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.

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.

An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data

This article proposes a new AdaBoost method with k′k-means Bayes classifier for imbalanced data. It reduces the imbalance degree of training data through the k′k-means Bayes method and then deals with the imbalanced classification problem using multiple iterations with weight control, achieving a good effect without losing any raw data information or needing to generate more relevant data manually. The effectiveness of the proposed method is verified by comparing it with other traditional methods based on numerical experiments. In the NSL-KDD data experiment, the F-score values of each minority class are also greater than the other methods.

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.

Intelligent predictions of Covid disease based on lung CT images using machine learning strategy.

Covid or Corona Virus, a term ruling the world from past two years and causes a huge destruction in all countries. One of the most important Covid disease identification method is Lung based Computed Tomography (CT) image scanning, in which it provides an effective disease identification means in clear manner. However, this Lung CT image based disease detection principles are complex to health care representatives and doctors to predict the Covid disease accurately. Several manual errors and medical flaws are raised day-by-day, so that a new systematic methodology is required to identify the Covid disease effectively with respect to machine learning principles. The machine learning principles are most popular to identify the respective disease efficiently as well as classify the disease in accurate manner without any time consumption. The infected portions of the chest are identified accurately and report to the respective person without any delay. In this paper, a new machine learning strategy is introduced called Hybrid Disease Detection Principle (HDDP), in which it is derived from the two classical machine learning algorithms called Convolutional Neural Network (CNN) and the AdaBoost Classifier. Both these algorithms are integrated together to produce a new strategy called HDDP, in which it process the lung CT image based on the machine learning factors such as pre-processing, feature extraction and classification. Based on these effective image processing strategies the proposed algorithm handles the CT images to predict the Covid disease and report to the respective user with proper accuracy ratio. This paper intends to provide effcient disease predictions as well as provide a sufficient support to medical people and patients in fine manner to assist them with modern classification algorithms.

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.

Prediction of COVID-19 & Pneumonia using Machine Learning & Deep Learning Model

As a result of the COVID-19 pandemic, medical examinations (RTPCR, X-ray, CT-Scan, etc.) may be required to make a medical decision. COVID-19's SARS-CoV-2 virus infects and spreads in the lungs, which can be easily recognized by chest X-rays or CT scans. However, along with COVID-19 instances, cases of another respiratory ailment known as Pneumonia began to rise. As a result, clinicians are having difficulty distinguishing between COVID-19 and Pneumonia. So, more tests were required to identify the condition. After a few days, the COVID-19 SARS-CoV-2 virus multiplied in the lungs, causing pneumonia and COVID-19 named Novel Corona virus infected Pneumonia. We employ Machine Learning and Deep Learning models to predict diseases such as COVID-19 Positive, COVID-19 Negative, and Viral Pneumonia in this research. A dataset of data is used in a Machine Learning model. A dataset of 120 images was used in the Machine Learning model. By extracting eight statistical elements from an image texture, we calculated accuracy. Adaboost, Decision Tree & Naive Bayes have overall accuracy of 88.46%, 86.4% and 80%, respectively. When we compared the algorithms, Adaboost algorithm performs the best, with overall accuracy of 88.46%, sensitivity of 84.62%, specificity of 92.31%, F1-score of 88% and Kappa of 0.8277. VGG16 Architecture is used in CNN model for 838 images in Deep Learning model. The model's total accuracy is 99.17 %. © 2022 IEEE.

Boosting the Performance of Artificial Intelligence-Driven Models in Predicting COVID-19 Mortality in Ethiopia.

Like other nations around the world, Ethiopia has suffered negative effects from COVID-19. The objective of this study was to predict COVID-19 mortality using Artificial Intelligence (AI)-driven models. Two-year daily recorded data related to COVID-19 were trained and tested to predict mortality using machine learning algorithms. Normalization of features, sensitivity analysis for feature selection, modelling of AI-driven models, and comparing the boosting model with single AI-driven models were the main activities performed in this study. Prediction of COVID-19 mortality was conducted using a combination of four dominant feature variables, and hence, the best determination of coefficient (DC) of AdaBoost, KNN, ANN-6, and SVM in the prediction process were 0.9422, 0.8618, 0.8629, and 0.7171, respectively. The Boosting model improved the performance of the individual AI-driven models KNN, SVM, and ANN-6 by 7.94, 22.51, and 8.02 percent, respectively, at the verification stage using the testing dataset. This suggests that the boosting model has the best performance for prediction of COVID-19 mortality in Ethiopia. As a result, it suggests a promising potential performance of boosting ensemble model to be applied in predicting mortality and cases from similarly recorded daily data to predict mortality due to COVID-19 in other parts of the world.

