2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)最新文献_第8页

Detection of Fake News using Recurrent Neural Network 基于递归神经网络的假新闻检测

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793155

G. Anusha, G. Praveen, D. Mounika, U. S. Krishna, R. Cristin

引用次数: 0

Smart Grid And The Importance Of Electric Vehicles 智能电网和电动汽车的重要性

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793038

Numaan Ahmed Daulatabad, R. M, M. M, Hemanth H A, Shobha Shankar

引用次数: 0

Comparison and Performance Analysis of Various Despeckling Methods on Echocardiographic Images 超声心动图图像去斑方法的比较与性能分析

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793244

Kalpana Chauhan, R. Chauhan, A. Saini

引用次数: 1

The Impact of COVID-19 Pandemic and Consumer Purchase Behaviour on the Automotive Industry COVID-19大流行和消费者购买行为对汽车行业的影响

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793111

Yew Liong Lim, Booma Poolan Marikannan, Leo Gertrude David

{"title":"The Impact of COVID-19 Pandemic and Consumer Purchase Behaviour on the Automotive Industry","authors":"Yew Liong Lim, Booma Poolan Marikannan, Leo Gertrude David","doi":"10.1109/icdcece53908.2022.9793111","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9793111","url":null,"abstract":"The outbreak of COVID-19 has forced countries to lock borders to prevent the spread of infection. It broadly affects numerous industries and economies globally termed 'Coronanomics.' Subsequently, many corporate performances have suffered during this time, leading to dramatic changes in business activities and consumer behaviour. The pandemic outbreak has disrupted and challenged many industries. Hence, this study explores how the pandemic affected in automotive industry and factors influencing consumers' purchasing intention. Discuss the major aspects that motivate vehicle purchase after the pandemic outbreak and how the pandemic’s negative effect partially reduced vehicle purchases' propensity and factors that could potentially affect the new car purchase decision. This project methodology adopted the CRISP-DM framework using power BI for data visualisation and R programming in implementation. This project focused on comparing traditional machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NeuralNet, NNet), and deep learning algorithms: Multilayer Perceptron (MLP) to predict how likely it a customer will purchase a vehicle after the pandemic. Establish the models using a confusion matrix, and evaluate the accuracy rate and low misclassification rate. The most suitable algorithm with a higher accuracy rate and lower error rate will be chosen in the final model comparison and evaluation section. Furthermore, the NeuralNet model, with its accuracy of 99.97%, is the best fit model to predict vehicle purchase intention.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122165399","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

Stock Market Price and Cryptocurrency Price Prediction 股票市场价格和加密货币价格预测

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793088

Shiva Agarwal, Naresh Babu Muppalaneni

引用次数: 4

Deep Learning-Based CSI Estimation Using Synthetic Dataset 基于深度学习的综合数据集CSI估计

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792900

Ravi Hosamani, Yerriswamy T

{"title":"Deep Learning-Based CSI Estimation Using Synthetic Dataset","authors":"Ravi Hosamani, Yerriswamy T","doi":"10.1109/icdcece53908.2022.9792900","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9792900","url":null,"abstract":"Multiple-input Multiple-output (MIMO) plays a vital role in 5G technology. The MIMO transmission is beneficial only when Channel State Information (CSI) is known. However, gathering CSI provides challenges like high dynamic channel and feedback overheads. In this paper, a CSI estimation technique using deep learning techniques is proposed for highly dynamic vehicular networks. The propagation environments like scatters and reflectors are almost the same. This allows the designed Deep Neural Network (DNN) architecture to experience with negligible overhead with the non-linear CSI relations. The proposed method focuses on synthetic data generation using MATLAB and training the neural network which will be used for the prediction of the CSI of the eMBB channel in the same cell. Hence resulting in the overhead reduction and increases in the threshold violations. The network used for the training results in proper convergence of the loss function. This indicates a reduction of CSI overhead of eMBB vehicles in order realize non-linear CSI with the DNN model. MATLAB is used to generate synthetic data. The trained neural network is implemented using TensorFlow 2.0 framework in python 3.6.0. using the Nvidia RTX2070ti graphic Card with CUDA and CUDNN support. From the simulation study, results show that the angular domain transformation is more logical with the real-time data as the data is already in complex number form so, to make some similarities, to improve in the estimation of CSI.","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"22 9-10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125620904","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

