Features of metaheuristic algorithm for integration with ANFIS model

Tavangari, Shirmohammad; Yelghi, Aref

doi:10.22541/au.165032264.41712561/v1

Cited by 1 publication

(1 citation statement)

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“…Yelghi et al suggested a modified version of the firefly technique utilizing metaheuristics. The Adaptive Neural Fuzzy Inference System (ANFIS) adjusts the parameters of the clustering algorithm in an attempt to address difficult problems such as neural networks [46][47][48][49][50]. They have attempted to use optimization techniques to address complicated issues in the fields of finance and software engineering [51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks

2023

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The early diagnosis of sepsis reduces the risk of the patient’s death. Gradient-based algorithms are applied to the neural network models used in the estimation of sepsis in the literature. However, these algorithms become stuck at the local minimum in solution space. In recent years, swarm intelligence and an evolutionary approach have shown proper results. In this study, a novel hybrid metaheuristic algorithm was proposed for optimization with regard to the weights of the deep neural network and applied for the early diagnosis of sepsis. The proposed algorithm aims to reach the global minimum with a local search strategy capable of exploring and exploiting particles in Particle Swarm Optimization (PSO) and using the mental search operator of the Human Mental Search algorithm (HMS). The benchmark functions utilized to compare the performance of HMS, PSO, and HMS-PSO revealed that the proposed approach is more reliable, durable, and adjustable than other applied algorithms. HMS-PSO is integrated with a deep neural network (HMS-PSO-DNN). The study focused on predicting sepsis with HMS-PSO-DNN, utilizing a dataset of 640 patients aged 18 to 60. The HMS-PSO-DNN model gave a better mean squared error (MSE) result than other algorithms in terms of accuracy, robustness, and performance. We obtained the MSE value of 0.22 with 30 independent runs.

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Section: Introductionmentioning

confidence: 99%