Search

BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. METHODS: The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. RESULTS: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. CONCLUSION: The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%. [ABSTRACT FROM AUTHOR]

Forecasting with a precise evaluation of new cases and the rate of occurrence is essential for the effective implementation of governmental initiatives and early prevention of any infectious illness. Despite the extensive research done on the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) virus since the outbreak, not enough knowledge could be gained about the virus in terms of immune function, virus-host interactions, pathogenesis, propagation, and mutations. In this paper, various statistical models, namely Supervised Machine Learning techniques (ML), are being discussed for previous diseases and for the recent COVID-19 pandemic. Namely, the use of the Support Vector Machine (SVM) model and a variety of time series regression models is demonstrated for several infectious diseases, including COVID-19. As infectious diseases evolve throughout time, they provide data on a single variable, that is, "the figure of contaminations that occurred over time"; thus, researchers tend to use time series models to fit the data and make predictions using different evaluation metrics to find the best-fitting model. This review developed ideas about how to enhance the current modeling techniques. Furthermore, findings of current Machine Learning Techniques are being evaluated, which attempts to estimate the COVID-19 spread. Researchers looking for approaches to advance SARS-CoV-2 research as well as individuals curious about the field's current condition will find this review to be helpful. [ABSTRACT FROM AUTHOR]

The exponential growth of web-based content has prompted governments and businesses to seek new strategies and tools for effective governance, navigation, and management of complex data. Since the start of SARS-CoV-2 (COVID-19), there have been numerous news related to coronavirus and its variants published on various platforms. Thus, it is important to introduce an intelligent classification technique for classifying the news related to COVID-19. Automated news classification refers to the process of categorising news articles into predetermined categories based on their content, using the confidence gained from the training news dataset. Therefore, this paper intends to develop a model capable of classifying Arabic news related to the COVID-19 Omicron variant. The model can classify related news into five classes: statistics, vaccines, restrictions, economy and health. The news corpus is comprised of 220,000 instances gathered from various Arabic newspapers. The dataset is pre-processed, and then the n-gram features are identified to train and test the models. This study experimented with various machine learning (ML) classifiers, i.e., Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), and Random Forest (RF) with various n-grams when creating the model. The results show that MNB with character n-gram outperformed baseline methods in terms of recall, precision and F1-score with average values of 86%, 87% and 86%, respectively. [ABSTRACT FROM AUTHOR]

A theoretical investigation into the hidden source field of mutation affecting the curvature change in the spike (S) protein of Omicron BA.5 and related variants is reported. The curvature in the open string shape of S protein is defined using the Yang-Mills field over a new type of connection known as quantum genotype. By adding more invariant properties of curvature two-forms to the adaptive tensor fields in DNA, RNA, and protein molecules, we redefined the hidden quantum biological co-state of the mutational field between the virus and the host cell. The new algorithm was applied to classify mutations in S protein with a support vector machine. The results showed that the average performance of the prediction of unknown amino acids in 14 variants including Omicron BA.1-BA.5 is 97.79%. Additionally, we demonstrated a new approach for the quantitative measurement of changing curvature of S protein mutations in each amino acid. The empirical analysis of the probability distribution of time series data showed the evolution of the quantum genotype over time, revealing a new direction of evolution in SARS-CoV-2's quantum genotype opposite to the period between 2020 and 2021. This work can be applied to detect new incoming novel variants of SARS-CoV-2 in the future and provide insight into the coupling between the passive and active sides of communication in the biomolecular layer. [ABSTRACT FROM AUTHOR]

We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism. [ABSTRACT FROM AUTHOR]

