Taguchi method: artificial neural network approach for the optimization of high-efficiency microfluidic biosensor for COVID-19

Romdhane, Imed Ben; Jemmali, Asma; Kaziz, Sameh; Echouchene, Fraj; Alshahrani, Thamraa; Belmabrouk, Hafedh

doi:10.1140/epjp/s13360-023-03988-1

Cited by 28 publications

(1 citation statement)

References 51 publications

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“…Duration is dramatically shortened using the NN-PSO approach, from 15 h to 17 min. In 2023, Romdhane et al 20 have enhances a microchip flow containment technique to transfer tiny sample quantities to sensor interfaces quickly. The tests were designed using Taguchi's L9(33) orthogonal array and a mathematical simulation.…”

Section: Literature Surveymentioning

confidence: 99%

Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection

2024

ECS J. Solid State Sci. Technol.

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Cancer diagnosis is difficult and costly due to the complexity of the brain. Photonic technology-based biosensors show potential for identifying malignant tissues, including brain tumors, but they are often costly, time-consuming, and computationally difficult. To address these challenges, we propose an enhanced probabilistic pyramid neural networks (EPPNN)-based hollow-core photonic crystal fiber (PCF) biosensor with terahertz refractive index (THzBio-ECPPN) for detection of cancerous brain tumors. The approach is divided into two stages: biosensor design and brain tumor detection. Initially, PCF geometry with suspended cladding and a spiral-shaped hollow-core in the terahertz (THz) band is proposed. The PCF biosensors' characteristics are then calculated using the EPPNN model. The EPPNN model's hyperparameters are modified using the circle-inspired optimization algorithm to maximize accuracy and minimize effective mode loss. The proposed biosensor is then used to identify brain tumors. Experimental evaluations utilizing MATLAB show that the suggested strategy surpasses earlier methods, with a higher sensitivity (98%). The sensor has exceptional performance characteristics, such as a high figure of merit of 1.25-1.35 RI range and sensitivity of 50000 nm/RIU, indicating its potential for precise detection of changes in refractive index. This combination of photonic crystal structures and neural networks has enormous potential for improving cancerous tumor accuracy to 99.92%, precision to 99.23%, specificity to 99.73%,and sensitivity to 99.36% of brain tumor diagnosis

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Section: Literature Surveymentioning

confidence: 99%

Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection

2024

ECS J. Solid State Sci. Technol.

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Modeling of the biosorption of Fe(III) ions by olive‐stone activated carbon

Massaoudi,

Echouchene,

Ayed

et al. 2024

Applied Organom Chemis

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This study deals with the optimization of Fe(III) ion removal using activated carbon from olive stone waste using advanced machine learning models. The main objective is to evaluate and compare the performance of machine learning models, specifically multilayer perceptron artificial neural network (MLP‐ANN), general regression artificial neural network (GR‐ANN), radial basis function artificial neural network (RBF‐ANN), and particle swarm optimization artificial neural network (PSO‐ANN) in predicting Fe(III) removal efficiency. Experimental data on adsorption parameters were used to train and test the models. Techniques such as tuning hidden layer neurons, optimizing propagation values, and using a Taguchi approach PSO algorithm were applied to improve the models. For the MLP‐ANN model, the optimal configuration contains 13 neurons in the hidden layer. Concerning the parameters involved in the PSO‐ANN model, the coefficient C2 and the particle have the main effect on the reduction of the error. Their contributions are respectively 49% and 19%. The PSO‐ANN model showed superior performance with the highest regression coefficient (0.9997) and remarkable prediction accuracy, surpassing other models such as MLP‐ANN and GR‐ANN. This research suggests that innovative optimization techniques, particularly using PSO algorithms, significantly enhance the predictive capabilities of machine learning models in complex adsorption processes, contributing to more accurate Fe(III) removal models.

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Taguchi method and neural network for efficientβ‐ketoenaminesynthesis in deionized water

Ghabi,

Landolsi,

Echouchene

et al. 2024

Can J Chem Eng

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The optimization of performance parameters, in particular the yield of the synthesis reaction of β‐enaminones in demineralized water, is crucial to improve their efficiency and accuracy. In this report, we investigate the optimization of the synthesis of β‐ketoenamines in deionized water by controlling several parameters such as reaction time, temperature, amine equivalent, acid percentage, and stirring rate. An orthogonal L16 (45) network was created using Taguchi's approach, allowing for the best possible parameters. To forecast the contribution of each parameter, analysis of variance (ANOVA) techniques are also used. Multiple linear and nonlinear regression (MLR, MNLR) and multilayer perception artificial neural network (MLP‐ANN) predictive models were developed. Analysis of the results led to optimized design parameters, with time = 6 h, temperature = 25°C, amine equivalent = 1.5, acid percentage = 20%, and stirring rate = 1000 rpm, leading to a maximum yield of 63%. ANOVA analysis revealed that temperature, stirring rate, acid percentage, and time are the parameters with the greatest influence. The least sensitive parameter is the amine equivalent. The two main interactions are temperature * acid % and amine equivalent * rpm. The MLP‐ANN predictions are in good agreement with the experimental values, resulting in a higher R2 compared to the quadratic regression model and the MLR model. By using molecular docking studies, the produced compounds' biological activity was investigated. Some of the synthesized compounds appear to be interesting and could be used for therapeutic applications. The results of this study give us insight into the gentle, cost‐effective, and biologically active synthesis of β‐enaminones in deionized water.

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Taguchi method: artificial neural network approach for the optimization of high-efficiency microfluidic biosensor for COVID-19

Cited by 28 publications

References 51 publications

Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection

Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection

Modeling of the biosorption of Fe(III) ions by olive‐stone activated carbon

Taguchi method and neural network for efficientβ‐ketoenaminesynthesis in deionized water

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