Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall. © 2023 CRL Publishing. All rights reserved.

The role of facilities management in fighting COVID-19 outbreak: Evidence from Malaysian public hospitals

The aim of this study is to provide a holistic review of the fight against COVID-19 in developing countries, particularly Malaysia. Specifically, the study aims to determine how facilities management delivery in public hospitals can be improved to ensure readiness in handling COVID-19 cases. We conducted a review of the literature and reliable media updates on COVID-19 and services management. A critical synthesis of COVID-19 information was conducted to scrutinise the technical aspects and highlight how facilities management can be improved to ensure hospital readiness in managing COVID-19 cases. The data and information used in the present study were collected up to the time of writing this paper, which leaves a room for further studies. Nonetheless, this study's recommendations are useful for understanding the present and future pandemics. This study is a first attempt to summarise the data on facilities management in relation to the COVID-19 pandemic in the Malaysian context. The study's findings are suitable for the developing countries in managing healthcare management practices in the fight against COVID-19. This study aims to highlight current issues in order to provide a more objective assessment of facilities management to ensure hospital readiness in handling COVID-19 cases.

Empathy of Healthcare Professionals During Covid-19 Crisis

Background: Empathy is one of the most vital parts of medical ethics that is a prerequisite for good clinical practice, yet the level of empathy of the doctors has not been up to the mark. Objective(s): To measure empathy of healthcare professionals during COVID-19 crisis Material(s) and Method(s): This was a cross Sectional Study done in almost all public and private tertiary care hospitals of Peshawar. The study duration was 6 months (10 September 2021-10 Febuary 2022) after the approval of research proposal. The sampling method was non probability convenient sampling. The inclusion criteria were health professionals involved in the care of the COVID 19 patients like pulmonogists, physicians, trainee doctors, nurses and chest physiotherapists. Exclusion criteria were the doctors from other specialties who were not taking in the management of such patients. Empathy scale was used to collect responses of the participants.The data was analyzed using SPSS latest version Results: Significant association was seen between Consultants, Nurses and PGRs with that they were not interested in patient's personal matters as these are not relevant to medical treatment. i.e. p-value <0.001. Significant association was seen between Consultants, Nurses and PGRs they Viewed things from patient' perspectives might confuse me and make me too distracted to take the right clinical decision (cognitive). I.e. p-value=0. 026 Conclusion(s): The level of empathy did get affected to a colossal extent during the COVID-19 pandemic as reflected by the above results. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

A comparative study of spreading of novel corona virus disease by ussing fractional order modified SEIR model

In this research work, a non-linear dynamical modified SEIR model of the recent pandemic, due to Coronavirus-19 disease (COVID-19) for different countries like Malaysia and Pakistan, is considered under nonsingular fractional order derivative. For this model, some qualitative results, existence theory, and numerical solution are studied by using fixed point approach and fractional Adams-Bashforth method. The results are simulated corresponding to some real data of various fractional order by using Matlab. Hence, the suitability of the considered COVID-19 model for the current outbreak in two different countries Malaysia and Pakistan are shown by simulation. © 2020 Faculty of Engineering, Alexandria University

Fuzzy fractional-order model of the novel coronavirus.

In this paper, a novel coronavirus infection system with a fuzzy fractional differential equation defined in Caputo's sense is developed. By using the fuzzy Laplace method coupled with Adomian decomposition transform, numerical results are obtained for better understanding of the dynamical structures of the physical behavior of COVID-19. Such behavior on the general properties of RNA in COVID-19 is also investigated for the governing model. The results demonstrate the efficiency of the proposed approach to address the uncertainty condition in the pandemic situation.