PurposeThe coronavirus disease 2019 (COVID-19) pandemic has spread to all countries in the world, and different countries have been impacted differently. The study aims to understand what factors contribute to different COVID-19 impacts at the country level.MethodsMultivariate statistical analyses were used to evaluate COVID-19 deaths and cases relative to nine other demographic and socioeconomic factors in all countries and regions of the world using data as of August 1, 2020. The factors analyzed in the study include a country's total COVID-19 deaths and cases per million population, per capita gross domestic product (GDP), population density, virus tests per million population, median age, government response stringency index, hospital beds availability per thousand population, extreme poverty rate, Bacille Calmette-Guérin (BCG) vaccination rate, and diphtheria-tetanus-pertussis (DTP3) immunization rate.ResultsThe study reveals that COVID-19 deaths per million population in a country most significantly correlates, inversely, with the country's BCG vaccination rate (r =  - 0.50, p = 5.3e-5), and also significantly correlates a country's per capita GDP (r = 0.39, p = 7.4e-3) and median age (r = 0.30, p = 0.042), while COVID-19 cases per million population significantly correlate with per capita GDP and tests per thousand population. To control for possible confounding effects of age, the correlation was assessed in countries propensity score matched for age. The inverse correlation between BCG vaccination rates and COVID-19 case (r =  - 0.30, p = 0.02) and death (r =  - 0.42, p = 0.0007) remained significant among the top 61 countries with the highest median age.ConclusionThis study contributes to a growing body of evidence supporting the notion that BCG vaccination may be protective against COVID-19 mortality.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges modern society has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Overall, this study suggests that there is valuable residual information in the rRT-PCR positive samples that can be used to identify patterns in the development of the SARS-CoV-2 pandemic. The successful application of supervised classification algorithms to detect these patterns demonstrates the potential of machine learning techniques to aid in understanding the spread of the virus and its variants. [ABSTRACT FROM AUTHOR]

Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) and genetic algorithm were successfully used to predict the changes in the prevalence rate (ΔPR) measured by the increase of reported cases per million population from the 16th to the 45th day during a nation's lockdown after the COVID-19 outbreak. The national cultural indices [individualism–collectivism (Ind), tightness–looseness (Tight)], and the number of people per square kilometer (Pop_density) were used to develop the SVM model of lnΔPR. The SVM model has R2 of 0.804 for the training set (44 samples) and 0.853 for the test set (11 samples), which were much higher than those (0.416 and 0.593) of the multiple linear regression model. The statistical results indicate that there are nonlinear relationships between lnΔPR and Tight, Ind, and Pop_density. It is feasible to build the model for lnΔPR with SVM algorithm. The results suggested that the risk of COVID-19 epidemic spread will be reduced if a nation implements severe measures to strengthen the tightness of national culture and individuals realize the importance of collectivism. [ABSTRACT FROM AUTHOR]

Corona virus is considered as a viral disease activated from the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) that leads to high illness and death globally. The reverse transcription polymerase chain reaction (RT-PCR) represents the standard test for the medical examination, but the test creates false negatives. In this paper, the ensemble Support vector machine with Ludo game-based swarm optimisation algorithm (ESLGSA) is proposed for the novel COVID-19 detection from the CT images and X-ray images. The proposed approach decreases the necessities of the physical labelling of the CT images and X-ray images. The proposed approach achieves the accurate infection recognition and separates the COVID-19 persons from the non-COVID-19 persons. The experimental results describes that the proposed approach enhances the accuracy value of 99.64% and the area under curve of 0.9257 in the detection of COVID-19 on CT image and X-ray image. The results described that the COVID-19 recognition using proposed approach in CT image and X-ray image is enhanced significantly than any other approaches. The results describes that the proposed approach outperforms the other approaches with respect to the measures such as accuracy, precision, recall and F-measure. [ABSTRACT FROM AUTHOR]

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity's body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease's risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19. [ABSTRACT FROM AUTHOR]

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%. [ABSTRACT FROM AUTHOR]

The rapid spread of the new Coronavirus, COVID-19, causes serious symptoms in humans and can lead to fatality. A COVID-19 infected person can experience a dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness, according to a clinical report. A chest X-ray (also known as radiography) or a chest CT scan are more effective imaging techniques for diagnosing lung cancer. Computed Tomography (CT) scan images allow for fast and precise COVID-19 screening. In this paper, a novel hybridized approach based on the Neighborhood Rough Set Classification method (NRSC) and Backpropagation Neural Network (BPN) is proposed to classify COVID and NON-COVID images. The proposed novel classification algorithm is compared with other existing benchmark approaches such as Neighborhood Rough Set, Backpropagation Neural Network, Decision Tree, Random Forest Classifier, Naive Bayes Classifier, K- Nearest Neighbor, and Support Vector Machine. Various classification accuracy measures are used to assess the efficacy of the classification algorithms. [ABSTRACT FROM AUTHOR]

Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use. The primary objective of this study is to analyse the public awareness and opinion toward the vaccination process and to develop a model that predicts the awareness and acceptability of SARS-CoV-2 vaccines in Saudi Arabia by analysing a dataset of Arabic tweets related to vaccination. Therefore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR), sideways with the N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) techniques for feature extraction and Long Short-Term Memory (LSTM) model used with word embedding. LR with unigram feature extraction has achieved the best accuracy, recall, and F1 score with scores of 0.76, 0.69, and 0.72, respectively. However, the best precision value of 0.80 was achieved using SVM with unigram and NB with bigram TF-IDF. However, the Long Short-Term Memory (LSTM) model outperformed the other models with an accuracy of 0.95, a precision of 0.96, a recall of 0.95, and an F1 score of 0.95. This model will help in gaining a complete idea of how receptive people are to the vaccine. Thus, the government will be able to find new ways and run more campaigns to raise awareness of the importance of the vaccine. [ABSTRACT FROM AUTHOR]

As TLR2 agonists, several lipopeptides had been proved to be candidate vaccine adjuvants. In our previous study, lipopeptides mimicking N-terminal structures of the bacterial lipoproteins were also able to promote antigen-specific immune response. However, the structure-activity relationship of lipopeptides as TLR2 agonists is still unclear. Here, 23 synthetic lipopeptides with the same lipid moiety but different peptide sequences were synthesized, and their TLR2 activities in vitro and mucosal adjuvant effects to OVA were evaluated. LP1-14, LP1-30, LP1-34 and LP2-2 exhibited significantly lower cytotoxicity and stronger TLR2 activity compared with Pam2CSK4, the latter being one of the most potent TLR2 agonists. LP1-34 and LP2-2 assisted OVA to induce more profound specific IgG in sera or sIgA in BALF than Pam2CSK4. Furthermore, the possibility of LP1-34, LP2-2 and Pam2CSK4 as the mucosal adjuvant for the SARS-CoV-2 recombinant RBD (rRBD) was investigated. Intranasally immunized with rRBD plus either the novel lipopeptide or Pam2CSK4 significantly increased the levels of specific serum and respiratory mucosal IgG and IgA, while rRBD alone failed to induce specific immune response due to its low immunogenicity. The novel lipopeptides, especially LP2-2, significantly increased levels of rRBD-induced SARS-CoV-2 neutralizing antibody in sera, BALF and nasal wash. Finally, Support vector machine (SVM) results suggested that charged residues in lipopeptides might be beneficial to the agonist activity, while lipophilic residues might adversely affect the agonistic activity. Figuring out the relationship between peptide sequence in the lipopeptide and its TLR2 activity may lay the foundation for the rational design of novel lipopeptide adjuvant for COVID-19 vaccine. [ABSTRACT FROM AUTHOR]

Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes the COVID-19 disease, which turns into a pandemic and threatens public health. Appearing of SARS-CoV-2 variants shows a significant challenge in determining the risk of infection, develop vaccines as well as antiviral agents, monitor the changes, and assess the evolution of SARS-CoV-2. In this study, we propose a method for identifying SARS-CoV-2 variants in Turkey. To achieve this goal, nucleotide occurrences are computed from the whole genome sequences that include four nucleotides, A, C, T, and G. Thus, 30 000 bps genome sequences are represented by only four integer numbers. After features are extracted, four classification methods, support vector machines, k-nearest neighbor, neural network, and decision tree are employed to identify SARS-CoV-2 variants. Experimental results are conducted on a dataset including 1403 genome sequences from Turkey and belonging to variants of SARS-CoV-2, B.l.1.7 (Alpha), B.1.351 (Beta), P.l. (Gamma), as well as B. 1.617 (Delta). Experimental results present that the KNN classifier achieves an accuracy of 0.94, a precision of 0.81, a recall of 0.80, and an F-score of 0.80 on average. [ABSTRACT FROM AUTHOR]

