2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)最新文献_第3页

Leveraging Smart Sensors for Human Function Traceability 利用智能传感器进行人体功能追溯

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141715

Harpreet Kaur, Khusdeep Kaur, Ramakant Kumar, Nirbhay Kumar Tagore

引用次数: 0

Image Analysis of Ultrasound Images Captured by a PVA/CNC/TiO2 Coated Transducer PVA/CNC/TiO2涂层传感器捕获超声图像的图像分析

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141777

P. L. J. Raj, K. Kalimuthu

引用次数: 0

Jaundice Recognition in Newborn Face, Chest and Abdomen using Spatial and Spectral Domain Graph Neural Network 基于空间与谱域图神经网络的新生儿脸、胸、腹黄疸识别

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141723

Shikha Prasher, Leema Nelson, Sangeetha Annam

{"title":"Jaundice Recognition in Newborn Face, Chest and Abdomen using Spatial and Spectral Domain Graph Neural Network","authors":"Shikha Prasher, Leema Nelson, Sangeetha Annam","doi":"10.1109/InCACCT57535.2023.10141723","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141723","url":null,"abstract":"Jaundice in newborns is common and generally no pain, but if it is not diagnosed and not handled with proper time, it will cause acute yellowing of the skin, which damages the brain and even death. Jaundice in a newborn manifest as yellowing of the infant’s face and chest. This is caused by the buildup of bilirubin in the blood of baby. Naturally, the liver of pregnant mother removes bilirubin from the baby, but adhering to delivery, thebody of baby does not begin to remove bilirubin, causing newborn jaundice. The infant’s face and chest turn yellow when bilirubin levels produce in the blood are too high and yellow coloration is present on the total serum bilirubin (TSB) level in the blood. Deep learning (DL) methods have been used to determine the degree of newborn jaundice using spectral and spatial graph neural networks (SSGNN). This jaundice prediction will improve the health and quality of life of a neonatal. It is a novel model based on graphical neural networks to extract information from photos of the face and the chest in the spatial and spectral domains.The image color information of the face and chest are used to predict the TSB levels. The combined impacts from spatial domain based on graph neural networks (SPAGNN) and the spectral domain based on graph neural networks (SPEGNN) with supplementary extraction will be carried out to maximize the intensity of the new model with higher accuracy. The performance of the SSGNN model is evaluated using recall,accuracy, specificity, and F1 score.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130594222","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

Heart Failure Prediction Using XGB Classifier, Logistic Regression and Support Vector Classifier 基于XGB分类器、逻辑回归和支持向量分类器的心力衰竭预测

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141752

Vinod Jain, Mayank Agrawal

引用次数: 1

Retreat of Gangotri glacier and find out the snout position using Remote Sensing and Geographic Information Systems 基于遥感和地理信息系统的甘戈特里冰川退缩及冰川口部位置研究

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141696

K. Rawat, Dharampal Singh

引用次数: 0

Pneumonia Classification Model using Deep Learning Algorithm 基于深度学习算法的肺炎分类模型

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141688

Sanchit Vashisht, Shweta Lamba, Bhanu Sharma, Avinash Sharma

{"title":"Pneumonia Classification Model using Deep Learning Algorithm","authors":"Sanchit Vashisht, Shweta Lamba, Bhanu Sharma, Avinash Sharma","doi":"10.1109/InCACCT57535.2023.10141688","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141688","url":null,"abstract":"The bacteria Streptococcus pneumoniae is the cause of pneumonia, a potentially fatal infectious disease that affects one or both lungs in humans. According to the World Health Organization (WHO), pneumonia is to blame for one in every three fatalities in India. Three classification categories are considered in this paper: Healthy, Viral and Bacterial infection. Chest X-rays that are used to diagnose pneumonia and must be evaluated by experienced radiotherapists in the medical sector. By combining three different classification techniques, a new hybrid Convolutional Neural Network (CNN) model is suggested in this regard. To classify CXR images, the first classification method makes use of Fully-Connected (FC) layers. The weights that result in the highest level of classification accuracy are retained after this model has been trained over a number of epochs. In the second method of classification, Machine Learning (ML) classifiers are used to classify the images, and the trained optimized weights are used to extract the features that are the most representative of CXR images. The proposed classifiers are used in an ensemble in the third classification method to classify CXR images. With an accuracy of 98.55 percent, the outcomes demonstrate that the proposed ensemble classifier, which combines Support Vector Machine (SVM), and other classifiers which performs the best. Finally, this model is used to create a Computer Automated Detection system that radiologists can use to accurately detect pneumonia.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123805213","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

