Data Mining Approach Research Articles

Antioxidant and anti-α-glucosidase activities of biotransformable dragon’s blood via predicted data mining approach

Bacillus megaterium tyrosinase (BmTYR) has been proven an applicable biocatalyst in the ortho-hydroxylation of flavonoids, which usually possess higher antioxidant activity than their precursors. With a predicted data mining approach (PDMA), putative novel bioactive flavonoids from known precursors may be good candidates for biotransformation by BmTYR. Two flavonoid precursors (loureirin A and loureirin B) were confirmed via this approach, which are the commercial constituents of the Chinese medicine dragon’s blood. These two flavonoids were then biotransformed by BmTYR for validation. The results found two novel flavonoids (3′-hydroxyloureirin A and 3′-hydroxyloureirin B), which gained 2.5 and 1.6 times of the antioxidant activity of ascorbic acid, respectively. In addition, all four flavonoids—loureirin A, loureirin B, and the two 3′-hydroxyl derivatives—possessed 4–22 times the anti-α-glucosidase activity of acarbose, a clinical anti-diabetes drug. A further kinetic analysis revealed that the inhibition mode of the four α-glucosidase inhibitors was non-competitive, and their kinetic constants (Ki values) were 64.3 ± 2.5 μM, 308.7 ± 10.4 μM, 69.4 ± 2.8 μM, and 325.3 ± 12.6 μM, respectively. The high antioxidant and anti-α-glucosidase activity suggest that the two novel flavonoids have potential future applications in pharmacology and biotechnology.

Updated guidelines for gene nomenclature in wheat

Key messageHere, we provide an updated set of guidelines for naming genes in wheat that has been endorsed by the wheat research community.The last decade has seen a proliferation in genomic resources for wheat, including reference- and pan-genome assemblies with gene annotations, which provide new opportunities to detect, characterise, and describe genes that influence traits of interest. The expansion of genetic information has supported growth of the wheat research community and catalysed strong interest in the genes that control agronomically important traits, such as yield, pathogen resistance, grain quality, and abiotic stress tolerance. To accommodate these developments, we present an updated set of guidelines for gene nomenclature in wheat. These guidelines can be used to describe loci identified based on morphological or phenotypic features or to name genes based on sequence information, such as similarity to genes characterised in other species or the biochemical properties of the encoded protein. The updated guidelines provide a flexible system that is not overly prescriptive but provides structure and a common framework for naming genes in wheat, which may be extended to related cereal species. We propose these guidelines be used henceforth by the wheat research community to facilitate integration of data from independent studies and allow broader and more efficient use of text and data mining approaches, which will ultimately help further accelerate wheat research and breeding.

Knowledge Maps as Support Tool for Managing Scientific Competences: A Case Study at a Portuguese Research Institute

In a research organization, finding someone who is an expert in a field and that can take up a given role, defining areas of excellence, or employing a new member all require understanding the competences that are available in-house. This work explores the idea of using knowledge or competence maps as support tools for managing scientific competences. We implemented a use case at the Institute of Electronics and Informatics Engineering of Aveiro, a research institute at the University of Aveiro, but the methodology we proposed can be adapted to virtually any research organization. Knowledge maps are visual representations of information that can be designed with variable granularities with respect to the knowledge assets of an organization. From a research management perspective, knowledge maps support the discovery of research competences and provide an instant overview of a topic by showing the main areas at a glance. This solution explored in this work employed data mining approaches for gathering information from public databases and presenting it using knowledge maps. Other visualization tools, such as bar graphs, tables, filters and search functionalities, were created and integrated into a web platform. When put together, these components could turn the platform into a key component for the administration of a research organization.

Early detection of student degree-level academic performance using educational data mining.

Higher educational institutes generate massive amounts of student data. This data needs to be explored in depth to better understand various facets of student learning behavior. The educational data mining approach has given provisions to extract useful and non-trivial knowledge from large collections of student data. Using the educational data mining method of classification, this research analyzes data of 291 university students in an attempt to predict student performance at the end of a 4-year degree program. A student segmentation framework has also been proposed to identify students at various levels of academic performance. Coupled with the prediction model, the proposed segmentation framework provides a useful mechanism for devising pedagogical policies to increase the quality of education by mitigating academic failure and encouraging higher performance. The experimental results indicate the effectiveness of the proposed framework and the applicability of classifying students into multiple performance levels using a small subset of courses being taught in the initial two years of the 4-year degree program.

