2022 Smart Technologies, Communication and Robotics (STCR)最新文献_第8页

Machine Learning Techniques for Parkinson's Disease Detection 帕金森病检测的机器学习技术

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009074

Sanjay V, S. P.

{"title":"Machine Learning Techniques for Parkinson's Disease Detection","authors":"Sanjay V, S. P.","doi":"10.1109/STCR55312.2022.10009074","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009074","url":null,"abstract":"A neurological disease is Parkinson's disease. It causes trembling in the hands, trouble walking, losing balance, and coordination. In the high-level stage, there is no access to medical care. Blood test reports, CT scan results, and X-ray reports are not accessible early enough. Early Parkinson’s disease detection is crucial to implement effective treatment. The purpose of the proposed effort was to identify Parkinson’s disease in early prediction using clinical imaging and machine learning technologies. Despite the fact that there are numerous methods for detecting Parkinson’s disease, using MRI scan images still it is a big challenge. In this study, an Adaboost classifier is used with a hybrid PSO algorithm to propose a novel technique for detecting Parkinson’s disease. Adaboost acted as the best classifier among other classifiers. Initially, MRI image best features are extracted and identified by the curvelet transform and principal component analysis. This Ad boost classifier receives optimal features as input. Finally, Adaboost classifieds the MRI images and gave excellent classification accuracy. To evaluate the proposed method three methods metrics namely accuracy, specificity, and sensitivity are used. Based on the results the proposed methods yield greater accuracy than the existing systems.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115282889","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

A Systematic Method of Stroke Prediction Model based on Big Data and Machine Learning 基于大数据和机器学习的中风预测模型系统方法

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009283

V. E., R. D

引用次数: 2

Energy Conservation for Environment Monitoring System in an IoT based WSN 基于物联网的WSN环境监测系统节能研究

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009100

Siva Satya Sreedhar, R. Anitha, Priya Rachel, S. Suganya, C. Ramesh Babu Durai, G. S. Uthayakumar

引用次数: 1

Identification and Analysis of Alzheimer’s Disease using DenseNet Architecture with Minimum Path Length Between Input and Output Layers 基于输入和输出层之间最小路径长度的密集网结构的阿尔茨海默病识别与分析

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009552

D. Deepa, M. S. Raj, S. Gowthami, K. Hemalatha, C. Poongodi, P. Thangavel

{"title":"Identification and Analysis of Alzheimer’s Disease using DenseNet Architecture with Minimum Path Length Between Input and Output Layers","authors":"D. Deepa, M. S. Raj, S. Gowthami, K. Hemalatha, C. Poongodi, P. Thangavel","doi":"10.1109/STCR55312.2022.10009552","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009552","url":null,"abstract":"Alzheimer’s Disease is a neurological brain disorder that damages the cells in brain and reduce the ability of the brain from the regular activities. It is a representation of the most common form of adult-onset dementias. Earlier detection of Alzheimer’s disease can be more helpful in predetermining the symptomatic conditions of patients suffering with this problem. By diagnosing the consequences of this disease, with the help of medical scan images, it would be more useful in classifying the patients whether they are suffering from this deadly disease. Machine Learning tends to be more beneficial in diagnosing diseases and implementation of this technique, to Magnetic Resonance Imaging (MRI) inputs in identification of Alzheimer’s disease, resulted in faster prediction of the disease and in the contribution of the evolution of the disease. Carrying out this technique, it is possible to diagnose and predict the individual dementia of adults by screening data of Alzheimer’s disease and inducing Machine Learning classifiers. This work focuses on building an evolving framework to detect Alzheimer’s disease efficiently with the help of neuroimaging technologies and prediction at a very earlier stage by using the data stacked up for Alzheimer’s disease patients.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130563487","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 Approach for Detecting Alzheimer’s Disease 一种检测阿尔茨海默病的改进方法

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009274

Sanjay V, S. P.

