Naïve Bayes

Webb, Geoffrey I.

doi:10.1007/978-1-4899-7502-7_581-1

Cited by 23 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…State-of-the-art result is achieved using SVM with accuracy 98.97%. Chandra et al 37 have used majority voting based ensemble of five classifiers—SVM 28 , KNN 22 , DT 36 , Artificial Neural Network (ANN) 38 , Naive Bayes (NB) 39 on the database consisting of three publicly available CXR image datasets: covid-chestxray dataset 23 , Montgomery dataset 40 , and NIH ChestX-ray14 dataset 41 . Among the total 8196 features extracted from all the pre-processed images, 8 are First Order Statistical Features (FOSF) 42 , 88 are Grey Level Co-occurrence Matrix (GLCM) 43 based features and the rest 8100 are Histogram of Oriented Gradients (HOG) 44 features.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Saha

Mukherjee

Singh

et al. 2021

Sci Rep

View full text Add to dashboard Cite

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Saha

Mukherjee

Singh

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…It has the advantage that it natively provides a probabilistic result; however, it is known to be relatively computationally intensive when applied to high dimensional data sets. The second contrasting nonlinear method tested is a Gaussian Naive Bayes model, based on applying Bayes' theorem while assuming conditional interdependence between features, given the assigned class (Pérez et al, 2006; Webb et al, 2011). In this formulation, the likelihood of the features is assumed to be Gaussian.…”

Section: Methods Models and Metricsmentioning

confidence: 99%

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Smith¹,

Rae

Forsyth³

et al. 2020

Space Weather

View full text Add to dashboard Cite

In this study we investigate the ability of several different machine learning models to provide probabilistic predictions as to whether interplanetary shocks observed upstream of the Earth at L1 will lead to immediate (Sudden Commencements, SCs) or longer lasting magnetospheric activity (Storm Sudden Commencements, SSCs). Four models are tested including linear (Logistic Regression), nonlinear (Naive Bayes and Gaussian Process), and ensemble (Random Forest) models and are shown to provide skillful and reliable forecasts of SCs with Brier Skill Scores (BSSs) of ∼0.3 and ROC scores >0.8. The most powerful predictive parameter is found to be the range in the interplanetary magnetic field. The models also produce skillful forecasts of SSCs, though with less reliability than was found for SCs. The BSSs and ROC scores returned are ∼0.21 and 0.82, respectively. The most important parameter for these predictions was found to be the minimum observed B Z. The simple parameterization of the shock was tested by including additional features related to magnetospheric indices and their changes during shock impact, resulting in moderate increases in reliability. Several parameters, such as velocity and density, may be able to be more accurately predicted at a longer lead time, for example, from heliospheric imagery. When the input was limited to the velocity and density the models were found to perform well at forecasting SSCs, though with lower reliability than previously (BSSs ∼ 0.16, ROC Scores ∼ 0.8), Finally, the models were tested with hypothetical extreme data beyond current observations, showing dramatically different extrapolations.

show abstract

“…For salmon classification, AutoML generated thirty-six pipelines, as shown in Figure 8. The RF, LDA, Adaboost [67], libsvm_svc, Extra Tree, PA, KNN, MLP, Decision Tree (DT) [68], Bernoulli naïve Bayes (bernoulli_nb) [69], and GB classifiers were tested. Pipelines #3, #4, #20, #22, #30, #31, #32, #33, #34, #35, and #36 were left out of the Ensemble.…”

Section: Resultsmentioning

confidence: 99%

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

2021

View full text Add to dashboard Cite

Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.

show abstract

Naïve Bayes

Cited by 23 publications

References 0 publications

RETRACTED ARTICLE: GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

RETRACTED ARTICLE: GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

Contact Info

Product

Resources

About