Using machine learning with case studies to identify practices that reduce greenhouse gas emissions across Australian grain production regions

It is difficult to identify farm management practices that consistently provide greenhouse gas (GHG) abatement at different locations because effectiveness of practices is greatly influenced by climates and soils. We address this knowledge gap by identifying practices that provide abatement in eight case studies located across diverse conditions in Australian’s grain-producing areas. The case studies focus on soil-based emissions of nitrous oxide (N2O) and changes in soil organic carbon (SOC), simulated over 100 years for 15 cropping management scenarios. Average changes in the balance of GHG from both N2O emissions and SOC sequestration (∆GHG balance) and gross margins compared to a high emissions baseline were determined over 25 and 100 simulated years. Because scenarios providing the greatest abatement varied across individual case studies, we aggregated the data over all case studies and analysed them with a random forest data mining approach to build models for predicting ∆GHG balance. Increased cropping intensity, achieved by including cover crops, additional grains crops, or crops with larger biomass in the rotation, was the leading predictor of ∆GHG balance across the scenarios and sites. Abatement from increased cropping intensity averaged 774 CO2-e ha−1 year−1 (25 years) and 444 kg CO2-e ha−1 year−1 (100 years) compared to the baseline, with reduced emissions from SOC sequestration offsetting increased N2O emissions for both time frames. Increased cropping intensity decreased average gross margins, indicating that a carbon price would likely be needed to maximise GHG abatement from this management. To our knowledge, this is the first time that the random forest approach has been applied to assess management practice effectiveness for achieving GHG abatement over diverse environments. Doing so provided us with more general information about practices that provide GHG abatement than would have come from qualitative comparison of the variable results from the case studies.

In silico assessment of mixture toxicity mechanisms involved in the pathogenesis of thyroid diseases: The combination of toxic metal(oid)s and decabrominated diphenyl ether

The current study aimed to assess the connection between the mixture of lead (Pb), cadmium (Cd), arsenic (As), methylmercury (MeHg) and decabrominated diphenyl ether (decaBDE) and thyroid function, by using in silico toxicogenomic data-mining approach. To obtain the linkage between investigated toxic mixture and thyroid diseases (TDs), the Comparative Toxicogenomics Database (CTD) was used, while gene ontology (GO) enrichment analysis was performed by ToppGeneSuite portal. The analysis has shown 10 genes connected to all chemicals present in the mixture and TDs (CAT, GSR, IFNG, IL1B, IL4, IL6, MAPK1, SOD2, TGFB1, TNF), most of which were in co-expression (45.68%), or belonged to the same pathway (30.47%). Top 5 biological processes and molecular functions affected by the investigated mixture emphasized the role of two common mechanisms – oxidative stress and inflammation. Cytokines and inflammatory response was listed as the main molecular pathway that may be triggered by simultaneous exposure to toxic metal(oid)s and decaBDE and connected to TDs. The direct relations between Pb/decaBDE and redox status impairment in thyroid tissue was confirmed by our chemical-phenotype interaction analysis, while the strongest linkage between Pb, As and decaBDE and thyroid disorders was found. The obtained results provide better understanding of molecular mechanisms involved in the thyrotoxicity of the investigated mixture, and can be used to direct further research.

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques-Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors-are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs.

