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Sven Nõmm
Sven Nõmm
Tenured associate professor, Department of Software Science, Tallinn University of Technology
Verified email at ttu.ee
Title
Cited by
Cited by
Year
Unsupervised Anomaly Based Botnet Detection in IoT Networks
S Nõmm, H Bahºi
2018 17th IEEE International Conference on Machine Learning and Applications …, 2018
1212018
Dimensionality Reduction for Machine Learning Based IoT Botnet Detection
H Bahºi, S Nõmm, FB La Torre
2018 15th International Conference on Control, Automation, Robotics and …, 2018
1142018
MedBIoT: Generation of an IoT Botnet Dataset in a Medium-sized IoT Network.
A Guerra-Manzanares, J Medina-Galindo, H Bahsi, S Nõmm
ICISSP, 207-218, 2020
1082020
KronoDroid: Time-based hybrid-featured dataset for effective android malware detection and characterization
A Guerra-Manzanares, H Bahsi, S Nõmm
Computers & Security 110, 102399, 2021
702021
On realizability of neural networks-based input–output models in the classical state-space form
Ü Kotta, FN Chowdhury, S Nõmm
Automatica 42 (7), 1211-1216, 2006
562006
Hybrid feature selection models for machine learning based botnet detection in IoT networks
A Guerra-Manzanares, H Bahsi, S Nõmm
2019 International Conference on Cyberworlds (CW), 324-327, 2019
542019
Neural networks based ANARX structure for identification and model based control
E Petlenkov, S Nomm, U Kotta
2006 9th International Conference on Control, Automation, Robotics and …, 2006
392006
On a new type of neural-network-based input-output model: the ANARMA structure
Ü Kotta, S Nõmm, FN Chowdhury
IFAC Proceedings Volumes 34 (6), 1535-1538, 2001
342001
Linear input-output equivalence and row reducedness of discrete-time nonlinear systems
Ü Kotta, Z Bartosiewicz, S Nomm, E Pawluszewicz
IEEE Transactions on Automatic Control 56 (6), 1421-1426, 2011
292011
Monitoring of the human motor functions rehabilitation by neural networks based system with kinect sensor
S Nomm, K Buhhalko
IFAC Proceedings Volumes 46 (15), 249-253, 2013
272013
Classical state space realizability of input-output bilinear models
Ü Kotta, S Nomm, ASI Zinober
International Journal of Control 76 (12), 1224-1232, 2003
272003
Detailed Analysis of the Luria's Alternating SeriesTests for Parkinson's Disease Diagnostics
S Nõmm, K Bardõ¹, A Toomela, K Medijainen, P Taba
2018 17th IEEE International Conference on Machine Learning and Applications …, 2018
262018
Quantitative analysis in the digital Luria's alternating series tests
S Nomm, A Toomela, J Kozhenkina, T Toomsoo
Control, Automation, Robotics and Vision (ICARCV), 2016 14th International …, 2016
262016
Tremor-related feature engineering for machine learning based Parkinson’s disease diagnostics
E Valla, S Nõmm, K Medijainen, P Taba, A Toomela
Biomedical Signal Processing and Control 75 (103551), 2022
252022
An alternative approach to measure quantity and smoothness of the human limb motions.
S Nõmm, A Toomela
Estonian Journal of Engineering 19 (4), 2013
252013
In-Depth Feature Selection for the Statistical Machine Learning-Based Botnet Detection in IoT Networks
R Kalakoti, S Nõmm, H Bahsi
IEEE Access 10, 94518 - 94535, 2022
242022
On realizability of neural networks-based input-output models
FN Chowdhury, U Kotta, S Nõmm
Proc. of the 3rd Int. Conf. on Differential Equations and Applications, St …, 2000
232000
Nn-based anarx model of the surgeon's hand for the motion recognition
貞弘晃宜, 宮脇富士夫
Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM), 19-24, 2007
202007
Recognition of the surgeon's motions during endoscopic operation by statistics based algorithm and neural networks based ANARX models
S Nomm, E Petlenkov, J Vain, J Belikov, F Miyawaki, K Yoshimitsu
IFAC Proceedings Volumes 41 (2), 14773-14778, 2008
192008
Application of self organizing Kohonen map to detection of surgeon motions during endoscopic surgery
E Petlenkov, S Nomm, J Vain, F Miyawaki
2008 IEEE International Joint Conference on Neural Networks (IEEE World …, 2008
182008
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