Enhancement of E-Learning Student's Performance Based on Ensemble Techniques

Educational institutions have dramatically increased in recent years, producing many graduates and postgraduates each year. One of the critical concerns of decision-makers is student performance. Educational data mining techniques are beneficial to explore uncovered data in data itself, creating a pattern to analyze student performance. In this study, we investigate the student E-learning data that has increased significantly in the era of COVID-19. Thus, this study aims to analyze and predict student performance using information gathered from online systems. Evaluating the student E-learning data through the data mining model proposed in this study will help the decision-makers make informed and suitable decisions for their institution. The proposed model includes three traditional data mining methods, decision tree, Naive Bays, and random forest, which are further enhanced by the use of three ensemble techniques: bagging, boosting, and voting. The results demonstrated that the proposed model improved the accuracy from 0.75 to 0.77 when we used the DT method with boosting. Furthermore, the precision and recall results both improved from 0.76 to 0.78. © 2023 by the authors.

A Hybrid Model for Covid-19 Detection using CT-Scans

Although some believe it has been wiped out, the coronavirus is striking again. Controlling this epidemic necessitates early detection of coronavirus disease. Computed tomography (CT) scan images allow fast and accurate screening for COVID-19. This study seeks to develop the most precise model for identifying and classifying COVID-19 by developing an automated approach using transfer-learning CNN models as a base. Transfer learning models like VGG16, Resnet50, and Xception are employed in this study. The VGG16 has a 98.39% accuracy, the Resnet50 has a 97.27% accuracy, and the Xception has a 96.6% accuracy;after that, a hybrid model made using the stacking ensemble method has an accuracy of 98.71%. According to the findings, hybrid architecture offers greater accuracy than a single architecture. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Ensemble Summarization Models to Leverage Performance

Summaries underpin a majority of relevant information needed to quickly make an informed decision from a large corpus of text;Natural Language techniques have been developed to generate these summaries using either ive or extractive methods. Presently, state-of-the-art approaches involve using neural network-based solutions akin to seq2seq, graph2seq, and other encoder-decoder architectures. These models make different contributions to prediction quality. In this paper, we build a model that ensembles two distinct pretraining NLP models to leverage their summarization performance using a TextRank process we constructed. We evaluate our model using the CoronaNet Research Project COVID-19 dataset, which contains how governments responded to the Covid-19 pandemic. We compared the ROUGE scores of the individual models on the test set to our ensemble method. The experiment results show that our proposed ensemble method performs better than using the models individually. © 2022 IEEE.

A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms.

One of the prominent uses of Predictive Analytics is Health care for more accurate predictions based on proper analysis of cumulative datasets. Often times the datasets are quite imbalanced and sampling techniques like Synthetic Minority Oversampling Technique (SMOTE) give only moderate accuracy in such cases. To overcome this problem, a two-step approach has been proposed. In the first step, SMOTE is modified to reduce the class imbalance in terms of Distance-based SMOTE (D-SMOTE) and Bi-phasic SMOTE (BP-SMOTE) which were then coupled with selective classifiers for prediction. An increase in accuracy is noted for both BP-SMOTE and D-SMOTE compared to basic SMOTE. In the second step, Machine learning, Deep Learning and Ensemble algorithms were used to develop a Stacking Ensemble Framework which showed a significant increase in accuracy for Stacking compared to individual machine learning algorithms like Decision Tree, Naïve Bayes, Neural Networks and Ensemble techniques like Voting, Bagging and Boosting. Two different methods have been developed by combing Deep learning with Stacking approach namely Stacked CNN and Stacked RNN which yielded significantly higher accuracy of 96-97% compared to individual algorithms. Framingham dataset is used for data sampling, Wisconsin Hospital data of Breast Cancer study is used for Stacked CNN and Novel Coronavirus 2019 dataset relating to forecasting COVID-19 cases, is used for Stacked RNN.

An Ensemble Learning Approach for COVID-19 Fact Verification

The rapid development of social media platforms has resulted in a fast-paced spread of misinformation, which is especially common in the COVID-19 pandemic. In the global pandemic, the amount of COVID-19 related fake news generated online becomes enormous, which negatively results in public tension. Moreover, rumours are spread across platforms from different countries in such a global pandemic. Thus, automated fact-checking, which refers to automatically verifying the correctness of a claim, is of great importance. In this paper, we propose and examine ensemble learning approaches that exploit the power of multiple large-scale pre-trained language models. We conduct extensive experiments on traditional approaches, learning-based approaches, and our proposed ensemble methods. We successfully advance state-of-the-art performance by a significant margin. Further, we show that our ensemble method is especially suited to tasks with scarce training data, making it more suitable for many real-world applications. © 2022 IEEE.

BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging than before. This paper explored machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. Also, we investigated a way to conduct such BCI experiments remotely via Zoom. The results showed that Random Forest and RBF SVM performed well for EEG classification tasks. The remote experiment during the pandemic yielded several challenges, and we discussed the possible solutions;nevertheless, we developed a protocol that grants non-experts who are interested a guideline for such data collection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Ensembling EEG bands for Mental State Assessment

The assessment of mental status is an important task in psychiatry. But the impact of the COVID-19 epidemic has reduced the number of face-to-face assessments with physicians, and thus making it difficult. In recent years, some studies have used EEG (electroencephalogram) to help assess depression or mental state. Users can thus further assess mental state through simple EEG measurement. Since the EEG measurement will obtain multiple frequency bands related to mental or emotion state, if only one frequency band is used to evaluate a specific emotion or mental state, it may be insufficient. Some studies have proposed an ensemble method of multiple frequency bands for emotion recognition. In this study, we will use ensemble multi-bands EEG frequency to do and assist mental state or depression assessment. Through the method of ensemble learning, we integrate the frequency bands which is mainly related to mental state to assist the evaluation of mental state. From the experimental results, we can find that this method has a good effect. © 2022 IEEE.

Multi-class Sentiment Classification for Customers’ Reviews

The rise of e-commerce due to the Covid-19 situation is becoming more significant in 2021. It could lead to great demands to understand customers’ opinions usually shown in their reviews. An e-commerce platform with the ability to be aware of its users’ viewpoint can have a higher possibility of meeting customer expectations, attracting new users, and increasing sales. With the tremendous data in e-commerce platforms presently, sentiment analysis is a powerful tool to understand users. However, the sentiment in reviews data may contain more than two states, positive and negative, and then a binary sentiment classifier may not be helpful in practice. According to our knowledge, research on this subject is often restricted access. Therefore, this paper presents a multi-class sentiment analysis for Vietnamese reviews on a large-scale dataset, including 480,702 reviews. We collected these reviews from popular Vietnamese e-commerce websites and manually did the labeling process with three classes of sentiments (positive, negative, and neutral). To build a suitable classification model for the main problem, we propose a deep learning approach using different architectures (LSMT, GRU, TextCNN, LSTM + CNN, and GRU+CNN) and compare the performance among other ensemble techniques. The experimental results show the outperformance of the ensemble techniques on the multi-class sentiment classification problem, and the combination of chosen architectures using the attention mechanism could obtain the best F-1 score of 73.64 %. © 2022, Springer Nature Switzerland AG.

An Ensemble Method for Covid-19 Positive Cases Detection using Machine Learning Algorithms

In recent days, people from all over the world is facing a severe issue related to their survival of life. Due to Covid-19, many people lost their loved ones. It was a major threat not only to human beings but also to all the living creatures. Many Organizations are trying to find out the solution and at the same time, the corona affected people's rate is also increasing tremendously. Thousands and thousands of people lost their lives. The reason behind the increased death rate is that, many people were not aware of whether they are infected with the Covid virus or not. So, to solve the issue and to take remedies before it is too late, a method is proposed which helps people to identify whether they were affected with the Covid virus or not. The data in the dataset contains 152 Covid-19 positive cases and 1143 negative or healthy cases in India. In order to identify the positive and healthy cases efficiently, preprocessing is done on the collected data and features are extracted using MFCC before classification. Using several classifiers, the level of accuracy has been predicted. The classifier which gives highest level of accuracy is considered as the best classifier and using the classifier, Covid-19 positive cases are identified. Oversampling is performed on the extracted features in order to provide good accuracy. Several metrics like precision, recall, confusion matrix, Mathew's Correlation coefficient, F1-score and accuracy has been calculated to produce efficient results. Finally, K-NN classifies the Covid affected patients with 92% of accuracy, also scored good results in all the metrics calculated. © 2022 IEEE.

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text.

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

An HMM-ensemble approach to predict severity progression of ICU treatment for hospitalized COVID-19 patients

COVID-19-related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble-based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1, 000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches. © 2021 IEEE.

An Ensemble Model for Face Mask Detection Using Faster RCNN with ResNet50

The computer vision which is an important aspect of Artificial Intelligence. The object detection is the most researchable area with deep learning algorithms. Now in the current COVID – 19 pandemics, the social distancing is a mandatory factor to prevent this transmission of this deadly virus. The government is struggling to handle the persons without wearing masks in public places. Our work concentrates on the object detection of face masks using the state-of-the-art methodologies like YOLO, SSD, RCNN, Fast RCNN and Faster RCNN with different backbone architectures like ResNet, MobileNet, etc. This paper brings out various ensemble methods by combining the state of art methodologies and compare those combinations to identify the best performance, in choice of the dataset of the application. We have obtained the highest performance benchmark with the usage of Faster RCNN – ResNet50 among the other ensemble methods. All the performance evaluation metrics are compared with one other with the same face mask detection image dataset. In this paper, we present a balancing collation of the ensemble methods of object detection algorithms. © 2022, Springer Nature Switzerland AG.

