Stacking Classifier-based Automated Insurance Fraud Detection System

During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile. © 2022 IEEE.

Research on Key technologies of network public opinion warning based on improved Stacking algorithm

Online public opinion warning for emergencies can help people understand the real situation, avoid panic, timely remind people not to go to high-risk areas, and help the government to carry out epidemic work.In this paper, key technologies of network public opinion warning were studied based on improved Stacking algorithm. COVID-19, herpangina, hand, foot and mouth, varicella and several emergency outbreaks were selected as public opinion research objects, and rough set was used to screen indicators and determine the final warning indicators.Finally, the warning model was established by the 50% fold Stacking algorithm, and the training accuracy and prediction accuracy experiments were carried out.According to the empirical study, the prediction accuracy of 50% Stacking is good, and the early warning model is practical and robust.This study has strong practicability in the early warning of the online public opinion of the sudden epidemic. © 2023 SPIE.

Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

In the current era, the Optical Character Recognition (OCR) model plays a vital role in converting images of handwritten characters or words into text editable script. During the COVID-19 pandemic, students' performance is assessed based on multiple-choice questions and handwritten answers so, in this situation, the need for handwritten recognition has become acute. Handwritten answers in any regional language need the OCR model to transform the readable machine-encoded text for automatic assessment which will reduce the burden of manual assessment. The single Convolutional Neural Network (CNN) algorithm recognizes the handwritten characters but its accuracy is suppressed when dataset volume is increased. In proposed work stacking and soft voting ensemble mechanisms that address multiple CNN models to recognize the handwritten characters. The performance of the ensemble mechanism is significantly better than the single CNN model. This proposed work ensemble VGG16, Alexnet and LeNet-5 as base classifiers using stacking and soft voting ensemble approaches. The overall accuracy of the proposed work is 98.66% when the soft voting ensemble has three CNN classifiers. © 2023 Lavoisier. All rights reserved.

A more accurate mask detection algorithm based on Nao robot platform and YOLOv7

With the global outbreak of Corona Virus Disease 2019(COVID-19), many countries had made it mandatory for people to wear masks in public places. This paper proposed a novel mask detection algorithm RMPC (Restructing the Maxpool layer and the Convolution layer)-YOLOv7 based on YOLOv7 for detecting whether people wear masks in public places. The RMPC-YOLOv7 algorithm reconstructed the downsampling structure in the original YOLOv7 algorithm. We changed the stacking of the maxpooling layer and the convolutional layer. This enabled the feature information to be fully integrated to achieve the accuracy improvement of the new model. Through comparison experiments, our proposed RMPC-YOLOv7 had was improved 0.9% and 1.2% for mAP0.5 and mAP0.5:0.95, respectively. The experimental results demonstrated the feasibility of RMPC-YOLOv7. © 2023 IEEE.

Ensemble Pre-Trained Deep Convolutional Neural Network Model for Classifying Medical Image Datasets

Over the last few years, Deep Learning models have shown prominent results in medical image analysis especially to predict disease at the earlier stages. Since Deep Neural Network require more training data for better prediction, it needs more computational time for training. Transfer learning is a technique which uses the learned knowledge to perform the classification task by minimizing the number of training data and training time. To increase the accuracy of a single classifier, ensemble learning is used as a meta-learner. This research work implements a framework Ensemble Pre-Trained Deep Convolutional Neural Network using Resnet50, InceptionV3 and VGG19 pre-trained Convolutional Neural Network models with modified top layers to classify the disease present in the medical image datasets such as Covid X-Rays, Covid CT scans and Brain MRI with less computational time. Further, these models are combined using stacking and bagging ensemble approach to increase the accuracy of single classifier. The datasets are distributed as train, test and validation data and the models are trained and tested for four epochs. All the models are evaluated using validation data and the result shows that the ensemble learning approach increases the prediction accuracy when compared to the single models for all the datasets. In addition, this experiment reveals that the stacked model attains higher test accuracy of 99% for chest X-Ray images, 100% for chest CT scan images and 98% for brain MRI, compared to the bagged models. © 2022 IEEE.

