scholarly journals Gender Classification Based on Iris Recognition Using Artificial Neural Networks

2021 ◽  
Vol 1 (2) ◽  
pp. 156-164
Author(s):  
Basna Mohammed Salih ◽  
Adnan Mohsin Abdulazeez ◽  
Omer Mohammed Salih Hassan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Credit Card ◽  
High Performance ◽  
Iris Recognition ◽  
Image Data ◽  
Gender Classification ◽  
Financial Transfers ◽  
Biometric Verification ◽  
Artificial Neural

Biometric authentication is one of the most quickly increasing innovations in today's world; this promising technology has seen widespread use in a variety of fields, including surveillance services, safe financial transfers, credit-card authentication. in biometric verification processes such as gender, age, ethnicity is iris recognition technology is considered the most accurate compared to other vital features such as face, hand geometry, and fingerprints.  Because the irises in the same person are not similar. In this work, the study of gender classification using Artificial Neural Networks (ANN) based on iris recognition. The eye image data were collected from the IIT Delhi IRIS Database. All datasets of images were processed using various image processing techniques using the neural network. The results obtained showed high performance in training and got good results in testing. ANN's training and testing process gave a maximum performance at 96.4% and 97% respectively.

Iris recognition using artificial neural networks

2011 ◽  
Vol 38 (5) ◽  
pp. 5940-5946 ◽  
Author(s):  
Fadi N. Sibai ◽  
Hafsa I. Hosani ◽  
Raja M. Naqbi ◽  
Salima Dhanhani ◽  
Shaikha Shehhi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Iris Recognition ◽  
Artificial Neural

scholarly journals Predicting the Cardioactivity of Tivela stultorum clam with Digoxin Using Artificial Neural Networks

2017 ◽  
Author(s):  
◽  
D. Flores
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Artificial Neural Networks ◽  
High Performance ◽  
Digoxin Concentration ◽  
Computational Method ◽  
Specific Concentration ◽  
Filling Time ◽  
Artificial Neural

Artificial neural networks (ANN) are a computational method that has been widely used to solve complex problems and carry out predictions on nonlinear systems. Multilayer perceptron artificial neural networks were used to predict the physiological response that would be obtained by adding a specific concentration of digoxin to Tivela stultorum hearts, this organism is a model for testing cardiac drugs that pretends to be used in humans. The MLPANN inputs were weight, volume, length, and width of the heart, digoxin concentration and volume used for diluting digoxin, and maximum contraction, minimum contraction, filling time, and heart rate before adding digoxin, and the outputs were the maximum contraction, minimum contraction, filling time, and heart rate that would be obtained after adding digoxin to the heart. ANNs were trained, validated, and tested with the results obtained from the in vivo experiments. To choose the optimal network, the smallest square mean error value was used. Perceptrons obtained a high performance and correlation between predicted and calculated values, except in the case of the filling time output. Accurate predictions of the T. stultorum clams cardioactivity were obtained when a specific concentration of digoxin was added using ANNs with one hidden layer; this could be useful as a tool to facilitate laboratory experiments to test digoxin effects.

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