Loss function values of target detection algorithms during training. by Guodong Su (7876934)

Comparison of the proposed algorithms with other ensemble models. by Kaveh Faraji Googerdchi (18778727)

Modeling effect of different automl algorithms. by Guangyuan Ge (11520226)

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... by Ross J. Burton (10941910)

Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…First, physico-chemical inputs as bulk density (BD), initial water content (W_i), saturated water content (W_s), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

The iterative process of genetic algorithm in the upper-level model. by Zhipeng Huang (1759759)

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Benchmark functions. by Peiwen Zhang (1863202)

Parameters of the genetic algorithm. by Jian Ma (170138)

The model of network adversarial attack. by Dingming Yang (8505990)

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Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. by Gursimran Singh (575288)

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Simplified diagram of the new solution model of MSA cell individual generation. by Qi Song (121209)

Model optimization flow by MFO algorithm. by Ruoqi Wang (14958062)

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Test functions. by Huazan Liu (9137461)

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Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

Convergence curves obtained by different algorithms on CEC2022 benchmark functions. by Yuqi Xiong (12343771)

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APSO–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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APSO–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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Auto-tuning algorithm of the weight factors in the cost function. by Syed Umaid Ali (14486372)

Penalty functions and estimators for some of the regularized regression methods used in this paper. by Dongsheng Li (222712)