Application of Horizontal Federated Learning for Critical Resource Allocation - Lessons from the COVID-19 Pandemic 横向联邦学习在关键资源分配中的应用——来自COVID-19大流行的经验教训

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141746

A. Kujur, V. Bharathi, Dhanya Pramod

{"title":"Application of Horizontal Federated Learning for Critical Resource Allocation - Lessons from the COVID-19 Pandemic","authors":"A. Kujur, V. Bharathi, Dhanya Pramod","doi":"10.1109/InCACCT57535.2023.10141746","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141746","url":null,"abstract":"The COVID-19 pandemic has caused disruption all over the industry. Significantly healthcare systems have been most affected due to a scarcity and acute demand for critical hospital resources. In this paper, we have proposed a framework for resource allocation using the characteristics of horizontal federated learning. We have used data of COVID cases in India and its states. We sourced the available critical hospital resources data and conducted the experimental analysis of the ten most vulnerable states of the country. The resource allocation method relies on the severity, spectrum of disease, and the patient’s length-of-stay in the hospital. The overall proposed methodology manages the limited resources and optimizes its use for the upcoming COVID cases. The collaborative feature of federated learning in the paper helped to update the information on patients, resources, and infection rates of different states, which in turn helped declare the overall severity of the pandemic in the country. Going further, our study will be helpful in the healthcare system’s authority to plan on hospital resources and manage them efficiently.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123834284","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

Sentiment Analysis Dashboard for Socia Media comments using BERT 使用BERT的社交媒体评论情感分析仪表板

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141803

Madhav Agarwal, P. Chaudhary, S. Singh, Charvi Vij

引用次数: 0

Underwater image re-enhancement with blend of Simplest Colour Balance and Contrast Limited Adaptive Histogram Equalization Algorithm 混合最简单色彩平衡和对比度限制的自适应直方图均衡化算法的水下图像再增强

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141807

G. Mageshwari, M. Chandralekha, Dharminder Chaudhary

引用次数: 0

Data driven approach to identify a flow-based Botnet Host using Deep Learning 使用深度学习识别基于流的僵尸网络主机的数据驱动方法

2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) Pub Date : 2023-05-05 DOI: 10.1109/InCACCT57535.2023.10141698

Aniket Mishra, I. Bharathi

{"title":"Data driven approach to identify a flow-based Botnet Host using Deep Learning","authors":"Aniket Mishra, I. Bharathi","doi":"10.1109/InCACCT57535.2023.10141698","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141698","url":null,"abstract":"The internet’s technological advancements exposed the globe to its weaknesses as well. The risk of exploitation has also increased as a result of larger network cores cooperating to combat cyber threats, which continue to be a severe problem for the entire world. Recurrent Neural Networks (RNN)-based deep learning techniques have recently advanced to new levels in a variety of fields and applications. The risk of forged accounts is greater than ever thanks to increased network use and traffic. The challenge to identify a malicious host on the internet has always been a challenge from the development perspective. The job of binary classification to label a host as a botnet has not made any significant progress and thus still, the internet faces the issue of botnets taking over many active and important connections exploiting the network, controlling compromised hosts to spam other hosts on the network, launch DDoS attacks and more. This paper attempts to provide a novel approach for evolving the comprehensive framework for controlling botnet host prediction and uses them to handle real time cases. To attain greater recognition accuracy, we use Gated Recurrent Unit (GRU) as a hybrid Recurrent Neural Network (RNN) model. We take an evolving time series input from a network station for several days which depicts data flow i.e., count of connections from different devices recognized by their IPs, and these features are used from the IP flow to provide capability to recognize the host on a network as a potential threat. Threat detection of such botnets is important not only from the perspective of stopping them but also to find significant insights about the targeted attack to understand future trends and make the networks persistent against them.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"34 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114022610","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