Results 301 to 310 of about 6,422,664 (364)

Automatic detection of laughter

Interspeech 2005, 2005
In the context of detecting ‘paralinguistic events’ with the aim to make classification of the speaker’s emotional state possible, a detector was developed for one of the most obvious ‘paralinguistic events’, namely laughter. Gaussian Mixture Models were trained with Perceptual Linear Prediction features, pitch&energy, pitch&voicing and modulation ...
Truong, K.P., Leeuwen, D.A. van
openaire   +2 more sources

Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network.

Gastrointestinal Endoscopy, 2020
BACKGROUND AND AIMS Protruding lesions of the small bowel vary in wireless capsule endoscopy (WCE) images, and their automatic detection may be difficult.
Hiroaki Saito, Tomonori Aoki, Kazuharu Aoyama, Yusuke Kato, A. Tsuboi, Atsuo Yamada, M. Fujishiro, S. Oka, S. Ishihara, T. Matsuda, M. Nakahori, Shinji Tanaka, K. Koike, T. Tada   +13 more
semanticscholar   +1 more source

Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.

Radiology, 2019
Purpose To develop and validate a deep learning-based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods For this
J. Nam, Sunggyun Park, Eui Jin Hwang, Jong Hyuk Lee, K. Jin, K. Lim, T. Vu, J. Sohn, Sangheum Hwang, J. Goo, C. Park   +10 more
semanticscholar   +1 more source

Automatic Symmetry Detection for Promela

Journal of Automated Reasoning, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alastair F. Donaldson, Alice Miller 0001
openaire   +1 more source

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists

Biological Reviews of The Cambridge Philosophical Society
Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated.
Arik Kershenbaum, Çağlar Akçay, Lakshmi Babu-Saheer, Alexander Barnhill, Paul Best, J. Cauzinille, D. Clink, Angela Dassow, Emmanuel Dufourq, Jonathan Growcott, Andrew Markham, Barbara Marti-Domken, Ricard Marxer, Jen Muir, Sam Reynolds, Holly Root-Gutteridge, S. Sadhukhan, Loretta Schindler, Bethany R Smith, Dan Stowell, C. Wascher, Jacob C Dunn   +21 more
semanticscholar   +1 more source

Automatic Arrhythmia Detection

2010
In the present chapter, the authors have developed a tool for the automatic arrhythmias detection, based on time-frequency features and using a Support Vector Machines (SVM) as classifier. Arrhythmia Database Massachusetts Institute of Technology (MIT) has been used in the work in order to detect eight different states, seven are pathologies and one is
Travieso, Carlos M., Alonso, Jesús B., Ferrer, Miguel A., Corsino, Jorge   +3 more
openaire   +2 more sources

Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms

Physica A: Statistical Mechanics and its Applications, 2019
Depending on the severity of the leaf spot disease in the field, it can cause a loss in sugar yield by 10% to 50%. Therefore, disease symptoms should be detected on-time and relevant measures should be taken instantly to prevent further spread or ...
M. Ozguven, K. Adem
semanticscholar   +1 more source

Automatic Detection of QRS Complexes Using Dual Channels Based on U-Net and Bidirectional Long Short-Term Memory

IEEE journal of biomedical and health informatics, 2020
Objective: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients.
Runnan He, Yang Liu, Kuanquan Wang, Na Zhao, Yongfeng Yuan, Qince Li, Henggui Zhang   +6 more
semanticscholar   +1 more source

Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram

Comput. Methods Programs Biomed., 2019
BACKGROUND AND OBJECTIVE This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal.
E. Urtnasan, Y. Kim, Jonguk Park, E. Joo, Kyoung-Joung Lee   +4 more
semanticscholar   +1 more source

Automatic detection of apnoea of prematurity

Physiological Measurement, 2011
The detection of the incidents of apnoea of prematurity (AP) in preterm infants is important in the intensive care unit, but this detection is often based on simple threshold techniques, which suffer from poor specificity. Three methods for the automatic detection of AP were designed, tested and evaluated using approximately 2426 h of continuous ...
Belal, Suliman Yousef, Emmerson, Anthony John, Beatty, Paul Charles William   +2 more
openaire   +3 more sources