Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

Rene, Eldon R.; López, M. Estefanía; Park, Hung‐Suck

doi:10.1155/2013/463401

Cited by 22 publications

(6 citation statements)

References 38 publications

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“…The process of gender classification was carried out using BPNN with two types of parameter setting. The first one was the standard parameter setting based on previous researchers (Tsai & Lee, 2011;Rene et al, 2013) and the second parameter setting was an Automated Parameter Tuning as shown in Figure 3. Automated parameter tuning was based on the parameter optimization incorporating grid search algorithm and cross-validation which was carried out in the experiment.…”

Section: Optimized Back Propagation Neural Networkmentioning

confidence: 99%

Gender Classification on Skeletal Remains: Efficiency of Metaheuristic Algorithm Method and Optimized Back Propagation Neural Network

Hairuddin

Yusuf²,

Othman³

2020

Journal of Information and Communication Technology

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In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classification in forensic anthropology. Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. Once the set of significant features was obtained, the learning rate and momentum of Back Propagation Neural Network (BPNN) were optimized. This was to obtain a good combination of parameters in order to produce a better gender classification. This study used 1,538 data samples from Goldman Osteometric Dataset which consisted of femur, humerus and tibia parts. Based on the feature selection results, the Optimized BPNN outperformed other methods for all datasets. The Ant Colony Algorithm-Optimized Back Propagation Neural Network produced the highest accuracy for all parts of the skeleton where for femur was 89.44%, the humerus with 88.97% and tibia with 87.52% accuracy. Hence, it can be concluded that

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Section: Optimized Back Propagation Neural Networkmentioning

confidence: 99%

Gender Classification on Skeletal Remains: Efficiency of Metaheuristic Algorithm Method and Optimized Back Propagation Neural Network

Hairuddin

Yusuf²,

Othman³

2020

Journal of Information and Communication Technology

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“…In the modeling process, training data were analyzed and then the decision was made through output neurons when new input variables were put in [31]. In most of the ANN models, the backpropagation (BP) network was the commonly used solution in dealing with the nonlinear relationship between input variables and output variables by constantly adapting the connection weight value between neurons and the error threshold in each layer to make the output variables approximately towards the expected outcome [32][33][34][35][36]. The BP algorithm was based on error gradient descent (Figure 1), which was aimed at finding the minimum error by adjusting weights of connections between neurons in the direction of lowest error [37].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Wei

Yang

2021

Computational and Mathematical Methods in Medicine

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Introduction. A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods. We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results. Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion. The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.

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“…To the best of the authors' knowledge, the application of ANN in desulfurizing BF modeling has been few reported so far [13,[36][37][38], despite the wide use of the desulfurizing BFs. Therefore, this study was conducted to enrich this research gap.…”

Section: Introductionmentioning

confidence: 99%

Predicting the performance of a desulfurizing bio-filter using an artificial neural network (ANN) model

Salehi¹,

Lestari

2020

Environmental Engineering Research

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The aim of this study was to develop a model for predicting the performance of a desulfurizing bio-filter (BF), without requiring prior information about H 2 S biodegradation kinetics and mechanism. A single hidden layer artificial neural network (ANN) model was developed and validated using the gradient descent backpropagation (GDBP) learning algorithm coupled with a learning rate and a momentum factor. The ANN model inputs were gas flow rate, residence time, and axial position in the BF bed. The removal efficiency of H 2 S was the model output. Various structures for ANN model, differing in the number of hidden layer neurons, were trained and an early stopping validation technique, the K-fold cross-validation, was used to determine the optimal structure with the best generalization ability. The modeling results showed that there was a good agreement between the experimental data and the predicted values, with a determination coefficient (R 2) of 94%. This implies that the ANN model might be an attractive and useful alternative tool for forecasting the performance of desulfurizing BFs.

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Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

Cited by 22 publications

References 38 publications

Gender Classification on Skeletal Remains: Efficiency of Metaheuristic Algorithm Method and Optimized Back Propagation Neural Network

Gender Classification on Skeletal Remains: Efficiency of Metaheuristic Algorithm Method and Optimized Back Propagation Neural Network

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research

Predicting the performance of a desulfurizing bio-filter using an artificial neural network (ANN) model

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