Implementation of Greenhouse Control and Monitoring System Using ESP32 利用ESP32实现温室控制与监测系统

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9792852

M. Sushmitha, V. Jyothi, A. Sain

引用次数: 0

An Artificial Intelligence Technique for Covid-19 Detection with eXplainability using Lungs X-Ray Images 基于肺部x射线图像的可解释性Covid-19检测人工智能技术

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793240

Pranshu Saxena, S. Singh, Gyanendra Tiwary, Yush Mittal, Ishika Jain

{"title":"An Artificial Intelligence Technique for Covid-19 Detection with eXplainability using Lungs X-Ray Images","authors":"Pranshu Saxena, S. Singh, Gyanendra Tiwary, Yush Mittal, Ishika Jain","doi":"10.1109/icdcece53908.2022.9793240","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9793240","url":null,"abstract":"According to the World Health Organization, the coronavirus outbreak poses a daily threat to the global health system. Almost all countries' health resources are insufficient or unequally distributed. There are several issues, such as a lack of health care workers, beds, and intensive care units, to name a few. The key to the country's health systems overcoming this epidemic is to use limited resources at optimal levels. Disease detection is critical to averting an epidemic. The greater the success, the more tightly the covid viral spread may be managed. PCR (Polymerase chain reaction) testing is commonly used to determine whether or not a person has a virus. Deep learning approaches can be used to classify chest X-RAY images in addition to the PCR method. By analyzing multi-layered pictures in one go and establishing manually entered parameters in machine learning, deep learning approaches have become prominent in academic research. This popularity has a favorable impact on the available health datasets. The goal of this study was to detect disease in persons who had x-rays done for suspected COVID-19 (Coronavirus Disease-2019). A bi-nary categorization has been used in most COVID-19 investigations. Chest x-rays of COVID-19 patients, viral pneumonia patients, and healthy patients were obtained from IEEE [17] (Institute of Electrical and Electronics Engineers) and Kaggle [18]. Before the classification procedure, the data set was subjected to a data augmentation approach. These three groups have been classified through multiclassclassification deep learning models. We are also debating a taxonomy of recent contributions on the eXplainability of Artificial Intelligence (XAI).","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127734342","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

Design of Multi-slotted Trapezoidal Cantilever for Low Frequency Devices 用于低频器件的多开槽梯形悬臂梁设计

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793163

Arvind Kumar, N. Chattoraj, Ashutosh Anand

引用次数: 0

Brain Tumor Recognition and Categorization in MRI Images Utilizing Optimal Deep Belief Network 基于最优深度信念网络的MRI图像脑肿瘤识别与分类

2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2022-04-23 DOI: 10.1109/icdcece53908.2022.9793211

Kamutam Sridharraj, S. Venkatesh, Sudati Akash Rao, N. Hathiram

{"title":"Brain Tumor Recognition and Categorization in MRI Images Utilizing Optimal Deep Belief Network","authors":"Kamutam Sridharraj, S. Venkatesh, Sudati Akash Rao, N. Hathiram","doi":"10.1109/icdcece53908.2022.9793211","DOIUrl":"https://doi.org/10.1109/icdcece53908.2022.9793211","url":null,"abstract":"Brain tumors are the most prevalent and deadly disease, with a life expectancy of only a few years at best. MRI images are used to investigate a brain tumor in this study. Although, the vast quota of data provided using a Magnetic Resonance Image scan necessitates physical tumor recognition for a certain moment. However, it has some limitations in the terms of providing reliable quantitative measures for a small number of photos. To prevent the death rate of a human, trustworthy and spontaneous grouping techniques are required. Automatic brain tumor classification is a tough undertaking because of the large geographical and structural diversity of the tumor’s outer areas. In this paper proposed CNN classifier is developed to detect brain tumors. An anisotropic filter is used to perform the morphological procedures. The previous methods do not have efficient feature extraction and classification. Medical images are choosen as dataset. The dataset consists of 3064 T1-weighted contrast-enhanced images with three types of brain tumors. Trimmed the mri images accordingly to give them as input to the model. The proposed method shows an average accuracy of 96.5%","PeriodicalId":417643,"journal":{"name":"2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130991974","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