scholarly journals COVID-19 Identification using Machine Learning Classifiers with GLCM Features of Chest X-ray Images

2021 ◽  
Vol 18 (23) ◽  
pp. 46
Author(s):  
Sudeep D. Thepade ◽  
Hrishikesh Jha
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Automated System ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Rapid Pace ◽  
Occurrence Matrix ◽  
Machine Learning Models

COVID-19 is an ongoing pandemic, and is also known by the name coronavirus. It was originally discovered in Wuhan, China, in December, 2019. Since then, it has been increasing rapidly worldwide. Since it has been increasing at such a rapid pace, testing equipment has limited availability. Also, this disease spreads very quickly, so it is better if it is detected earlier, in order so that it can be stopped from spreading. Therefore, the importance of early detection has increased; however, because of the shortage of testing sets, it is a necessity to develop an automated system that can detect whether the COVID-19 disease is present in a person or not as early as possible. Therefore, in this work, to extract features from X-ray images of the chest, we have made use of the Gray Level Co-occurrence Matrix (GLCM). After extracting these features for the classification of the images, we used different machine learning models, and an ensemble of machine learning models, to classify X-ray images of the chest as COVID-19, Normal, Pneumonia-bac, or Pneumonia-vir. Considering the average of performance metrics, the ensemble of Random Forest-MLP gave the best result among the variations.

scholarly journals COVID-19 detection using federated machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252573
Author(s):  
Mustafa Abdul Salam ◽  
Sanaa Taha ◽  
Mohamed Ramadan
Keyword(s):  
Machine Learning ◽  
Model Prediction ◽  
Prediction Accuracy ◽  
Activation Function ◽  
Learning Model ◽  
Learning Models ◽  
X Ray ◽  
Machine Learning Model ◽  
Chest X Ray ◽  
Machine Learning Models

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning

2021 ◽  
Author(s):  
Chenxi Huang ◽  
Shu-Xia Li ◽  
César Caraballo ◽  
Frederick A. Masoudi ◽  
John S. Rumsfeld ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Performance Metrics ◽  
Prediction Models ◽  
Learning Models ◽  
Risk Prediction Models ◽  
Clinical Risk ◽  
Machine Learning Models

Background: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics. Methods and Results: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics. Conclusions: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models.

scholarly journals Multivariate Classification of Drugs using Parametric and Nonparametric Machine Learning Models

2020 ◽  
Vol 9 (3) ◽  
pp. 2021-2027
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Biological Activities ◽  
Biological Effects ◽  
Recursive Feature Elimination ◽  
Drug Candidate ◽  
Learning Models ◽  
Machine Learning Models ◽  
Non Parametric

In pharmaceutical research, traditional drug discovery process is time consuming and expensive, where several compounds are experimentally tested for their biological activities. Series of lab experiments are conducted to analyze newly synthesized drug’s pharmaceutical activities and its biological effects on human. With every new drug discovery, the required clinical properties can be determined using machine learning models and this greatly reduces the experimental cost. This paper explores parametric and non-parametric machine learning models to classify administration properties of drugs and its toxicity. The multinomial classification of drugs was based on their physicochemical and ADMET properties. Balanced data samples were drawn from chEMBL and was pre-processed. Features were reduced using Recursive Feature Elimination and the attributes were ranked based on their importance to reduce highly correlated attributes. The performance of parametric and non-parametric machine learning models was analyzed on cheminformatic data that includes physiochemical, biological and pharmaceutical properties of the drug molecules. Selecting the potent drug candidate along with its administration properties greatly reduces wet lab experimental time and cost. Multiclass classification can be determined efficiently using non-parametric machine learning model. Optimal feature engineering, tuning hyperparameters and adopting hybrid algorithms would result in more accurate predictions in future for cheminformatics data.

The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models

2020 ◽  
pp. 219-226
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Assessment of Machine Learning Algorithms for Network Intrusion Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1667-1671
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Network Intrusion Detection ◽  
Learning Models ◽  
Network Intrusion ◽  
Tree Classifier ◽  
Machine Learning Models

A Network Intrusion Detection System (NIDS) is a framework to identify network interruptions as well as abuse by checking network traffic movement and classifying it as either typical or strange. Numerous Intrusion Detection Systems have been implemented using simulated datasets like KDD’99 intrusion dataset but none of them uses a real time dataset. The proposed work performs and assesses tests to overview distinctive machine learning models reliant on KDD’99 intrusion dataset and an ongoing created dataset. The machine learning models achieved to compute required performance metrics so as to assess the chosen classifiers. The emphasis was on the accuracy metric so as to improve the recognition pace of the interruption identification framework. The actualized calculations showed that the decision tree classifier accomplished the most noteworthy estimation of accuracy while the logistic regression classifier has accomplished the least estimation of exactness for both of the datasets utilized.

scholarly journals Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Manjit Kaur ◽  
Vijay Kumar ◽  
Vaishali Yadav ◽  
Dilbag Singh ◽  
Naresh Kumar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Huge Number ◽  
X Ray ◽  
Screening Model ◽  
Proposed Model ◽  
Strength Pareto Evolutionary Algorithm ◽  
Chest X Ray ◽  
Deep Learning Model

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

scholarly journals Detection of Online Fake News Using Blending Ensemble Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Arvin Hansrajh ◽  
Timothy T. Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Fake News ◽  
Learning Models ◽  
Linear Discriminant ◽  
Proposed Model ◽  
Machine Learning Models

The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

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