Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Cai, Chengfeng; Gou, Bingchen; Khishe, Mohammad; Mohammadi, Mokhtar; Rashidi, Shima; Moradpour, Reza; Mirjalili, Seyedali

doi:10.1016/j.eswa.2022.119206

Cited by 61 publications

(13 citation statements)

References 48 publications

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“…The positions of other chimps in the population are determined by the positions ( ) of Attacker, Barrier, Chaser, and Driver, and the position update Eqs. ( 12 ), ( 13 ), ( 14 ), and ( 15 ) 58 . …”

Section: Methodsmentioning

confidence: 99%

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Ahmed,

Alsenany,

Abdelaliem

et al. 2023

Sci Rep

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The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.

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Section: Methodsmentioning

confidence: 99%

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Ahmed,

Alsenany,

Abdelaliem

et al. 2023

Sci Rep

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show abstract

“…The information provided by the top four outcomes will help the remaining residents upgrade their location. Equations ( 12), ( 13), (14), and (15) provide their statistical proof, respectively [36].…”

Section: Chimp Optimization Algorithm (Choa)mentioning

confidence: 98%

A novel approach to optimize LSTM Hyperparameter using the Chimp Optimization Algorithm for the Pressure Ventilator Prediction

Ahmed,

Alsenany,

Abdelaliem

et al. 2023

Preprint

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Mechanical ventilation plays a vital role in the treatment of patients suffering from severe lung disease. In times of a pandemic, it becomes crucial to develop ventilators that can automatically adjust parameters during the treatment process. To address this need, a study was conducted to predict the pressure exerted on the patient by the ventilator. This prediction was based on various factors, including the ventilator's parameters and the patient's condition, using a specialized model known as Long Short-Term Memory (LSTM). In order to optimize the LSTM model and improve the accuracy of the prediction results, an algorithm called Chimp Optimization Algorithm (ChoA) was employed. The combination of LSTM and ChoA resulted in the creation of the LSTM-ChoA model, which effectively addressed the challenge of selecting appropriate hyperparameters for the LSTM model. Experimental findings demonstrated that the LSTM-ChoA model outperformed other optimization algorithms such as whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO), as well as regression models including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in terms of accurately predicting ventilator pressure.

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“…Emambocus in 2023 made a Survey on training neural network using different types of optimization algorithms [77]. And others researchers have recently been using recent-swarm intelligence algorithms for training feedforward neural networks [78][79][80][81][82][83][84].…”

Section: Related Workmentioning

confidence: 99%

A modified weighted chimp optimization algorithm for training feed-forward neural network

Atta

Ali²,

Elshamy³

2023

PLoS ONE

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Swarm intelligence algorithms (SI) have an excellent ability to search for the optimal solution and they are applying two mechanisms during the search. The first mechanism is exploration, to explore a vast area in the search space, and when they found a promising area they switch from the exploration to the exploitation mechanism. A good SI algorithm can balance the exploration and the exploitation mechanism. In this paper, we propose a modified version of the chimp optimization algorithm (ChOA) to train a feed-forward neural network (FNN). The proposed algorithm is called a modified weighted chimp optimization algorithm (MWChOA). The main drawback of the standard ChOA and the weighted chimp optimization algorithm (WChOA) is they can be trapped in local optima because most of the solutions update their positions based on the position of the four leader solutions in the population. In the proposed algorithm, we reduced the number of leader solutions from four to three, and we found that reducing the number of leader solutions enhances the search and increases the exploration phase in the proposed algorithm, and avoids trapping in local optima. We test the proposed algorithm on the Eleven dataset and compare it against 16 SI algorithms. The results show that the proposed algorithm can achieve success to train the FNN when compare to the other SI algorithms.

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Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Cited by 61 publications

References 48 publications

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

A novel approach to optimize LSTM Hyperparameter using the Chimp Optimization Algorithm for the Pressure Ventilator Prediction

A modified weighted chimp optimization algorithm for training feed-forward neural network

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