ADASEML: Hospitalization Period Prediction of COVID-19 Patients Using ADASYN and Stacking Based Ensemble Learning

The COVID-19 pandemic places additional constraints on hospitals and medical services. Understanding the period for support requirements for COVID-19 infected admitted to hospitals is critical for resource distribution planning in hospitals, particularly in resource-reserved settings. Machine Learning techniques are being used to approximate a patient's duration of stay in the hospital. This research uses Decision Tree, Random Forest and K-Nearest Neighbors, Voting classifiers, and Stacking classifiers to predict patients' length of stay in the hospital. Due to the imbalance in the dataset, Adaptive Synthetic (ADASYN) was used to resolve the issue, and the permutation feature importance method was employed to find the feature importance scores in identifying important features during the models' development process. The proposed "ADASEML” has shown superior performance to the earlier works, with an accuracy of 80%, precision of 78%, and recall of 80%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Epidemic Outbreak Prediction with Ensemble of Deep Learning Models

The ability of today's technology has proved it's significance and dire need in the world yet again, with COVID-19 being a global pandemic. Various techniques are being incorporated and researches being conducted everyday in order to mitigate this pandemic. Forecasting of COVID-19 cases is one such task in machine learning which is being researched intensively to develop reliable forecasting models.In the proposed work, we have forecasted the number of COVID-19 confirmed,recovered and death cases globally using time series data with machine learning and deep learning ensemble models. The purpose of this study is to prove that ensemble of several week learners that we have developed can result in a better performing model. Deep learning models always tend to perform better than machine learning and traditional linear models due to their non-linearity. Our study concludes that deep learning ensemble model achieves better performance than the machine learning ensemble (Random forest) and the individual base learners used in ensemble model itself in COVID-19 forecasting. © 2022 IEEE.

COVID-19 Patient Recovery Prediction Using Efficient Logistic Regression Model

This research develops a COVID-19 patient recovery prediction model using machine learning. A publicly available data of infected patients is taken and pre-processed to prepare 450 patients' data for building a prediction model with 20.27% recovered cases and 79.73% not recovered/dead cases. An efficient logistic regression (ELR) model is built using the stacking of random forest (RF) and logistic regression (LR) classifiers. Further, the proposed model is compared with state-of-art models such as logistic regression (LR), support vector machine (SVM), decision tree (C5.0), and random forest (RF). All the models are evaluated with different metrics and statistical tests. The results show that the proposed ELR model is good in predicting not recovered/dead cases and handling imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Stacking-based modelling for improved over-indebtedness predictions

In a world now starkly divided into pre- and post-COVID times, it's imperative to examine the impact of this public health crisis on the banking functions - particularly overindebtedness risks. In this work, a flexible analytics-based model is proposed to improve the banking process of detecting customers who are likely to have difficulty in managing their debt. The proposed model assists the banks in improving their predictions. The proposed meta-model extracts information from existing data to determine patterns and to predict future outcomes and trends. We test and evaluate a large variety of Machine Learning Algorithms (MLAs) by using new techniques like feature selection. Moreover, models of previous months are combined in order to build a meta-model representing several months using stacked generalisation technique. The new model will identify 91% of the customers potentially unable to repay their debt six months ahead and enable the bank to implement targeted collections strategies. Copyright © 2022 Inderscience Enterprises Ltd.

An Empirical Comparison of Students' Performance in Online vs Offline Platforms Using Ensemble Learning Models

Online learning is a paradigm shift from traditional offline education;recently there has been a remarkable surge in e-learning platforms due to Covid 19 outbreaks. There is a significant difference in students' performance on both platforms. The primary focus of this study is to investigate how the students perform in both learning methods. Moreover, five ensemble-learning approaches are compared to predict student performance in online and offline education platforms. Ensemble learning is a prominent machine learning meta-approach that integrates predictions from several models to improve prediction. Students' performance data for both offline and online platforms were extracted from a private university's student database. Five ensemble-learning methods were applied to both datasets for predictive analysis. According to the findings of this study, students do better on online platforms than in traditional education systems. Furthermore, XGBoost, Gradient Boost, and Stacking KNN fared better for online data, whereas stacking neural networks and stacking random forest performed better for offline data. The findings of this study will assist educational instructors to concentrate more on students' performance based on their particular learning system. © 2022 ACM.