引用次数: 0

Electricity Price Forecasting using Multilayer Perceptron Optimized by Particle Swarm Optimization 基于粒子群优化的多层感知器电价预测

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009414

S. Udaiyakumar, CL Chinnadurrai, C. Anandhakumar, S. Ravindran

{"title":"Electricity Price Forecasting using Multilayer Perceptron Optimized by Particle Swarm Optimization","authors":"S. Udaiyakumar, CL Chinnadurrai, C. Anandhakumar, S. Ravindran","doi":"10.1109/STCR55312.2022.10009414","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009414","url":null,"abstract":"In this paper, electricity price forecasting using a hybrid multilayer perceptron, back propagation and modified particle swarm optimization is implemented. Here modified particle swarm optimization technique is used to improve the performance of the backpropagation algorithm while training the multilayer perceptron. Two different MLP are used for electricity price forecasting one MLP is with a single hidden layer and another MLP is with three hidden layers, both the neural networks are trained by BP and initial parameters such as weights between different layers, the bias of the layers, and activation function of each layer except input layer are selected by MPSO. Normally MLP trained by BP uses linear activation functions for all layers and neurons, but in this case, we use three different functions namely linear function, sigmoid function, and tangent function as activation functions. These three different activation functions are independently selected for each neuron by MPSO based on the data set which is used. Because of the independent selection of activation function to each neuron the overall performance, convergence time, and convergence efficiency of the BP are greatly improved. The proposed method is implemented to predict Austria and Northern Italy electricity price.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124584250","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

An Application of Embedded System and IOT: Development of SpO2 based Simple Healthcare System 嵌入式系统与物联网的应用:基于SpO2的简易医疗保健系统的开发

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009089

Kalpesh P. Modi, S. Chakole, Sandeep R Sonaskar, Neema Ukani

引用次数: 0

Mathematical Model for Anisotropic diffusion Filter and GLRLM Feature Extraction to Detect Covid-19 from Chest X-Ray Images 基于各向异性扩散滤波和GLRLM特征提取的胸部x线图像Covid-19检测数学模型

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009430

S. Sanjayprabu, R. Sathish Kumar, K. Somasundaram, R. Karthikamani

引用次数: 3

Viral Pneumonia and Covid Screening on Lung Ultrasound 病毒性肺炎和新冠肺炎肺部超声筛查

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009501

R. K, G. Flora, S. K, Lakshmi Priya. P, N. V

{"title":"Viral Pneumonia and Covid Screening on Lung Ultrasound","authors":"R. K, G. Flora, S. K, Lakshmi Priya. P, N. V","doi":"10.1109/STCR55312.2022.10009501","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009501","url":null,"abstract":"The rise of Covid-19 pandemic has exaggerated the necessity for safe, quick and sensitive diagnostic tools to confirm the protection of tending employees and patients. Although ML has shown success in medical imaging, existing studies concentrate on Covid-19 medicine victimization using Deep Learning (DL) with X-ray and computed axial Tomography (CT) scans. During this study we tend to aim to implement CNN model on Lung Ultrasound (LUS), to assist doctors with the designation of Covid-19 patients. We selected LUS since it's quicker, cheaper and additional out there in rural areas compared to CT and X- ray. We have used the biggest public dataset containing LUS pictures and videos of Covid, Pneumonia and healthy patients that has been collected from totally different resources. We tried out frame level approach that extracted 5 frames per patient video. We'll use this dataset to experiment with a CNN model that has hyper parameter calibration. We conjointly enclosed explainable AI using Grad-CAM that uses gradients of a selected target that flows through the convolutional network to localize and highlight regions of the target within the image. Moreover, we'll experiment with completely different data preprocessing techniques that may aid with pattern recognition and increasing the DL model’s accuracy like histogram equalization, standardization, Principle Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE). Lastly, we tend to create a straightforward application that diagnoses LUS videos with our CNN model, and shows the frame results with visual illustration of why the model has taken certain prediction with the help of Gradient-Weighted category Activation Mapping (Grad-CAM).","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"107 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121559820","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

Machine Learning System for Recognition and Classification of Overlapped Fingerprints 重叠指纹识别与分类的机器学习系统

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009199

N. Sowmya, I. Babu

引用次数: 1