Hybrid deep learning models for effective COVID-19 diagnosis with chest x-rays

The survey on COVID-19 test kits RT-PCR (reverse transcription-polymerase chain reaction) concludes the hit rate of diagnosis and detection is degrading. Manufacturing these RT-PCR kits is very expensive and time-consuming. This work proposed an efficient way for COVID detection using a hybrid convolutional neural network (HCNN) through chest x-rays image analysis. It aids to differentiate non-COVID patient and COVID patients. It makes the medical practitioner to take appropriate treatment and measures. The results outperformed the custom blood and saliva-based RT-PCR test results. A few examinations were carried out over chest X-ray images utilizing ConvNets that produce better accuracy for the recognition of COVID-19. When considering the number of images in the database and the COVID discovery season (testing time = 0.03 s/image), the design reduced the computational expenditure. With mean ROC AUC scores 96.51 & 96.33%, the CNN with minimised convolutional and fully connected layers detects COVID-19 images inside the two-class COVID/Normal and COVID/Pneumonia orders. © 2023, IGI Global. All rights reserved.

Dynamic Modeling of COVID-19 Disease with Impact of Lockdown in Pakistan & Malaysia

Being researchers, it is an utmost responsibility to provide insight on social issues thus, this work addresses the dynamic modeling of first and most contagious disease named as COVID-19 caused by coronavirus. The first case of COVID-19 appeared in Pakistan was on 26th February 2020 and in Malaysia on 27th February 2020;both patients had foreign travel history. In the paper, the number of total affected cases and total deaths in both countries, are quite the same up till 12th April 2020 but the frequency of new cases per day and recovery rate are different from one another. The movement control approach had also been imposed on 18th March 2020 by both countries. Keeping these facts and figures, the paper proposes a mathematical model based on Lotka-Volterra equations and provides numerical solution of differential equations using the suspectable, exposed, infected, and recovered people data to estimate future consequences and address the difference in the growth rate of COVID-19 patients before and after locked down to reduce the spread further by taking pro-active approaches i.e., social distancing and being quarantined for the essential time frame. © 2021 IEEE

Designing an IoT-based disinfectant gate for reducing the reproductive coefficient of the novel coronavirus COVID-19 in Malaysia

Purpose The novel coronavirus (COVID-19) has almost affected more than two million people and has taken more than one hundred thousand lives around the globe. At this current state, researchers are trying their best level to drive the permanent solution for this menace;hence, till now social distancing and hygienic lifestyle are the only solutions. This paper proposes a smart entrance disinfectant gate based on the sanitizer spray station and ultraviolet irradiation mechanisms. This innovative and embedded system design-oriented gate will first capture the image of the entrant, second, measure the temperature, third, spray the sanitizers and, last, provide the ultraviolet irradiation to make sure that the person entering any space may have fewer chances to carry coronavirus. The purpose of this study is to enable the IoT feature that helps the government officials to keep the data record of suspectable, exposed, infected and recovered people which will later help to reduce the reproductive co-efficient Ro of COVID-19 within any state of Malaysia. Design/methodology/approach In the current manuscript, design proposes a smart entrance disinfectant gate based on the sanitizer spray station and ultraviolet irradiation mechanisms. This design of the gate is enabled with the feature of the internet of things (IoT) and some efficient sensors along with computer vision facilities. Findings This paper bridges an academic research on COVID-19 and addresses IoT and data prediction-based solution to compute the reproductive number for this novel coronavirus. Originality/value This paper with the features such as hardware design, IoT and, last but not the least, data prediction and visualization makes this prototype one of its kind and provides approximate results for reproductive number (Ro)