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Found 9 papers, 0 papers with code
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis
To characterize this mosquito neural activity, it is essential to classify the generated electrical spikes.
AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach
The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others.
Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit
Streamlined weirs which are a nature-inspired type of weir have gained tremendous attention among hydraulic engineers, mainly owing to their established performance with high discharge coefficients.
FCM-DNN: diagnosing coronary artery disease by deep accuracy Fuzzy C-Means clustering model
First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models.
Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals.
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods
This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series.
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients
To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images.
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data.
