This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity ...
This is an agile repository to perform cell unsupervised clustering using Hibou-L model to extract the cell embeddings. Each embedding has a size of 1024 for which dimensionality reductions is also ...
Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!
The advancement of tactile sensing in robotics and prosthetics is constrained by the trade-off between spatial and temporal resolution in artificial tactile sensors. To address this limitation, we ...
ABSTRACT: Polychlorinated biphenyls are toxic, man-made, organic chemicals that have hazardous effects on the environment and our health, yet their occurrence in sediments and water from drinking ...
Abstract: Large-scale sparse multiobjective optimization problems (LSMOPs) are of great significance in the context of practical applications, such as critical node detection, feature selection, and ...
Abstract: Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data ...
This project classifies text messages as either spam or ham (not spam) for feature extraction and Support Vector Machines (SVM) for classification. Python: The programming language used for model ...
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