Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Nicolaos B, Karayiannis"'
Autor:
Eli M. Mizrahi, Joyeeta Mitra, Periklis Y. Ktonas, Arun Thitai Kumar, Richard A. Hrachovy, Amit Mukherjee, Nicolaos B. Karayiannis, J.R. Glover, James D. Frost
Publikováno v:
Journal of Clinical Neurophysiology. 26:218-226
This paper describes the design and test results of a three-stage automated system for neonatal EEG seizure detection. Stage I of the system is the initial detection stage and identifies overlapping 5-second segments of suspected seizure activity in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52d1688b4998d60ff0cc08b82adeb0b5
https://doi.org/10.1097/wnp.0b013e3181b2f29d
https://doi.org/10.1097/wnp.0b013e3181b2f29d
Publikováno v:
Computer Communications. 29:3182-3196
This paper introduces an entropy-constrained algorithm for routing of communication networks. The proposed formulation of the routing problem allows multiple nodes to compete for each position in the route, with the associated uncertainty measured by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46151374768c32678f0cbfc44d868640
https://doi.org/10.1016/j.comcom.2006.04.016
https://doi.org/10.1016/j.comcom.2006.04.016
Autor:
Nicolaos B. Karayiannis, Eli M. Mizrahi, Guozhi Tao, Merrill S. Wise, Richard A. Hrachovy, James D. Frost
Publikováno v:
Clinical Neurophysiology. 117:1585-1594
Objective This study was aimed at the development of a seizure detection system by training neural networks using quantitative motion information extracted by motion segmentation methods from short video recordings of infants monitored for seizures.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6193377bdbabc20640511a286050b562
https://doi.org/10.1016/j.clinph.2005.12.030
https://doi.org/10.1016/j.clinph.2005.12.030
Autor:
Richard A. Hrachovy, Eli M. Mizrahi, Guozhi Tao, Merrill S. Wise, Nicolaos B. Karayiannis, Yaohua Xiong, James D. Frost
Publikováno v:
Epilepsia. 47:966-980
Summary: Purpose: This study aimed at the development of a seizure-detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of the myoclonic and focal clonic types and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e2e2a5a59cd2dbfe860dc24e25ebc4b
https://doi.org/10.1111/j.1528-1167.2006.00571.x
https://doi.org/10.1111/j.1528-1167.2006.00571.x
Autor:
Nicolaos B. Karayiannis, Guozhi Tao
Publikováno v:
Image and Vision Computing. 24:27-40
This paper presents a procedure developed to extract quantitative motion information from video recordings of neonatal seizures in the form of temporal motion strength signals. Temporal motion strength signals are obtained from a sequence of video fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e26ee4a12b34cf6bdab13b791fff7948
https://doi.org/10.1016/j.imavis.2005.09.009
https://doi.org/10.1016/j.imavis.2005.09.009
Publikováno v:
International Journal of Neural Systems. 15:323-338
This paper proposes a framework for training feedforward neural network models capable of handling class overlap and imbalance by minimizing an error function that compensates for such imperfections of the training set. A special case of the proposed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74a6e9c5c488bb8019e0904862ed086d
https://doi.org/10.1142/s012906570500030x
https://doi.org/10.1142/s012906570500030x
Publikováno v:
Information Sciences. 174:177-196
This paper proposes a new approach to control nonlinear discrete dynamic systems, which relies on the identification of a discrete model of the system by a neural network. A locally equivalent optimal linear model is obtained from the neural network
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::adc0da631dc4efad60e63fb73e9d3837
https://doi.org/10.1016/j.ins.2004.08.002
https://doi.org/10.1016/j.ins.2004.08.002
Autor:
J.R. Glover, James D. Frost, Amit Mukherjee, Nicolaos B. Karayiannis, Eli M. Mizrahi, Richard A. Hrachovy
Publikováno v:
Soft Computing. 10:382-396
This paper presents the results of an experimental study that evaluated the ability of quantum neural networks (QNNs) to capture and quantify uncertainty in data and compared their performance with that of conventional feedforward neural networks (FF
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e5640ee333e8ac908b0e67ef94c1f11
https://doi.org/10.1007/s00500-005-0498-4
https://doi.org/10.1007/s00500-005-0498-4
Publikováno v:
IEEE Transactions on Neural Networks. 16:423-435
This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c4e7af0ee94b077d0f2f0815a4e5d12
https://doi.org/10.1109/tnn.2004.841778
https://doi.org/10.1109/tnn.2004.841778
Publikováno v:
International Journal of Intelligent Systems. 20:591-605
This article presents the results of a study aimed at the development of a system for short-term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural network
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a7f78609fc96e29b33a82ac4a83c9f3
https://doi.org/10.1002/int.20084
https://doi.org/10.1002/int.20084