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Found 9 papers, 0 papers with code
Standardized Analysis Ready (STAR) data cube for high-resolution Flood mapping using Sentinel-1 data
Floods are one of the most common disasters globally.
Introducing Feature Attention Module on Convolutional Neural Network for Diabetic Retinopathy Detection
The simultaneous learning of attention weights for the features and thereupon the combination of attention-modulated features within the feature attention block facilitates the network's ability to focus on relevant information while reducing the impact of noisy or irrelevant features.
Transfer-Ensemble Learning based Deep Convolutional Neural Networks for Diabetic Retinopathy Classification
This article aims to classify diabetic retinopathy (DR) disease into five different classes using an ensemble approach based on two popular pre-trained convolutional neural networks: VGG16 and Inception V3.
T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple Localizations based Spatial Attention Mechanisms for Covid-19 Detection
The proposed T-Fusion Net and the homogeneous ensemble model exhibit better performance, as compared to other state-of-the-art methods, achieving accuracy of 97. 59% and 98. 4%, respectively.
Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN
The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods.
MUTE-Reco: MUTual Information Assisted Ensemble Feature RECOmmender System for Healthcare Prognosis
The performance of the proposed recommender is compared with four state-of-the-art methods using recommender systems' performance metrics like average precision@K, precision@K, recall@K, F1@K, reciprocal rank@K. Experimental results show that the model built with the recommended features can attain a higher accuracy (96. 6% and 98. 6% using support vector machine and neural network, respectively) for classifying different stages of ccRCC with a reduced feature set as compared to existing methods.
Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks
But, these models fail to handle sudden and drastic changes in the price trend.
Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks
This method with two countries, Spain and Brazil, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.
RelDenClu: A Relative Density based Biclustering Method for identifying non-linear feature relations
For all the datasets the performance of the proposed method is compared with that of seven different state-of-the-art algorithms and the proposed algorithm is seen to produce better results.
