Erratum: Text compression as rule based pattern recognition

1993 ◽  
Vol 29 (24) ◽  
pp. 2155
Author(s):  
H.U. Khan ◽  
J. Ahmad ◽  
A. Mahmood ◽  
H.A. Fatmi
Keyword(s):  
Pattern Recognition ◽  
Text Compression ◽  
Rule Based

Text compression as rule based pattern recognition

1993 ◽  
Vol 29 (20) ◽  
pp. 1752 ◽  
Author(s):  
H.U. Khan ◽  
J. Ahmad ◽  
A. Mahood ◽  
H.A. Fatmi
Keyword(s):  
Pattern Recognition ◽  
Text Compression ◽  
Rule Based

Corner Detection Using Fuzzy Principles

2013 ◽  
pp. 498-512
Author(s):  
Erik Cuevas ◽  
Daniel Zaldivar ◽  
Marco Perez-Cisneros
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Important Task ◽  
Fuzzy Rules ◽  
Corner Detection ◽  
Illumination Change ◽  
Benchmark Test ◽  
Rule Based ◽  
Rule Based System

Reliable corner detection is an important task in pattern recognition applications. In this chapter an approach based on fuzzy-rules to detect corners even under imprecise information is presented. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, et cetera. Fuzzy systems are well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model corner properties by a fuzzy rule-based system. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm in noise presence.

scholarly journals Deep rule-based classifier for finger knuckle pattern recognition system

2020 ◽  
Author(s):  
Abdelouahab Attia ◽  
Zahid Akhtar ◽  
Nour Elhouda Chalabi ◽  
Sofiane Maza ◽  
Youssef Chahir
Keyword(s):  
Pattern Recognition ◽  
Recognition System ◽  
Rule Based ◽  
Pattern Recognition System ◽  
Rule Based Classifier

scholarly journals Hybrid neural network and rule-based pattern recognition system capable of self-modification

1990 ◽  
Author(s):  
Charles W. Glover ◽  
Mike Silliman ◽  
Mark Walker ◽  
Phil Spelt ◽  
Nageswara S. V. Rao
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition System ◽  
Hybrid Neural Network ◽  
Rule Based ◽  
Pattern Recognition System

scholarly journals TCP FIN Flood Attack Pattern Recognition on Internet of Things with Rule Based Signature Analysis

2019 ◽  
Vol 15 (07) ◽  
pp. 124
Author(s):  
Deris Stiawan ◽  
Dimas Wahyudi ◽  
Ahmad Heryanto ◽  
Samsuryadi Samsuryadi ◽  
Mohd. Yazid Idris ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Internet Of Things ◽  
Confusion Matrix ◽  
Feature Selection Method ◽  
Signature Analysis ◽  
Rule Based ◽  
Average Percentage ◽  
Attack Pattern ◽  
Precision Level ◽  
Rate Evaluation

<p class="0abstract">Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenario normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done based on observation and analysis of packet attribute from raw data (pcap) using a feature extraction and feature selection method. Further testing was conducted using snort as an IDS. The results of the confusion matrix detection rate evaluation against the snort as IDS show the average percentage of the precision level.</p>

Rule-based systems and pattern recognition

1987 ◽  
Vol 5 (4) ◽  
pp. 267-272 ◽  
Author(s):  
E. Giakoumakis ◽  
G. Papaconstantinou ◽  
E. Skordalakis
Keyword(s):  
Pattern Recognition ◽  
Rule Based ◽  
Rule Based Systems

Corner Detection Using Fuzzy Principles

2012 ◽  
pp. 270-285
Author(s):  
Erik Cuevas ◽  
Daniel Zaldivar ◽  
Marco Perez-Cisneros
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Important Task ◽  
Fuzzy Rules ◽  
Corner Detection ◽  
Illumination Change ◽  
Benchmark Test ◽  
Rule Based ◽  
Rule Based System

Reliable corner detection is an important task in pattern recognition applications. In this chapter an approach based on fuzzy-rules to detect corners even under imprecise information is presented. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, et cetera. Fuzzy systems are well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model corner properties by a fuzzy rule-based system. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm in noise presence.

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