Improving Classification Accuracy of Detecting Error-Related Potentials using Two-stage Trained Neural Network Classifier

2020 ◽  
Author(s):  
Praveen K. Parashiva ◽  
A. P. Vinod
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Neural Network Classifier ◽  
Two Stage ◽  
Related Potentials ◽  
Trained Neural Network

A New Optimization Strategy for Chemical Mechanical Polishing Process

2000 ◽  
Author(s):  
Gou-Jen Wang ◽  
Jau-Liang Chen ◽  
Ju-Yi Hwang
Keyword(s):  
Neural Network ◽  
Taguchi Method ◽  
Signal To Noise Ratio ◽  
Global Optimum ◽  
Optimum Process ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Two Stage ◽  
Chemical Mechanical Polishing Process ◽  
Trained Neural Network

Abstract In this paper, a systematic approach to achieve global optimum CMP process is carried out. In this new approach, orthogonal array technique adopted from the Taguchi method is used for efficient experiment design. The neural network (NN) technique is then applied to model the complex CMP process. Signal to Noise Ratio (S/N) Analysis (ANOVA) technique used in the conventional Taguchi method is also implemented to obtain the local optimum process parameters. Successively, the global optimum parameters are acquired in terms of the trained neural network. In order to increase the CMP throughput, a two-stage optimal strategy is also proposed. Experimental results demonstrate that the two-stage strategy can perform better then the original approach even though the polishing time is reduced by 1/6.

Two-Stage Neural Network Classifier for the Data Imbalance Problem with Application to Hotspot Detection

2021 ◽  
Author(s):  
Bingshu Wang ◽  
Lanfan Jiang ◽  
Wenxing Zhu ◽  
Longkun Guo ◽  
Jianli Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Network Classifier ◽  
Hotspot Detection ◽  
Two Stage ◽  
Data Imbalance ◽  
Imbalance Problem

BRIGHTNESS PRESERVING HISTOGRAM EQUALIZATION (BPHE) TECHNIQUE FOR CLASSIFICATION OF DATES

2013 ◽  
pp. 200-202
Author(s):  
MANICKAVASAGAN. A ◽  
GABRIEL THOMAS ◽  
AL-YAHYAI, R ◽  
HEMA, M
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classification Accuracy ◽  
Histogram Equalization ◽  
Neural Network Classifier ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural ◽  
Hidden Neurons ◽  
Classification Efficiency

Brightness preserving histogram equalization (BPHE) technique was used to enhance the features to discriminate three dates varieties (Khalas, Fard and Madina). Mean, entropy and kurtosis features were computed from the enhanced images and used in an Artificial Neural Network classifier. The classification efficiency of 4 sets of hidden neurons (5, 10, 20, and 30) was tested and the network with 5 neurons yielded the highest classification accuracy of 95.2%.

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis

2020 ◽  
Vol 28 (5) ◽  
pp. 923-938
Author(s):  
Amine Ben Slama ◽  
Hanene Sahli ◽  
Aymen Mouelhi ◽  
Jihene Marrakchi ◽  
Seif Boukriba ◽  
...  
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Quantitative Evaluation ◽  
Clinical Manifestation ◽  
Vestibular System ◽  
Classification Accuracy ◽  
Normal Subjects ◽  
Ellipse Fitting ◽  
Neural Network Classifier ◽  
Vestibular Disorder

BACKGROUD AND OBJECTIVE: The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. METHODS: In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects. RESULTS: The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. CONCLUSIONS: The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.

scholarly journals Neural network classifier for hyperspectral images of skin pathologies

2021 ◽  
Vol 2127 (1) ◽  
pp. 012026
Author(s):  
V Vinokurov ◽  
Yu Khristoforova ◽  
O Myakinin ◽  
I Bratchenko ◽  
A Moryatov ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Malignant Neoplasms ◽  
Neural Network Classifier ◽  
Training Set ◽  
The Neural Network ◽  
2D Images ◽  
Entire Spectral Range

Abstract This paper describes the use and results of a neural network classifier trained on a set of hyperspectral images of benign and malignant neoplasms. The analysis is carried out on 2D images extruded from hyperspectral data. The ranges of wavelengths at which the research is carried out is represented by the intervals 530–570 nm and 600–606 nm, which is caused by the assumption that the analysis of the entire spectral range is redundant and the prospect of saving resources. Melanoma, basal cell carcinoma (BCC), nevus and papilloma are accepted as primary classes, as the most dangerous, most common and non-malignant types of neoplasms, respectively. The neural network classifier is based on a three-block VGG network. With a training set included 1944 images, the classification accuracy for 4 types of samples was 92%.

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