Development of Contactless Individual Management System with Covid - 19 Preemptive Measures

The significance of social distancing and non-contact habits was emphasized by the Covid-19 pandemic. Even after the pandemic, everyone should adhere to the same hygiene procedures. Preventive measures must be implemented prior to the individuals' return. These include identifying people's presence and monitoring their health. This research focuses on using sensor fusion and deep learning technology to create a contactless individual management system. It is capable of carrying out the attendance routine without compromising the precautionary measures. Persons can be identified without removing the mask by employing random Quick Response (QR) code recognition. While recognising the QR, the system will double-verify the individual by identifying the Media Access Control (MAC) address of the user's mobile Bluetooth at the backend. Then the system employs a pre-trained deep learning model to detect masks. The Convolutional Neural Network (CNN) technique produces a deep learning model that can distinguish between Faces with and without masks. The system then monitors the body temperature with an Infrared (IR) temperature sensor followed by dispensing sanitizer. The response for the entire procedure will be updated in both the person's mobile application and the Management Authority. © 2022 IEEE.

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

The Canny Blocks for Generation of Secured Epass in Pandemic COVID-19

The SARS COVID-19 has immensely affected human life across the world. The transport which completely blocked was released by issuing epass to the travelers with valid emergency. The purpose of issuing the epass is not only to make the restriction of frequent travel but also to control the traffic and to save the livelihood of people in local zones. There are distinct challenges in epass generation and issuance. The existing system uses the traditional cloud database for storing the user-sensitive data who applied for epass. It may result in data leakage and security threats due to centralized server. The unique technology which meets the current requirement of highly secured sensitive data available to all authorized person is Blockchain Technology. Blockchain is expounded as distributed decentralized ledger, maintains the non-modifiable ledger at all nodes in the network. In this paper, we proposed blockchain technology-based framework that can be deployed for generating and issuing the epass to improve the availability of sensitive data to the authorized person for effective controlled movement between the country. This decentralized distributed blockchain-based epass maintains the history of the user applied for the epass properly in the non-modifiable ledger properly. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.