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Found 8 papers, 0 papers with code
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning
The existing approaches employed very large and complex deep ConvNet or 2SRNN-based domain adaptation methods to approximate the distribution shift caused by these inter-session and inter-subject data variability.
AI2: The next leap toward native language based and explainable machine learning framework
The primary contribution of the AI$^{2}$ framework allows a user to call the machine learning algorithms in English, making its interface usage easier.
ck-means, a novel unsupervised learning method that combines fuzzy and crispy clustering methods to extract intersecting data
The algorithm is also able to find the optimal number of clusters for the FCM and the k-means algorithm, according to the consistency of the clusters given by the Silhouette Index (SI).
Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation
This paper proposes a novel metric named "Explainable Global Error Weighted on Feature Importance"(xGEWFI).
DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction
The method applies a regression or a classification, evaluates the results, and gives a diagnosis about the best dimensionality reduction process in this specific supervised learning context.
Feature selection or extraction decision process for clustering using PCA and FRSD
A clustering algorithm is an unsupervised method.
Unsupervised Machine learning methods for city vitality index
Based on the resulting clusters and VI, a linear regression is applied to predict the VI of each district of a city.
S-ConvNet: A Shallow Convolutional Neural Network Architecture for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG Images
Without using any pre-trained models, our proposed S-ConvNet and All-ConvNet demonstrate very competitive recognition accuracy to the more complex state of the art for neuromuscular activity recognition based on instantaneous HD-sEMG images, while using a ~ 12 x smaller dataset and reducing learning parameters to a large extent.
