Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates
M Szaleniec - Pharmacological Reports, 2012 - Elsevier
Abstract Background Artificial Neural Networks (ANNs) are introduced as robust and
versatile tools in quantitative structure-activity relationship (QSAR) modeling. Their …
versatile tools in quantitative structure-activity relationship (QSAR) modeling. Their …
Signatures verification based on PNN classifier optimised by PSO algorithm
In this paper, we propose a new biometric pattern recognition method. In classical
techniques only features of raw objects are compared. In our approach we will use …
techniques only features of raw objects are compared. In our approach we will use …
[图书][B] Advanced neural network-based computational schemes for robust fault diagnosis
M Mrugalski - 2014 - Springer
The quality of models of systems and processes determines the effectiveness of numerous
contemporary technical systems ie, control systems, fault diagnosis systems or fault tolerant …
contemporary technical systems ie, control systems, fault diagnosis systems or fault tolerant …
Artificial neural network modelling of the results of tympanoplasty in chronic suppurative otitis media patients
J Szaleniec, M Wiatr, M Szaleniec, J Składzień… - Computers in biology …, 2013 - Elsevier
The application of computer modelling for medical purposes, although challenging, is a
promising pathway for further development in the medical sciences. We present predictive …
promising pathway for further development in the medical sciences. We present predictive …
Non-destructive building investigation through analysis of GPR signal by S-transform
P Szymczyk, M Szymczyk - Automation in Construction, 2015 - Elsevier
The aim of the paper is to present one, two and three-dimensional S-transforms. The paper
also shows the ability to study one, two and three-dimensional waveforms in frequency …
also shows the ability to study one, two and three-dimensional waveforms in frequency …
Parallel architectures for learning the RTRN and Elman dynamic neural networks
J Bilski, J Smoląg - IEEE Transactions on Parallel and …, 2014 - ieeexplore.ieee.org
A major problem encountered by researchers of dynamic neural networks is the
computational complexity increasing the learning time. In this paper the parallel realization …
computational complexity increasing the learning time. In this paper the parallel realization …
How does generalization and creativity come into being in neural associative systems and how does it form human-like knowledge?
A Horzyk - Neurocomputing, 2014 - Elsevier
This paper explains and models selected associative processes that take place in biological
associative neural systems. Such associative systems allow us to form, expand, and exploit …
associative neural systems. Such associative systems allow us to form, expand, and exploit …
Classification of geological structure using ground penetrating radar and Laplace transform artificial neural networks
P Szymczyk, M Szymczyk - Neurocomputing, 2015 - Elsevier
This paper focuses on a new kind of artificial neural networks–the Laplace transform artificial
neural networks (LTANN). It is proposed to use the Laplace transform instead of ordinary …
neural networks (LTANN). It is proposed to use the Laplace transform instead of ordinary …
Classification of tea specimens using novel hybrid artificial intelligence methods
Two innovative systems based on feed-forward and recurrent neural network used for
qualitative analysis has been applied to specimens of different fruit tea. Their performance …
qualitative analysis has been applied to specimens of different fruit tea. Their performance …
[PDF][PDF] Approximation of phenol concentration using novel hybrid computational intelligence methods
P Plawiak, R Tadeusiewicz - International Journal of Applied …, 2014 - sciendo.com
This paper presents two innovative evolutionary-neural systems based on feed-forward and
recurrent neural networks used for quantitative analysis. These systems have been applied …
recurrent neural networks used for quantitative analysis. These systems have been applied …