Tangent and Cotangent Similarity Measures of Pentapartitioned Neutrosophic Pythagorean Set in Virtual Education During Covid Pandemic

In this paper, a new tangent and cotangent similarity measures between two Pentapartitioned Neutrosophic Pythagorean [PNP] sets with truth membership, falsity membership, ignorance and contradiction membership as dependent Neutrosophic component is proposed and its properties are investigated. The unknown membership alone will be considered as independent Neutrosophic components. Also, the weighted similarity measures are also studied with a decision making problem © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Improved correlation coefficients in neutrosophic statistics for COVID patients using pentapartitioned neutrosophic sets

A correlation coefficient is a statistical measure that contributes in deciding the degree to which changes in one variable predict changes in another. Wang's single-valued neutrosophic sets have still continued to improve to pentapartitioned neutrosophic sets. In this article, we analyze the characteristics of pentapartitioned neutrosophic [PN] sets and interval-valued pentapartitioned neutrosophic sets [IVPN] with improved correlation coefficients. We have also used the same approach in multiple attribute decision-making methodologies including one with a pentapartitioned neutrosophic environment. Finally, we implemented the above technique to the problem of multiple attribute group decision-making. © 2023 Elsevier Inc. All rights reserved.

Effectiveness on impact of COVID vaccines on correlation coefficients of pentapartitioned neutrosophic pythagorean statistics

Correlation is a statistical measure that expresses the extent to which two variables are linearly related. It is a common tool for describing simple relationships without making a statement about cause and effect. Correlations are useful for describing relationships among data. In this paper, we apply the correlation coefficient to pentapartitioned neutrosophic Pythagorean sets (PNPSs). Also, we have introduced the new concept of interval-valued pentapartitioned neutrosophic Pythagorean set (IVPNPS), and some of its basic operations are also investigated. Also, the correlation measure of IVPNPS is proposed, and some of its properties are discussed. The concept of this correlation measure of PNPS is the extension of correlation measures of Pythagorean fuzzy set and pentapartitioned neutrosophic set. Then, using the correlation of PNP and IVPNP set measure, the application of COVID injection is given. © 2023 Elsevier Inc. All rights reserved.

Effective DDoS Attack Detection using Deep Generative Radial Neural Network in the Cloud Environment

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity. © 2022 IEEE.

Lung Ultrasound COVID-19 Detection Using Deep Feature Recursive Neural Network

Coronavirus disease (COVID-19) is a universal illness that has been prevalent since December 2019. COVID-19 causes a disease that extends to more serious illnesses than the flu and is formulated from a large group of viruses. COVID-19 has been announced as a global epidemic that has greatly affected the global economy and society. Recent studies have great promise for lung ultrasound (LU) imaging, subjects infected by COVID-19. Extensively, the growth of an impartial, fast, and accurate automated method for evaluating LU images is still in its infancy. The present algorithms provide results of LU detecting COVID-19, are very time consuming, and provide high false rate for early detection and treatment of affected patients. Today, accurate detection of COVID-19 usually takes a long time and is prone to human error. To resolve this problem, Information Gain Feature Selection (IGFS) based on Deep Feature Recursive Neural Network (DFRNN) algorithm is proposed to detect the COVID-19 automatically at an early stage. The LU images are preprocessed using Gaussian filter approach, then quality enhanced by Watershed Segmentation (WS) algorithm, and later trained into IGFS algorithm to detect the finest features of COVID-19 to improve classification performance. Thus, the proposed algorithm detects whether the person is COVID-19 affected or not, from his LU image, in an efficient manner. The proposed experimental results show improved precision, recall, F-measure, and classification performance with low time complexity and less false rate performance, compared to the previous algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Lung Ultrasound Covid-19 Disease Patients based Convolution Multifacet Analytics using Deep Learning

