“…As shown in Table 3, our TSCNN-I yields the highest recognition accuracy (72.74%) and F 1 -score (0.7236) among other state-of-the-art approaches [16], [22]- [25], [50], [51], [53], [54], [56], [58]- [60], [63]- [69], [72]- [75]. Compared with the best results of other methods (Bi-WOOF+Phase [67], TIM+DCNN+ SVM [75], Dual-Inception Network [25], SSSN [23], DSSN [23], 3D-CNNs [22], OFF-ApexNet [24], and 3D-FCNN [74]), our method yields 4.45%, 6.84%, 6.74%, 9.33%, 9.33%, 6.44%, 4.96%, and 17.25% better recognition accuracy, respectively. 3) Comparison of results using the SAMM database: As shown in Table 4, our TSCNN-I yields a recognition accuracy of 63.53% and an F 1 -score of 0.6065, which are considerably better than the other methods [5], [9], [20], [23], [61], [62], [66].…”