Acceleration artificial bee colony optimization-artificial neural network for optimal feature selection over big data

2017 ◽  
Author(s):  
S. Meera ◽  
B. Rosiline Jeetha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Big Data ◽  
Artificial Bee Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Optimal Feature Selection ◽  
Optimal Feature

scholarly journals Pareto front feature selection based on artificial bee colony optimization

2018 ◽  
Vol 422 ◽  
pp. 462-479 ◽  
Author(s):  
Emrah Hancer ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
Dervis Karaboga ◽  
Bahriye Akay
Keyword(s):  
Feature Selection ◽  
Pareto Front ◽  
Artificial Bee Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

scholarly journals Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving

2020 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Wan Nazirah Wan Md Adnan ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Measurement And Verification ◽  
Bee Colony Optimization ◽  
Validation Technique ◽  
Artificial Neural ◽  
Verification Model ◽  
Hybrid Artificial Neural Network

<span lang="EN-US">This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving. </span>

Comparative Dynamic Control of SynRM Servodrive Continuously Variable Transmission System Using Blend Amend Recurrent Gegenbauer-Functional-Expansions Neural Network Control and Altered Artificial Bee Colony Optimization

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Artificial Bee Colony ◽  
Continuously Variable Transmission ◽  
Control Performance ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Functional Expansions ◽  
Variable Transmission ◽  
Bee Colony Optimization

In comparison control performance with more complex and nonlinear control methods, the classical linear controller is poor because of the nonlinear uncertainty action that the continuously variable transmission (CVT) system is operated by the synchronous reluctance motor (SynRM). Owing to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma, and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization (ABCO) yields two varied learning rates for two parameters to find two optimal values, which helped improve convergence. Finally, the experimental results with various comparisons are demonstrated to confirm that the proposed control system can result in better control performance.

A Hermite neural network incorporating artificial bee colony optimization to model shoreline realignment at a reef-fronted beach

2018 ◽  
Vol 280 ◽  
pp. 32-45 ◽  
Author(s):  
George E. Tsekouras ◽  
Vasilis Trygonis ◽  
Andreas Maniatopoulos ◽  
Anastasios Rigos ◽  
Antonios Chatzipavlis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Artificial Bee Colony Optimization for Feature Selection of Traffic Sign Recognition

2017 ◽  
Vol 8 (2) ◽  
pp. 50-66 ◽  
Author(s):  
Diogo L. da Silva ◽  
Leticia M. Seijas ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Feature Selection ◽  
High Performance ◽  
Artificial Bee Colony ◽  
Information Gain ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Sign Recognition ◽  
Bee Colony Optimization

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.

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