A minimum cross-entropy multi-thresholds segmentation algorithm based on improved WOA

Zhu, Zhenkun; Sun, Yingxiang

doi:10.1051/matecconf/202133607003

Cited by 2 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…WOA will lose the ability to jump out of the local optimum when the number of iterations reaches half of the maximum number of iterations set earlier. Consequently, WOA is accompanied by a certain risk of falling into the local optimum, leading to inaccurate results of prediction [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…Levy flight is a kind of random search that relies on Levy distribution, which has been applied many times in the optimization field in recent years. Levy flight is able to improve cuckoo and particle swarm optimization algorithms [23][24][25] and so on. LWOA owns a faster convergence speed and higher convergence accuracy; LWOA has a better ability to jump out of the local optimum.…”

Section: Principle Of Lwoamentioning

confidence: 99%

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Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

Zhang

Sheng

et al. 2022

Computational Intelligence and Neuroscience

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The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.

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Section: Methodsmentioning

confidence: 99%

Section: Principle Of Lwoamentioning

confidence: 99%