Classification of On-Line Mathematical Symbols with Hybrid Features and Recurrent Neural Networks

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

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Detection of Roller Bearing Conditions Using Mechatronics Approach

10.1115/imece1999-0732 ◽

1999 ◽

Author(s):

T. I. Liu ◽

F. Ordukhani

Keyword(s):

Neural Networks ◽

Roller Bearing ◽

Diagnostic System ◽

Feature Selection Technique ◽

Vibration Signals ◽

Selection Technique ◽

On Line ◽

Line Classification ◽

Nonlinear Pattern

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

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Automate Labeling Of Bugs and Tickets Using Attention-Based Mechanism in Recurrent Neral Networks

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38982 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1411-1418

Author(s):

Ravi Kauthale

Keyword(s):

Neural Networks ◽

Text Classification ◽

Recurrent Neural Networks ◽

Support Systems ◽

The Neural Networks

Abstract: The aim here is to explore the methods to automate the labelling of the information that is present in bug trackers and client support systems. This is majorly based on the classification of the content depending on some criteria e.g., priority or product area. Labelling of the tickets is important as it helps in effective and efficient handling of the ticket and help is quicker and comprehensive resolution of the tickets. The main goal of the project is to analyze the existing methodologies used for automated labelling and then use a newer approach and compare the results. The existing methodologies are the ones which are based of the neural networks and without neural networks. In this project, a newer approach based on the recurrent neural networks which are based on the hierarchical attention paradigm will be used. Keywords: Automate Labeling, Recurrent Neural Networks, Hierarchical Attention, Multi-class Text Classification, GRU

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