Improved Adaptive Neuro-Fuzzy Inference System Based on Particle
Swarm Optimization Algorithm for Predicting Labor Productivity

Elshaboury, Nehal; Al-Sakkaf, Abobakr; Alfalah, Ghasan; Abdelkader, Eslam Mohammed

doi:10.11159/iccefa21.117

Cited by 2 publications

(1 citation statement)

References 30 publications

(33 reference statements)

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“…FCM separates the input space into fuzzy areas, making it easier to identify the input variables that are most relevant to the expected output of the models [50]. FCM is utilized to obtain a smaller number of fuzzy rules [51].…”

Section: Fuzzy C-mean Clustering (Fcm)mentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination (R2), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model.

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Section: Fuzzy C-mean Clustering (Fcm)mentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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Intelligent Data-Driven Models for Simulating Formwork Labour Productivity in High Rise Buildings

Elshaboury¹,

Al-Sakkaf²,

Alfalah³

et al. 2022

IJCI

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Forecasting accurate labour productivity is critical in construction project management because construction projects are labour-intensive. This study proposes eight intelligent data-driven models for emulating formwork labour productivity in high rise buildings. These models encompass an adaptive neuro-fuzzy inference system trained using genetic algorithm (ANFIS-GA), an adaptive neuro-fuzzy inference system trained using particle swarm optimization algorithm (ANFIS-PSO), generalized regression neural network (GRNN), back-propagation artificial neural network (BP-ANN), Elman neural network (ENN), regression trees (RT), support vector machines (SVM) and Gaussian process regression (GPR). The models are applied to two high-rise buildings in Montreal, Canada to test their prediction capabilities. The accuracies of the developed data-driven models are investigated using the performance metrics of mean absolute percentage error (MAPE), mean absolute error (MAE), root-mean squared error (RMSE), root relative squared error (RRSE) and relative absolute error (RAE).The assessment metrics show that the GRNN model exhibits better and stable performance than the remainder of the prediction models (MAPE 8.98%, MAE 0.13, RMSE 0.19, RAE 0.45 and RRSE 0.54). It is also derived that the work method and temperature sustain the high influence on labor productivity. It can be anticipated that the developed GRNN model, can be a valuable decision-making tool for forecasting construction labour productivity in construction projects.

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Improved Adaptive Neuro-Fuzzy Inference System Based on Particle Swarm Optimization Algorithm for Predicting Labor Productivity

Cited by 2 publications

References 30 publications

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Intelligent Data-Driven Models for Simulating Formwork Labour Productivity in High Rise Buildings

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