Data Mining Techniques for Extraction and Analysis of Covid-19 Data

Artificial intelligence has played a crucial role in medical disease diagnosis. In this research, data mining techniques that included deep learning with different scenarios are presented for extraction and analysis of covid-19 data. The energy of the features is implemented and calculated from the CT scan images. A modified meta-heuristic algorithm is introduced and then used in the suggested way to determine the best and most useful features, which are based on how ants behave. Different patients with different problems are investigated and analyzed. Also, the results are compared with other studies. The results of the proposed method show that the proposed method has higher accuracy than other methods. It is concluded from the results that the most crucial features can be concentrated on during feature selection, which lowers the error rate when separating sick from healthy individuals. © 2022 IEEE.

Text Based Diagnosis of COVID-19 Using Data Mining Techniques: A Comparative Study

In the course of the recent pandemic, we have witnessed non-clinical approaches such as data mining and artificial intelligence techniques being exceedingly utilized to restrain and combat the increase of COVID-19 across the globe. The emergence of artificial intelligence in the medical field has helped in reducing the immense burden on medical systems by providing the best means for diagnosis and prognosis of COVID-19. This work attempts to analyze & evaluate superlative models on robust data resources on symptoms of COVID-19, consisting of age, gender, demographic information, pre-existing medical conditions, and symptoms experienced by patients. This study establishes paradigmatic pipeline of supervised learning algorithms coupled with feature extraction techniques and surpassing the current state-of-the-art results by achieving an accuracy of 93.360. The optimal score was found by performing feature extraction on the data using principal component analysis (PCA) followed by binary classification using the AdaBoost classifier. In addition, the present study also establishes the contribution of various symptoms in the diagnosis of the malady. © 2022 IEEE.

Innovations in courts: Validation of a scale of technological innovation

The development of studies on technological innovations in the public sector, specifically in justice system, is still little explored in the literature. This article aimed to develop and validate a scale of technological innovation in the justice system during the period of the COVID-19 pandemic. Data collection procedures were carried out by means of questionnaires sent to 20.727 e-mails of civil servants and judges of the state courts of justice in Brazil. The relationships among the innovation variables that make up the technological innovation construct in the Brazilian judiciary were studied. The factor analyses resulted in the main factors listed by the respondents, as the innovative trend factor (IT);technological resources factor (TR);governance factor and its evidence (G);and innovation and technology factor (IT). For responses to the studies, descriptive statistical analysis was performed, and the innovative sensitivity and technological integration variables presented greater commonalities, and the two factors extracted explain 74% and 67% of the variance. After the descriptive statistical treatment, the confidence level was 99% and the error margin was 4.87%, resulting in a sample of 679 respondents.

Predicting Study or Work from Home Satisfaction using Data Mining Technique: A Case Study in Malaysia

The COVID-19 pandemic has urged the government of Malaysia to implement Movement Control Order (MCO) which forces working people to work from home while students to study from home. People's satisfaction on work from home is crucial in determining their work productivity and efficiency whereas student's satisfaction on study from home is important for their learning effectiveness. There is no work has been done yet for exploring data mining techniques to build a model for predicting work or study from home satisfaction using Malaysia as a case study. This paper aimed to identify the best data mining model for predicting the work or study from home satisfaction. The prediction model is learned by analyzing the demographic, the personality traits, and the work from home experience collected from a group of Malaysia people. This study attempts to investigate four data mining techniques that are the decision tree, linear kernel support vector machine, polynomial support vector machine, and radial basis support vector machine. Experiment results show that the radial basis support vector machine outperformed other techniques in predicting the work or study from home satisfaction of Malaysia's community. © 2022 IEEE.

Prediction of Work From Home Post COVID-19 using Classification Model

This study aims to predict the continuance of the adoption of WFH after COVID-19 and other factors that also influence these preferences. This study was conducted using data mining techniques with the CRISP-DM framework based on the Decision Tree, Naïve Bayes, and Random Forest Algorithms. The dataset was taken based on a questionnaire survey distributed online via Google Forms with a target of 200 respondents and returned from 183 respondents from four divisions. The results of this study indicate the Decision Tree model has the best performance with an accuracy of 85.45%. Based on the prediction and questionnaire results, employees tend to agree to continue implementing WFH after COVID-19 with a Hybrid working model. These preferences influence work improvement, employee performance, and work environment. © 2022 IEEE.