A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases

The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB's role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region's propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of "high-confidence" hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

An intelligent heart disease prediction system using hybrid deep dense Aquila network

This paper proposes an intelligent HD prediction system based on a hybrid deep dense Aquila network for predicting HD at the early stage. The main intension of the proposed system is to provide an integrated deep learning model with advanced data mining approaches for framing effective decisions and accurate disease prediction. The different processing steps used in the proposed scheme are data acquisition, data pre-processing, optimized feature selection, and disease classification. Initially, the proposed system performs data acquisition to collect heart disease data from different public data sources. Then, the acquired data are pre-processed using noisy data elimination, mean computation and z-score or zero mean normalization. Next, an optimized feature selection model based on an enhanced sparrow search algorithm (E-SSA) is used to minimize the data dimensionality and selects the most optimal features from the pre-processed data. Finally, the selected optimal features are given as input to the deep-dense residual attention Aquila convolutional network (Deep-DenseAquilaNet) based disease classification model in which the weight is updated using the Aquila optimization algorithm (AOA). The proposed scheme is simulated in the Python platform and evaluated the performance in terms of different performance metrics using HD datasets (Statlog + Hungary + Cleveland + Switzerland + long beach VA datasets). Additionally, the performance of the proposed scheme is compared with recent existing algorithms. The maximum accuracy reached through the proposed scheme is 99.57%. Subsequently, the simulated results proved that the proposed scheme had achieved better performance than the existing schemes.

Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation

In this paper, a database of biomass production from Yarrowia lipolytica fermentation is prepared and constructed using machine learning and data mining approaches. The database is curated from 15 publications and consists of 301 rows of data with 25 predictors and 1 label. The predictors include inoculum size, temperature, pH, and time, while the label is the corresponding biomass production. The database is then divided into training, validation, and test datasets and analyzed as a supervised machine learning task for regression. Twenty-six regression models are employed and compared for their performance in predicting biomass production. The best-performing model is the Matern 5/2 Gaussian process regression model, which has the lowest root-mean-squared error of 0.75 g/L, the highest R squared of 0.90, and the lowest mean absolute error of 0.52 g/L. The t-test is used to identify the most important predictors, and 14 predictors are sufficient for creating an accurate model. These 14 predictors are fermentation time, peptone, temperature, total Kjeldahl nitrogen, shaking rate, total nitrogen, inoculum size, yeast extract, crude glycerol, glucose, oil and grease, media pH, ammonium sulfate, and olive oil. This research demonstrates the application of machine learning and data mining to estimate biomass production and gives insight into which parameters are essential for Yarrowia lipolytica fermentation.

Investigating Problem Solving on Calculator Items in a Large-Scale Digitally Based Assessment: A Data Mining Approach

This exploratory study investigated the behaviors and content of onscreen calculator usage by a nationally representative sample of eighth-grade students who responded to items from the 2017 National Assessment of Educational Progress mathematics assessment. Meaningful features were generated from the process data to infer whether students spontaneously used calculators for mathematical problem solving, how frequently and when they used them, and the nature of the operations performed on calculators. Sequential pattern mining was applied on sequences of calculator keystrokes to obtain patterns of operations that were representative of students’ problem-solving strategies or processes. Results indicated that higher scoring students not only were more likely to use calculators, but also used them in a more goal-driven manner than lower scoring students.

Unfolded protein response and angiogenesis in malignancies.

Cellular stress, arising from accumulation of unfolded proteins, occurs frequently in rapidly proliferating cancer cells. This cellular stress, in turn, activates the unfolded protein response (UPR), an interconnected set of signal transduction pathways that alleviate the proteostatic stress. The UPR is implicated in cancer cell survival and proliferation through upregulation of pro-tumorigenic pathways that ultimately promote malignant metabolism and neoangiogenesis. Here, we reviewed mechanisms of signaling crosstalk between the UPR and angiogenesis pathways, as well as transmissible ER stress and the role in tumor growth and development. To characterize differences in UPR and UPR-mediated angiogenesis in malignancy, we employed a data mining approach using patient tumor data from The Cancer Genome Atlas (TCGA). The analysis of TCGA revealed differences in UPR between malignant samples versus their non-malignant counterparts.

