Speech Emotion Recognition using Manta Ray Foraging Optimization Based Feature Selection

Chattopadhyay, Soham; Dey, Arijit; Basak, Hritam; Saha, Sriparna

doi:10.1201/9781003109327-9

Cited by 5 publications

(1 citation statement)

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“…Ouyang et al used MRFO to determine the K-means' initial center of clustering, which optimized the image segmentation efficiency [50]. Chattopadhyay et al deployed an MRFO in feature selection for recognizing speech emotion, which increased the classification accuracy significantly [51]. Tiwari et al minimized the total operating cost for distributed generator evaluated by load dispatch [52], while Sultan et al used MRFO to solve multi-objective problems of sizing components of hybrid PV, wind turbine, and fuel cell system [53].…”

Section: Introductionmentioning

confidence: 99%

On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Rizk‐Allah

Zineldin

Mousa

et al. 2022

Int J Comput Intell Syst

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In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.

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Section: Introductionmentioning

confidence: 99%