An artificial neural network based on adaptive resonance theory for fault classification on an automated assembly machine

2014 ◽  
Author(s):  
Heshan Fernando ◽  
Brian Surgenor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Resonance Theory ◽  
Fault Classification ◽  
Automated Assembly ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Artificial Neural

PASSPHRASE AUTHENTICATION BASED ON TYPING STYLE THROUGH AN ART 2 NEURAL NETWORK

2002 ◽  
Vol 02 (02) ◽  
pp. 131-152 ◽  
Author(s):  
JASON BECHTEL ◽  
GURSEL SERPEN ◽  
MARCUS BROWN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Authentic Sample ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Algorithm

This study proposes the use of an artificial neural network algorithm to perform passphrase authentication based on the typing style of a user. The only hardware required is a keyboard. Prior studies have demonstrated the feasibility of this approach and its limitations, one of which was the need for collection of impostor samples for training the artificial neural network based classifier algorithm. This requirement is rather impractical for most application domains. The proposed study eliminates the need to collect impostor samples by employing an unsupervised and self-organizing artificial neural network algorithm, the Adaptive Resonance Theory 2 neural network, and therefore pushes the passphrase authentication technology one step closer to the realm of practical implementation. The preliminary study performed demonstrates that it is possible to train an Adaptive Resonance Theory 2 neural network using only authentic sample data and still provide a relatively low impostor pass rate. Given the minimal cost and easy in-field trainability of the proposed passphrase authentication system, the developed system can greatly enhance the security of computing environments with wide acceptance.

Detection of malware using an artificial neural network based on adaptive resonant theory

2021 ◽  
pp. 69-82
Author(s):  
D. G. Bukhanov ◽  
◽  
V. M. Polyakov ◽  
M. A. Redkina ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Significant Loss ◽  
Malicious Code ◽  
Resonance Theory ◽  
Similarity Parameter ◽  
Adaptive Resonance ◽  
Artificial Neural ◽  
And Behavior ◽  
Classification Time

The process of detecting malicious code by anti-virus systems is considered. The main part of this process is the procedure for analyzing a file or process. Artificial neural networks based on the adaptive-resonance theory are proposed to use as a method of analysis. The graph2vec vectorization algorithm is used to represent the analyzed program codes in numerical format. Despite the fact that the use of this vectorization method ignores the semantic relationships between the sequence of executable commands, it allows to reduce the analysis time without significant loss of accuracy. The use of an artificial neural network ART-2m with a hierarchical memory structure made it possible to reduce the classification time for a malicious file. Reducing the classification time allows to set more memory levels and increase the similarity parameter, which leads to an improved classification quality. Experiments show that with this approach to detecting malicious software, similar files can be recognized by both size and behavior.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

2013 ◽  
Vol 838-841 ◽  
pp. 3287-3290 ◽  
Author(s):  
Adriano dos Santos e Souza ◽  
Fábio Roberto Chavarette ◽  
Fernando Parra dos Anjos Lima ◽  
Mara Lúcia Martins Lopes ◽  
Simone Silva Frutuoso de Souza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Integrity ◽  
Base Line ◽  
Structural Failure ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Artificial Neural ◽  
Identification And Characterization

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.

scholarly journals LUNG DISORDERS DETECTION BASED ON IRISES IMAGE USING COMPUTATIONAL INTELLIGENT ART

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Anna Triwijayanti K. ◽  
Hadi Suwastio ◽  
Rini Damayanti
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Digital Camera ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Artificial Neural

Iridology as a way of revealing human organs and tissues conditions is done by iridologist by taking the image of both irises of the patients. This can be done by using a digital camera and observe each iris on the LCD display or connect the camera to a computer or a television set and observe it through the display. Research on computerized iridology has been performed before by using artificial neural network of back propagation, which is a kind of supervised learning algorithm, as the classifier [13]. Such system should be able to retain its stability while still being plastic enough to adapt to arbitrarily input patterns. Adaptive Resonance Theory (ART), another kind of artificial neural network which uses unsupervised learning algorithm, has some important traits, such as real-time learning, self-stabilizing memory in response to arbitrarily many input patterns, and fast adaptive search for best match of input-to-stored patterns [9]. That way, ART architecture is expected to be the best stable and adaptable solution in changing environment of pattern recognition. In this research, the lung disorders detection is simply designed through the steps of segmentation, extraction of color variations, transformation of lung and pleura representation area in iris image to binary form as the input of ART 1, and pattern recognition by ART 1 neural network architecture. With 32 samples and 4 nodes of output layer of ART1, the system is able to determine the existences of the four stadiums of lung disorders (acute, subacute, chronic and degenerative) in relatively short time process (approximately 1.8 to 3.2 seconds) with the accuracy of stadium recognition 91.40625% by applying the vigilance parameter value of 0.4.Keywords: iridology, lung, pleura, segmentation, ART 1 neural network

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