A machine learning framework for identifying influenza pneumonia from bacterial pneumonia for medical decision making

Zhang, Qian; Huang, Anran; Shao, Lianyou; Wu, Peiliang; Heidari, Ali Asghar; Cai, Zhennao; Liang, Guoxi; Chen, Huiling; Alotaibi, Fahd Saleh; Mafarja, Majdi; Ouyang, Jinsheng

doi:10.1016/j.jocs.2022.101871

Cited by 8 publications

(1 citation statement)

References 123 publications

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“…To diagnose epileptic EEGs, Liu et al [9] suggested a modified binary grey wolf optimizationbased fuzzy KNN method. A GSHHO-FKNN machine learning model was created by Zhang et al [10] by combining an enhanced Harris hawk's optimization (GSHHO) based on the Gaussian mutation mechanism and a simulated annealing approach with a fuzzy KNN. Using a machine learning technique that combines an enhanced binary mutant quantum grey wolf optimizer (MQGWO) with FKNN, Hu et al [11] examined 3069 data points from 314 HD patients.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines

Zhu,

Li,

Heidari

et al. 2023

Sensors

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Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of CCRIME, the key parameters of FKNN were optimized using a binary conversion method. Finally, a discrete CCRIME-based BCCRIME was developed, which uses an S-shaped function transformation approach to address the feature selection issue by converting the search result into a real number that can only be zero or one. The performance of CCRIME was examined in this study from various perspectives, utilizing 30 benchmark functions from IEEE CEC2017. Basic algorithm comparison tests and sophisticated variant algorithm comparison experiments were also carried out. In addition, this paper also used collected microseismic and blasting data for classification prediction to verify the ability of the BCCRIME-FKNN model to process real data. This paper provides new ideas and methods for real-time monitoring of rock mass stability during deep well mineral resource mining.

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Section: Introductionmentioning

confidence: 99%