Discrete learning-based intelligent methodology for heart disease diagnosis

Classification is one of the most frequently used data mining approaches which has been broadly applied in different fields of sciences, such as engineering, finance, energy, environments, transportation, etc., especially medicine, successfully. Over the years, various intelligent modeling techniques with different properties have been proposed to yield more accurate and more efficient classification results. However, in spite of the different appearance of all of the developed models, the same basic methodology is applied to the learning processes. Based on this methodology, a continuous distance-based cost function is considered and optimized for estimating the unknown parameters in the learning procedures. While using a continuous cost function in the classification field in which the goal function is discrete, is unreasonable or at least quite inefficient. In this paper, in contrast to conventional continuous distance-based methodologies, a novel discrete learning-based methodology is proposed for classification purposes. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. The multilayer perceptron (MLP) which is one of the most widely-used intelligent classification approaches, is exemplarily chosen in order to implement the proposed methodology. Although it can be generally demonstrated that the classification rate of the proposed discrete learning-based MLP (DIMLP) model will not be worse than its conventional continuous learning-based one. However, in order to determine the superiority of the proposed DIMLP model, it is exemplarily evaluated on the heart disease diagnosis benchmark data set and several other medical datasets, and its performance is compared to the classic multilayer perceptron model. Empirical results illustrate that, as pre-expected, the classification rate of the proposed model is higher than its conventional version in all data sets. Obtained results indicate that the proposed DIMLP classifier can yield a 94.27 % classification rate in heart disease diagnosis, which approximately indicates a 9.35 % improvement over the classic version, which can only produce an 86.21 % classification rate. Therefore, the proposed methodology is an appropriate and effective alternative learning process for intelligent classification approaches, especially when more accurate results and/or a more reasonable model are required.

The Use of Artificial Intelligence (AI) in Online Learning and Distance Education Processes: A Systematic Review of Empirical Studies

Artificial intelligence (AI) technologies are used in many dimensions of our lives, including education. Motivated by the increasing use of AI technologies and the current state of the art, this study examines research on AI from the perspective of online distance education. Following a systematic review protocol and using data mining and analytics approaches, the study examines a total of 276 publications. Accordingly, time trend analysis increases steadily with a peak in recent years, and China, India, and the United States are the leading countries in research on AI in online learning and distance education. Computer science and engineering are the research areas that make the most of the contribution, followed by social sciences. t-SNE analysis reveals three dominant clusters showing thematic tendencies, which are as follows: (1) how AI technologies are used in online teaching and learning processes, (2) how algorithms are used for the recognition, identification, and prediction of students’ behaviors, and (3) adaptive and personalized learning empowered through artificial intelligence technologies. Additionally, the text mining and social network analysis identified three broad research themes, which are (1) educational data mining, learning analytics, and artificial intelligence for adaptive and personalized learning; (2) algorithmic online educational spaces, ethics, and human agency; and (3) online learning through detection, identification, recognition, and prediction.

Analytical Study On Prevention And Detection Of Financial Cybercrime And Frauds Using Transaction Pattern Generation Tool

E-commerce is a vital sales avenue for multinational businesses in today's technology environment. Due to the fast growth of e-commerce, credit card sales have increased. Unfortunately, criminals have profited from credit card theft. The discovery of safety faintness in standard credit card dispensation schemes has increased credit card theft, costing billions of dollars yearly. Modern credit card thieves are agile and use cutting-edge tactics. Global fraud complicates credit card issues for banks and other financial businesses. Many techniques, such as First Virtual, Cyber Cash, and SET, are employed to avoid financial cybercrime. Although customers and businesses rarely use these systems, they are very secure. These models protect our online transactions, but they cannot prevent fraud if a customer's credit card information is physically lost or falls into the wrong hands. The study is distinctive in that it uses data mining, statistics on one stage for modeling portion. Effort detailed in thesis necessity be beneficial to academics; in particular, a literature review of data mining techniques is an effort to offer a roadmap for the researchers to explore and choose the best data mining approach before putting it into practice. Additionally, building additional financial applications benefits from an considerate of the role data mining plays in detecting economic misconduct.Although the programme was developed with online transactions in mind, cardholders can also use it for offline transactions.

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data.

Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. This study aimed to identify methods for the early detection of a wide range of ADR signals. First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. ARM of claims data may be effective in the early detection of a wide range of ADR signals.

