Efficient Bag-of-Features using Improved Whale Optimization Algorithm for Histopathological Image Classification

2019 ◽  
Vol 12 (4) ◽  
pp. 269-279 ◽  
Author(s):  
Varun Tiwari ◽  
Sushil C. Jain
Keyword(s):  
Image Classification ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Local Optima ◽  
Bag Of Features ◽  
Histopathological Image ◽  
Whale Optimization ◽  
Image Dataset ◽  
Histopathological Image Classification

Background: The whale optimization algorithm is one of the popular meta-heuristic algorithms which has successfully been applied in various application areas such as image analysis and data clustering. However, the slow convergence rate and chances of sticking into the local optima due to improper balance of its exploration and exploitation phases are some of its pitfalls. Therefore, in this paper, a new improved whale optimization algorithm has been proposed. Moreover, the proposed method has been used in bag-of-features method for histopathological image classification. Methods: The new algorithm, improved whale optimization algorithm, modifies the encircling phase of original whale optimization algorithm. The proposed algorithm has been used to cluster the extracted features for finding the relevant codewords to be used in the bag-of-features method for histopathological image classification. Results: The efficiency of proposed algorithm has been analyzed on 23 benchmark functions in terms of mean fitness, standard deviation values, and convergence behavior. The performance of the improved whale optimization algorithm based histopathological image classification method has been analyzed on blue histology image dataset and compared with other meta-heuristic based bagof- features methods in terms of recall, precision, F-measure, and accuracy. The experimental results validate that the proposed method outperforms the considered state-of-the-art methods and attains 12% increase in the histopathological image classification accuracy. Conclusion: In this paper, a new improved whale optimization algorithm has been proposed and applied in bag-of-features method for histopathological image classification. The results of proposed method outperform the other existing meta-heuristic methods over standard benchmark functions and histopathological image dataset.

scholarly journals Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Athraa Jasim Mohammed ◽  
Khalil Ibrahim Ghathwan
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Space ◽  
Input Image ◽  
Whale Optimization Algorithm ◽  
Color Image Segmentation ◽  
Local Optima ◽  
Whale Optimization

Color image segmentation is widely used methods for searching of homogeneous regions to classify them into various groups. Clustering is one technique that is used for this purpose. Clustering algorithms have drawbacks such as the finding of optimum centers within a cluster and the trapping in local optima. Even though inspired meta-heuristic algorithms have been adopted to enhance the clustering performance, some algorithms still need improvements. Whale optimization algorithm (WOA) is recognized to be enough competition with common meta-heuristic algorithms, where it has an ability to obtain a global optimal solution and avoid local optima. In this paper, a new method for color image based segmentation is proposed based on using whale optimization algorithm in clustering. The proposed method is called the whale color image based segmentation (WhCIbS). It was used to divide the color image into a predefined number of clusters. The input image in RGB color space was converted into L*a*b color space. Comparison of the proposed WhCIbS method was performed with the wolf color image based segmentation, cuckoo color image based segmentation, bat color image based segmentation, and k-means color image based segmentation over four benchmark color images. Experimental results demonstrated that the proposed WhCIbS had higher value of PSNR and lower value of RMSR in most cases compared to other methods.

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

scholarly journals Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
Author(s):  
Wei Chen ◽  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Himan Shahabi ◽  
Yi Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Landslide Susceptibility ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Economic Losses ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Prediction Ability ◽  
Inference System ◽  
Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

Improved whale optimization algorithm based on random hopping update and random control parameter

2021 ◽  
Vol 40 (1) ◽  
pp. 363-379
Author(s):  
Yanju Guo ◽  
Huan Shen ◽  
Lei Chen ◽  
Yu Liu ◽  
Zhilong Kang
Keyword(s):  
Optimization Algorithm ◽  
Control Parameter ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Multimodal Functions ◽  
Whale Optimization ◽  
Update Strategy ◽  
Random Control ◽  
Better Than

Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

scholarly journals Loss Minimization by Reconfiguration in Radial Distribution Network using Whale Optimization Technique

2020 ◽  
Vol 9 (2) ◽  
pp. 1746-1751
Keyword(s):  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Heuristic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Function Evaluation ◽  
Voltage Profile ◽  
Whale Optimization

