Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Prediction of Stable Angina for Coronary Artery Diseases using Boosting Algorithm

Narrowed arteries block the blood flow to the heart muscle and other parts of the body, which can cause chest pain. Coronary arteries disease (CAD) can weaken the heart muscle causing heart failure, in which the heart cannot pump blood. A person with underlying diseases is more prone to get highly affected by COVID-19 because of the decreased immunity. Cardiovascular disease and coronary heart disease have been associated with worsened outcomes of COVID-19 patients. Thus, detecting CAD at a proper stage is crucial to avoid any further serious issues. This paper is an empirical analysis to predict stable angina for CAD using Histogram gradient boosting (HGB) and Adaboost (ADB) classifier algorithm and compared the performance with traditional Naïve Bayes (NB) algorithm. © 2023 IEEE.

Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

An Exploratory Analysis of Delhi Air Quality Using Statistics and Machine Learning Models

Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.

A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations

Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology. © 2023 IEEE.

COVID-19 Diagnostic System Using Medical Image Classification and Retrieval: A Novel Method for Image Analysis

With the rapid increase in the number of people infected with COVID-19 disease in the entire world, and with the limited medical equipment used to detect it (testing kit), it becomes necessary to provide another detection method that mainly relies on Artificial Intelligence and radiographic Image Analysis to determine the disease infection. In this study, we proposed a diagnosis system that detects the COVID-19 using chest X-ray or computed tomography (CT) scan images knowing that this system does not eliminate the reverse transcription-polymerase chain reaction test but rather complements it. The proposed system consists of the following steps, starting with extracting the image's features using Visual Words Fusion of ResNet-50 (deep neural network) and Histogram of Oriented Gradient descriptors based on Bag of Visual Word methodology. Then training the Adaptive Boosting classifier to classify the image to COVID-19 or NOTCOVID-19 and finally retrieving the most similar images. We implemented our work on X-ray and CT scan databases, and the experimental results demonstrate the effectiveness of the proposed system. The performance of the classification task in terms of accuracy was as follows: 100% for classifying the input image to X-ray or CT scan, 99.18% for classifying X-ray image to COVID-19 or NOTCOVID-19 and 97.84% for classifying CT scan to COVID-19 or NOTCOVID-19. © 2021 The British Computer Society.

Black Fungus Classification using Adaboost with SVM-based classifier and Compare accuracy with Support Vector Machine

India recognize a severe public health issue in addition to the COVID-19 outbreak and the growing percentage of patients with related mucormycosis from 2021. An uncommon condition known as mucormycosis is brought on by fungus in the family Mucorales. Mucormycosis is a fairly uncommon illness that is caused by common environmental moulds that may be found in soil and decomposing organic materials. Spores develop into hyphae in a susceptible individual, which subsequently infect nearby tissue, including blood vessels, leading to hemorrhagic infarction. Doctors have offered many hypotheses on this. The issue is if black fungus is present in other countries given how uncontrolled it is growing in India. Patients in India with weakened immune systems are more susceptible to illnesses other than corona virus infection. The revised machine learning strategy which will be created in this work is Adaboost with an Support Vector Machine-based classifier (ASVM). Due of the difficulties in learning SVM and the differential in variety as well as efficiency over straightforward SVM classifiers, ASVM classifier is frequently believed to violate the Boosting principle. The Adaboost classifier used in the study gradually replaces SVM as the primary classifier when the weight value of the training sample changes. On testing data, the mean accuracy of the classification was 97.1%, which was much higher than that of SVM classifiers without Adaboost. © 2022 IEEE.

Lung Lesion Images Classification Based on Deep Learning Model and Adaboost Techniques

Today, the medical industry is promoting the research and application of artificial intelligence in disease diagnosis and treatment. The development of diagnostic methods with the support of electronic devices and information technology can help doctors save time in diagnosing and treating diseases, especially medical images. Diagnosis of lung lesions based on lung images is a case study. This paper proposed a method for lung lesion images classification based on modified U-Net and VGG-19 combined on adaboost techniques. The modified U-Net architecture with 5 pooling and 5 unpooling. It has the unpooling layer with kernels of size 2 × 2, stride 2 × 2 to get output consistent with the adaboost. The result of the proposed method is about 97.61% and better results than others in the Covid-19 radiography dataset. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. © 2023 Elsevier Ltd

Construction and Application of Bid Fraud Prediction Model Based on AdaBoost Algorithm

In recent years, with the development of Internet big data technology and e-commerce platform, many active offline transaction methods have gradually shifted to online. Online auctions have come a long way due to COVID-19, but bidding fraud has seriously disrupted the health of the industry. In this paper, the AdaBoost model is used to build a bidding fraud prediction model, and the prediction performance of the model is verified by data experiments, and it is found that it has a high accuracy for identifying bidding fraud. At present, there are few prediction models for bidding fraud, and it has broad development prospects. © 2022 IEEE.

Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends

With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.

Multi-branch sustainable convolutional neural network for disease classification

Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease-19 (COVID-19), brain stroke, and cancer are at their peak. Different machine learning and deep learning-based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double-branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi-branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID-19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K-nearest neighbor (K-NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID-19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%). © 2023 Wiley Periodicals LLC.