"Novel Coronavirus", also known as COVID-19 has spread globally which is sparked in Wuhan, the capital city of Hubei in China. It is a big problem for the world's health organization as the number of affected cases as well as deaths increases in each country. Several Machine Learning models are designed to predict and forecast the COVID-19 outbreak. In this paper, we are designed to understand the current scenario of coronavirus, and predicting and forecasting the total number of confirmed cases of novel COVID-19 in India using linear regression, polynomial regression, and support vector machine regression models. The decision-making process of an organization depends on the data analysis and it is presenting with interactive visual representation. It is not possible to predict without having necessary information from the past and the present. Trends over time tell us where we were and where we can go with our model. The model is predicting the number of confirmed cases from 30th May 2021 to 15th June 2021on the data available from 22 January 2020 to 29 May 2021. Forecasting the future of confirmed cases can help health organizations to plan medical facilities. Moreover, the time series forecasting technique is being engaged to forecast the overall total of confirmed cases in the upcoming. [ABSTRACT FROM AUTHOR]

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy. [ABSTRACT FROM AUTHOR]

Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan. This research also compares the performance of machine learning classifiers (i.e., Decision Tree (DT), Naive Bayes (NB), Support Vector Machine, and Logistic Regression) on the COVID-19 dataset collected in Pakistan. According to the experimental results, DT and NB classifiers outperformed the other classifiers. In addition, the classified data is categorized by implementing a BayesianRegularizationArtificialNeuralNetwork (BRANN) classifier. The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world's health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators. [ABSTRACT FROM AUTHOR]

COVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The experimental outcomes of this study confirmed that the supported vector machine technique has high accuracy and excellent performance in the classification of the disease, as reflected by values of 91.8% accuracy, 91.7% sensitivity, 95.9% specificity, 91.8% F1-score, and 97.6% AUC. [ABSTRACT FROM AUTHOR]

Diabetes is a life-long condition that is well-known in the 21st century. Once known as a disease of the West, the rise of diabetes has been fed by a nutrition shift, rapid urbanization and increasingly sedentary lifestyles. In late 2019, a new public health concern was emerging (COVID-19), with a particular hazard concerning people living with diabetes. Medical institutes have been collecting data for years. We expect to achieve predictions for pathological complications, which hopefully will prevent the loss of lives and improve the quality of life using data mining processes. This work proposes a comparative study of data mining techniques for early diagnosis of diabetes. We use a publicly accessible data set containing 520 instances, each with 17 attributes. Naive Bayes, Neural Network, AdaBoost, k-Nearest Neighbors, Random Forest and Support Vector Machine methods have been tested. The results suggest that Neural Networks should be used for diabetes prediction. The proposed model presents an AUC of 98.3% and 98.1% accuracy, an F1-Score, Precision and Sensitivity of 98.4% and a Specificity of 97.5%. [ABSTRACT FROM AUTHOR]

Novel Coronavirus-19 (COVID-19) is a newer type of coronavirus that has not been formally detected in humans. It is established that this disease often affects people of different age groups, particularly those with body disorders, blood pressure, diabetes, heart problems, or weakened immune systems. The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates. Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans. It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken. The latest global coronavirus epidemic (COVID- 19) has brought new challenges to the scientific community. Artificial Intelligence (AI)-motivated methodologies may be useful in predicting the conditions, consequences, and implications of such an outbreak. These forecasts may help to monitor and prevent the spread of these outbreaks. This article proposes a predictive framework incorporating Support Vector Machines (SVM) in the forecasting of a potential outbreak of COVID-19. The findings indicate that the suggested system outperforms cutting-edge approaches. The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance. The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity. The proposed SVM system model exhibits 98.88% and 96.79% result in terms of accuracy during training and validation respectively. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) enables quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, helping in outcome prediction. Methods: From 1 to 22 March 2020, patients with pneumonia symptoms, positive lung CT scan, and confirmed SARS-CoV-2 on reverse transcription-polymerase chain reaction (RT-PCR) were consecutively enrolled. Clinical data was collected. Outcome was defined as favourable or adverse (i.e., need for mechanical ventilation or death) and registered over a period of 10 days following CT. Volume of disease (VoD) on CT was calculated semi-automatically. Multiple linear regression was used to predict VoD by clinical/laboratory data. To predict outcome, important features were selected using a priori analysis and subsequently used to train 4 different models. Results: A total of 106 consecutive patients were enrolled (median age 63.5 years, range 26–95 years; 41/106 women, 38.7%). Median duration of symptoms and C-reactive protein (CRP) was 5 days (range 1–30) and 4.94 mg/L (range 0.1–28.3), respectively. Median VoD was 249.5 cm3 (range 9.9–1505) and was predicted by lymphocyte percentage (p = 0.008) and CRP (p < 0.001). Important variables for outcome prediction included CRP (area under the curve [AUC] 0.77), VoD (AUC 0.75), age (AUC 0.72), lymphocyte percentage (AUC 0.70), coronary calcification (AUC 0.68), and presence of comorbidities (AUC 0.66). Support vector machine had the best performance in outcome prediction, yielding an AUC of 0.92. Conclusions: Measuring the VoD using a simple CT post-processing tool estimates SARS-CoV-2 burden. CT and clinical data together enable accurate prediction of short-term clinical outcome. [ABSTRACT FROM AUTHOR]

