Covid-19 was an outbreak of unfamiliar diseases affecting the majorly respiratory system. The disease gradually exacerbates and inscribed the whole world. Majorly deteriorating population. The well versed and optimized methodology to predict Covid-19 is still questionable. However, state of the art techniques of Deep Learning clearly created a new path of superior prediction and forecasting. This study consists of two dimensions. First Dimension is that a new method of prediction has been established via COVID-19 patients registration slips. ResNet-101 has been applied to indigenous data set of COVID-19 patients registration slips. The dataset includes 5003 E-registration slips with exact timings. The accuracy of forecasting with respect to time was 82%. Forecasting for COVID-19 positive cases of the following day was determined. Error framework was also established inscribing of MOE, MAE juxtaposition. The second Dimension includes the prediction of COVID-19 via Chest X-Ray. Indigenous dataset of 8009 chest X-Ray was collected. Three Neural Networks were implied incriminating Faster R-CNN, Mask-CNN, and ResNet-50. Faster R-CNN shows the best accuracy of 87%. Mask R-CNN accuracy was 83% and resNet-50 ended up at 72%. Performance parameters were in terms of ACC, PRC, and RCL. The batch normalization technique was added to improve SVM performance..

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This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading to the misjudgment of targets. To address this challenge, TensorFlow and PyTorch were used to initialize various distributed model parallel and data parallel techniques. Despite the success of these techniques, communication constraints were observed along with certain speed issues. As a result, a hybrid pipeline was proposed, combining both dataset and model distribution through an all-reduced algorithm and NVlinks to prevent miscommunication among gradients. The proposed approach was tested on both an edge cluster and Google cluster environment, demonstrating superior performance compared to other test settings, with the quality of the bounding box detection system meeting expectations with increased reliability. Performance metrics, including total training time, images/second, cross-entropy loss, and total loss against the number of the epoch, were evaluated, revealing a robust competition between TensorFlow and PyTorch. The PyTorch environment’s hybrid pipeline outperformed other test settings.

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.