Analysis of the impact of COVID-19 on student life based on the results of a survey

The spread of COVID-19 has adversely affected many sectors, including tourism, retail, and manufacturing. The educational field is no exception, and many universities, including our own, have taken measures to prevent infection, such as implementing online classes and banning the use of facilities. These infection control measures are expected to change the living environment of students. If students are unable to lead their lives as before due to changes in their living environment, this may lead to a decline in academic performance and poor health. Therefore, it is very important for universities to understand how COVID-19 affects students' lives in order for them to lead healthy student lives. Therefore, this study aims to understand the impact of COVID-19 on students' lives by using data mining techniques to analyze the response data from a survey conducted for students, and to provide appropriate support and infection prevention measures for students. As a result, we identified a tendency for students to feel anxious about infection with COVID-19 and changes in students' evaluations of classes conducted in a face-to-face format under infection prevention measures. We believe that these results can contribute to reconsideration of support for students and class formats. © 2022 IEEE.

Educational Data Mining tools and Techniques used for Prediction of Student's Performance: A Study

In the digital era, enormous amounts of data have been generated in education that has led to data driven approaches which in turn help effective decision making. Educational data mining has been used as a very effective tool for identifying the hidden patterns in educational data, predicting students' performance and to enhance the teaching /learning environment. Educational data mining tools and techniques help institutes in providing information about students e.g., about enrolment of students, weak students can be identified earlier so that various corrective strategies can be applied, and various resources can be allocated to enhance their performance and their success in courses they enrolled. This paper examines the research efforts that have been made in the field of educational data mining and the various educational data mining tools and techniques used in recent years for predicting student's performance. We live in a world where enormous volumes of data are collected, but if these data are not further examined, they remain nothing more than enormous amounts of data We may use this data, analyze it, and gain a significant edge by using new approaches and procedures. Data mining is the ideal approach in this situation. Extraction of hidden and valuable information and patterns from massive data sets is known as data mining. It has already been widely used in several fields, including banking, sales, marketing, telecommunications, and finance. This essay aims to introduce a unique use of data mining for education, known as educational data mining. An interdisciplinary study topic called Educational Data Mining (EDM) was established to apply data mining to the educational sector. To examine the data gathered during teaching and learning, it employs a variety of tools and methodologies from machine learning, statistics, data mining, and data analysis. The process of turning large educational databases' raw data into useful information that can be used for decision-making in educational systems as well as for a better understanding of students and their learning circumstances is known as educational data mining. © 2022 IEEE.

Data mining techniques for patients healthcare analysis during COVID-19 Pandemic conditions

Nowadays various health-related surveys use data mining and machine learning techniques for the analysis and prediction of health-related records. Current day, people are suffering from COVID-19 health issues, which cause serious health issues around the world. To predict health-related issues, classification techniques are used. Within the classification techniques, one can process a large amount of data. Previous research uses various classification techniques for data mining applications that are k-nearest neighbour, Naives Bayes, ANN, and SVM, which takes much time to execute the result. The proposed research work uses an Ordered support vector machine (O-SVM) learning algorithm with the advance in kernel-based technique. In health-related research, the health records are collected from different sources and the algorithm will identify the research-related records in the training process. The training data sets are mentioning the normal and abnormal conditions of the patients. By using the proposed classification technique, the medical images are classified by various regions to identify the defect. This paper is mainly used for COVID-19 detection and prediction using image processing and data mining techniques. The image processing techniques are used to identify the defect presented in the image. This proposed model is done by MATLAB in the adaptation of 2018a. The proposed research work provides the best result as compared to the most recent related literature. © 2022, Scibulcom Ltd.. All rights reserved.

Extraction And Analysis Of Covid-19 Data Using Techniqus K-Means Clustering By Data Mining Techniques

