Price Forecasting by Back Propagation Neural Network Model

Zaw, Thura; Tun, Khin Mo Mo; Oo, Aung Nway

doi:10.1109/aitc.2019.8921396

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…To assess the performance of the proposed approach, the proposed RBF-TLLH was applied to the driving fatigue classification dataset and compared with the state-of-the-art neuron network models, including the RBF based on the ROLS+D-opt algorithm (RBF-ROLS+D-opt) (Chen et al, 2003 ), three-layer forward ANN with back-propagation (ANN-BP) (Zaw et al, 2019 ; Zhang and Pu, 2020 ), and three-layer forward ANN with PSO optimization (ANN-PSO) (Li and Liu, 2016 ). The RBF based on the ROLS+D-opt algorithm (RBF-ROLS+D-opt) has been widely used because of its robustness, sparsity of the parameters, and easy implementation (Chen et al, 2003 ).…”

Section: Methodsmentioning

confidence: 99%

“…The RBF based on the ROLS+D-opt algorithm (RBF-ROLS+D-opt) has been widely used because of its robustness, sparsity of the parameters, and easy implementation (Chen et al, 2003 ). ANN-BP has the ability to approximate the non-linear function with arbitrary accuracy; therefore, it has been widely applied to various classification problems (Zaw et al, 2019 ; Zhang and Pu, 2020 ). The three-layer forward ANN with PSO optimization (ANN-PSO) is also widely used due to its advantages such as easy implementation, fewer adjustment parameters, and fast convergence (Li and Liu, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

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EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function

Ren¹,

Li²,

Chen³

et al. 2021

Front. Neurorobot.

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Electroencephalography (EEG)-based driving fatigue detection has gained increasing attention recently due to the non-invasive, low-cost, and potable nature of the EEG technology, but it is still challenging to extract informative features from noisy EEG signals for driving fatigue detection. Radial basis function (RBF) neural network has drawn lots of attention as a promising classifier due to its linear-in-the-parameters network structure, strong non-linear approximation ability, and desired generalization property. The RBF network performance heavily relies on network parameters such as the number of the hidden nodes, number of the center vectors, width, and output weights. However, global optimization methods that directly optimize all the network parameters often result in high evaluation cost and slow convergence. To enhance the accuracy and efficiency of EEG-based driving fatigue detection model, this study aims to develop a two-level learning hierarchy RBF network (RBF-TLLH) which allows for global optimization of the key network parameters. Experimental EEG data were collected, at both fatigue and alert states, from six healthy participants in a simulated driving environment. Principal component analysis was first utilized to extract features from EEG signals, and the proposed RBF-TLLH was then employed for driving status (fatigue vs. alert) classification. The results demonstrated that the proposed RBF-TLLH approach achieved a better classification performance (mean accuracy: 92.71%; area under the receiver operating curve: 0.9199) compared to other widely used artificial neural networks. Moreover, only three core parameters need to be determined using the training datasets in the proposed RBF-TLLH classifier, which increases its reliability and applicability. The findings demonstrate that the proposed RBF-TLLH approach can be used as a promising framework for reliable EEG-based driving fatigue detection.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function

Ren¹,

Li²,

Chen³

et al. 2021

Front. Neurorobot.

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“…As PNN has speed of learning capacity, it can adjust its learning progressively. Hein Tun Zaw et al [6] proposed technique can help the clinical staffs, for example, specialists and radiologists to analyze the brain malignant growth from MRI images particularly for GBM which requires the discovery of all conceivable spreading destructive regions. In this strategy, brain tumors have been identified utilizing Naïve Bayes classification with the assistance of most extreme entropy edge.…”

Section: Related Work a Related Workmentioning

confidence: 99%

A Review on Automatic Brain Tumor Classification from MRI Imaging

Kumar¹

2023

IJRASET

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Brain tumor is an irrepressible development of cells that may spread in different tissues. It can be recognized through Magnetic Resonance Imaging (MRI) which is a non-surgical investigation of organ for diagnosing any disease related to the symptoms. Tumors may be cancerous or non-cancerous or it can be considered as life threatening or less dangerous. A tumor belongs to two distinct categories such as benign or malignant. Here benign tumor that has been detected is considered as the non-cancerous or less dangerous and it does not spread to the other part of the brain. It has solid boundaries or contouring that indicates the particular shade of the tumor but malignant is the cancerous tumor which is highly dangerous and it can be spread to the other part of the brain by itself. The boundaries of the malignant tumor are not solid in appearance, instead of that it appears as faded in nature. The intension of the paper is to review different approaches regarding brain tumor classification and finding out certain flaws present in the existing systems.

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Forecasting of the WPI of Textiles in India: An Neural Approach

Das

Chakrabarti

2021

Advanced Techniques for IoT Applications

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Price Forecasting by Back Propagation Neural Network Model

Cited by 5 publications

References 3 publications

EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function

EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function

A Review on Automatic Brain Tumor Classification from MRI Imaging

Forecasting of the WPI of Textiles in India: An Neural Approach

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