 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":"128422426","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

Modified design of STBC Encoder for reducing Non-Linear distortions in OFDM Channel Estimation 改进的STBC编码器设计以减少OFDM信道估计中的非线性失真

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807993

S. Singh, Amit Kumar

{"title":"Modified design of STBC Encoder for reducing Non-Linear distortions in OFDM Channel Estimation","authors":"S. Singh, Amit Kumar","doi":"10.1109/ICAECT54875.2022.9807993","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807993","url":null,"abstract":"The non-orthogonal encoding in MIMO communication cause fading in OFDM Channel which further increase the BER. For Orthogonal Frequency Division Multiplexing (OFDM) systems, this work develops a new Quasi Space-time block codes (STBC) Encoder of while maintain full rate in Multiple Input Multiple Output (MIMO) communication. This work shows that not only the Alamouti-scheme which was useful only for STBC for two transmit antennas but with four transmit antenna we can also archive optionality with full rate of communication. This work is carried out on 4x4 Orthogonal Frequency Division Multiplexing (OFDM) framework and use encoder having 12 full orthogonal sets outs of total 16 possible combination of fading matrix. Non-Linear distortions due to High power amplifiers (HPA) at transmitters of MIMO communication system are one of the major causes of Low SNR (Signal to Noise Ratio), Proposed STBC encoder fading matrix also kept the input signals symbols linear with output signal symbols. This work is carried out on MATLAB-2018b EDA device and tried on something very similar with different commotion channels conditions.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"199 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":"117063666","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

Automated Approach to Detect and Monitor the Development of Turner’s Syndrome 自动检测和监测特纳氏综合征发展的方法

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807872

R. R, G. N, A. Chokkalingam

引用次数: 0

Generating Electrical Energy from Living plants Using Plant-Microbial Fuel Cell Technology 利用植物微生物燃料电池技术从活的植物中产生电能

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807933

J. Dharinee, K. Dhayalini, R. Ranjeet Skanda

{"title":"Generating Electrical Energy from Living plants Using Plant-Microbial Fuel Cell Technology","authors":"J. Dharinee, K. Dhayalini, R. Ranjeet Skanda","doi":"10.1109/ICAECT54875.2022.9807933","DOIUrl":"https://doi.org/10.1109/ICAECT54875.2022.9807933","url":null,"abstract":"Hydro, coal and wind energy are nonrenewable and are main source for production of electricity. It is good to have an efficient way for the production of energy and sustainable form of renewable energy. This project uses sustainable energy for the development of electricity. It is carried out through a natural process and which is very safe for both the surrounding environment and the green plant. This project utilizes the humidity, temperature and soil moisture data obtained from a living plant and which is used for the production of electricity and analysis were carried out to analyze their impact to the environment. Solar energy is absorbed for the photosynthesize process of the plants organic matter through carbon dioxide, water for its growth. A small amount of the organic matter which are excreted through the roots of the plants into the soil is considered as the waste product. Bacteria’s which are created naturally are electrochemically active in nature and it break downs the organic matter in the rhizosphere and produce electrons. When these electrodes are placed near the roots of the plants these ions travel through the soil, intern the ions move towards the electrodes and produces electricity from this surge.","PeriodicalId":346658,"journal":{"name":"2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"72 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":"117160468","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

Proficient Evaluation of Visual Cryptography using Transposition Cipher and Bit Reversal Techniques 使用换位密码和位反转技术的视觉密码学的熟练评估

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Pub Date : 2022-04-21 DOI: 10.1109/ICAECT54875.2022.9807843

Archishman Udaysinha Zanzaney, Chaitanya Krishna Sharma, Lakshya Jain, C. Gururaj

引用次数: 0