The goal from this study Data mining techniques like K-Means Clustering are used to investigate the Covid-19 Coronavirus Diseases. Researcher's prediction will not only allow detection and pipeline to predict how much money their detection method for COVID-19 will make, but it will also allow them to justify their characteristics, such as type of infection and choice of vaccine in order to reach a certain detection using data mining-based model. In this way, the lack of cultural data was no longer an issue with new COVID-19 predictions. With the data mining algorithm, researchers provide prediction at 15 to 20 different methods with an accuracy above 80% after training. The training is performed on 80% of data while the testing is done on remaining 20% of data. Such prediction will also allow other interested third parties to predict the success of a COVID-19 detection before it is released on open-source community. In the process of prediction, some researchers found the variables most associated with COVID-19 detection, and to see how the various prediction models are affected by them. Nevertheless, those data mining-based methods can greatly benefit from modern artificial intelligence techniques for this purpose that can handle complex features and give out great prediction results. Therefore, employing historical COVID-19 data and using them in data mining algorithms to predict disease could save companies millions of dollars on rather unsuccessful detection. The results were adopted by quantitative prediction identical to the classification of COVID-19 using artificial intelligence. The results achieved by the SMV model with ML sentiment analysis have a very high accuracy in predicting behavior (87.71 %). Correcting many types of behavior for different people and its ability to perform is much better than predicting all COVID-19 with a decrease in loss. © 2022 IEEE.

Analysis of Sentiments of Twitter Data on Remote Working

COVID-19 has significantly increased interest in remote working. The phenomenon of this increase was examined in this paper by examining tweets on Twitter by analysis the sentiments of people working remotely. Data were collected by downloading tweets by using keywords “#remoteworking.” The study also explored magazines like HR people matters, HBR, and articles from reputed journals, and by visiting regularly NASSCOM Web site and newspapers. Compiled data are then refined through data mining techniques, and then, sentiment analyses have been deployed. Sentiment index was found greater than one which reflected that people are very happy with having remote working. It was also found that the issue of remote work increased nearly 15 times in a year, reaching an epidemic peak in March 2020. The study has shown that in the post-COVID period it will stay permanently. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

USING DATA MINING TECHNIQUES TO MEASURE STUDENTS’ ACADEMIC PERFORMANCE AND THE EFFECTIVENESS OF LEARNING METHODS: A LITERATURE REVIEW

Analyzing students' academic performance in online learning to improve the overall quality and effectiveness of education has been one of the main focuses of Higher Educational Institutions (HEIs). A practical analysis utilizing the academic performance data to improve the quality of online learning has become a vital issue urgently required to guide HEIs for the improvement of the academic performance of students. The changes affected in the Covid-19 framework have affected the academic performance of students and educators. This study aims to summarize the various aspects of educational data mining and how it can be utilized to improve the teaching process. Using EDM, the study analyzed students' academic performance for the past five years. It focused on the various learning methods that the students used. The study provided a detailed analysis of the multiple attributes that influenced the students' academic performance. We presented 12 out of 56 papers/documents that fit the inclusion and exclusion criteria of students' academic performance based on the educational setting. This study revealed that the most commonly used methods for assessing students' academic performance are not done in the face-to-face learning method. © 2022 Little Lion Scientific.

An Analysis of Students’ Academic Performance Using K-Means Clustering Algorithm

A massive amount of data is often used to evaluate the academic performance of students in higher education. Analysis can solve this challenge through various strategies and methods. Due to the spread of the pandemic Covid-19, traditional modes of education have shifted to include online learning. This study aims to analyze the academic performance of students through data mining techniques. The objective aims to investigate the academic performance of business students at a private university in Malaysia using Educational Data Mining techniques. Students’ academic performance data of a private university in Malaysia is used to analyze students’ performance using demographic and academic attributes. This study used students’ academic performance in the learning method to identify the patterns before and during Covid-19 using the K-Means data mining clustering technique. The results of the k-means clustering analysis showed that students were achieving higher CGPA during Covid-19 online learning compared to before Covid-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Novel Approach for Tracking the Spread of COVID-19 Disease and Discovering the Symptom Patterns of COVID-19 Patients Using Association Rule Mining

Coronavirus disease also known as COVID-19 is an infectious disease which is caused by the SARS-CoV-2 virus. People who get infected by the virus will experience respiratory diseases and might recover easily, but few will become seriously ill and require medical attention. Top researchers from across the world are working on cures for it. A lot of research is being conducted on the spread of the disease and cures for it. Only developing cure for this virus would not be enough. With the current technology, advanced data mining techniques can be used to track and trace the virus which is quicker and faster solution which will change the course of the pandemic. The rate at which people are getting affected, tracking becomes vital. It is necessary to alert a person when they are near the infected person. With the help of GPS and Bluetooth technologies, the users are alerted. Data mining also known as knowledge discovery of data, is the process of analyzing huge chunks of data in different ways and converting it into useful information. This process plays a significant role in various fields with vast data. Healthcare industries generate and store large amounts of data like name of the patient, disease, diagnosis method, resources, this is where data mining techniques can be applied. So the data which can be obtained from COVID datasets can be used by healthcare industries to improve the tracking and spreading of COVID-19. We propose a method to identity patterns using the factors in the dataset which can be used to predict if a person having similar symptoms is affected by the virus or not. Clustering, association rule mining, classification and other various data science concepts are used to analyze the spread of COVID in India. © 2022 IEEE.

