Deep CNN and LSTM Architecture-Based Approach for COVID-19 Detection

The severe acute respiratory syndrome(SARS-CoV2) led to a pandemic of respiratory disease, namely COVID19. The disease has scaled worldwide and has become a global health concern. Unfortunately, the pandemic not just cost several individuals their lives but also, resulted in many people losing their jobs and life savings. In times like these, ordinary people become fearful of their resources in a world that gives its best resources to the wealthiest beings. Following the pandemic, the world suffered greatly and survival was rather difficult. As a result, numerous analytical techniques were developed to address this issue, with the key one being the discovery that the efficacy of clinically tested vaccines is actually quite poor. When researchers and medical professionals were unable to find a cure, radiologists and engineers created techniques to detect infected chests with the help of X-rays. Our proposed solution involves a CNN + LSTM model which has secured an accuracy of 98% compared to 95% of the trusted VGG-16 architecture. Our model's area under the curve (AUC) scores reached 99.458% while using RMSprop. A crucial feature of image processing till depth is accessible through scanning features from the layers of images using CNN. Our model uses 5 convolution blocks to detect the features. The coordination of activator functions, learning rates, and flattening has enabled accurate in-point predictions. With merely X-rays, models like ours ensure that anyone can easily detect covid-19. The best results obtained were at a learning rate =0.01 with RMSprop and Adam functions. The model has good fortune in detecting any other lung disease which occurs in the near future, as our data collectively rounds up to 4.5 gigabytes of data providing higher precision. © 2023 IEEE.

Detection of Covid-19 using Mask R-CNN

Deep learning has been widely used to analyze radiographic pictures such as chest scans. These radiographic pictures include a wealth of information, including patterns and cluster-like formations, which aid in the discovery and conformance of COVID-19-like pandemics. The COVID-19 pandemic is wreaking havoc on global well-being and public health. Until present, more than 27 million confirmed cases have been recorded globally. Due to the increasing number of confirmed cases and issues with COVID-19 variants, fast and accurate categorization of healthy and infected individuals is critical for COVID-19 management and treatment. In medical image analysis and classification, artificial intelligence (AI) approaches in general, and region-based convolutional neural networks (CNNs) in particular, have yielded promising results. In this study, a deep Mask R-CNN architecture based on chest image classification is suggested for the diagnosis of COVID-19. An effective and reliable Mask R-CNN classification was difficult due to a lack of sufficient size and high-quality chest image datasets. These complications are addressed with Mask Region-based convolutional neural networks (R-CNNs) as a framework for detecting COVID-19 patients from chest pictures using an open-source dataset. First, the model was evaluated using 100 photos from the original processed dataset, and it was found to be accurate. The model was then validated against an independent dataset of COVID-19 X-ray pictures. The suggested model outperformed all other models in general and specifically when tested using an independent testing set. © 2023 IEEE.

Study of Zero Trust Architecture for Applications and Network Security

As a result of globalization, the COVID-19 pandemic and the migration of data to the cloud, the traditional security measures where an organization relies on a security perimeter and firewalls do not work. There is a shift to a concept whereby resources are not being trusted, and a zero-trust architecture (ZTA) based on a zero-trust principle is needed. Adapting zero trust principles to networks ensures that a single insecure Application Protocol Interface (API) does not become the weakest link comprising of Critical Data, Assets, Application and Services (DAAS). The purpose of this paper is to review the use of zero trust in the security of a network architecture instead of a traditional perimeter. Different software solutions for implementing secure access to applications and services for remote users using zero trust network access (ZTNA) is also summarized. A summary of the author's research on the qualitative study of 'Insecure Application Programming Interface in Zero Trust Networks' is also discussed. The study showed that there is an increased usage of zero trust in securing networks and protecting organizations from malicious cyber-attacks. The research also indicates that APIs are insecure in zero trust environments and most organization are not aware of their presence. © 2022 IEEE.

Architecture-based Evaluation of VGG16 and ResNet Models for an Online Deep Learning Environment for Medical Applications

Image classification using deep learning models has evolved impressively well in the past decade. Datasets containing millions of images grouped into thousands of classes have been used to train and test these models. Medical image classification however still faces the challenge of scarcity in datasets. Gathering data from various locations and placing it in a commonly accessed dataset is highly time-consuming. Diseases need real-Time response just like any other mission-critical operation and online deep learning could be handy. There are many pre-Trained models which acquired good accuracy on large datasets. But as the depth of the model increases the time taken to train the model and the number of computations also increase. In this paper, we evaluated two models with different architectures. VGG16 is a 16-layer normal stack of convolutional layers and ResNet50V2 is a stack of residual blocks with skip connections and 50 layers. We used a Computer Tomography (CT) Lung image dataset to classify images into COVID, healthy and pneumonia images. We found that VGG16 is taking lesser time and computations with reduced loss when compared to the ResNet50V2 model. We finally conclude that ResNet50V2 is taking more time to train images as the model is 50 layers deep, whereas the VGG16 model is only 16 layers deep. Also, images that show mild infection were predicted as healthy images by ResNet50V2 but predicted correctly by the VGG16 model. © 2022 IEEE.

