Artificial Intelligence Framework for COVID Protocol Detection

Artificial intelligence is now penetrating into all the domains. Any domain can incorporate artificial intelligence to automate their process. In the outbreak of COVID pandemic, artificial intelligence has been very useful in many ways. artificial intelligence helps in automating process where it's not always possible for people to do and to reduce the wastage of human resource. Here we proposed a frame work to automate the detection of covid protocol violation in public places. Our work detecting people with & without masks and detects social distancing with a single model. The best performing model from the standard convolution neural network architectures namely VGG16 and MobileNetV2 are used in the present work, from the experiments it's found that MobileNetV2 outperformed VGG16. The developed system can easily be integrated/implemented on various embedded devices with limited computational capacity by using the MobileNetV2 architecture. Compared to other previous works, our work outstands by having good accuracy and compatible to use in real life application because of its requirement of less computational complexity. © 2022 IEEE.

Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction

Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging ‘off-the-shelf’ pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural networks to reduce reliance on computational capacity. This reduction of computational capacity is of critical importance in time-limited or resource-constrained circumstances, such as the early stages of a pandemic. On a dataset of 49 patients, comprising over 20,000 images, we demonstrate that the use of existing methods as feature extractors results in the effective classification of COVID-19-related pneumonia severity while requiring only minutes of training time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity score scale and provides comparable per-patient region and global scores compared to expert annotated ground truths. These results demonstrate the capability for rapid deployment and use of such minimally-adapted methods for progress monitoring, patient stratification and management in clinical practice for COVID-19 patients, and potentially in other respiratory diseases. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Anvil - System Architecture and Experiences from Deployment and Early User Operations

Anvil is a new XSEDE advanced capacity computational resource funded by NSF. Designed with a systematic strategy to meet the ever increasing and diversifying research needs for advanced computational capacity, Anvil integrates a large capacity high-performance computing (HPC) system with a comprehensive ecosystem of software, access interfaces, programming environments, and composable services in a seamless environment to support a broad range of current and future science and engineering applications of the nation's research community. Anchored by a 1000-node CPU cluster featuring the latest AMD EPYC 3rd generation (Milan) processors, along with a set of 1TB large memory and NVIDIA A100 GPU nodes, Anvil integrates a multi-tier storage system, a Kubernetes composable subsystem, and a pathway to Azure commercial cloud to support a variety of workflows and storage needs. Anvil was successfully deployed and integrated with XSEDE during the world-wide COVID-19 pandemic. Entering production operation in February 2022, Anvil will serve the nation's science and engineering research community for five years. This paper describes the Anvil system and services, including its various components and subsystems, user facing features, and shares the Anvil team's experience through its early user access program from November 2021 through January 2022. © 2022 Owner/Author.