Protein-protein interaction is one of the ways viruses interact with their hosts. Therefore, identifying protein interactions between viruses and hosts helps explain how virus proteins work, how they replicate, and how they cause disease. SARS-CoV-2 is a new type of virus that emerged from the coronavirus family in 2019 and caused a worldwide pandemic. Detection of human proteins interacting with this novel virus strain plays an important role in monitoring the cellular process of virus-associated infection. Within the scope of the study, a natural language processing-based collective learning method is proposed for the prediction of potential SARS-CoV-2-human PPIs. Protein language models were obtained with the prediction-based word2Vec and doc2Vec embedding methods and the frequency-based tf-idf method. Known interactions were represented by proposed language models and traditional feature extraction methods (conjoint triad and repeat pattern), and their performances were compared. The interaction data were trained with support vector machine, artificial neural network (ANN), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT), and ensemble algorithms. Experimental results show that protein language models are a promising protein representation method for protein-protein interaction prediction. The term frequency-inverse document frequency-based language model performed the SARS-CoV-2 protein-protein interaction estimation with an error of 1.4%. Additionally, the decisions of high-performing learning models for different feature extraction methods were combined with a collective voting approach to make new interaction predictions. For 10,000 human proteins, 285 new potential interactions were predicted, with models combining decisions. [Display omitted] • We proposed a robust protein language model for predicting SARS-CoV2-human PPIs. • We show that tf-Idf and Word2Vec can capture context information from proteins. • The NLP-based method outperformed than several existing protein encoding methods. • With an ensemble learning approach, 285 highly possible interactions were predicted. • Protein type of SARS-CoV-2 is determined using a gene ontology-based approach. [ABSTRACT FROM AUTHOR]

Rapid and sensitive pathogen detection is important for prevention and control of disease. Here, we report a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) and machine learning for the rapid and accurate detection of thirteen respiratory virus species including SARS-CoV-2, common human coronaviruses, influenza viruses, and others. Virus detection and measurement have been performed using highly sensitive SiO 2 coated silver nanorod array substrates, allowing for detection and identification of their characteristic SERS peaks. Using appropriate spectral processing procedures and machine learning algorithms (MLAs) including support vector machine (SVM), k-nearest neighbor, and random forest, the virus species as well as strains and variants have been differentiated and classified and a differentiation accuracy of >99% has been obtained. Utilizing SVM-based regression, quantitative calibration curves have been constructed to accurately estimate the unknown virus concentrations in buffer and saliva. This study shows that using a combination of SERS, MLA, and regression, it is possible to classify and quantify the virus in saliva, which could aid medical diagnosis and therapeutic intervention. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 caused by SARS-CoV-2 urges the development of rapidly and accurately diagnostic methods. Here, one high-sensitivity and point-of-care detection method based on magnetic SERS biosensor composed of Fe 3 O 4 -Au nanocomposite and Au nanoneedles array was developed to detect SARS-CoV-2 directly. Among, the magnetic Fe 3 O 4 -Au nanocomposite is applied to capture and separate virus from nasal and throat swabs and enhance the Raman signals of SARS-CoV-2. The magnetic SERS biosensor possessed high sensitivity by optimizing the Fe 3 O 4 -Au nanocomposite. More significantly, the on-site detection of inactivated SARS-CoV-2 virus was achieved based on the magnetic SERS biosensor with ultra-low limit of detection of 100 copies/mL during 15 mins. Furthermore, the contaminated nasal and throat swabs samples were identified by support vector machine, and the diagnostic accuracy of 100% was obtained. The magnetic SERS biosensor combined with support vector machine provides giant potential as the point-of-care detection tool for SARS-CoV-2. • A magnetic SERS biosensor based Fe 3 O 4 -Au and Au nanoneedles array was developed for rapid detection of SARS-CoV-2. • The magnetic SERS biosensor can identify 100 copies/mL SARS-CoV-2 within 15 mins by portable Raman spectrometer. • The viral samples can be accurately identified by SERS spectra and SVM, which is expected to restrain the COVID-19 epidemic [ABSTRACT FROM AUTHOR]