Answering Questions About COVID-19 Vaccines Using ChatBot Technologies

Chatbots are a powerful tool to design and implement sophisticated computer systems able to interact with human users through natural language. Chatbots are considered more friendly to users than other sources of information, and consequently, they have been largely applied to various domains. In this work, we propose a chatbot application aimed at answering questions about COVID-19 vaccines. Besides the interesting application domain and the knowledge engineering behind this development, we also introduce a modular chatbot architecture based on an easy-to-update database and natural language templates in which new information (for example, new vaccines) can be added without the need for retraining the chatbot. Furthermore, in this paper, we provide an empirical evaluation of the proposed chatbot application. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Age Estimation with Synthetic Mask Generation Based on MobileNet and Facial Keypoint Detection

Facial age estimation is one of the most important tasks in the field of face recognition and recommendation system. Since the COVID-19 pandemic, people have been required to wear masks, which can be a challenge for traditional recognition methods. In this paper, an improved convolutional neural network architecture based on MobileNet is proposed to perform age estimation. For the challenge of masked faces, an innovative mask generation method using face keypoint detection is adopted, extracting the key points of the faces in order to add synthetic masks to simulate the real situations. Then we compare the estimation results of the original images and the synthetic images. Our method is applied to the WIKI Face dataset containing more than 150,000 images, and achieves MAE of 3.79 and 6.54 on unmasked and masked faces, respectively, which demonstrates the effectiveness of the proposed model. © 2022 IEEE.

Comparison of deep learning architectures for COVID-19 diagnosis using chest X-ray images

The implementation of architectures based on artificial intelligence and deep learning to support COVID-19 diagnosis has great potential. However, especially in architectures designed at the beginning of the pandemic, they use different databases that do not contain a good amount of chest X-ray images of COVID-19 patients. The present work presents a comparison of three deep learning architectures (COVID-Net, CovXNet and DarkCovidNet) for COVID-19 diagnosis using chest Xray images. First, the architectures were implemented with the databases provided by the authors, to compare the results with those presented in the state of the art. Then, a new database with more than 9000 chest X-ray images of patients with COVID-19, pneumonia and healthy (3305 images for each class), was elaborated using databases from four different institutions around the world. Finally, the database was used to evaluate the original architectures, retrain them and, finally, evaluate the performance of the retrained architectures and compare results. It was identified that the architectures with the best performance and generalizability are DarkCovidNet and CovXNet with a support vector machine stacking algorithm, with an accuracy of 94.04% and 92.02% respectively, for the test data of the new database. 2022 SPIE. © 2022 SPIE. All rights reserved.

Automated Diagnosis of COVID-19 Using Squeeze Net Architecture Based on Deep Learning

Coronavirus infection (COVID-19) is an extremely contagious infection produced by severe acute respiratory coronavirus syndrome 2. The infection started in Wuhan, China, in December 2019, and has extended worldwide to more than 200 countries since then. The effect is such that the World Health Organization (WHO) has announced a Global Health Emergency of International Significance on the present pandemic of COVID-19. As many countries gets affected by this rampant virus, it is important for the healthcare workers to keenly observe every patient and give the accurate results like if they been affected or not. As we know healthcare worker are the real worriers as they sacrifice their lives to save others, so helping them with advance technologies will be a big deal. So, in this paper a CNN model is been used for precisely classify patients as they are affected or not. Experimentation results depicts that the proposed model attains an accuracy of 93.9%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

IoT Architecture Based Water Resources Conservation Management Using LoRa

Urbanization and anthropogenic activities are impacting the biodiversity of the water stream. Consequently, it interrupts water supply for daily purpose and sanitization requirement during pandemic Covid19. Water quality monitoring program designed to control water pollution. Development of IoT contributed to environment conservation including monitoring purpose for support decision system. This paper aims to study the integration of LoRa network with automatic water quality monitoring system. The proposed prototype of the system built with one gateway as base station and wireless sensor nodes (WSN) that embedded with water quality sensor of pH, turbidity, total dissolved solid (TDS), dissolved oxygen (DO) and temperature. The daily water quality status can be viewed in developed mobile application dashboard. Result shows that LoRa capabilities were affected from non-line of sight condition, transmission power and Spread Factor (SF) value. In conclusion, LoRa is compatible to be integrated with water quality monitoring system in urban environment. © 2021 IEEE.