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Found 9 papers, 0 papers with code
Self-supervised Learning for Electroencephalogram: A Systematic Survey
2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods.
Guidelines in Wastewater-based Epidemiology of SARS-CoV-2 with Diagnosis
With the global spread and increasing transmission rate of SARS-CoV-2, more and more laboratories and researchers are turning their attention to wastewater-based epidemiology (WBE), hoping it can become an effective tool for large-scale testing and provide more ac-curate predictions of the number of infected individuals.
Infrared Small Target Detection Using Double-Weighted Multi-Granularity Patch Tensor Model With Tensor-Train Decomposition
First, to capture different granularity information of tensor from multiple modes, a multi-granularity infrared patch tensor (MGIPT) model is constructed by collecting nonoverlapping patches and tensor augmentation based on the tensor train (TT) decomposition.
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks
The presence of residual and dense neural networks which greatly promotes the development of image Super-Resolution(SR) have witnessed a lot of impressive results.
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis
Futhermore, we abandoned this paper due to its limitation only applied on medical images.
Medical image super-resolution method based on dense blended attention network
In order to address the issue that medical image would suffer from severe blurring caused by the lack of high-frequency details in the process of image super-resolution reconstruction, a novel medical image super-resolution method based on dense neural network and blended attention mechanism is proposed.
What works and what does not: Classifier and feature analysis for argument mining
This paper offers a comparative analysis of the performance of different supervised machine learning methods and feature sets on argument mining tasks.
Vehicle Speed Prediction using Deep Learning
The purpose of this paper is to research the application of Deep Learning techniques to this problem to identify at the beginning of a drive cycle the driver specific vehicle speed profile for an individual driver repeated drive cycle, which can be used in an optimization algorithm to minimize the amount of fossil fuel energy used during the trip.