This paper provides a new approach for solving the problem of network reconfiguration in the presence of Whale Optimization Algorithm (WOA). It is aimed at reducing actual power loss and enlightening the voltage profile in the supply system. The voltage and branch current capacity constraints have been included in the objective function evaluation. The method has been evaluated at three separate heuristic algorithms on 33-bus radial distribution systems to demonstrate the performance and effectiveness of the proposed method. In this paper the comparison of performance of two latest optimization techniques such as Whale Optimization Algorithm (WOA) with classic optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The new optimization technique produces better result compare to other two optimization logarithm..

scholarly journals Near Infrared and Visible Image Registration using Whale Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Image Registration ◽  
Optimization Algorithm ◽  
Near Infrared ◽  
Optimization Problems ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Visible Image ◽  
Local Optima ◽  
Multimodal Image Registration ◽  
Whale Optimization

This paper reports the use of a nature-inspired metaheuristic algorithm known as ‘Whale Optimization Algorithm’ (WOA) for multimodal image registration. WOA is based on the hunting behaviour of Humpback whales and provides better exploration and exploitation of the search space with small possibility of trapping in local optima. Though WOA is used in various optimization problems, no detailed study is available for its use in image registration. For this study different sets of NIR and visible images are considered. The registration results are compared with the other state of the art image registration methods. The results show that WOA is a very competitive algorithm for NIR-visible image registration. With the advantages of better exploration of search space and local optima avoidance, the algorithm can be a suitable choice for multimodal image registration.

Grey Relational Analysis based Keypoints Selection in Bag-of-Features for Histopathological Image Classification

2019 ◽  
Vol 12 (4) ◽  
pp. 260-268 ◽  
Author(s):  
Raju Pal ◽  
Mukesh Saraswat
Keyword(s):  
Image Classification ◽  
Vector Quantization ◽  
Grey Relational Analysis ◽  
Clinical Practices ◽  
Bag Of Features ◽  
Relational Analysis ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Histopathological Image Classification ◽  
Grey Relational

Background: With the expeditious development of current medical imaging technology, the availability of histopathological images has been increased in a large number. Hence, histopathological image classification and annotation have emerged as the prime research fields in the pathological diagnosis and clinical practices. Several methods are available for the automation of image classification. Methods: Recently, the bag-of-features appeared as a successful histopathological image classification method. However, all the extracted keypoints in bag-of-features are not relevant and generally have very high dimensions, which degrade the performance of a classifier. Therefore, this paper introduces a new Grey relational analysis-based bag-of-features method to search the relevant keypoints. Results: The efficacy of the proposed method has been analyzed on animal diagnostics lab histopathological image datasets having healthy and inflamed images of three organs. The average accuracy of the proposed method is 88.3%, which is the highest among other state-of-the-art methods. Conclusion: This paper introduced a new Grey relational analysis-based bag-of-features which improves the efficiency of vector quantization step of the standard bag-of-features method. The method used Grey relational analysis for similarity measure in vector quantization method of bag-offeatures. The proposed method has been validated in terms of precision, recall, G-mean, F1 score, and radar charts on three datasets, Kidney, Lung, and Spleen of ADL histopathological images.

Optimal total harmonic distortion minimization in multilevel inverter using improved whale optimization algorithm

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Aala Kalananda Vamsi Krishna Reddy ◽  
Komanapalli Venkata Lakshmi Narayana
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Whale Optimization Algorithm ◽  
Matlab Simulation ◽  
Multilevel Inverters ◽  
Whale Optimization ◽  
Distortion Minimization ◽  
Optimum Solution

AbstractThis paper presents the solution to mitigate the total harmonic distortion (THD) in multilevel inverters (MLIs) using novel improved whale optimization algorithm (IWOA). The IWOA falls under the category of swarm-based nature inspired optimization algorithms. It uses a novel diffusion process using a random walk technique and utilizes an additional ranking system to estimate the optimum solution to minimize THD. Moreover, THD minimization is further accomplished through nine various meta-heuristic algorithms for investigation and comparative analysis. The selected algorithms along with the proposed IWOA are rigorously tested on single phase 5 and 7 level cascaded H-Bridge MLIs for various performance parameters such as consistency, computational efficiency and speed of convergence. It is found that the proposed algorithm outperforms the nine algorithms and is efficient for THD minimization for modulation index (MI) in the range of 0–1. The results are analyzed and reported after thorough verification using MATLAB simulation.

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