“…In this experiment, we compared our proposed technique with other MS identification methods from machine and deep learning studies, such as Haar Wavelet Transform, principal component analysis, and Logistic Regression (HWT + PCA + LR) 61 , Wavelet Entropy and Feedforward Neural Network Trained by Adaptive Genetic Algorithm (WE + FNN + AGA) 44 , Hu Moment Invariant and Feedforward Neural Network Trained by Particle Swarm Optimization (HMI + FNN + PSO) 46 , gray level co-occurrence matrix and Feedforward Neural Network (GLCM + FNN) 45 , multiscale amplitude-modulation frequency-modulation (MAMFM) 62 , gray level www.nature.com/scientificreports/ co-occurrence matrix and ensemble Learning along with LogitBoost algorithm (GLCM + ensemble + Logit-Boost) 41 , gray level co-occurrence matrix and the gray level run length matrix (GLCM + GLRLM) 63 , 6-layer stochastic pooling convolutional neural network (6l-CNN) 64 , discrete wavelet transform, principal component analysis, and least squares support vector machine (DWT + PCA + LS-SVM) 65 , Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization (WE + HBP) 66 , discrete wavelet transform and probabilistic principal component analysis with random forests (DWT + PPCA + RF) 67 , fractional Fourier entropy, multilayer perceptron, and Self-adaptive Three-segment-encoded Jaya algorithm (FRFE + MLP + ST-Jaya) 16 , Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression (BWT + RKPCA + LR) 38 , Minkowski-Bouligand dimension, single hidden layer neural network, and three-segment representation biogeography-based optimization (MBD + SHLNN + TSR-BBO) 68 , stationary wavelet entropy and k-nearest neighbors (SWE + KNN) 37 , and biorthogonal wavelet features and fitness-scaled adaptive genetic algorithm (BWF + FAGA) 69 . All the techniques were tested on the same open-access dataset as ours.…”