The emergence of machine learning-based in silico tools has enabled rapid and high-quality predictions in the biomedical field. In the COVID-19 pandemic, machine learning methods have been used in many topics such as predicting the death of patients, modeling the spread of infection, determining future effects, diagnosis with medical image analysis, and forecasting the vaccination rate. However, there is a gap in the literature regarding identifying epitopes that can be used in fast, useful, and effective vaccine design using machine learning methods and bioinformatics tools. Machine learning methods can give medical biotechnologists an advantage in designing a faster and more successful vaccine. The motivation of this study is to propose a successful hybrid machine learning method for SARS-CoV-2 epitope prediction and to identify nonallergen, nontoxic, antigen peptides that can be used in vaccine design from the predicted epitopes with bioinformatics tools. The identified epitopes will be effective not only in the design of the COVID-19 vaccine but also against viruses from the SARS family that may be encountered in the future. For this purpose, epitope prediction performances of random forest, support vector machine, logistic regression, bagging with decision tree, k-nearest neighbor and decision tree methods were examined. In the SARS-CoV and B-cell datasets used for education in the study, epitope estimation was performed again after the datasets were balanced with the synthetic minority oversampling technique (SMOTE) method since the epitope class samples were in the minority compared to the nonepitope class. The experimental results obtained were compared and the most successful predictions were obtained with the random forest (RF) method. The epitope prediction performance in balanced datasets was found to be higher than that in the original datasets (94.0% AUC and 94.4% PRC for the SMOTE-SARS-CoV dataset; 95.6% AUC and 95.3% PRC for the SMOTE-B-cell dataset). In this study, 252 peptides out of 20312 peptides were determined to be epitopes with the SMOTE-RF-SVM hybrid method proposed for SARS-CoV-2 epitope prediction. Determined epitopes were analyzed with AllerTOP 2.0, VaxiJen 2.0 and ToxinPred tools, and allergic, nonantigen, and toxic epitopes were eliminated. As a result, 11 possible nonallergic, high antigen and nontoxic epitope candidates were proposed that could be used in protein-based COVID-19 vaccine design ("VGGNYNY", "VNFNFNGLTG", "RQIAPGQTGKI", "QIAPGQTGKIA", "SYECDIPIGAGI", "STFKCYGVSPTKL", "GVVFLHVTYVPAQ", "KNHTSPDVDLGDI", "NHTSPDVDLGDIS", "AGAAAYYVGYLQPR", "KKSTNLVKNKCVNF"). It is predicted that the few epitopes determined by machine learning-based in silico methods will help biotechnologists design fast and accurate vaccines by reducing the number of trials in the laboratory environment. [Display omitted] • A new approach (SMOTE-RF-SVM) is proposed to identify SARS-CoV-2 epitopes that can be used in vaccine design. • Epitope candidates were determined using machine learning-based in silico and bioinformatics tools. • In the unbalanced dataset, generating artificial data with the SMOTE technique increased the model performance. • Nonallergic, high antigen (antigen score ≥1.0) and nontoxic 11 possible epitopes candidates were proposed. • The search space for vaccine studies was narrowed by SMOTE-RF-SVM. [ABSTRACT FROM AUTHOR]