CLASSIFICATION OF COVID-19 PATIENT'S CHEST X-RAY IMAGES USING MACHINE LEARNING TECHNIQUES - A COMPARATIVE ANALYSIS

Covid-19 pandemic is a major health thread all over the world. Early detection is the only solution to control the spread of disease. Chest X-rays plays a key role in the diagnosis of Covid-19 since the viral test and the antibody test may take time to get the result. These tests sometimes give the result negative for infected persons. Chest X-rays are also cost effective when compared to other diagnosis tests for Covid-19 patients. Medical image analysis requires more efforts as the data increases rapidly. Due to high risk of work in this area, a Computer aided technique can lead to diagnose Covid-19 accurately than the radiologist. Better solution is to use machine learning techniques for risk assessment and treatment planning. This model can classify Covid-19 patients, Pneumonia patients and healthy patients based on their chest X-rays. Statistical measures are used in machine learning to retrieve the hidden information present in the image that may be used for good decision-making. X-ray images are gray scale images with almost the same textural features. In our model the traditional textural feature Gray Level Co-occurrence matrix (GLCM) is used to extract the information of pixel intensities between neighbouring pixels in a small region in the chest X-ray images of the patients. Then these extracted features of the patients are given to different conventional machine learning techniques like K-Nearest neighbor, Naive-Bayes Classifier, Support Vector machine for classification. Comparison of these classifiers are done on the basis of accuracy and found to be less. Then advanced machine learning ensemble methods were tried for classification. The ensemble methods like Random forest and XGBoost are used for classification. The comparative study of the model shows that classifying the X-ray image dataset with the combination of GLCM and ensemble methods gives better result than using GLCM with traditional machine learning methods. Our model has less computation time and it requires less memory (cost effective).

Ensemble feature selection: Are stability metrics a proxy or a complement to predictive performance?

Proper identification of biomarkers, used in the development of drugs, is critical as has been shown with the race to find a vaccine for the Covid19. Gene-expression based marker discovery often entails that feature selection be performed. However, a plethora of feature selection methods exist and they do not result in the selection of the same feature subsets for the same dataset. Often, users are faced with having to select which subset to use. To help in this conundrum, several approaches have been proposed to guide feature subset selection, among which the use of ensemble methods (i.e., combining subsets from multiple methods) has gained attention recently. In an ensemble approach there are two issues that deserve attention: the stability of the feature subsets being combined and the classification performance of the combined feature subsets. Hence the interest in exploring how stability and performance relate, which is the central topic investigated in this paper. First 5/6 different feature selection methods are used to create feature subsets for 3 different transcriptomics datasets. Then, the stability and performance of these feature subsets under a given merging strategy are computed using 5 stability metrics and 3 performance metrics for 3 different classifiers. Our results suggest that performance and stability criteria are complementary and conflicting and that both must be considered to decide on the final selected feature subsets. We use two reference metrics to illustrate such selection. © 2021 ACM.

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.

Coronavirus19 is caused due to infection in the respiratory system. It is the type of RNA virus that might infect animal and human species. In the severe stage, it causes pneumonia in human beings. In this research, hand-crafted and deep microscopic features are used to classify lung infection. The proposed work consists of two phases; in phase I, infected lung region is segmented using proposed U-Net deep learning model. The hand-crafted features are extracted such as histogram orientation gradient (HOG), noise to the harmonic ratio (NHr), and segmentation based fractal texture analysis (SFTA) from the segmented image, and optimum features are selected from each feature vector using entropy. In phase II, local binary patterns (LBPs), speeded up robust feature (Surf), and deep learning features are extracted using a pretrained network such as inceptionv3, ResNet101 from the input CT images, and select optimum features based on entropy. Finally, the optimum selected features using entropy are fused in two ways, (i) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are horizontally concatenated/fused (ii) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are combined/fused with deep features. The fused optimum features vector is passed to the ensemble models (Boosted tree, bagged tree, and RUSBoosted tree) in two ways for the COVID19 classification, (i) classification using fused hand-crafted features (ii) classification using fusion of hand-crafted features and deep features. The proposed methodology is tested /evaluated on three benchmark datasets. Two datasets employed for experiments and results show that hand-crafted & deep microscopic feature's fusion provide better results compared to only hand-crafted fused features.

An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans.

The novel coronavirus outbreak of 2019 reached pandemic status in March 2020. Since then, many countries have joined efforts to fight the COVID-19 pandemic. A central task for governments is the rapid and effective identification of COVID-19 positive patients. While many molecular tests currently exist, not all hospitals have immediate access to these. However, CT scans, which are readily available at most hospitals, offer an additional method to diagnose COVID-19. As a result, hospitals lacking molecular tests can benefit from it as a way of mitigating said shortage. Furthermore, radiologists have come to achieve accuracy levels over 80% on identifying COVID-19 cases by CT scan image analysis. This paper adds to the existing literature a model based on ensemble methods and 2-stage transfer learning to detect COVID-19 cases based on CT scan images, relying on a simple architecture, yet complex enough model definition, to attain a competitive performance. The proposed model achieved an accuracy of 86.70%, an F1 score of 85.86% and an AUC of 90.82%, proving capable of assisting radiologists with COVID-19 diagnosis. Code developed for this research can be found in the following repository: https://github.com/josehernandezsc/COVID19Net.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.