Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated ...
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Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are *** enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting *** most common symptoms of COVID-19 are fever,dry cough and sore *** symptoms may lead to an increase in the rigorous type of pneumonia with a severe *** medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death ***,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and *** approach integrates the union of deep features with the help of Inception 14 and VGG-16 *** last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of *** the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is *** experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.
In this modern era, the use of vehicles is increasing tremendously due to the increase in the population. The escalating issue of traffic congestion has emerged as a critical challenge in the real world. We currently ...
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Highways serve as vital connectors between cities, yet they often suffer from traffic congestion as the population continues to grow. Various intelligent frameworks or models for traffic status prediction have been em...
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Highways serve as vital connectors between cities, yet they often suffer from traffic congestion as the population continues to grow. Various intelligent frameworks or models for traffic status prediction have been employed in the Intelligent Transport System (ITS) to provide services for convenient and safe traveling, effective traffic management, and smart signal control. Most of these frameworks typically involve learning processes that utilize learning algorithms and requires training data. For highway traffic, the Greenshields model offers a practical relationship among vehicle speeds, traffic flows, and traffic density, which can serve as fundamental knowledge for developing intelligent traffic management systems. This paper proposes a fuzzy logic system based on the Greenshields model as the knowledge base for quickly predicting highway traffic congestion without extensive preparing data. Our system operates in two modes: jam and non-jam modes. In each model, the two inputs of vehicle speed and traffic flow are processed respectively with specified membership functions for effective fuzzification. The set of rules and conditions guided by the Greenshields theory is governed by the inference mechanism, which makes decisions according to the input field. Subsequently, the defuzzification process converts the fuzzy sets obtained by the inference engine into a congestion level as the output. To validate the accuracy of our system, a polynomial regression model utilizing realistic data from roadside equipment on the Sun Yat-Sen Highway in Taiwan is established for comparison. Comparing the observed data points from the polynomial regression model with the outputs obtained from our system using the same inputs, both predicting outputs are found to be consistent, affirming the practical feasibility of the proposed system. Moreover, our proposed scheme is adaptable to suit diverse road conditions without extensive training data and possesses a short memory to perform
Blindness, largely resulting from conditions such as Diabetic Retinopathy, Glaucoma, and Cataract, stands as a significant health concern. This paper introduces a novel approach proposing an automatic, self-diagnosing...
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An innovative deep learning structure, PulmoNetX, integrates the capabilities of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance pneumonia detection in chest X-ray imagery. During prepro...
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The project aims to create an automated system for detecting fungal contamination in dried red chilies using advanced deep learning algorithms such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG16),...
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The deployment of fifth-generation (5G) networks across various industry verticals is poised to transform communication and data exchange, promising unparalleled speed and capacity. However, the security concerns rela...
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This research investigates a transformative shift in managing placement related data within the contemporary academic landscape. A dedicated open-source Flask application, Placement-Manager, is examined for its role i...
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Depression is a significant global health concern, especially among social media users who experience mental pressures. It is a vital mental health issue due to undiagnosed and untreated that can lead to chronic menta...
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INTRODUCTION Store signboards provide important information in street view images,andcharacter recognition in natural scenes is an important research direction in computer *** view store signboard character recognitio...
INTRODUCTION Store signboards provide important information in street view images,andcharacter recognition in natural scenes is an important research direction in computer *** view store signboard character recognition technology,acombination of the two,
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