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Speaker-turn aware diarization for speech-based cognitive assessments

Frontiers in neuroscience, 2024-01, Vol.17, p.1351848

2024

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Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks

IEEE journal of biomedical and health informatics, 2019-07, Vol.23 (4), p.1574-1584

2019

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Inter-patient ECG classification with i-vector based unsupervised patient adaptation

Expert systems with applications, 2022-12, Vol.210, p.118410, Article 118410

2022

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A new optimization algorithm for power saving in backbone networks

2014 23rd International Conference on Computer Communication and Networks (ICCCN), 2014, p.1-6

2014

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A new terminating condition to identify the convergence of the learning process in multi-layer feed-forward neural networks

2015 International Joint Conference on Neural Networks (IJCNN), 2015, p.1-6

2015

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I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification

IEEE journal of biomedical and health informatics, 2020-03, Vol.24 (3), p.717-727

2020

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Deep neural networks versus support vector machines for ECG arrhythmia classification

2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2017, p.127-132

2017

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A systematic algorithm to escape from local minima in training feed-forward neural networks

2016 International Joint Conference on Neural Networks (IJCNN), 2016, p.396-402

2016

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Speaker Turn Aware Similarity Scoring for Diarization of Speech-Based Cognitive Assessments

2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2021, p.1299-1304

2021

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Age-Invariant Speaker Embedding for Diarization of Cognitive Assessments

2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2021, p.1-5

2021

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Patient-Specific Heartbeat Classification Based on I-Vector Adapted Deep Neural Networks

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, p.784-787

2018

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Further enhancements in WOM algorithm to solve the local minimum and flat-spot problem in feed-forward neural networks

2014 International Joint Conference on Neural Networks (IJCNN), 2014, p.1225-1230

2014

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Solving the local minimum and flat-spot problem by modifying wrong outputs for feed-forward neural networks

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013, p.1-7

2013

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A new fast learning algorithm with promising global convergence capability for feed-forward neural networks

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013, p.1-6

2013

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A fast learning algorithm with Promising Convergence Capability

The 2011 International Joint Conference on Neural Networks, 2011, p.937-942

2011

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Enhanced Two-Phase method in fast learning algorithms

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, p.1-7

2010

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Speaker-turn aware diarization for speech-based cognitive assessments

Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks

Inter-patient ECG classification with i-vector based unsupervised patient adaptation

A new optimization algorithm for power saving in backbone networks

A new terminating condition to identify the convergence of the learning process in multi-layer feed-forward neural networks

I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification

Deep neural networks versus support vector machines for ECG arrhythmia classification

A systematic algorithm to escape from local minima in training feed-forward neural networks

Speaker Turn Aware Similarity Scoring for Diarization of Speech-Based Cognitive Assessments

Age-Invariant Speaker Embedding for Diarization of Cognitive Assessments

Patient-Specific Heartbeat Classification Based on I-Vector Adapted Deep Neural Networks

Further enhancements in WOM algorithm to solve the local minimum and flat-spot problem in feed-forward neural networks

Solving the local minimum and flat-spot problem by modifying wrong outputs for feed-forward neural networks

A new fast learning algorithm with promising global convergence capability for feed-forward neural networks

A fast learning algorithm with Promising Convergence Capability

Enhanced Two-Phase method in fast learning algorithms

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