Fitness dependent optimizer with neural networks for COVID-19 patients.

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.

Graph-Based Text Summarization and Its Application on COVID-19 Twitter Data

Large volumes of structured and semi-structured data are being generated every day. Processing this large amount of data and extracting important information is a challenging task. The goal of an automatic text summarization is to preserve the key information and the overall meaning of the article to be summarized. In this paper, a graph-based approach is followed to generate an extractive summary, where sentences of the article are considered as vertices, and weighted edges are introduced based on the cosine similarities among the vertices. A possible subset of maximal independent sets of vertices of the graph is identified with the assumption that adjacent vertices provide sentences with similar information. The degree centrality and clustering coefficient of the vertices are used to compute the score of each of the maximal independent sets. The set with the highest score provides the final summary of the article. The proposed method is evaluated using the benchmark BBC News data to demonstrate its effectiveness and is applied to the COVID-19 Twitter data to express its applicability in topic modeling. Both the application and comparative study with other methods illustrate the efficacy of the proposed methodology.

Deep Learning Analysis for COVID-19 Using Neural Network Algorithms

The COVID-19 pandemic threatens to devastatingly impact the global population’s safety. A successful surveillance of contaminated patients is a crucial move in the battle against COVID-19, and radiological photographs via chest X-ray are one of the main screening strategies. Recent research showed that patients have abnormalities in photographs of chest X-ray that are characteristic of COVID-19 infects. This has inspired a set of deep learning artificial intelligence (AI) programs, and it has been seen that the precision of the identification of COVID-19 contaminated patients utilizing chest X-rays has been quite positive. However, these built AI schemes, to the extent of their author’s awareness, have become closed sources and not accessible for further learning and expansion by the scientific community, so they are not open to the general public. This thesis therefore implements COVID-Net to identify COVID-19 cases of chest X-rays images, an open source, accessible to the general public, a deep neural network architecture adapted to the detection. The COVID-Net data collection, which is referred to as COVIDx which includes 13,800 chest X-ray photographs of 13,725 patients from 3 open-access data sources, one of which we launched, are also addressed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning Analysis for COVID 19 Using Neural Network Algorithms

The COVID-19 pandemic threatens to devastatingly impact the global population’s safety. A successful surveillance of contaminated patients is a crucial move in the battle against COVID-19 and radiological photographs via chest X-ray are one of the main screening strategies. Recent research showed that patients have abnormalities in photographs of chest X-ray that are characteristic of COVID-19 infects. This has inspired a set of deep learning artificial intelligence (AI) programs, and it has been seen that the precision of the identification of COVID-19 contaminated patients utilizing chest X-rays has been quite positive. However, these built AI schemes, to the extent of their author's awareness, have become closed sources and not accessible for further learning and expansion by the scientific community, so they are not open to the general public. This thesis therefore implements COVID-Net, an Internet of Things (IoT) hand-accessible Machine Learning (ML) network mode to identify COVID-19 cases using the chest X-ray images. This investigation utilize the COVID cases database images from an open source that are accessible to the general public, employs Deep Neural Network (DNN) architecture for the detection and analyzing the disease using Machine Learning (ML) e-network based COVID-Net system. The COVID-Net data collection, which is referred to as COVIDx which includes 13,800 chest X-ray photos of 13,725 patients from 3 open-access data sources, one of which we launched, are also addressed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Peptide-based epitope design on non-structural proteins of SARS-CoV-2 (preprint)

The SARS-CoV-2 virus has caused the severe pandemic, COVID19 and since then its been critical to produce a potent vaccine to prevent the quick transmission and also to avoid alarming deaths. Among all type of vaccines peptide based epitope design tend to outshine with respect to low cost production and more efficacy. Therefore, we started with obtaining the necessary protein sequences from NCBI database of SARS-CoV-2 virus and filtered with respect to antigenicity, virulency, pathogenicity and non- homologous nature with human proteome using different available online tools and servers. The promising proteins was checked for containing common B and T- cell epitopes. The structure for these proteins were modeled from I-TASSER server followed by its refinement and validation. The predicted common epitopes were mapped on modeled structures of proteins by using Pepitope server. The surface exposed epitopes were docked with the most common allele DRB1*0101 using the GalaxyPepDock server. The epitopes, ELEGIQYGRS from Leader protein (NSP1), YGPFVDRQTA from 3c-like proteinase (nsp5), DLKWARFPKS from NSP9 and YQDVNCTEVP from Surface glycoprotein (spike protein) are the epitopes which has more hydrogen bonds. Hence these four epitopes could be considered as a more promising epitopes and these epitopes can be used for future studies.

Convalescent Plasma Therapy in COVID 19 an Indian Scenario: Comprehensive Review

The recent pandemic due to Corona virus more popularly known as COVID 19 has reassessed the usefulness of historic convalescent plasma transfusion. (CPT) The CPT is one of the promising therapies in the current pandemic situation. This review was conducted to evaluate the effectiveness of CPT therapy in COVID 19 patients based on the publications reported till date. PubMed, EMBASE and Medline databases were screened up to 30 April 2021. All the records were screened as per the protocol eligibility criteria. The main features of the studies reviewed were, convalescent plasma can reduce mortality in severely ill patients, an increase in neutralizing antibodies titre and disappearance of SARS CoV 2 RNA was observed in all the patients on CPT therapy and over all a beneficial effect on clinical symptoms after administration of CP. Based on the review findings and the limited scientific data, CPT therapy in COVID 19 patients appear safe, clinically effective and reduces mortality. However, the need of a multicentre clinical trials, unequivocal proof of efficacy, effectiveness and the need for the standardisation of the CPT needs to be addressed immediately for the full utilisation of potential of CPT.

Word and graph embeddings for covid-19 retweet prediction

In this paper, we present our solution for COVID-19 retweet prediction challenge. The proposed approach consists of feature engineering and modeling. For feature engineering, we leverage both hand-crafted and unsupervised learning features. As the provided data set is large, we implement auto-encoding algorithms to reduce feature dimension. To develop predictive models, we utilize ensemble learning and deep learning algorithms. We then combine these models to generate the final blended model. Moreover, to stabilize the predictions, we also apply bagging as well as down-sampling techniques to remove the tweets where number of retweets equals to zero. Our solution is ranked First on the public test set and second on the private test set. © 2020 CEUR-WS. All rights reserved.

Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

Purpose: The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images. Design/methodology/approach: We used machine learning techniques with convolutional neural network. Findings: Detecting COVID-19 symptoms from patient CT scan images. Originality/value: This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images. © 2021, Emerald Publishing Limited.

Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

[This corrects the article DOI: 10.1007/s00779-021-01541-4.].

Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.