Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Deif, Mohanad A.; Solyman, Ahmed A. A.; Alsharif, Mohammed H.; Uthansakul, Peerapong

doi:10.3390/s21196379

Cited by 39 publications

(20 citation statements)

References 33 publications

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“…In the disciplines of energy, ecology, hydrology, and economics, SVM has numerous applications [45,46]. In a regression issue, the training set is defined as [46][47][48] x j , y j x j ,…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Hammam

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

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One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness R a values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments.

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“…In the disciplines of energy, ecology, hydrology, and economics, SVM has numerous applications [45,46]. In a regression issue, the training set is defined as [46][47][48] x j , y j x j ,…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Hammam

Attar

Amer

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…It is ideally suited to small sample sizes and has a strong statistical base [27]. In the disciplines of energy, ecology, hydrology, and economics, SVM has a wide range of applications [28][29][30][31][32]. In a regression issue, the training set is defined as [33,34] x j ,…”

Section: Variable Type Abbreviation (Unit) Descriptionmentioning

confidence: 99%

A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

et al. 2021

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Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.

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“…Due to the rise in diagnostic imaging usage, researchers have been able to investigate AI applications in medical image processing (13). Particularly in the diagnosis of pathological images, deep learning (DL) has demonstrated excellent effectiveness in resolving a number of medical image processing difficulties (14; 15; 16). There have been a number of studies done on the analysis of medical images through smartphones that use artificial intelligence-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

An ensemble deep learning model with empirical wavelet transform feature for oral cancer histopathological image classification

Deo¹,

Pal

Panigrahi

et al. 2022

Preprint

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Oral squamous cell carcinoma (OSCC) has become quite prevalent across many countries and poor prognosis is one of the major reasons for the ensuing high mortality rate. It mainly occurs in sites such as tongue, tonsil, oropharynx, gum, floor and other parts of the mouth. For early detection, the widely used approach is biopsy, in which a small portion of the tissue is taken from the mouth and examined under a disinfected and secure microscope. However, these observations do not effortlessly distinguish between normal and cancerous cells. Diagnosis of OSCC is generally done by pathologists who mostly rely on their years of empirical experience from tissue biopsy sections. The possibilities of human errors increase while detecting the cells using microscopy biopsy images physically. With the growth of artificial intelligence, deep learning models have gained immense importance in recent years and have become one of the core technologies in numerous fields including the prediction of lung cancer, breast cancer, oral cancer, and various medical diagnosis. It not only enhances accuracy, but also fastens the image classification process, as a result, lowering human errors andworkload. Here,we have made use of a customized deep-learning model for aiding pathologists in better OSCC detection from histopathological images.We accumulated and analyzed a complete set of 696 histopathological oral images, amongst them 80% have been taken in the training set, 10% of the images are included in the validation set, and the rest 10% for testing purposes. In this study, 2D empiricalwavelet transformis used to extract features from the images; later an ensemble of two pre-trained models, namely Resnet50 and Densenet201 are used for the classification of images into normal and OSCC classes. The efficacy of the model is assessed and compared in terms of accuracy, sensitivity, specificity, and ROC AUC scores. The simulation results show that the proposed model has achieved an accuracy of 92.00%. Thus, this method may be utilized for assisting in the binary classification of oral histopathological images.

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Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Cited by 39 publications

References 33 publications

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization

A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

An ensemble deep learning model with empirical wavelet transform feature for oral cancer histopathological image classification

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