Neural emulator for nonlinear systems based on PSO algorithm: real-time validation

Farhat, Yassin; Atig, Asma; Zribi, Ali; Ben Abdennour, Ridha

doi:10.1007/s10586-023-04166-9

Cited by 1 publication

(1 citation statement)

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“…It is widely applied in the literature for addressing various optimization problems. PSO operates by facilitating social information sharing within the swarm, enabling individuals' positions to converge towards the best position within the swarm [15]. Figure 1 illustrates the flow diagram of the PSO algorithm: In the flow diagram illustrated in Figure 1, the algorithm commences by randomly dispersing particles, each representing a potential solution for the problem, across the solution space.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Bülbül,

Işık

2024

Biomimetics

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The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients’ clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman’s Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman’s Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%