Restaurant Data Analytics to Improve Business Verticals

Due to the Covid-19 pandemic, hospitality industry witnessed a massive decline in their revenues. In our research we realized that one of the most effective ways to aid customer retention and boost the revenue of this Our research shows that currently the data analysts in this industry only use the traditional tools for predictive analysis, offering from a limited range of offers that lack customization as per user purchase history. Hence, we put forward a proof of concept for a tool where we make a machine learning model that learns from the historic data of each restaurant, including customer segments and coupon parameters, and predicts the probability of a coupon to work on a specific sub-category of customers. This would thereby increase the chances of transaction and thus boost the revenue. We worked with several classification algorithms, like Logistic Regression, AdaBoost, Random Forest, Gradient Boosting, and realized that Random Forest Classifier was producing the best results. Thus we selected it for building our model. As a result, we have built a web-based tool that can be used by Analysts or the business person themselves, to find out what coupon offers would best suit a particular subset of customers. This would help them make better business decisions, gain more customer traction and retention, and consequently boost their revenue. © 2022 IEEE.

Ensemble Approach for Detecting COVID-19 Propaganda on Online Social Networks

COVID-19 affected the entire world due to the unavailability of the vaccine. The social distancing was a contributing factor that gave rise to the usage of Online Social Networks. It has been seen that people share the information that comes to them without verifying its source . One of the common forms of information that is disseminated that have a radical purpose is propaganda. Propaganda is organized and conscious method of molding conclusions and impacting an individual's contemplations to accomplish the ideal aim of proselytizer. For this paper, different propagandistic tweets were shared in the COVID-19 Era. Data regarding COVID-19 propaganda was extracted from Twitter. Labelling of data was performed manually using different propaganda identification techniques and Hybrid feature engineering was used to select the essential features. Ensemble machine learning classifiers were used for performing the binary classification. Adaboost shows an accuracy of 98.7%, which learns from a weak learning algorithm by updating the weights. © 2022 University of Baghdad-College of Science. All rights reserved.

Global Economic Effects of COVID-19: Analysis Based on Hybrid Model

Global economy has been destroyed by the COVID-19 pandemic, which has rendered many of the world's population impoverished. More uncertainties about the social policies will appear. Meanwhile, there are many researchers devoted themselves into using machine learning to analyze the economics. tarting from the decrease of population, the health crisis has translated to an economic crisis. The spread of the virus encouraged social distancing which led to the shutdown of financial markets, corporate offices, business and event. In this paper, we use the dataset provided by Kaggle platform to analyze the economic effects COVID-19 brings. We choose serval metrics, such as the Human Development Index, the total death caused by virus. The model is a hybrid one which combine AdaBoost and Linear Regression. AdaBoost is a kind of boosting model with an optimal performance. We also do the compared experiments using the metric: MSE, the result shows that our model owns the best performance with the lowest MSE score 7.23. The KNN, Random Forest are respectively 2.58 and 2.55 higher than that of our hybrid model. © 2022 SPIE. All rights reserved.

Research on Credit Assessment and Prediction Based on Deep Learning

The huge pressure of market demand and competitive environment makes supply chain finance the choice of most enterprises. The emergence of public health emergencies such as the COVID-19 epidemic has made it particularly urgent to improve the risk management capabilities of the pharmaceutical industry's supply chain in a transitional period. In-depth exploration of the key factors affecting the financial credit risk of pharmaceutical companies' supply chain, and the construction of a high-accuracy forecast model is of great significance to the stability of the macroeconomy. Combining the characteristics of the pharmaceutical manufacturing industry, this paper builds a financial credit risk assessment system for the pharmaceutical supply chain. On the basis of Factor Analysis and Random Forest variable screening, the AdaBoost algorithm is used to build the prediction model. By comparing basic machine learning models such as SVM model, decision tree, logistic regression, Bayesian classifier, BP neural network, and integrated learning models such as Random Forest, Bagging meta-estimator, GBM, and XGBoost, the study found that the AdaBoost model has higher accuracy. And through the data forecast in 2020, the superiority and effectiveness of the model for credit risk assessment in the pharmaceutical industry are further verified. According to the prediction results, this paper finds that the epidemic has no obvious negative impact on pharmaceutical manufacturing enterprises and proposes suggestions from the perspectives of the government and enterprises for reference. © 2022 SPIE.

COVID-19 and SARS Virus Function Sites Classification with Machine Learning Methods

COVID-19 and SARS virus are two related coronaviruses. In recent years, the increasingly serious epidemic situation has become the focus of all human beings, and has brought a significant impact on daily life. So, we proposed a link analysis of the two viruses. We obtained all the required COVID-19 and SARS virus data from the Uniprot database website, and we preprocessed the data after obtaining the data. In the prediction of the binding site of the COVID-19 and SARS, it is to judge the validity between the two binding sites. In response to this problem, we used Adaboost, voting-classifier and SVM classifier, and compared different classifier strategies through experiments. Among them, Metal binding site can effectively improve the accuracy of protein binding site prediction, and the effect is more obvious. Provide assistance for bioinformatics research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A COVID-19 PREDICTION BASED on MACHINE LEARNING ALGORITHMS - A LITERATURE REVIEW

