Impact of e-Learning in Education Sector: A Sentiment Analysis View

The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy. © 2022 IEEE.

Prediction of in-hospital mortality rate in COVID-19 patients with diabetes mellitus using machine learning methods.

Background: Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority. Method and Material: The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records. Results: The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model. Conclusion: Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.

Analysis of Covid-19 using machine learning techniques

Covid-19 is caused by a newly detected coronavirus (SARS-CoV-2). It is a respiratory infection that usually spreads from individual to individual through sneezing or coughing. The disease, which was first detected in the province of Wuhan, China, had effected more than one continent and was declared as a pandemic by the World Health Organization (WHO). The pandemic has affected health, social, economic, and psychological segments of life for billions of people. Though vaccines have been developed and are made available, we are still prone to the virus, which is similar to any other flu. This chapter presents an analysis of the symptoms of the disease and identifies significant symptoms that impact the cause of the illness. Machine learning techniques like multiple regression, support vector machine (SVM), Decision Tree, Random Forest, and Logistic Regression are applied to understand the evaluation with respect to the measures like coefficient of determination, and mean-squared error. Hypothesis testing is used to determine whether at least one of the features is useful in the diagnosis of the disease. Further feature selection process is used to identify the most significant symptoms that will cause the virus. Different visualization methods are used to figure the substantial reasoning from the model's prediction and perform analysis on the results obtained. © 2023 Elsevier Inc. All rights reserved.

COVID19: INTELLIGENT CHATBOT AND FINANCIAL AID APPLICATION

Covid19 has devastated all continents causing disasters not only on the health sector but also at social, economic, and at political levels. The world is still trying to eradicate the virus. One of the measures taken is to inform citizens about the virus in order to avoid contamination as much as possible. Several people lost their jobs, and found themselves without any income. The whole world is confined, and the poor can no longer endure this critical situation. Financial assistance is therefore necessary in order to reduce the impact. This paper aims to propose an intelligent financial support application that computes the eligibility for a citizen to get a support during the pandemic;and to explain steps for chatbot using DialogFlow. The training realized using a machine learning algorithm was chosen after making a comparison between some other algorithms. Gradient Boosting Classifier algorithm was the accurate and most efficient for the application. It is possible to train the system again using other data set to make any adaptive results or computations. Copyright © (2022) by International Association for Development of the Information Society (IADIS). All rights reserved.

Comparison of Machine Learning Models for predicting Covid-19 patients' recovery in India

The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.

Analysis of Covid-19 Fake News on Indian Dataset Using Logistic Regression and Decision Tree Classifiers

Since the approach of the internet, many fake news and fabricated articles/contents observed widely. With the growing utilization of advancement and social media, buyers are making and sharing more information than some other time in recent memory. However, some individuals distributed counterfeit news with no significance to reality just to build the readership. Gossip distinguishing on social media is an essential issue. This paper talks about the methodology of machine learning and natural language processing to solve this problem. Use of TF-IDF (TermFrequencyInverse Document Frequency) and trained the data on four classifiers to explore which amongst them works well for this Indian dataset (https://github.com/Aks121/Fake-News-Analysis-on-Indian-Dataset ).The recall, precision and F1 scores help us figure out which model works best. The accuracy achieved so far is 95 on the ratio of 70:30 split dataset. The reason for this work is to approach the mechanized arrangement of the news stories utilizing machine learning. This can be used by the users to identify through the locales containing fake news. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diabetes Analysis with a Dataset Using Machine Learning

Diabetes is a disease that actually impacts the capacity of the body to obtain blood glucose, which is usually referred to as blood sugar. At the end of 2019, a new public health problem (COVID-19) emerged. This disease has greatly harmed people with diabetes. Therefore, we intend to make use of data mining algorithms to prevent death and improve the quality of life through the prediction of diabetes. In this paper, four different algorithms have been used to analyze Diabetes from DAT260x Lab01: Logistic, Decision Tree Classifier, Xgboost and SVC. The models are evaluated for which algorithm is much effective. The paper then provides a quick overview of both the set of data and the fieldwork carried out on the subject. In the adjoining step, the dataset and its features are discussed. In addition, the paper explains the four algorithms and virtual environments that have been used to clarify the variables, which have the largest impact on raw data. The findings are obtained by evaluating the confusion matrix applied to the whole selected algorithm. The paper outlines the full observations and conclusions taken based on the results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prediction of Data Breaches using Classification Algorithms

Security across network has become a major concern during this Covid-19 scenario. Security threats happens due to variety of reasons like theft of analytical property, software attacks, identity theft, stealing of equipment or information, sabotage, and information extraction. The wrong use of protocols over network also causes security threat. Introduction of data mining techniques in network security field plays a major role with data extraction, data transformation and analysation of the huge amount of data. The various data mining algorithms provides an insight to analyse and predict the data and the threats over the computer networks. This paper focusses on the approaches to predict security threats over networks using various classification algorithms. The four-classification algorithm majorly focussed here is Naive Bayes Classifier, Decision Tree Classifier, K Nearest Neighbours and Logistic Regression. It compares the performance of the above-mentioned classification algorithms to detect the threats.

Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19.

The rapid spread of novel coronavirus (namely Covid-19) worldwide has alarmed a pandemic since its outbreak in the city of Wuhan, China in December 2019. While the world still tries to wrap its head around as to how to contain the rapid spread of the novel coronavirus, the pandemic has already claimed several thousand lives throughout the world. Yet, the diagnosis of virus spread in humans has proven complexity. A blend of computed tomography imaging, entire genome sequencing, and electron microscopy have been at first adapted to screen and distinguish SARS-CoV-2, the viral etiology of Covid-19. There are a less number of Covid-19 test kits accessible in hospitals because of the expanding cases every day. Accordingly, it is required to utensil a self-exposure framework as a fast substitute analysis to contain Covid-19 spreading among individuals considering the world at large. In the present work, we have elaborated a prudent methodology that helps identify Covid-19 infected people among the normal individuals by utilizing CT scan and chest x-ray images using Artificial Intelligence (AI). The strategy works with a dataset of Covid-19 and normal chest x-ray images. The image diagnosis tool utilizes decision tree classifier for finding novel corona virus infected person. The percentage accuracy of an image is analyzed in terms of precision, recall score and F1 score. The outcome depends on the information accessible in the store of Kaggle and Open-I according to their approved chest X-ray and CT scan images. Interestingly, the test methodology demonstrates that the intended algorithm is robust, accurate and precise. Our technique accomplishes the exactness focused on the AI innovation which provides faster results during both training and inference.