Analysis of COVID-19 vaccine adverse event using language model and unsupervised machine learning.

After the COVID-19 pandemic, the world has made efforts to recover from the chaotic situation. Vaccination is a way to help control infectious diseases, and many people have been vaccinated against COVID-19 by this point. However, an extremely small number of those who received the vaccine have experienced diverse side effects. In this study, we examined people who experienced adverse events with the COVID-19 vaccine by gender, age, vaccine manufacturer, and dose of vaccinations by using the Vaccine Adverse Event Reporting System datasets. Then we used a language model to vectorize symptom words and reduced their dimensionality. We also clustered symptoms by using unsupervised machine learning and analyzed the characteristics of each symptom cluster. Lastly, to discover any association rules among adverse events, we used a data mining approach. The frequency of adverse events was higher for women than men, for Moderna than for Pfizer or Janssen, and for the first dose than for the second dose. However, we found that characteristics of vaccine adverse events, including gender, vaccine manufacturer, age, and underlying diseases were different for each symptom cluster, and that fatal cases were significantly related to a particular cluster (associated with hypoxia). Also, as a result of the association analysis, the {chills ↔ pyrexia} and {vaccination site pruritus ↔ vaccination site erythema} rules had the highest support value of 0.087 and 0.046, respectively. We aim to contribute accurate information on the adverse events of the COVID-19 vaccine to relieve public anxiety due to unconfirmed statements about vaccines.

Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques

Every farmer requires access to rainfall prediction (RP) to continue their exploration of harvest yield. The proper use of water assets, the successful collection of water, and the successful pre-growth of water construction all depend on an accurate assessment of rainfall. The prediction of heavy rain and the provision of information regarding natural catastrophes are two of the most challenging factors in this regard. In the twentieth century, RP was the most methodically and technically complicated issue worldwide. Weather prediction may be used to calculate and analyse the behaviour of weather with unique features and to determine rainfall patterns at an exact locale. To this end, a variety of methodologies have been used to determine the rainfall intensity in Saudi Arabia. The classification methods of data mining (DM) approaches that estimate rainfall both numerically and categorically can be used to achieve RP. This study, which used DM approaches, achieved greater accuracy in RP than conventional statistical methods. This study was conducted to test the efficacy of several machine learning (ML) approaches for forecasting rainfall, utilising southern Saudi Arabia’s historical weather data obtained from the live database that comprises various meteorological data variables. Accurate crop yield predictions are crucial and would undoubtedly assist farmers. While engineers have developed analysis systems whose performance relies on several connected factors, these methods are seldom used despite their potential for precise crop yield forecasts. For this reason, agricultural forecasting should make use of these methods. The impact of drought on crop yield can be difficult to forecast and there is a need for careful preparation regarding crop choice, planting window, harvest motive, and storage space. In this study, the relevant characteristics required to predict precipitation were identified and the ML approach utilised is an innovative classification method that can be used determine whether the predicted rainfall will be regular or heavy. The outcomes of several different methodologies, including accuracy, error, recall, F-measure, RMSE, and MAE, are used to evaluate the performance metrics. Based on this evaluation, it is determined that DT provides the highest level of accuracy. The accuracy of the Function Fitting Artificial Neural Network classifier (FFANN) is 96.1%, which is higher than that of any of the other classifiers currently used in the rainfall database.

A Comparison Between the Performance of Features Selection Techniques: Survey Study

Feature selection is one of the most popular and crucial methods of data processing used in different machine learning and data mining approaches to avoid high dimensionality and increase classification accuracy. Additionally, attribute selection aids in accelerating machine learning algorithms, improving prediction accuracy, data comprehension, decreasing data storage space, and minimizing the computational complexity of learning algorithms. For this reason, several feature selection approaches are used. To determine the essential feature or feature subsets needed to achieve classification objectives, several feature selection techniques have been suggested in the literature. In this research, different widely employed feature selection strategies have been evaluated by using different datasets to see how efficiently these techniques may be applied to achieve high performance of learning algorithms, which improves the classifier's prediction accuracy.

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.

The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing. In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected" neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19. With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies. These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.