scholarly journals A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans

2020 ◽  
Vol 26 (4) ◽  
pp. 3088-3105 ◽  
Author(s):  
Mohamed Abdel-Basst ◽  
Rehab Mohamed ◽  
Mohamed Elhoseny
Keyword(s):  
Ct Scans ◽  
Detection Accuracy ◽  
Ideal Solution ◽  
Accurate Evaluation ◽  
Proposed Model ◽  
Rapid Spread ◽  
The World ◽  
Uncertainty Environment ◽  
Internet Of Things Technology ◽  
Best Worst Method

The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%.

scholarly journals Deep Learning Assisted Covid-19 Detection using full CT-scans

2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Detection Accuracy ◽  
Residual Network ◽  
Excellent Performance ◽  
Examination Procedure ◽  
Medical Institutions ◽  
Proposed Model ◽  
Learning Technique ◽  
Intensive Approach

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>

scholarly journals Deep Learning Assisted Covid-19 Detection using full CT-scans

2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Detection Accuracy ◽  
Residual Network ◽  
Excellent Performance ◽  
Examination Procedure ◽  
Medical Institutions ◽  
Proposed Model ◽  
Learning Technique ◽  
Intensive Approach

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>

scholarly journals INTEGRATING THE BWM AND TOPSIS ALGORITHM TO EVALUATE THE OPTIMAL TOKEN EXCHANGES PLATFORM IN TAIWAN

2021 ◽  
Vol 0 (0) ◽  
pp. 1-23
Author(s):  
Wei-Yuan Wang ◽  
Yeh-Cheng Yang ◽  
Chun-Yueh Lin
Keyword(s):  
Linear Programming ◽  
Delphi Method ◽  
Optimal Solution ◽  
Ideal Solution ◽  
Token Exchange ◽  
Modified Delphi ◽  
Proposed Model ◽  
Best Worst Method ◽  
Topsis Model ◽  
The Ideal

This research presents procedures for determining the optimal solution of token exchanges platform for investors in Taiwan via integrating the best-worst method (BWM) and the technique for ordering preference by similarity to the ideal solution (TOPSIS). Firstly, this research applies the modified Delphi method to develop the perspectives and factors via literature review and experts opinion. Secondly, the BWM is implemented to obtain weights of perspectives and factors on the linear programming concept. Thirdly, the TOPSIS model is used to rank the optimal solution of the token exchange for investors or corporations. Finally, the proposed model BWMTOPSIS-based procedures will list the optimal token exchanges platform on the three token exchange platforms to investors or corporations in Taiwan on the basis of their rankings in the architecture. The proposed combination framework is able to provide academic and commerce support to investors or corporations in implementing the token into their portfolio as a valuable objective guide to determine the optimal token exchange platform.

scholarly journals Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

2021 ◽  
Vol 18 (12) ◽  
pp. 6429
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Malak Aljabri ◽  
Sumayh S. Aljameel ◽  
Mariam Moataz Aly Kamaleldin ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Computational Intelligence ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Detection And Identification ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The World ◽  
Detection And Diagnosis

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1-19
Author(s):  
Sreelakshmy I. J. ◽  
Binsu C. Kovoor
Keyword(s):  
Patch Size ◽  
Image Inpainting ◽  
Qualitative Evaluation ◽  
Natural Images ◽  
External Information ◽  
Image Editing ◽  
Model Combining ◽  
Proposed Model ◽  
The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

scholarly journals Importance of immunoglobulin therapy for COVID-19 patients with lymphocytopenia

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Heba K. Nabih
Keyword(s):  
Passive Immunization ◽  
Immunoglobulin Therapy ◽  
World Health ◽  
Clinical Feature ◽  
Main Body ◽  
Plasma Therapy ◽  
Rapid Spread ◽  
The World ◽  
Health Organization

Abstract Background The global coronavirus disease 2019 (COVID-19) was announced as pandemic by the World Health Organization (WHO). With the increased number of infected and dead victims daily all over the world, it becomes necessary to stop or overcome its rapid spread. Main body Although the production of vaccine or even specified effective anti-virus may take about six months to a year, intravenous immunoglobulin (IVIg) may be clinically used as a safe treatment to save and improve the quality of life of patients with a variety of immunodeficiency diseases such as lymphocytopenia, which is a common clinical feature in COVID-19. Conclusion Through the current review, it was concluded that this passive immunization may promote the immunity to better fight against the virus, so the survival of the patients could be kept longer. The efficacy of immunotherapy with IVIg would be greater if the immune IgG antibodies were collected from convalescent plasma therapy.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mingying Xu ◽  
Junping Du ◽  
Feifei Kou ◽  
Meiyu Liang ◽  
Xin Xu ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Feature Representation ◽  
Search Model ◽  
Model Based ◽  
Proposed Model ◽  
Cloud Server ◽  
Weakly Supervised ◽  
Internet Of Things Technology ◽  
Academic Search

Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long-distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge-enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word-level interactive matching and sentence-level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.

scholarly journals Coronavirus Disease 2019 Occupational Considerations for Surgeons: A Review Article

2021 ◽  
Vol 10 (1) ◽  
pp. 36-41
Author(s):  
Seyed Hesamaddin Banihashemi ◽  
Ahmadreza Karimi ◽  
Hasti Nikourazm ◽  
Behnaz Bahmanyar ◽  
Dariush Hooshyar
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
World Health Organization ◽  
Medical Staff ◽  
Review Article ◽  
World Health ◽  
Published Data ◽  
Rapid Spread ◽  
The World ◽  
Health Organization ◽  
And Control

The severe acute respiratory syndrome coronavirus 2 virus and its associated disease, called coronavirus disease 2019 (COVID-19), first appeared in Wuhan, China in December 2019 and quickly spread around the world. Coronavirus was officially named COVID-19 by the World Health Organization and was recognized as a pandemic due to its rapid spread worldwide. Based on the published data, it is hoped to provide a source for later studies and to help prevent and control the contagious COVID-19 and its characteristics, and considerations that surgeons and medical staff must observe during the epidemic.

scholarly journals Wellness Constraints Among International University Students Transitioning Through Covid 19

2021 ◽  
Vol 58 (3) ◽  
pp. 3444-3456
Author(s):  
Mr J Dorasamy, Et. al.
Keyword(s):  
World Health Organization ◽  
Social Economic ◽  
World Health ◽  
Social Emotional ◽  
Global Pandemic ◽  
Rapid Spread ◽  
The World ◽  
International University ◽  
Novel Coronavirus ◽  
Health Organization

The World Health Organization (Who) In March 2020 Declared Covid 19 A Pandemic, Due To The  Global And Rapid Spread Of A Novel Coronavirus (Who, 2020). The Covid 19 Pandemic Being Highly Infectious And Unpredictable, Has  Disrupted  Social, Economic, Environmental And Political Spheres Of Life. Globally, People Have Ventured Into A “Lockdown World”, Increasing Uncertainty About Their Future Amidst The Covid 19 Pandemic. As A Result Of The Pandemic, Social Alteration Has Taken The Form Of Social Distancing, Self-Isolation And Self-Quarantine.  Many Were Unprepared For The Shift From The “Normal”, Propelling  Undue  Stress Under The New Normal Way Of Doing Things During The Current Global Pandemic Crisis. This Has Been Accompanied By Social, Emotional And Mental Effects, As The Ongoing And Fluid Nature Of The Pandemic Has Created Uncertainty For Many People. The Covid 19 Pandemic, As A Multidimensional Stressor Affecting Wellbeing, Has Affected Individuals, Families, Educational, Occupational, And Broader Societal Systems.  

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