An Improved Back Propagation Neural Network Algorithm on Classification Problems

Nawi, Nazri Mohd; Ransing, R. S.; Salleh, Mohd Najib Mohd; Ghazali, Rozaida; Hamid, Norhamreeza Abdul

doi:10.1007/978-3-642-17622-7_18

Cited by 47 publications

(15 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BP (back propagation) network is a back propagation algorithm by former Multilayer feed-forward network, is currently the most widely used one neural network model [3]. The BP neural network algorithm is also known as the error back propagation algorithm.…”

Section: A the Principle And Algorithm Of Bp Neural Networkmentioning

confidence: 99%

Research And Realization of Aluminum Plate Surface Defects Classification Based on a Combination of BP Neural Network and SVM

Liu¹,

Li²

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

View full text Add to dashboard Cite

Abstract-Based on a combination of BP neural network and SVM, aluminum plate surface defects classification was discussed. In order to detect the defects, the target image is binaried by adaptive threshold method. After binarizing the target image, extract the characteristic value of six kinds of aluminum plate surface defect images and formed twentyfour dimensional feature vector. The principle and algorithm of BP neural network and support vector machine are introduced, given a way about combination of BP neural network and SVM, and about the important parameters optimization was carried out. The results verify the efficiency, accuracy and robustness of the algorithm about the BP neural network and support vector machine (SVM) classification of combining.

show abstract

Section: A the Principle And Algorithm Of Bp Neural Networkmentioning

confidence: 99%

Research And Realization of Aluminum Plate Surface Defects Classification Based on a Combination of BP Neural Network and SVM

Liu¹,

Li²

2015

Proceedings of the International Conference on Chemical, Material and Food Engineering

View full text Add to dashboard Cite

show abstract

“…The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy. Furthermore, the popularity of artificial neural networks (ANN) in data mining field had gained great attention from researchers to incorporate the technique into different kind of applications and classification problems [20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Parkinson disease classification: a comparative analysis on classification techniques

Nawi

Makhtar

Afip

et al. 2020

IJEECS

Self Cite

View full text Add to dashboard Cite

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.

show abstract

“…To achieve this objective, a classification task from a data mining methodology is proposed [6,9,11,12]. Classification involves determining an object into predefined groups of classes [1,5,14].…”

Section: Introductionmentioning

confidence: 99%

Classification of Alcohol Consumption among Secondary School Students

Palaniappan

Hameed

Samsudin

2017

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Abstract-In 2016, the National Institute of Health reported that 26% of 8th graders, 47% of 10th graders, and 64% of 12th graders have all had experience in consuming alcoholic drinks. This finding indicates an accelerating trend in alcohol use among school students, hence a growing concerns among the public. To address this issue, this paper is set to model the alcohol consumption data among the secondary school students and attempt to predict the alcohol consumption behaviors among them. A set of classification experiments are carried out and the classification accuracies are compared between two variations of neural network algorithms; a self-tuning multilayer perceptron classifier (AutoMLP) against the standard MLP using the student alcohol consumption dataset. It is found that AutoMLP produced better accuracy of 64.54% than neural network with 61.78%.

show abstract

An Improved Back Propagation Neural Network Algorithm on Classification Problems

Cited by 47 publications

References 25 publications

Research And Realization of Aluminum Plate Surface Defects Classification Based on a Combination of BP Neural Network and SVM

Research And Realization of Aluminum Plate Surface Defects Classification Based on a Combination of BP Neural Network and SVM

Parkinson disease classification: a comparative analysis on classification techniques

Classification of Alcohol Consumption among Secondary School Students

Contact Info

Product

Resources

About