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Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset
TLDR
A deep learning-based model is developed that can detect COVID-19 patients with better accuracy both on CT scan and chest X-ray image dataset and test results demonstrate that it is conceivable to interpret top features that should have worked to build a trust AI framework to distinguish between patients with CO VID-19 symptoms with other patients.
Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance
TLDR
CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score, and the study outcomes demonstrate that the model’s performance varies depending on the data scaling method.
COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities
TLDR
Results demonstrate that the proposed models can identify the infectious regions and top features; ultimately, it provides a potential opportunity to distinguish between COVID-19 patients with others.
Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME
TLDR
Six different Deep Convolutional Neural Networks models are implemented and the process of feature extraction is explained using Local Interpretable Model-Agnostic Explanations (LIME), which contributes to a better understanding of what features in CT/X-ray images characterize the onset of COVID-19.
Study of Queuing System of a Busy Restaurant and a Proposed Facilitate Queuing System
TLDR
Queuing theory is suitable to be applied in a restaurant setting since it has an associated queue or waiting line where customers who cannot be served immediately have to queue (wait) for service.
Extending the Storage Capacity And Noise Reduction of a Faster QR-Code
TLDR
A faster QR code which has more storage and can scan faster is proposed, which takes twice the time than normal QR code but it can also store double QR code data and is as faster as other QR code scanning technique.
Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients
TLDR
This study develops a COVID-19 diagnosis model using Multilayer Perceptron and Convolutional Neural Network for mixed-data (numerical/categorical and image data) so that early diagnosis of the virus can be initiated, leading to timely isolation and treatments to stop further spread of the disease.
A Review on Comparative Remarks, Performance Evaluation and Improvement Strategies of Quadrotor Controllers
TLDR
A thorough analysis of the current literature on the effects of multiple controllers on quadrotors, focusing on two separate approaches: controller hybridization and controller development are conducted.
Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence
TLDR
Six modified deep learning models are proposed and tested to detect SARS-CoV-2 infection from chest X-ray images, with promising results by detecting COVID-19, normal, and Pneumonia patients and a pilot test of VGG16 models on a multi-class dataset shows promising results.
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