The SAR.CoV2 disease 2019(covid-19) pandemic affected many countries of the world. Actually, almost all the countries presented Covid-19 positive cases and governments are choosing different health policies to stop the infection and many programs are conducted with aware common people. Then number of positive cases increasing rapidly everyday around the world. This paper is going to propose a prediction on what basis common people getting affected and how to reduce the spreading of disease. Machine learning algorithms have been used in all the fields in predicting. Especially in medicine and enriches the applications of machine learning which are accurate and robust in selecting attributes. Here we investigate some of the machine learning models namely Decision Tree, Random Forest, Adaboost and Logistic Regression to predict accuracy of getting affected. In our experiment shows prediction result accuracies 70.1, 70.3, 67.9, 70.6 respectively. © 2022 University of Stockholm. All rights reserved.

Improved Performance Analysis for Cerebellar Ataxia disease Classification using AdaBoost

Artificial intelligence (AI) is the emerging field to diagnose and analyze chronic illnesses like Cerebellar Ataxia (CA), Spinocerebellar Ataxia (SCA), and Parkinson's disease. AI technologies such as machine learning and deep learning assist many doctors, diagnosis departments, and medical personnel in identifying and analyzing neurological disorders. Nowadays, AI used in most of the health care applications. Our research paper proffers an innovative approach to classify neurological disorders with various Machine learning algorithms. Existing research works experimented with machine learning algorithms like Support Vector machine and KNN, the performance of these algorithm is good, when the data is less and binary classified. In the proposed work, we have applied SVM, KNN, Decision tree and AdaBoost algorithms on the CA Data set. The performance of proposed methods exhibit improved accuracy when compared with the existing works. The results of the proposed work are tabulated for comparative analysis. We found that the AdaBoost algorithm shows the better classification result for Cerebellar Ataxia disease severity.

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures.

Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a large-scale, and with real time answers. A novel approach for COVID-19 assessment is adopted here, discriminating negative subjects versus positive or recovered subjects. The scope is to identify potential discriminating features, highlight mid and short-term effects of COVID on the voice and compare two custom algorithms. A pool of 310 subjects took part in the study; recordings were collected in a low-noise, controlled setting employing three different vocal tasks. Binary classifications followed, using two different custom algorithms. The first was based on the coupling of boosting and bagging, with an AdaBoost classifier using Random Forest learners. A feature selection process was employed for the training, identifying a subset of features acting as clinically relevant biomarkers. The other approach was centered on two custom CNN architectures applied to mel-Spectrograms, with a custom knowledge-based data augmentation. Performances, evaluated on an independent test set, were comparable: Adaboost and CNN differentiated COVID-19 positive from negative with accuracies of 100% and 95% respectively, and recovered from negative individuals with accuracies of 86.1% and 75% respectively. This study highlights the possibility to identify COVID-19 positive subjects, foreseeing a tool for on-site screening, while also considering recovered subjects and the effects of COVID-19 on the voice. The two proposed novel architectures allow for the identification of biomarkers and demonstrate the ongoing relevance of traditional ML versus deep learning in speech analysis.

CoviFacts—A COVID-19 Fake News Detector Using Natural Language Processing

Fake news confronts us on a daily basis in today’s fast-paced social media world. While some instances of fake news might seem innocuous, there are many examples that prove to be menacing. Misinformation or disinformation which takes the form of these weaponized lies which eventually amount to defective information, defamatory allegations, and hoaxes. The only motive behind such a malicious act is to engender emotional instability among the public. One such prevalent example today is COVID-19 which has caused an unprecedented paradigm shift in numerous businesses and quotidian activities across the globe. One of the primary activities is being news reporting. On average, people are spending almost one hour a day reading news via many different sources. The development in technology has obviated the barriers between sharing of information, thereby truly making the industry cosmopolitan. Therefore, it is paramount to curb fake news at source and prevent it from spreading to a larger audience. This paper describes a system, where the user can identify apocryphal news related to COVID-19 so as to ensure its authenticity. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Real-time Smile Detection using Integrated ML Model

This study investigates the ways to provide unhindered entertainment solutions to artists and common people. The research has led to the conclusion that a new social media platform can be built for artists around the globe. The aim of the study is to merge video conferencing features with face detection and smile detection machine learning algorithms to provide an interactive environment to audience virtually over Internet. The findings of the study suggest that people from different age groups and places can find the comedy shows of common interests for enjoyment. The project can be launched as application to build a worldwide community of comedians. The paper is intended to provide a fast and efficient Machine learning model to detect smiling faces in real time environment with low latency and high accuracy. The comparison between some smile detection models is done among which our model has proven to be more effective than 2 pre-existing models with accuracy score of 91.97%. Although the accuracy of this model is less than other one having 94.87% accuracy. © 2022 IEEE.