Improved predictive capability of coagulation process by extreme learning machine with radial basis function

Jayaweera, Chamanthi Denisha; Othman,; Aziz, N.

doi:10.1016/j.jwpe.2019.100977

Cited by 26 publications

(10 citation statements)

References 32 publications

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“…The low turbidity model was able to predict coagulant dosage with a correlation coefficient greater than 0.97. 22 The high turbidity model was able to predict coagulation dosage with a reasonably acceptable correlation coefficient of at least 0.80. 22 Another study was performed by a hybrid of k-signifies an adaptive neuro-fuzzy inference system (k-means-ANFIS) for the turbidity of settled water prediction and optimal determination of the coagulant dose using historical data at large scale.…”

Section: Introductionmentioning

confidence: 90%

“…22 The high turbidity model was able to predict coagulation dosage with a reasonably acceptable correlation coefficient of at least 0.80. 22 Another study was performed by a hybrid of k-signifies an adaptive neuro-fuzzy inference system (k-means-ANFIS) for the turbidity of settled water prediction and optimal determination of the coagulant dose using historical data at large scale. 23 To construct a well adaptive model to different states of inflow water process, raw water quality data was classified into four groups according to its properties by a k-means clustering technique.…”

Section: Introductionmentioning

confidence: 90%

“…21 Other researchers used extreme machine learning (ELM) coupled with radial base function (RBF) neural networks to predict coagulation doses. 22 The coagulation data was divided into two categories based on low and high turbidity. The optimal number of input parameters for low turbidity water coagulation modelling was found to be 3, while the optimal number of input parameters for turbidity water coagulation modelling were found to be 4.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

Tahraoui

Belhadj

Moula

et al. 2021

Kemija U Industriji

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Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

Tahraoui

Belhadj

Moula

et al. 2021

Kemija U Industriji

View full text Add to dashboard Cite

“…Yan [203] adopted both ELM and RBF to achieve higher accuracy of prediction in their experiments. And research of similar direction was concerned constantly in recent years [65,201].…”

Section: Elm Vs Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

271

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…Radial Basis Function (RBF) merupakan bagian dari metode Artificial Neural Network (ANN) berdasarkan fungsi radial basis sebagai fungsi aktivasi [15], [16]. Arsitektur RBF terdapat tiga lapisan yaitu lapisan input, tersembunyi, dan output.…”

Section: Klasifikasiunclassified

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

Liantoni

Santoso

2020

SINTECH Journal

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In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

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Improved predictive capability of coagulation process by extreme learning machine with radial basis function

Cited by 26 publications

References 32 publications

Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

Optimisation and Prediction of the Coagulant Dose for the Elimination of Organic Micropollutants Based on Turbidity

A review on extreme learning machine

Perbaikan Kontras Citra Mammogram Pada Klasifikasi Kanker Payudara Berdasarkan Fitur Gray-Level Co-Occurrence Matrix

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