Generative Adversarial Networks in Medical Image augmentation: A review

https://doi.org/10.1016/j.compbiomed.2022.105618

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

Slime Mould Algorithm (SMA) has recently received much attention from researchers because of its simple structure, excellent optimisation capabilities, and acceptable convergence in dealing with various types of complex real-world problems. this study aims to retrieve, identify, summarise and analyse critical studies related to SMA development. Based on this, 98 SMA-related studies in the Web of Science were retrieved, selected, and identified. The two main review vectors were advanced versions of SMAs and application domains. First, we counted and analysed various advanced versions of SMAs, summarised, classified, and discussed their improvement methods and directions. Secondly, we sort out the application domains of SMA and analyse the role, development status, and shortcomings of SMA in each domain. A survey based on the existing literature shows that SMAs clearly outperform some established metaheuristics in terms of speed and accuracy in handling various benchmark problems and solving multiple realistic optimization problems. This review not only suggests possible future directions in the field but, due to the inclusion of graphical and tabular comparisons of various properties, also provides future researchers with a comprehensive source of information about SMA and advanced versions of SAMs and the scope of adaptation for multiple application domains.

Slime mould algorithm: a comprehensive review of recent variants and applications

Performance optimization of annealing salp swarm algorithm: frameworks and applications for engineering design

An effective model for predicting serum albumin level in hemodialysis patients

An effective model for predicting serum albumin level in hemodialysis patients.

Parameter estimation of static solar photovoltaic models using Laplacian Nelder-Mead hunger games search

In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability. 

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Farah Farooqi

Papers

An effective model for predicting serum albumin level in hemodialysis patients

An effective model for predicting serum albumin level in hemodialysis patients.

Parameter estimation of static solar photovoltaic models using Laplacian Nelder-Mead hunger games search

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction