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A Survey on Hand Gesture Recognition Systems

Ankith A., E. Sasikala
2018 International Journal of Computer Applications  
Deep learning Gestures can be classified using artificial neural networks. The systems proposed in [7] and [12] use neural networks to classify gestures. Figure 6.  ...  The systems specified in uWave [4] and Sony [5] , [6] are designed to classify dynamic gestures exclusively.  ... 
doi:10.5120/ijca2018916759 fatcat:zgpkim6wpncppcigkiwyjg24di

Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection Zero-day Phishing Email [article]

Ammar ALmomani, B. B. Gupta, Tat-Chee Wan, Altyeb Altaher, Selvakumar Manickam
2013 arXiv   pre-print
The implementation of the proposed framework adapts the evolving clustering method (ECM) as a part of the dynamic evolving neural fuzzy inference system (DENFIS) in an online mode(N.  ...  Kasabov & Song, 2002; snjezana soltic, 2006) along the dynamic neural fuzzy inference system (DyNFIS) to enhance the rule creation in an offline mode (Y. C. Hwang & Q. Song, 2009 ).  ... 
arXiv:1302.0629v1 fatcat:u6kwl3cfeze37b3fpfslz7fl4e

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Georgios Batsis, Panagiotis Partsinevelos, Georgios Stavrakakis
2021 Energies  
The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of  ...  For the sake of completeness and flexibility, a Multi-Task Neural Network is developed.  ...  Energies 2021, 14, x FOR PEER REVIEW 18 of 32 Figure 7 . 7 Decision-making system flowchart in Mode II. Figure 7 . 7 Decision-making system flowchart in Mode II.  ... 
doi:10.3390/en14206773 fatcat:24hrnet5sreoflniirnkndnpn4

Page 375 of Neural Computation Vol. 8, Issue 2 [page]

1996 Neural Computation  
To study such questions we inves- tigate a symmetric neural system, in which all possible waveforms can be classified by symmetry and followed through numerical simulations.  ...  Moreover, by tracing the way this dynamic system operates, we find that the phenomenon of subharmonic oscillations, which can be obtained only in nonlinear systems, is respon- sible for segmentation.  ... 

Neural Network-based Actuator Fault Diagnosis for Attitude Control Subsystem of an Unmanned Space Vehicle

I.A. AlZyoud, K. Khorasani
2006 The 2006 IEEE International Joint Conference on Neural Network Proceedings  
A generalized embedded structure for the dynamic neuron model is considered in the DMLP network.  ...  Second, a static neural classifier is developed based on the Learning Vector Quantization (LVQ) network to serve as an isolation technique.  ...  In this work, the healthy mode is assigned for class No. 1, while the faulty mode is assigned for two classes (motor current fault is assigned for class No. 2, and bus voltage fault is assigned for class  ... 
doi:10.1109/ijcnn.2006.247383 dblp:conf/ijcnn/Al-ZyoudK06 fatcat:2odk6znl3vfn5j7uvbfgjx7cym

Sensor Fault Diagnosis in State Feedback Systems using Artificial Neural Networks

V Manikandan, K Ramakrishan, N Devarajan, C.K Babu, R VenkatateswaraBhupathi
2007 International Journal on Intelligent Electronic Systems  
The Integral Square Error (ISE) criterion is employed for extracting the signature of the fault and the classification is done using Artificial Neural Network (ANN) classifier.  ...  The proposed diagnosis approach is applied to a dc motor system to validate the effectiveness of the technique.program inspections, static & dynamic analysis and V&V techniques  ...  Fig. 4 . 4 a System states -current and speed -vs Time Fig. 4. B Error dynamics for current Fig. 4.  ... 
doi:10.18000/ijies.30010 fatcat:sol2ub4ua5cwpbu3rr46ufy244

Dynamical Behaviors of Periodically Forced Hindmarsh-Rose Neural Model: The Role of Excitability and `Intrinsic' Stochastic Resonance

Yuqing Wang, Z. D. Wang, W. Wang
2000 Journal of the Physical Society of Japan  
, bi-modal firing, and intrinsic oscillation, in terms of which we can roughly classify the relevant experimental observations on the periodically forced sensory neural systems through their dynamical  ...  In the presence/absence of external noise, dynamical behaviors of periodically forced neural systems and firing modes of interspike interval (ISI) are investigated by employing the Hindmarsh-Rose model  ...  Remarks and Conclusions Previous studies classified the ISIHs merely in terms of their detail distributions or shapes. 12, 21) Here, for the periodically forced sensory neural systems, the ISIHs are  ... 
doi:10.1143/jpsj.69.276 fatcat:bsicg5dnz5b3fpeimum5jaz4fi

ART-based multiple neural networks for monitoring offshore platforms

Lalu Mangal, V.G. Idichandy, C. Ganapathy
1996 Applied Ocean Research  
A multiple neural network system is adopted which enables the problem to be decomposed into smaller ones, facilitating easier solution.  ...  An adaptive resonance theory (ART) neural network is used for damage diagnosis and its advantages and limitations are investigated.  ...  This mode is also capable of detecting slow changes. These three modes are very useful in online monitoring systems for offshore platforms.  ... 
doi:10.1016/0141-1187(96)00024-7 fatcat:afvf767xcbcblkjioirnosn6zm

Off-Line Signature Recognition Systems

H B Kekre, V A Bharadi
2010 International Journal of Computer Applications  
We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system.  ...  We review existing techniques, their performance and method for feature extraction.  ...  J. hasna [22] have proposed a neural network based prototype for dynamic signature recognition, the system used method of verification by the Conjugate Gradient Neural Network (NN), and the FRR achieved  ... 
doi:10.5120/499-815 fatcat:4gjgolb3pzcanid6wor4k7aklq

Neurocomputing Techniques to Predict the 2D Structures by Using Lattice Dynamics of Surfaces

A. Belayadi, B. Bourahla, F. Mekideche-Chafa
2017 Acta Physica Polonica. A  
A theoretical study of artificial neural network modelling, based on vibrational dynamic data for 2D lattice, is proposed in this paper.  ...  Results showed that the method of collecting the dataset was very suitable for building a neurocomputing model that is able to predict and classify the 2D surface of the crystals.  ...  Dynamic of the diatomic perfect 2D structure The model system studied, in this work, is schematized in Fig. 1 .  ... 
doi:10.12693/aphyspola.132.1314 fatcat:3sqzmlevajgllio3d6kckrrqvu

Neural Network Based Intelligent Retrieval System for Verifying Dynamic Signatures

Ankita Wadhawan, Avani Bhatia
2015 International Journal of Advanced Science and Technology  
In this paper a neural network based approach of data mining is used to verify dynamic signature patterns.  ...  The results show that the system with neural network has better performance as compared to support vector machines.  ...  In this research online signature verification system for verifying Punjabi signatures is proposed. Signatures are classified by using neural networks based predictive model.  ... 
doi:10.14257/ijast.2015.83.03 fatcat:wpark5zqbzek5oqrtgkhvhnxba

Robust fault diagnosis for an exothermic semi-batch polymerization reactor under open-loop

Abdelkarim M. Ertiame, Dingli Yu, Feng Yu, J.B. Gomm
2014 Systems Science & Control Engineering  
The independent Radial Basis Function (RBF) Neural Network (RBFNN) is employed here for on-line diagnosis of faults on the actuator, sensors, and reactor components when the system is subjected to system  ...  Secondly, an additional RBF neural network is developed as a classifier to isolate faults from the generated residuals.  ...  Independent Model of RBF Modelling Using RBFNN for modelling, a non-linear dynamic system can be modelled in two modes: a dependent mode and an independent mode.  ... 
doi:10.1080/21642583.2014.984356 fatcat:5g2n2ia7nncf7fny7tjao3dobu

Wearable networked sensing for human mobility and activity analytics: A systems study

Bo Dong, Subir Biswas
2012 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012)  
This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis.  ...  Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors  ...  Further details about the recognition inaccuracies for the layer-2 dynamic classifier are shown in the form a confusion matrix (for Subject 1 using Neural Networks) in Fig. 7 .  ... 
doi:10.1109/comsnets.2012.6151376 pmid:25530911 pmcid:PMC4269838 dblp:conf/comsnets/DongB12 fatcat:774plant7rcslmcphfdrd4hywa

Patient prognosis from vital sign time series: Combining convolutional neural networks with a dynamical systems approach

Li-wei Lehman, Mohammad Ghassemi, Jasper Snoek, Shamim Nemati
2015 2015 Computing in Cardiology Conference (CinC)  
In this work, we propose a stacked switching vectorautoregressive (SVAR)-CNN architecture to model the changing dynamics in physiological time series for patient prognosis.  ...  modes.  ...  patterns among the dynamical modes using Convolutional Neural Networks (CNNs).  ... 
doi:10.1109/cic.2015.7411099 pmid:27790623 pmcid:PMC5079526 dblp:conf/cinc/LehmanGSN15 fatcat:2nte4e6qgjccvpdfar6ukbm6m4

Physically-interpretable classification of biological network dynamics for complex collective motions [article]

Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara
2020 arXiv   pre-print
Here we apply a data-driven spectral analysis called graph dynamic mode decomposition, which obtains the dynamical properties for collective motion classification.  ...  Our approach contributes to the understanding of principles of biological complex network dynamics from the perspective of nonlinear dynamical systems.  ...  For the neural network approaches [46, 36] , we used the default softmax layer as classifiers.  ... 
arXiv:1905.04859v2 fatcat:ble6ayvjdrcpldbetz3lazmffy
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