Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

Table 6

Comparison of ANFIS with other well-known models.

Model	MSE	RMSE
Proposed model	0.000609	0.026026
Random search: convolutional neural networks [46]	314.9	17.7
Particle swarm optimization: convolutional neural networks [46]	194.5	13.9
Firefly algorithm: convolutional neural networks [46]	179.9	13.4
Modified firefly algorithm: multichannel convolutional neural network [46]	120.9	10.9
Genetic algorithm: long short-term memory [47]	0.007	0.089
Differential evolution: functional link artificial neural network [47]	0.009	0.03
Genetic algorithm: radial basis function [47]	0.003	0.056
Cat swarm optimization: auto regressive moving average [47]	0.001	0.038
Particle swarm optimization: ELMAN [47]	0.004	0.07
Particle swarm optimization: multilayer perceptron [47]	0.002	0.052
Biogeography-based optimization: multilayer perceptron [47]	0.001	0.043
Cat swarm optimization: multilayer perceptron [47]	0.0008	0.029
Gated recurrent unit based on complete ensemble empirical mode decomposition of adaptive noise: wavelet [48]	496.07	22.27
Long short-term memory [48]	693.79	26.34
Autoregressive integrated moving average [48]	769.68	27.74
Gated recurrent unit [48]	552.99	23.51
Convolutional neural network: bidirectional long short-term memory [48]	589.52	24.28
Support vector machine [49]	0.003	0.058
Back propagation neural network [49]	0.017	0.132
Traditional convolutional neural networks [50]	0.68	0.824666
Denoised traditional convolutional neural networks [50]	0.468	0.683787
Traditional convolutional neural networks LightGBM [50]	0.382	0.617769
Denoised traditional convolutional neural networks LightGBM [50]	0.215	0.463204
ResNet [50]	0.508	0.713024
Denoised ResNet [50]	0.424	0.65097
ResNet LightGBM [50]	0.365	0.603879
Denoised ResNet LightGBM [50]	0.156	0.395185