A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

Li, Ling; Pan, Jeng‐Shyang; Zhuang, Zhongjie; Chu, Shu-Chuan

doi:10.1007/978-3-031-03948-5_3

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…. Equation (10), which expresses the quality function J, was put out to provide a well-balanced search approach.…”

Section: Narrowed Exploitationmentioning

confidence: 99%

“…One of the better algorithms is the AO method, which Abualigah proposed in 2021 [9], because it is simple to build, has consistent performance, and few configurable parameters. Its strong optimization capabilities have helped with a variety of global optimization problems, including feature selection [10], vehicle route planning [11], and machine scheduling [12].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

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One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

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“…. Equation (10), which expresses the quality function J, was put out to provide a well-balanced search approach.…”

Section: Narrowed Exploitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

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“…Li et al [ 32 ] developed a method for performing feature selection (FS) from the CT image feature sets utilized for COVID-19 identification in order to increase detection accuracy and speed. AO, a population-based intelligent optimization technique, was employed FS in this study.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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“…For example, the AO was utilized as an FS technique to enhance the intrusion detection system in the IoT and cloud environments [ 7 , 8 ]. In [ 9 ], the AO was employed as an FS for COVID-19 image classification. The AOA was utilized in various FS tasks such as osteosarcoma detection [ 10 ] and our previous study of HAR [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

et al. 2022

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In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications. The applied MH were the Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA), artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the motion patterns and reduce the time complexities. Second, we developed a light feature extraction technique using advanced deep learning approaches. The developed model was ResRNN and was composed of several building blocks from deep learning networks including convolution neural networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we applied the mentioned MH algorithms to select the optimal features and boost classification accuracy. Finally, the support vector machine and random forest classifiers were employed to classify each activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary classification. We used seven different and complex datasets for the multi-classification case: the PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the results of the nine MH optimization methods using different performance indicators. We concluded that MH optimization algorithms had promising performance in HAR and fall detection applications.

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A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

Cited by 4 publications

References 22 publications

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

A Comprehensive Survey on Aquila Optimizer

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

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