COVID-19 disease causes serious respiratory illnesses. Therefore, accurate identification of the viral infection cycle plays a key role in designing appropriate vaccines. The risk of this disease depends on proteins that interact with human receptors. In this paper, we formulate a novel model for COVID-19 named "amino acid encoding based prediction" (AAPred). This model is accurate, classifies the various coronavirus types, and distinguishes SARS-CoV-2 from other coronaviruses. With the AAPred model, we reduce the number of features to enhance its performance by selecting the most important ones employing statistical criteria. The protein sequence of SARS-CoV-2 for understanding the viral infection cycle is analyzed. Six machine learning classifiers related to decision trees, k-nearest neighbors, random forest, support vector machine, bagging ensemble, and gradient boosting are used to evaluate the model in terms of accuracy, precision, sensitivity, and specificity. We implement the obtained results computationally and apply them to real data from the National Genomics Data Center. The experimental results report that the AAPred model reduces the features to seven of them. The average accuracy of the 10-fold cross-validation is 98.69%, precision is 98.72%, sensitivity is 96.81%, and specificity is 97.72%. The features are selected utilizing information gain and classified with random forest. The proposed model predicts the type of Coronavirus and reduces the number of extracted features. We identify that SARS-CoV-2 has similar physicochemical characteristics in some regions of SARS-CoV. Also, we report that SARS-CoV-2 has similar infection cycles and sequences in some regions of SARS CoV indicating the affectedness of vaccines on SARS-CoV-2. A comparison with deep learning shows similar results with our method. • We formulate a novel model for COVID-19 that classifies the coronavirus types and differentiates SARS-CoV-2 from other coronaviruses, reducing the number of features of the model to enhance its performance. • We use machine learning techniques for evaluating the model in terms of accuracy, precision, sensitivity, and specificity. • We analyze the protein sequence of SARS-CoV-2 for understanding the viral infection cycle and obtain results that are implemented computationally and compared to deep learning tools. • We implement the obtained results computationally and apply them to real-world data. [ABSTRACT FROM AUTHOR]

Prediction of linear B cell epitopes is of interest for the production of antigen-specific antibodies and the design of peptide-based vaccines. Here, we present BCEPS, a web server for predicting linear B cell epitopes tailored to select epitopes that are immunogenic and capable of inducing cross-reactive antibodies with native antigens. BCEPS implements various machine learning models trained on a dataset including 555 linearized conformational B cell epitopes that were mined from antibody–antigen protein structures. The best performing model, based on a support vector machine, reached an accuracy of 75.38% ± 5.02. In an independent dataset consisting of B cell epitopes retrieved from the Immune Epitope Database (IEDB), this model achieved an accuracy of 67.05%. In BCEPS, predicted epitopes can be ranked according to properties such as flexibility, accessibility and hydrophilicity, and with regard to immunogenicity, as judged by their predicted presentation by MHC II molecules. BCEPS also detects if predicted epitopes are located in ectodomains of membrane proteins and if they possess N-glycosylation sites hindering antibody recognition. Finally, we exemplified the use of BCEPS in the SARS-CoV-2 Spike protein, showing that it can identify B cell epitopes targeted by neutralizing antibodies. [ABSTRACT FROM AUTHOR]

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature. [ABSTRACT FROM AUTHOR]

[Display omitted] In response to the pandemic caused by SARS-CoV-2, we constructed a hybrid support vector machine (SVM) classification model using a set of publicly posted SARS-CoV-2 pseudotyped particle (PP) entry assay repurposing screen data to identify novel potent compounds as a starting point for drug development to treat COVID-19 patients. Two different molecular descriptor systems, atom typing descriptors and 3D fingerprints (FPs), were employed to construct the SVM classification models. Both models achieved reasonable performance, with the area under the curve of receiver operating characteristic (AUC-ROC) of 0.84 and 0.82, respectively. The consensus prediction outperformed the two individual models with significantly improved AUC-ROC of 0.91, where the compounds with inconsistent classifications were excluded. The consensus model was then used to screen the 173,898 compounds in the NCATS annotated and diverse chemical libraries. Of the 255 compounds selected for experimental confirmation, 116 compounds exhibited inhibitory activities in the SARS-CoV-2 PP entry assay with IC 50 values ranged between 0.17 µM and 62.2 µM, representing an enrichment factor of 3.2. These 116 active compounds with diverse and novel structures could potentially serve as starting points for chemistry optimization for COVID-19 drug discovery. [ABSTRACT FROM AUTHOR]

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel ( C S V M P o l ) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models. [ABSTRACT FROM AUTHOR]

Background: In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.Objective: This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.Results: We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.Conclusions: The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI. [ABSTRACT FROM AUTHOR]

Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis.Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People's Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation.Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease. [ABSTRACT FROM AUTHOR]