Predicting Depression from Social Networking Data using Machine Learning Techniques

In this era of COVID-19 pandemic, as more people self-isolate themselves, psychological health issues like depression, anxiety, and stress is an increasing concern all over the world. The purpose of this study is to investigate the data from social forums, where we found communities of depressed people sharing their thoughts and emotions in the forums, these forums also receive advices and support. In this paper, we will analyse the "depressed"text;by manipulating the data, extracting features, categorising, and try to understand what are the attributes of "depressed"text, and how we can "predict"whether a text should be marked as depressed or not. Using text analysis and text data mining techniques, the text obtained from the social forums was analysed and three different machine learning algorithms were used to predict depression. After cross validation overall accuracy of 99.69% was obtained as the best score using the proposed system. This study definitively answers the question regarding using human basic language and communication of personal experiences, for the prediction of depression and can be reached easily. Furthermore, not only actions, habits and behaviour of a person, text too can be used for accurate diagnosis of depression. © 2021 IEEE.

Predicting Covid-19 Test Report Using Data Mining Techniques Based on The Second Wave in Bangladesh

Coronavirus (Covid-19) is a disease that spreads from one person to another very quickly. The whole world is facing this Covid-19 pandemic now and Bangladesh is also not out of it. After the first wave, now the second wave is going on in Bangladesh. As the second wave is spreading faster than the first wave and the test process of Covid-19 is very time-consuming. As a result, before getting the test report, a person infected with Covid-19 and spreads this virus to other people as he doesn't know whether he is infected with Coronavirus or not. To create a dataset, we have asked some patients from our nearby people who live in Faridpur, Joypurhat, and Cumilla district and collected their data who have tested for Covid-19 during the second wave. We have collected some of their symptoms that appeared before their Covid-19 test. With this dataset, we have used some data mining approach to predict whether a patient is tested positive or negative. We have applied two algorithms here. Among them, Naive Bayes gives the highest accuracy which is 80%. © 2021 IEEE.

Supervised Clustering for Subgroup Discovery: An Application to COVID-19 Symptomatology

Subgroup discovery is a data mining technique that attempts to find interesting relationships between different instances in a dataset with respect to a property of interest. Cluster analysis is a popular method for extracting homogeneous groups from a heterogeneous population, however, it often yields results that are challenging to interpret and action. In this work, we propose a novel, multi-step clustering methodology based on SHAP (SHapley Additive exPlanation) values and dimensionality reduction, for the purpose of subgroup discovery. Our method produces well-separated clusters that can be readily differentiated by simple decision rules, to yield interpretable subgroups in relation to a target variable. We illustrate our approach using self-reported COVID-19 symptom data across 2,479 participants who tested positive for COVID-19, resulting in the identification of 16 distinct symptom presentations. Future work will investigate common demographic and clinical features exhibited by each cluster cohort, and map clusters to outcomes to better understand the clinical presentation, risk factors and prognosis in COVID-19, as a timely and impactful application of this methodology. © 2021, Springer Nature Switzerland AG.

Data Mining in Healthcare and Prediction Model Using Data Mining Technique on COVID-19

The number of COVID-19 cases has reached millions globally, and those taking on the conflict against the pandemic have been roused to actualize inventive strategies to help foresee the spread of the outbreak. It has been seen from the past few months that various concerns are engaged with the COVID-19 retaliation over a globe embracing data mining tools and techniques to help with breaking down the spread of the virus. Such forward-thinking methods have been developed in pervasiveness, as different concerns have made their product and information accessible for free. The main purpose of data mining, whether its actuality charity in healthcare or business, is to recognize beneficial and reasonable arrangements by examining big circles of statistics. In this chapter, we provide a prediction, conferring the probable impact of data mining to combat against COVID-19 and the current restraints on these aids. © 2022, Springer Nature Switzerland AG.