Blockchain based access control for home hospitalization during covid-19

With the emergence of the Covid-19 disease, hospitals and health care professionals (HP) had to deal with a huge number of patients presenting with the virus that kept increasing every day, resulting in the increase of the pandemic transmission. To deal with this issue, minimize patients' and healthcare professionals' exposure, and be able to treat all patients, HP turned to home hospitalization. In-home hospitalization, doctors need to monitor their patient's health status remotely, and patients need to share this data with them. But in this scenario, patient privacy is exposed to several external threats and intrusions, sometimes resulting in the loss of patient life. To deal with the above issues, our focus in this article is on access control (AC). Thus, we propose a Blockchain (BC) smart contract-based model, where each object owner creates one ACC (Access control contract) for each subject in the system and defines his access policies in it. © 2022 IEEE.

Optimized convolutional neural network for automatic detection of COVID-19

The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing followed by CNN-based feature extraction. Then, Multi-objective Black Widow Optimization (MBWO) technique is applied to fine tune the hyperparameters of CNN. Finally, Extreme Learning Machine with autoencoder (ELM-AE) is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels. The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques. The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%. The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19. © 2021 Tech Science Press. All rights reserved.