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Found 6 papers, 0 papers with code
Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window
Recently, the EAGL-I system was developed to rapidly create massive labeled datasets of plants intended to be commonly used by farmers and researchers to create AI-driven solutions in agriculture.
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
The tools and technology that are currently used to analyze chemical compound structures that identify polymer types in microplastics are not well-calibrated for environmentally weathered microplastics.
Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions
We present the comprehensive taxonomy of multimodal co-learning based on the challenges addressed by co-learning and associated implementations.
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography
Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI.
1-Dimensional polynomial neural networks for audio signal related problems
We show that this non-linearity enables the model to yield better results with less computational and spatial complexity than a regular 1DCNN on various classification and regression problems related to audio signals, even though it introduces more computational and spatial complexity on a neuronal level.
Near real-time map building with multi-class image set labelling and classification of road conditions using convolutional neural networks
The EfficientNet-B4 framework was found to be most suitable to this problem, achieving validation accuracy of 90. 6%, although EfficientNet-B0 achieved an accuracy of 90. 3% with half the execution time.
