Helmet-Mounted Display System Based on IoT

Many people enjoy motorcycle riding and there are thousands of people who have lost their lives due to road accidents. This is mainly due to the delay in the state of emergency that must be provided to the victims. The helmet-mounted display system that uses the Internet of Things (IoT) reduces accidents and informs its contacts in emergencies so the helmet module contains sensors to determine the passenger\u27s pulse rate, alcohol content, and vibration intensity. The pulse rate sensor is used to determine whether the rider has worn the helmet and which will be connected to the rider\u27s start of his trip on the road. That\u27s why we implemented a prototype proposal using the IoT to connect all devices and make it easier for the user to reduce road accidents by displaying all their needs in full on the helmet screen. So, in the implementation of our proposal, we made several systems connected with Raspberry Pi 4 which are Global Positioning System (GPS) applications, camera systems, and sensors that display all output data in the background, after that will transmit all these data from Raspberry Pi 4 to Raspberry Pi 3 through User Datagram Protocol (UDP), which Raspberry Pi 3 connected with Digital Light Processing )DLP) projector to display all background data as a hologram to the user giving him safety on the road without any distractions

A robust CNN Model for Diagnosis of COVID-19 based on CT scan images and DL techniques

The 2019 Coronavirus (COVID-19) virus has caused damage on people\u27s respiratory systems over the world.  Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model\u27s classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.

A robust CNN Model for Diagnosis of COVID-19 based on CT scan images and DL techniques

The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world.  Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.