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Dimensionality Reduction

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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning.

PloS one 39913503
Autonomous transportation systems have the potential to greatly impact the way we travel. A vital aspect of these systems is their connectivity, facilitated by intelligent transport applications. However, the safety ensured by the vehicular network c...
Algorithms Deep Learning Dimensionality Reduction Humans Principal Component Analysis
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Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

IEEE journal of biomedical and health informatics 39509308
In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality...
Adult Algorithms Brain Brain-Computer Interfaces Dimensionality Reduction Electroencephalography Humans Machine Learning Neural Networks, Computer Principal Component Analysis Signal Processing, Computer-Assisted
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A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques.

Food research international (Ottawa, Ont.) 40356129
Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This study quantified 15 PFAS compounds across various tissues (liver, kidney, gill, muscle, skin, lung, b...
Animals Birds Cluster Analysis Dimensionality Reduction Environmental Monitoring Fishes Fluorocarbons Food Contamination Liver Livestock Machine Learning Tissue Distribution Water Pollutants, Chemical
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EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA.

Scientific reports 40274853
This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. The goal is to evaluate the effectiveness and performance of these models in accurately identifying epilepti...
Algorithms Databases, Factual Dimensionality Reduction Electroencephalography Epilepsy Humans Neural Networks, Computer Principal Component Analysis Support Vector Machine
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