scholarly journals Towards an Automated Recognition System for Chat-based Social Engineering Attacks in Enterprise Environments

2018 ◽  
Author(s):  
Nikolaos Tsinganos ◽  
Georgios Sakellariou ◽  
Panagiotis Fouliras ◽  
Ioannis Mavridis
Keyword(s):  
Social Engineering ◽  
Recognition System ◽  
Automated Recognition

Ensemble Learning based Age Invariant Fea-ture Recognition Using Soft Computing

2021 ◽  
pp. 7278-7290
Author(s):  
Divyanshu Sinha, Dr J. P. Pandey, Dr. Bhavesh Chauhan
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Matlab Simulation ◽  
Automated Recognition ◽  
Face Recognition System ◽  
Proposed Model ◽  
Overall Performance ◽  
Vision Application

Face recognition system is a state-of-the-art computer vision application within the artificial intelligence arena. Face recognition is the automated recognition of humans for their names/unique ID. The age invariant face recognition is a challenge task in the field of face recog-nition. In this work, we have introduced a stacked support vector machine where kernel activation of prototype examples is combined in nonlinear ways. The proposed work integrates soft compu-ting-based support vector machine (SVM) with deep SVM. The proposed model uses the implied relation between the variables described above in order to optimize their overall performance. Specifically, our method uses three different stages of complex convolution neural networks that detect and analyze the location of faces position and landmarks. This work has introduced cross-age celebrity dataset (CACD) for both single as well as cross-database enabling the transition of age. The proposed work has been implemented in the MATLAB simulation tool considering CACD dataset. Experimental results indicate that our techniques significantly outperform other strategies across a range of challenging metrics.

scholarly journals The Potential of Using Brain Images for Authentication

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Fanglin Chen ◽  
Zongtan Zhou ◽  
Hui Shen ◽  
Dewen Hu
Keyword(s):  
Gray Matter ◽  
Recognition System ◽  
Identity Authentication ◽  
Data Sets ◽  
Brain Images ◽  
Automated Recognition ◽  
Accuracy Requirement ◽  
Potential Possibility ◽  
Two Stages ◽  
The Brain

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

Automated recognition system for ELM classification in JET

2009 ◽  
Vol 84 (2-6) ◽  
pp. 712-715 ◽  
Author(s):  
N. Duro ◽  
R. Dormido ◽  
J. Vega ◽  
S. Dormido-Canto ◽  
G. Farias ◽  
...  
Keyword(s):  
Recognition System ◽  
Automated Recognition

scholarly journals Automated recognition system of musical score (vision system of the WABOT-2)

1985 ◽  
Vol 3 (4) ◽  
pp. 354-361 ◽  
Author(s):  
Toshiaki MATSUSHIMA ◽  
Katsuhiro KANAMORI ◽  
Sadamu OHTERU
Keyword(s):  
Vision System ◽  
Recognition System ◽  
Musical Score ◽  
Automated Recognition
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