An Extensive Study on HAR Systems to Recognize Daily Activities using Deep Learning Approaches

"Human Activity Recognition" (HAR) refers to the ability to recognise human physical movements using wearable devices or IoT sensors. In this epidemic, the majority of patients, particularly the elderly and those who are extremely ill, are placedin isolation units. Because of the quick development of COVID, it's tough for caregivers or others to keepan eye on them when they're in the same room. People are fitted with wearable gadgets to monitor them and take required precautions, and IoT-based video capturing equipment is installed in the isolation ward. The existing systems are designed to record and categorise six common actions, including walking, jogging, going upstairs, downstairs, sitting, and standing, using multi-class classification algorithms. This paper discussed the advantages and limitations associated with developing the model using deep learning approaches on the live streaming data through sensors using different publicly available datasets. © 2022 IEEE

Early prediction and analysis of corona pandemic outbreak using deep learning technique

Purpose: The purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus. Design/methodology/approach: This research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time. Findings: LSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set. Originality/value: The fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge. © 2021, Emerald Publishing Limited.

Role of digital technologies to combat COVID-19 pandemic

Purpose: The unexpected epidemic of the latest coronavirus in 2019, known as COVID-19 by the Globe, a number of governments worldwide have been put in a vulnerable situation by the World Health Organization. The effect of the COVID-19 outbreak, previously experienced by China’s citizens alone, has now become more pronounced. For practically every nation in the world, this is a matter of grave concern. The lack of assets to withstand the infection of COVID-19, mixed with the perception of overwhelmed medical mechanisms, pressured a number of places in a state of partial or absolute lockdown. Design/methodology/approach: The medical photos such as computed tomography (CT) and X-ray playa key role in the worldwide battle against COVID-19, while artificial intelligence (AI) has recently appeared. The power of imaging is further increased by technology tools and support for medical specialists. In comparison to the related direct health effects because of the COVID-19 disaster, this research identifies its impacts on the overall society. Findings: This paper hereby examines the rapid answers in the medical imaging community toward COVID-19 (empowered by AI). For example, the acquisition of AI-empowered images will significantly assist automate the scanning process and reshape the procedure as well. AI, too, may improve the quality of the job by correctly delineating X-ray and CT image infections, promoting subsequent infections, quantification. In addition, computer-aided platforms support radiologists make medical choices, i.e. for illness tracking, diagnosis and prognosis. Originality/value: This research encompasses the whole medical imaging pipeline and methods for research related to COVID-19, include a collection of images, segmentation, diagnosis and monitoring. In drawing stuff to minimize the effects of the COVID-19 epidemic, this paper is investigating the use of technologies such as the internet of things, unmanned aerial vehicles, blockchain, AI, big data and 5G. © 2021, Emerald Publishing Limited.