Deep Learning techniques for ultrasound images, from the front end to the most advanced applications, are the potential effect of deep learning methods on many aspects of the analysis of the ultrasound images. The Covid-19 epidemic has exposed global health care vulnerabilities, especially in developing countries. Lung Ultra-Sound (LUS) imaging as a real-time analytic tool for lung injuries is superior to X-rays and similar to CT, enabling real-time diagnosis. Relying on operator training and experience is the main limitation of the range. COVID-19 lung ultrasonography mainly reflects the pattern of pneumonia, and pleural effusion is not common. The previous system does not provide image accuracy, clarity, it is cost-effective screening large-scale traditional tests are not possible. To overcome the issues, this work proposed the method Convolutional Multi -Facet Analytics (CMFA) algorithm for using the Lung Ultra-Sound (LUS) imaging. Initially start the Preprocessing step based on the Geometric Image Noise Filtering (GINT) for removed the image noises, and unwanted values from the images, second steps of the image processing for Feature selection using the K-Nearest Neighbor (KNN) and Adaptive Gradient Boosting Algorithm (AGBA) for optimizing the image feature od efficient to reduce the same information form he original dataset. And then bagging with K-Nearest Neighbor (KNN) and Adaptive Gradient Boosting Regression (AGBR) Algorithm estimate the images feature weights like (shape, size, etc.) to test, and verify the best combined classifier model splitting training and testing for feature selection and evaluating the results in Softmax activation function. Classified the train and test features using the Convolutional Multi-Facet Analytics (CMFA) algorithm for analyzing the variety of different important features from the dataset. The simulation results show that Sensitivity, specificity, accuracy, and Error rate score shows better results. © 2022 IEEE.

Exploring workplace dignity from managerial lens: MRN

Purpose>Understanding managers’ experiences of workplace dignity (WPD) is critical to working with others in an organization. However, there is limited research available on this subject. This study aims to expand the knowledge of WPD by exploring managers’ understanding of WPD and their experiences of both affirmation and denial of dignity at work.Design/methodology/approach>Critical incident technique (CIT) has been used to explore the themes related to managers’ perceptions of WPD through their lived experiences. Affective event theory supports the use of CIT in the current study context.Findings>Findings unfolded many new aspects of WPD, which have not been explored in the past. An exploration and analysis of the three research questions related to managers’ understanding, affirmation and denial experiences of WPD have added new insights to the existing literature. These have been further segregated under the following four main factors: internal, external, process and feelings. Finally, the authors conclude that external factors that arrive during exchange relationships play an important role in managers’ understanding and experiences of WPD in India.Originality/value>To the best of the knowledge, this is a seminal study to have explored managers’ understanding of WPD in India. It aims to add to the literature by enriching the construct of WPD. Practical implications include a deeper managerial understanding of the affirmational practices and factors which will positively impact WPD.

Potential drug candidates for treatment of Covid-19

The late December of 2019 witnessed an outbreak of viral pneumonia of unknown etiology (VPUE) in the Wuhan city of Hubei province, China. Later it was identified as a novel strain of β-genus Coronavirus, which is similar to the Severe Acute Respiratory Syndrome (SARS) virus, which was a global pandemic during 2002-03. This novel coronavirus is rapidly spreading with an R0 of 2 and has an incubation period of 2-14 days. It spreads through human-to-human transmission and by fomites (articles and surfaces contaminated by affected persons). The World Health Organization acted immediately to prevent the spread of the virus by declaring it as a Public Health Emergency of International Concern (PHEIC). It has been declared as a pandemic, and several sets of guidelines have been issued by WHO, including social distancing norms and hygiene practices yet, this novel coronavirus continues to spread at an alarming rate leading to many fatalities across the globe. Since this novel coronavirus shares similar characteristics with other coronaviruses, some of the known treatment options can be reused in the treatment of this novel coronavirus. As of now, no vaccine has been approved for the prevention of this rapidly spreading novel coronavirus. Hence, the management of affected persons with the available treatment options becomes indispensable. This article briefs about the treatment options available, which include antiviral drugs like Remdesivir, Ribavarin, anti-retroviral drugs like Lopinavir and Ritonavir, antimalarial drugs like Chloroquine and Hydroxychloroquine, Tocilizumab, Ivermectin, and also traditional medicine.