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Pattern Recognition, Automated

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Deep Learning with Taxonomic Loss for Plant Identification.

Computational intelligence and neuroscience
Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which ...

Deep supervised learning with mixture of neural networks.

Artificial intelligence in medicine
Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single mo...

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L-Regularization.

Computational intelligence and neuroscience
Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust mode...

Fuzzy cognitive maps (FCMs) for the analysis of team communication.

Applied ergonomics
Communication in teams plays a vital role in team success. This work proposes Fuzzy Cognitive Maps (FCMs) as a formalized, team communication methodology for the analysis of content and flow, simultaneously, of naturalistic team communication in a st...

Adaptive Calibration of Electrode Array Shifts Enables Robust Myoelectric Control.

IEEE transactions on bio-medical engineering
OBJECTIVE: The objective of this work is to develop a novel method for adaptive calibration of the electrode array shifts toward achieving robust myoelectric pattern-recognition control.

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features.

Medical hypotheses
Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods...

A Multibranch Object Detection Method for Traffic Scenes.

Computational intelligence and neuroscience
The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its...

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.

Medical image analysis
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we pro...

Computational Approaches to Comics Analysis.

Topics in cognitive science
Comics are complex documents whose reception engages cognitive processes such as scene perception, language processing, and narrative understanding. Possibly because of their complexity, they have rarely been studied in cognitive science. Modeling th...

Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Medical image analysis
Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which results in little to no contrast at the lung boundary. For segmentation of severely injured lungs, loc...