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Found 11 papers, 7 papers with code
SyMPox: An Automated Monkeypox Detection System Based on Symptoms Using XGBoost
Monkeypox is a zoonotic disease.
Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods
The research aims to provide an ML model based on symptoms for the diagnosis of monkeypox.
PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard Voting Ensemble Method for Parkinson's Disease
Parkinson's disease (PD) is the most widespread movement condition and the second most common neurodegenerative disorder, following Alzheimer's.
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition
In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output.
Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Method
Moreover, the Hard Voting Ensemble Method was determined based on the performance of the four classifiers.
Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods
The Inception-ResNet-v2 which has both residual and inception networks profits together has shown the best feature extraction capability in the case of breast cancer histopathology images among all tested CNNs.
Ranked #1 on
Breast Cancer Detection
on BreakHis
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction
MFRFNN consists of two fuzzy neural networks with Takagi-Sugeno-Kang fuzzy rules, one is used to produce the output, and the other to determine the system’s state.
Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM
To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used.
Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM
The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak.
A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images
The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives.
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
In late 2019 and after COVID-19 pandemic in the world, many researchers and scholars have tried to provide methods for detection of COVID-19 cases.