Using Tree-Based Gradient Boosting to Distinguish Between Lymphoma and COVID-19

Over 600,000 new lymphoma cases and around 280,000 lymphoma-related deaths were reported in 2020. The delayed diagnosis of lymphoma has long been a problem. However, the advent of the COVID-19 pandemic, which disrupted healthcare services worldwide, may have caused more significant delays in lymphoma diagnoses. Since lymphomas can sometimes present with symptoms like COVID-19 and can affect the lungs, there is also a risk of misdiagnosis. We collected 505 lymphoma and 180 COVID-19 case reports from ScienceDirect and applied boosting methods to classify each patient as having COVID-19 or lymphoma based on the patient's age, gender and reported symptoms. LightGBM had the highest ROC AUC (0.89), meaning it best differentiates between the two diseases. Therefore, this model can be used as a screening tool to reduce the delay in lymphoma diagnosis and improve the patients' chances of survival. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Data analysis of Global Perceived Stress Scores during Covid-19

The Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. This work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. Machine Learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 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.

Detection of ADR on Covid Vaccine Safety Data

Due to pandemic vaccines are developed at a rapid pace. There is a requirement to ensure proper post-market pharmacovigilance. The proposed model will help speed up this process by classifying the Adverse Drug Reactions (ADRs) of the vaccines based on the severity. This will help vaccine manufacturers take necessary and timely action. The model will input the patient data (such as symptoms, vaccination details, and patient health details), which will be preprocessed and cleaned. The ADR will then be classified as a minor, major, or deadly reaction. The system made use of Count Vectors (CV), Word TF-IDF, N-gram TFIDF, and Character TF-IDF feature with Naive Bayes, Random Forest, Logistic Regression, Gradient Boost, and Adaboost machine learning classifiers. Using Random Forest with word-level TF-IDF comparatively a higher accuracy of 93.83% and an F1 score of 0.92 was achieved. © 2022 IEEE.

Detection of Face Wearing Mask Based on AdaBoost and YCrCb

At the beginning of 2020, COVID-19 broke out and swept the world. Wearing masks remains an important means of preventing epidemics. Many scholars have developed and studied mask wearing detection based on YOLO algorithm, and have made some achievements. AdaBoost algorithm has the advantages of high precision and low complexity, and is also suitable for solving this problem. This paper uses OpenCV to propose a face detection algorithm based on AdaBoost. This algorithm is based on face detection, including initialization of background estimation example, background subtraction preprocessing, obtaining eye position, face detection and other steps. LBP features are used as the training basis of the classifier. The trained classifier is generated and used as a function in the mask detection algorithm. At present, there are two problems in the research of mask wearing detection: first, only consider whether the tested object wears a mask, but not analyze the non-standard wearing of masks;Secondly, due to the influence of light and other external environments, the real-time detection effect of targets in complex scenes changes greatly. In view of the above problems, this paper adopts the following methods to solve them: pre-processing the image to reduce noise, light spots and other external environmental interference;For the case that the mask is not standardized, the condition that the mask covers the nose and mouth shall be detected. Finally, the Adaboost algorithm for facial mask wearing detection is obtained. Experiments show that the algorithm has high adaptability, robustness and accuracy, and can be used to promote the development of epidemic prevention. © 2022 IEEE.

Face Mask Detection Using Viola-Jones and Cascade Classifier

Globally, the COVID-19 coronavirus outbreak is causing chaos in human health and therefore, the healthcare sector is in serious disarray. Many precautions have been taken to prevent the spread of this disease, including the usage of masks, which is strongly recommended by the World Health Organization (WHO). This research study has used the Viola-Jones algorithm for detecting face masks, where Histogram Equalization, Unsharp Filter and Gamma Correction are used as the preferred image pre-processing techniques to improve the overall accuracy. Haar Feature Selection is applied for creating integral images and AdaBoost training is performed on these images. Cascade classifier, a machine learning-based approach, is also integrated with the base algorithm where a cascade function assists Viola-Jones in accurately detecting objects in images. A total number of 1670 images is used in this work and our system is compared with four other machine learning algorithms, where Viola-Jones outperforms these ML-based classifiers and the overall accuracy obtained is 96%. © 2022 IEEE.

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.

Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress

Mental Health Issues are a hidden pandemic which will emerge in the upcoming years. As the world witnessed COVID-19 pandemic and went into lockdown, the cases of Depression, Anxiety and Stress skyrocketed than ever before. This has given rise to the need for exploring the interdisciplinary field of Artficial Intelligence and Psychometry. In this paper, we propose compare various machine learning and ensemble learning methods, on the survey dataset comprising of the DASS-42 Psychometric Test Results and Demographic information. Random Forest, Decision Tree, Support Vector Machine (SVM), AdaBoost, CatBoost, and Extreme Gradient Boosting (XGBoost) are used to classify the level of Depression, Anxiety and Stress into normal, mild, moderate, severe and extremely severe categories. In our experiments on the dataset, Support Vector Machine outperformed and reached a final F1-measure of 94%, 95% and 91% in the prediction of Depression, Anxiety and Stress, respectively. © 2022 IEEE.