Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

2020 ◽  
pp. 036119812096794
Author(s):  
Eslam Mohammed Abdelkader ◽  
Osama Moselhi ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Statistical Significance ◽  
Optimization Method ◽  
Bridge Management ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Elman Neural Network ◽  
Existing Bridges ◽  
Value Decomposition

Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

scholarly journals Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment

2021 ◽  
Vol 7 (09) ◽  
pp. 1-6
Author(s):  
Adil Hussain Mohammed
Keyword(s):  
Neural Network ◽  
Feature Reduction ◽  
Training Dataset ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Detection Model ◽  
Network Training ◽  
Proposed Model ◽  
Trained Neural Network ◽  
Good Set

Cloud provide support to manage, control, monitor different organization. Due to flexible nature f cloud chance of attack on it increases by means of some software attack in form of ransomware. Many of researcher has proposed various model to prevent such attacks or to identify such activities. This paper has proposed a ransomware detection model by use of trained neural network. Training of neural network was done by filter or optimized feature set obtained from the feature reduction algorithm. Paper has proposed a Invasive Weed Optimization algorithm that filter good set of feature from the available input training dataset. Proposed model test was performed on real dataset, have set sessions related to cloud ransomware attacks. Result shows that proposed model has increase the comparing parameter values.

scholarly journals A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

2020 ◽  
Vol 26 (4) ◽  
pp. 643-661
Author(s):  
Eslam Mohammed Abdelkader ◽  
Osama Moselhi ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multi Objective

scholarly journals Rolling Bearing Fault Diagnosis under Variable Conditions Using Hilbert-Huang Transform and Singular Value Decomposition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hongmei Liu ◽  
Xuan Wang ◽  
Chen Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Singular Value ◽  
Instantaneous Amplitude ◽  
Elman Neural Network ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Value Decomposition

Fault diagnosis precision for rolling bearings under variable conditions has always been unsatisfactory. To solve this problem, a fault diagnosis method combining Hilbert-Huang transform (HHT), singular value decomposition (SVD), and Elman neural network is proposed in this paper. The method includes three steps. First, instantaneous amplitude matrices were obtained by using HHT from rolling bearing signals. Second, the singular value vector was acquired by applying SVD to the instantaneous amplitude matrices, thus reducing the dimension of the instantaneous amplitude matrix and obtaining the fault feature insensitive to working condition variation. Finally, an Elman neural network was applied to the rolling bearing fault diagnosis under variable working conditions according to the extracted feature vector. The experimental results show that the proposed method can effectively classify rolling bearing fault modes with high precision under different operating conditions. Moreover, the performance of the proposed HHT-SVD-Elman method has an advantage over that of EMD-SVD or WPT-PCA for feature extraction and Support Vector Machine (SVM) or Extreme Learning Machine (ELM) for classification.

scholarly journals Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Yanyan Tan ◽  
Xue Lu ◽  
Yan Liu ◽  
Qiang Wang ◽  
Huaxiang Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multiobjective Optimization Problems ◽  
Distinct Features

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

scholarly journals A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

2020 ◽  
Vol 10 (20) ◽  
pp. 7320
Author(s):  
Phu-Nguyen Le ◽  
Hee-Jun Kang
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Kinematic Model ◽  
Calibration Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Suggested Technique ◽  
Calibration Algorithm ◽  
Identification Technique ◽  
Position Precision

The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.

scholarly journals The Hybrid Invasive Weed Optimization-Shuffled Frog-leaping Algorithm Applied to Optimal Design of Frame Structures

2019 ◽  
Author(s):  
Ali Kaveh ◽  
Siamak Talatahari ◽  
Nima Khodadadi
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Optimization Method ◽  
Population Based ◽  
Frame Structures ◽  
Invasive Weed Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Invasive Weed ◽  
Shuffled Frog Leaping

In this article, an efficient hybrid optimization algorithm based on invasive weed optimization algorithm and shuffled frog-leaping algorithm is utilized for optimum design of skeletal frame structures. The shuffled frog-leaping algorithm is a population-based cooperative search metaphor inspired by natural memetic, and the invasive weed optimization algorithm is an optimization method based on dynamic growth of weeds colony. In the proposed algorithm, shuffled frog-leaping algorithm works to find optimal solution region rapidly, and invasive weed optimization performs the global search. Different benchmark frame structures are optimized using the new hybrid algorithm. Three design examples are tested using the new method. This algorithm converges to better or at least the same solutions compared the utilized methods with a smaller number of analyses. The outcomes are compared to those obtained previously using other recently developed meta-heuristic optimization methods.

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