Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation

Hao, Shuhui; Huang, Changcheng; Heidari, Ali Asghar; Xu, Zhen; Chen, Huiling; Althobaiti, Maha M.; Mansour, Romany F.; Chen, Xiaowei

doi:10.1016/j.bspc.2022.104139

Cited by 9 publications

(3 citation statements)

References 150 publications

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“…The covariance matrix can be used for image feature extraction to achieve downstream image analysis tasks such as image segmentation [25][26][27] and classification 28,29 . For image classification, images are divided into regions using commonly used region partitioning algorithms, such as K-means clustering algorithm or region growing algorithm.…”

Section: Related Work Covariance Matrix and Its Applicationsmentioning

confidence: 99%

Convolutional Correlation Branch Network for Image Classification

Li,

Luo,

Zhu

2023

Preprint

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ResNets are currently the mainstream networks used in engineering and research. However, cellular neural networks have inherent flaws, including typical problems such as structural defects and loss of convolutional information. In view of this, we propose a multi-branch deep network based on the correlation features of feature maps. Convolution-correlated features are derived from covariance matrices of feature maps within network layers. Because the covariance matrix belongs to a Riemannian manifold, in the network branch we use a self-attention network to map it to a linear space and combine it with advanced deep features to achieve image recognition. Using the covariance information of feature maps enables the network to effectively compensate for important information that may be lost in ResNet, and the multi-branch structure of the network effectively solves the problem of gradient disappearance or explosion during deep network training.The experimental results of image classification on four data sets: Cifar-10, Cifar-100,Flower-102 and Car-196 show that our proposed network significantly improves the performance of existing networks. It is worth noting that the multi-branch network model based on cooperative prevention difference we proposed can also be extended to the Transformer network, so it has good application prospects.

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Section: Related Work Covariance Matrix and Its Applicationsmentioning

confidence: 99%

Convolutional Correlation Branch Network for Image Classification

Li,

Luo,

Zhu

2023

Preprint

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“…It has garnered significant attention from the academic community and has been rapidly applied in various practice environments, particularly achieving enormous success in engineering applications. For example, they have gained wide application and achieved good results in areas such as combinatorial optimization [ 42 , 43 ], data mining [ 44 , 45 ], energy scheduling [ 46 , 47 ], medicine [ 48 , 49 ] and image classification [ 50 , 51 ], bankruptcy prediction [ 52 ], economic emission dispatch [ 53 ], feature selection [ 54 – 58 ], numerical optimization [ 59 – 61 ], scheduling optimization [ 62 , 63 ], multi-objective optimization [ 64 ], large-scale complex optimization [ 65 ], global optimization [ 66 – 70 ], feed-forward neural networks [ 71 ], and target tracking [ 72 ]. Therefore, to better explore the future development trend of sharing economy, this study proposes an effective prediction model for the future development trend of sharing economy.…”

Section: Introductionmentioning

confidence: 99%

An enhanced decision-making framework for predicting future trends of sharing economy

Wu,

Tang,

et al. 2023

PLoS ONE

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This work aims to provide a reliable and intelligent prediction model for future trends in sharing economy. Moreover, it presents valuable insights for decision-making and policy development by relevant governmental bodies. Furthermore, the study introduces a predictive system that incorporates an enhanced Harris Hawk Optimization (HHO) algorithm and a K-Nearest Neighbor (KNN) forecasting framework. The method utilizes an improved simulated annealing mechanism and a Gaussian bare bone structure to improve the original HHO, termed SGHHO. To achieve optimal prediction performance and identify essential features, a refined simulated annealing mechanism is employed to mitigate the susceptibility of the original HHO algorithm to local optima. The algorithm employs a mechanism that boosts its global search ability by generating fresh solution sets at a specific likelihood. This mechanism dynamically adjusts the equilibrium between the exploration and exploitation phases, incorporating the Gaussian bare bone strategy. The best classification model (SGHHO-KNN) is developed to mine the key features with the improvement of both strategies. To assess the exceptional efficacy of the SGHHO algorithm, this investigation conducted a series of comparative trials employing the function set of IEEE CEC 2014. The outcomes of these experiments unequivocally demonstrate that the SGHHO algorithm outperforms the original HHO algorithm on 96.7% of the functions, substantiating its remarkable superiority. The algorithm can achieve the optimal value of the function on 67% of the tested functions and significantly outperforms other competing algorithms. In addition, the key features selected by the SGHHO-KNN model in the prediction experiment, including " Form of sharing economy in your region " and " Attitudes to the sharing economy ", are important for predicting the future trends of the sharing economy in this study. The results of the prediction demonstrate that the proposed model achieves an accuracy rate of 99.70% and a specificity rate of 99.38%. Consequently, the SGHHO-KNN model holds great potential as a reliable tool for forecasting the forthcoming trajectory of the sharing economy.

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“…This variant was abbreviated ICS and its outcomes were better than those of the competing algorithms. There are several other metaheuristic algorithms proposed recently for tackling this problem, some of them are the water cycle algorithm [14], the growth optimizer [15], and the opposition-based Runge Kutta optimizer [16].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Multilevel Threshold Segmentation Method for Breast Cancer Imaging Based on Metaheuristics Algorithms: Analysis and Validations

Abdel‐Basset

Mohamed

Abouhawwash

et al. 2023

Int J Comput Intell Syst

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Breast cancer is a hazardous disease that should be seriously tackled to reduce its danger in all aspects of the world. Therefore, several imaging ways to detect this disease were considered, but the produced images need to be accurately processed to effectively detect it. Image segmentation is an indispensable step in image processing to segment the homogenous regions that have similar features such as brightness, color, texture, contrast, form, and size. Several techniques like region-based, threshold-based, edge-based, and feature-based clustering have been developed for image segmentation; however, thresholding, which is divided into two classes: bilevel and multilevel, won the highest attention by the researchers due to its simplicity, ease of use and accuracy. The multilevel thresholding-based image segmentation is difficult to be tackled using traditional techniques, especially with increasing the threshold level; therefore, the researchers pay attention to the metaheuristic algorithms which could overcome several hard problems in a reasonable time. In this paper, a new hybrid metaheuristic algorithm based on integrating the jellyfish search algorithm with an effective improvement method is proposed for segmenting the color images of breast cancer, namely the hybrid jellyfish search algorithm HJSO. Experiments are extensively performed to appear the superiority of the proposed algorithm, including validating its performance using various breast cancer images and conducting an extensive comparison with several rival algorithms to explore its effectiveness. The experimental findings, including various performance metrics like fitness values, CPU time, Peak signal-to-noise ratio (PSNR), standard deviation, Features similarity index (FSIM), and Structural similarity index (SSIM), totally show the efficiency of HJSO.

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Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation

Cited by 9 publications

References 150 publications

Convolutional Correlation Branch Network for Image Classification

Convolutional Correlation Branch Network for Image Classification

An enhanced decision-making framework for predicting future trends of sharing economy

An Efficient Multilevel Threshold Segmentation Method for Breast Cancer Imaging Based on Metaheuristics Algorithms: Analysis and Validations

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