An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Neggaz, Imène; Fizazi, Hadria

doi:10.21203/rs.3.rs-636151/v1

Cited by 4 publications

(1 citation statement)

References 63 publications

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“…al. 2021 ), Wind Speed Forecasting (Zhang et al 2021a ), Industrial Optimization (Yıldız et al 2021 ), Wind Energy Generation System (Li et al 2021 ), PEM Fuel Cell Parameter Identification (Houssein et al 2021c ; Sun et al 2021a ; Yao and Hayati 2021 ), Power Systems (Aribowo et al 2021a) , Feature Selection, Classification (Desuky et al 2021 ; Chen and Rezaei 2021 ; Annrose et al 2021 ; Neggaz and Fizazi 2021 ) Hashim et. al.…”

Section: Survey On Recent Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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Section: Survey On Recent Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

Neggaz

Fizazi

2022

Neural Comput & Applic

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Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).

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Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Dhal

Ray

Rai

et al. 2023

Arch Computat Methods Eng

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The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes Optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes Optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired Optimization Algorithms (PIOA), the Archimedes Optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

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An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Cited by 4 publications

References 63 publications

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

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