Res-Dense Net for 3D Covid Chest CT-Scan Classification

One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images. In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves a competitive performance on some evaluation metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

MKRF Stacking-Voting: A Data Mining Technique for Predicting Educational Satisfaction Level of Bangladeshis Student During Pandemic

Data mining is most efficient when used deliberately to achieve a corporate goal, answer business or research questions, or contribute to a problem-solving solution. Data mining aids in the accurate prediction of outcomes, the recognition of patterns and anomalies, and frequently inform forecasts. Online education is becoming more popular all around the world because of the COVID-19 pandemic. The main goal of this research is to Predict Educational Satisfaction Level of Bangladeshis Students During the Pandemic using data mining approaches by only filling up with some basic questionnaires which are related to the satisfaction level of online education collected through a public survey. By surveying 1004 students from various academic institutions, schools, colleges, and universities on the quality of online education in COVID-19 pandemic scenarios, we were able to determine how productive it would be. Influence how online learning is measured and how satisfied people are with it. To achieve our aim of predicting satisfaction levels, we used a total of eight classifiers, six of which were based classifiers, which we combined with the best three top-scoring classifiers to build a novel ensemble approach called MKRF Stacking and MKRF Voting ensemble classifier. Among those classifiers, the Random Forest classifier outperforms the other six base classifiers with 97.21% accuracy. Our proposed data mining ensemble approaches MKRF Stacking and MKRF Voting outperform applied classifiers. Typically, voting ensemble classifiers outperform voting ensemble classifiers, but in this case, MKRF Stacking defeated MKRF Voting and all applied classifiers with a supreme accuracy of 97.68% (Average). The proposed method would be used in a framework where education counselors find the root causes and minor explanations for dissatisfaction in online education among students so that they can better understand all aspects and provide them with the best advice and solutions to their problems. © 2022 IEEE.

Sentiment Analysis of COVID-19 Vaccination Responses From Twitter Using Ensemble Learning

Despite the evidence that shows the benefits and safety of immunizations, the widespread vaccine-related misinformation and conspiracy theories online have fueled a general vaccine hesitancy, and coronavirus disease (COVID-19) vaccinations are no exception. COVID-19 vaccine hesitancy is considered a global threat to public health that undermines the efforts to control the COVID-19 pandemic. Twitter and other social media platforms allow people to exchange information and express concerns and emotions on COVID-19-related issues. This research aims to understand people's sentiment on COVID-19 vaccines from data collected from Twitter. Analyzing the public attitude toward the vaccines helps the authorities to make better decisions and reach the intended herd immunity. In this paper, we utilize the state-of-the-art transformer-based classification models, RoBERTa and BERT, along with multiple task-specific versions, to classify people's opinions about COVID-19 vaccinations into positive, negative, and neutral. A Twitter dataset that consists of people's opinions about vaccines is used to train and evaluate the presented models. Two ensemble learning techniques that aggregate the individual classifiers are presented for further performance improvement: majority voting and stacking with Support Vector Machine (SVM) as meta-learner. The results also show that applying ensemble learning significantly outperforms the individual classifiers using all evaluation measures. We also found that ensembling with stacking has an advantage over simple majority voting. © 2022 IEEE.

Cough Sound based COVID-19 Detection with Stacked Ensemble Model

COVID-19 pandeamic has affected people all over the world. COVID-19 may manifest with different severity in different people, however, it predominantly affects respiratory system. Symptoms may vary from sore throat and cough to shortness of breath and damaged lungs. This work focusses on developing a smart system for early detection of COVID-19 based on cough sounds and machine learning algorithms. Such a system would be easily accessible and may provide initial screening for detection of COVID-19. Moreover, cough sounds may be recorded by the person on smartphone avoiding the need for visiting a hospital or testing facility and getting exposed to the disease during the pandeamic. First, the duration of cough sound is determined in the recorded audio signal using thresholding. Then, statistical features are extracted for cough sound and normalized. Finally, the performance of 10 different machine learning algorithms are compared for automatic detection of COVID-19. The proposed stacked ensemble of machine learning models yields the best performance, with an accuracy of 79.86% and area under region of convergence curve of 0.797 for cough sounds of new patients. © 2022 IEEE

Covid-19 Severity Classification Using Supervised Learning Approach

This paper presented work on supervised machine learning techniques using K-NN, Linear SVM, Naïve Bayes, Decision Tree (J48), Ada Boost, Bagging and Stacking for the purpose to classify the severity group of covid-19 symptoms. The data was obtained from Kaggle dataset, which was obtained through a survey collected from the participant with varying gender and age that had visited 10 or more countries including China, France, Germany Iran, Italy, Republic of Korean, Spain, UAE, other European Countries (Other-EUR) and Others. The survey is Covid-19 symptom based on guidelines given by the World Health Organization (WHO) and the Ministry of Health and Family Welfare, India which then classified into 4 different levels of severity, Mild, Moderate, Severe, and None. The results from the seven classifiers used in this study showed very low classification results. © 2021 IEEE.