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Hits 1 – 20 of 28
1
Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal
Seung-Hyun Kim; Su-Min Jeon; Eui Chul Lee
In: Sensors; Volume 22; Issue 8; Pages: 3070 (2022)
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2
Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks
Jiawei Li; Zhidong Zhang; Xiaolong Zhu; Yunlong Zhao; Yuhang Ma; Junbin Zang; Bo Li; Xiyuan Cao; Chenyang Xue
In: Micromachines; Volume 13; Issue 4; Pages: 501 (2022)
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3
Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model
Kanchon Kanti Podder; Muhammad E. H. Chowdhury; Anas M. Tahir; Zaid Bin Mahbub; Amith Khandakar; Md Shafayet Hossain; Muhammad Abdul Kadir
In: Sensors; Volume 22; Issue 2; Pages: 574 (2022)
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4
TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants
Guillermo Cámbara; Fernando López; David Bonet; Pablo Gómez; Carlos Segura; Mireia Farrús; Jordi Luque
In: Applied Sciences; Volume 12; Issue 4; Pages: 1974 (2022)
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5
Comparing Encoder-Decoder Architectures for Neural Machine Translation: A Challenge Set Approach ...
Doan, Coraline
. - : Université d'Ottawa / University of Ottawa, 2021
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6
Keyword Detection Based on RetinaNet and Transfer Learning for Personal Information Protection in Document Images
Guo-Shiang Lin
;
Jia-Cheng Tu
;
Jen-Yung Lin
In: Applied Sciences ; Volume 11 ; Issue 20 (2021)
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7
Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges
Sang Hoon Kim
;
Yun Jeong Lim
In: Diagnostics ; Volume 11 ; Issue 9 (2021)
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8
A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost
Aya Ismail
;
Marwa Elpeltagy
;
Mervat S. Zaki
...
In: Sensors ; Volume 21 ; Issue 16 (2021)
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9
Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique
Fuad Al Abir; Md. Al Siam; Abu Sayeed; Md. Al Mehedi Hasan; Jungpil Shin
In: Sensors; Volume 21; Issue 24; Pages: 8407 (2021)
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10
Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process
Mehrez Boulares
;
Reem Alotaibi
;
Amal AlMansour
;
Ahmed Barnawi
In: International Journal of Environmental Research and Public Health ; Volume 18 ; Issue 20 (2021)
Abstract:
Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.
Keyword:
convolutional neural network
;
CVD
;
deep learning
;
denoising
;
heart sounds
;
PCG
;
segmentation
URL:
https://doi.org/10.3390/ijerph182010952
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11
Robust 3D Face Reconstruction Using One/Two Facial Images
Ola Lium
;
Yong Bin Kwon
;
Antonios Danelakis
...
In: Journal of Imaging ; Volume 7 ; Issue 9 (2021)
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12
Dynamic gesture classification of American Sign Language using deep learning
Vaghasiya, Devina
. - : Laurentian University of Sudbury, 2021
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13
Comparing Encoder-Decoder Architectures for Neural Machine Translation: A Challenge Set Approach
Doan, Coraline
. - : Université d'Ottawa / University of Ottawa, 2021
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14
СПОСОБ ПОЛУЧЕНИЯ ИНФОРМАЦИИ ОБ ОБЪЕКТЕ НА ОСНОВЕ АНАЛИЗА ЕГО ИЗОБРАЖЕНИЙ ... : THE CHOICE OF THE METHOD OF OBTAINING INFORMATION ABOUT THE OBJECT BASED ON THE ANALYSIS OF ITS IMAGES ...
Локтев, Даниил Алексеевич
;
Локтев, Алексей Алексеевич
. - : Институт проблем управления им. В. А. Трапезникова РАН, 2020
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15
A Deep Neural Network-Based Model for Named Entity Recognition for Hindi Language
Sharma, Richa
;
Agarwal, Basant
;
Khan, Mohammad S.
...
In: ETSU Faculty Works (2020)
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16
Automatic Language Identification Using Speech Rhythm Features for Multi-Lingual Speech Recognition
Hwamin Kim
;
Jeong-Sik Park
In: Applied Sciences ; Volume 10 ; Issue 7 (2020)
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17
An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism
Li
;
Liu
;
Zhang
In: Future Internet ; Volume 11 ; Issue 4 (2019)
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18
Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps
Yue Sun
;
Songmin Dai
;
Jide Li
...
In: Future Internet ; Volume 11 ; Issue 2 (2019)
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19
An Optimized Abstractive Text Summarization Model Using Peephole Convolutional LSTM
Rahman
;
Siddiqui
In: Symmetry ; Volume 11 ; Issue 10 (2019)
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20
Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China
Bai
;
Sun
;
Zang
...
In: Energies ; Volume 12 ; Issue 17 (2019)
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