A Deep learning based Safe Travel Advisory Tool for Covid-19

Looking at the massive spread of SARS CoV2(COVID-19), it not only requires medical solutions at this point but different alternatives must also be examined to prevent its contagious nature getting its hands on a large number of individuals. Getting some prior information before its actual cause can help us to prepare ourselves to fight this pandemic better. It can assist authorities and administration to make better decisions in relatively less time to figure out the most suitable solutions. Since it is difficult to devise a permanent solution to this kind of pandemic, such data analysis can be used to strategize ourselves to cope with it. This study focuses on the forecasting of the number of active cases using deep neural networks. The models used in this approach are Multilayer Perceptron(MLP), Convolution Neural Networks(CNN) and Long Short Term Memory(LSTM). The performance of all three models is analyzed and although all of them are reasonably well, the MLP model outperforms the other two. These models can be used to predict the number of cases on a given day and a potential future outbreak. © 2022 IEEE.

Usage of ML and IoT in Healthcare Diagnose During Pandemic

The pandemic crisis has obliterated human existence as we know it, as well as regional, social, and commercial action, as well as compelled human civilization in living inside the defined perimeter. Uses of IoT with ML in health care applications is described in this article. The created ML with IoT dependent observation prototype assists for tracing COVID-19 positive detected persons using prior information and isolates them from non-infected individuals. By anticipating as well as analyzing information with AI, proposed ML-IoT system employs parallel computing to track pandemic sickness and also to avoid pandemic disease. The use of machine learning-dependent IoT for COVID in health conditions diagnose likely to be demonstrated the effectiveness for detection and prevention of CORONAVIRUS transmission. It still effects in better way on lowering preventive expenditures also leds to better treatment for infected individuals. In terms of monitoring and tracking, the recommended technique is 95% accurate. The findings will aid for stopping the pandemic's spread and providing assistance to the healthcare sector. © 2022 IEEE.

Learning upper patch attention using dual-branch training strategy for masked face recognition

In the context of pandemic, COVID-19, recognition of masked face images is a challenging problem, as most of the facial components become invisible. By utilizing prior information that mask-occlusion is located in the lower half of the face, we propose a dual-branch training strategy to guide the model to focus on the upper half of the face to extract robust features for Masked face recognition (MFR). During training, the features learned at the intermediate layers of the global branch are fed to our proposed attention module, named Upper Patch Attention (UPA), which acts as a local branch. Both branches are jointly optimized to enhance the feature extraction from non-occluded regions. We also propose a self-attention module, which integrates into the backbone network to enhance the interaction between the channels and spatial locations in the learning process. Extensive experiments on synthetic and real-masked face datasets demonstrate the effectiveness of our method. © 2022 Elsevier Ltd