Hybrid Data Mining Approach for Image Segmentation Based Classification

Biometrics ◽  
2017 ◽  
pp. 1543-1561 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Aboul Ella Hassanien ◽  
Ajith Abraham
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Image Segmentation ◽  
Search Algorithm ◽  
Rbf Neural Network ◽  
Harmony Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Harmony Search Algorithm ◽  
Data Mining Technique

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

Hybrid Data Mining Approach for Image Segmentation Based Classification

2016 ◽  
Vol 3 (2) ◽  
pp. 65-81 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Aboul Ella Hassanien ◽  
Ajith Abraham
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Image Segmentation ◽  
Search Algorithm ◽  
Rbf Neural Network ◽  
Harmony Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Feature Subset ◽  
Data Mining Technique

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

scholarly journals Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

2018 ◽  
Vol 8 (6) ◽  
pp. 4593 ◽  
Author(s):  
Erwin Erwin ◽  
Saparudin Saparudin ◽  
Wulandari Saputri
Keyword(s):  
High Performance ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Multilevel Thresholding ◽  
Improved Harmony Search ◽  
High Degree ◽  
Improved Harmony Search Algorithm

This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.

Optimal hybrid scheme of dynamic neural network and PID controller based on harmony search algorithm to control a PWM-driven pneumatic actuator position

2017 ◽  
Vol 24 (16) ◽  
pp. 3538-3554 ◽  
Author(s):  
Mahmood Mazare ◽  
Mostafa Taghizadeh ◽  
Mohammad Ghasem Kazemi
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Pulse Width Modulation ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Pneumatic Actuator ◽  
Hybrid Scheme ◽  
Dynamic Neural Network ◽  
Switching Valve

In this paper, the position of a pulse width modulation (PWM)-driven pneumatic actuator has been controlled using a dynamic neural network (DNN) and Proportional Integral Derivative (PID) controller. The harmony search algorithm (HSA) has been used to unravel the optimization problem. The DNN controller is optimally designed to control the position of the actuator. As to the performance of the PID controller, it can assist the DNN controller to give better results. Therefore, an optimal hybrid scheme with both DNN and PID controllers based on HSA is suggested. A pneumatic circuit containing a fast-switching valve is used to reduce the complexity of the PWM-driven servo pneumatic system along with its cost price.

Normal Parameter Reduction in Soft Set Based on Harmony Search Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2173-2176
Author(s):  
Zhi Kong ◽  
Guo Dong Zhang ◽  
Li Fu Wang
Keyword(s):  
Data Mining ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Soft Set ◽  
Intelligent Optimization ◽  
Parameter Reduction ◽  
Intelligent Optimization Algorithm ◽  
Normal Parameter

The normal parameter reduction in soft set is difficult to application in data mining because of great calculation quantity. In this paper, the intelligent optimization algorithm, the harmony search algorithm, is applied to solve the problem. The normal parameter reduction model is constructed and the harmony search algorithm is designed. Experience has shown that the method is feasible and fast..

scholarly journals VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS

2018 ◽  
Author(s):  
Moh’d Khaled Yousef Shambour
Keyword(s):  
Convergence Rates ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Search Performance ◽  
Search Spaces ◽  
Search Mechanism ◽  
Heuristic Technique

Recently, various variants of evolutionary algorithms have been offered to optimize the exploration and exploitation abilities of the search mechanism. Some of these variants still suffer from slow convergence rates around the optimal solution. In this paper, a novel heuristic technique is introduced to enhance the search capabilities of an algorithm, focusing on certain search spaces during evolution process. Then, employing a heuristic search mechanism that scans an entire space before determining the desired segment of that search space. The proposed method randomly updates the desired segment by monitoring the algorithm search performance levels on different search space divisions. The effectiveness of the proposed technique is assessed through harmony search algorithm (HSA). The performance of this mechanism is examined with several types of benchmark optimization functions, and the results are compared with those of the classic version and two variants of HSA. The experimental results demonstrate that the proposed technique achieves the lowest values (best results) in 80% of the non-shifted functions, whereas only 33.3% of total experimental cases are achieved within the shifted functions in a total of 30 problem dimensions. In 100 problem dimensions, 100% and 25% of the best results are reported for non-shifted and shifted functions, respectively. The results reveal that the proposed technique is able to orient the search mechanism toward desired segments of search space, which therefore significantly improves the overall search performance of HSA, especially for non-shifted optimization functions.   

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