Optimizing Feedforward neural networks using Krill Herd algorithm for E-mail spam detection

2015 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Ja'far Alqatawna
Keyword(s):  
Neural Networks ◽  
Feedforward Neural Networks ◽  
Spam Detection ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
E Mail

scholarly journals Training Neural Networks with Krill Herd Algorithm

2015 ◽  
Vol 44 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Piotr A. Kowalski ◽  
Szymon Łukasik
Keyword(s):  
Neural Networks ◽  
Krill Herd Algorithm ◽  
Krill Herd

scholarly journals Asymptotic Prediction Error Variance for Feedforward Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 1108-1113
Author(s):  
Magnus Malmström ◽  
Isaac Skog ◽  
Daniel Axehill ◽  
Fredrik Gustafsson
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Feedforward Neural Networks ◽  
Error Variance ◽  
Prediction Error Variance

scholarly journals Hybrid Krill Herd-ANN Model for Prediction Strength and Stiffness of Bolted Connections

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 229
Author(s):  
Iman Faridmehr ◽  
Mehdi Nikoo ◽  
Mohammad Hajmohammadian Baghban ◽  
Raffaele Pucinotti
Keyword(s):  
Correlation Coefficient ◽  
Predictive Accuracy ◽  
Supply And Demand ◽  
Steel Structures ◽  
Ann Model ◽  
Krill Herd Algorithm ◽  
Seat Angle ◽  
Taylor Diagram ◽  
Krill Herd ◽  
Strength And Stiffness

The behavior of beam-to-column connections significantly influences the stability, strength, and stiffness of steel structures. This is particularly important in extreme non-elastic responses, i.e., earthquakes, and sudden column removal, as the fluctuation in strength and stiffness affects both supply and demand. Accordingly, it is essential to accurately estimate the strength and stiffness of connections in the analysis of and design procedures for steel structures. Beginning with the state-of-the-art, the capacity of three available component-based mechanical models to estimate the complex mechanical properties of top- and seat-angle connections with double-web angles (TSACWs), with variable parameters, were investigated. Subsequently, a novel hybrid krill herd algorithm-artificial neural network (KHA-ANN) model was proposed to acquire an informational model from the available experimental dataset. Using several statistical metrics, including the corresponding coefficient of variation (CoV), correlation coefficient (R), and the correlation coefficient provided by the Taylor diagram, this study revealed that the krill herd-ANN model achieved the most reliable predictive accuracy for the strength and stiffness of top- and seat-angle connections with double web angles.

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