Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

Low, Cheng-Yaw; Park, Jaewoo; Teoh, Andrew Beng Jin

doi:10.1109/tcyb.2019.2908387

Cited by 42 publications

(29 citation statements)

References 50 publications

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“…In the optimization of the new-generation neural network, it also has the ability of multidimensional function mapping. Compared with the simple perceptron, it expands the scope of solving problems, which cannot be solved in many previous studies and have been broken through the limitations of the algorithm [ 6 , 7 ]. Its structure mainly includes three components: input layer, hidden layer, and output layer.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] English Feature Recognition Based on GA‐BP Neural Network Algorithm and Data Mining

Shen

2021

Computational Intelligence and Neuroscience

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With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.

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Section: Introductionmentioning

confidence: 99%

[Retracted] English Feature Recognition Based on GA‐BP Neural Network Algorithm and Data Mining

Shen

2021

Computational Intelligence and Neuroscience

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“…Ensemble learning is one of the most useful strategies to improve generalization performance of prediction model, with a core of training strategy for base classifiers, such as bagging, boosting, and stacking [ 29 ]. Bagging and boosting build the base learners from a single dataset, having an impact on diversity, while stacking learning method uses the multiple classifiers by taking the prediction of the previous level as input variables for the next level [ 30 ]. Therefore, stacking learning strategy is considered to construct the ensemble learning framework for hand segmentation.…”

Section: Methodsmentioning

confidence: 99%

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Jia

et al. 2020

International Journal of Biomedical Imaging

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In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.

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“…On the other hand, DNN models have achieved human-level performance and have shown great success in different real-world applications, including computer vision [ 16 ], textile process, biomedical engineering [ 17 ], material engineering [ 18 ]. DNN is an efficient machine learning tool suitable for the prediction of output parameters from input variables where there is an unknown relationship exists between input and output variables [ 19 , 20 , 21 ]. In recent years, DNN has been widely used to predict various properties of textiles.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

2021

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This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO2 NPs) through deep neural network (DNN). In the first step, TiO2 NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO2 NPs was tested against MB dye removal and in the final step, DNN was used for the prediction. DNN is an efficient machine learning tools and widely used model for the prediction of highly complex problems. However, it has never been used for the prediction of MB dye removal. Therefore, this paper investigates the prediction accuracy of MB dye removal under the influence of TiO2 NPs using DNN. Furthermore, the proposed DNN model was used to map out the complex input-output conditions for the prediction of optimal results. The amount of chemicals, i.e., amount of TiO2 NPs, amount of ehylene glycol and reaction time were chosen as input variables and MB dye removal percentage was evaluated as a response. DNN model provides significantly high performance accuracy for the prediction of MB dye removal and can be used as a powerful tool for the prediction of other functional properties of nanocomposites.

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Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

Cited by 42 publications

References 50 publications

[Retracted] English Feature Recognition Based on GA‐BP Neural Network Algorithm and Data Mining

[Retracted] English Feature Recognition Based on GA‐BP Neural Network Algorithm and Data Mining

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

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