Offline Handwritten Character & Numeral Recognition A Kernel Based Approach

Automatic Character Recognition for the handwritten Indic script has listed up as most the challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, but all the state-of-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level Fast Discrete Curvelet Transform (FDCT) to get higher dimension feature vector. After that, Kernel-Principal Component Analysis (K-PCA) considered to obtained optimal features from FDCT feature. Finally, the classification is performed by using Probabilistic Neural Network (PNN) on handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of proposed scheme outperforms better as compared to existing model with optimized Gaussian kernel-based feature set.

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Uncertain Nonlinear Process Monitoring Using Interval Ensemble Kernel Principal Component Analysis

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2021.p0101 ◽

2021 ◽

Vol 25 (1) ◽

pp. 101-109

Author(s):

Xianrui Wang ◽

Guoxin Zhao ◽

Yu Liu ◽

Shujie Yang ◽

◽

...

Keyword(s):

Principal Component Analysis ◽

Process Monitoring ◽

Principal Component ◽

Nonlinear Process ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Gaussian Kernel ◽

Bayesian Decision ◽

Symbolic Data ◽

Nonlinear Process Monitoring

To solve uncertainties in industrial processes, interval kernel principal component analysis (IKPCA) has been proposed based on symbolic data analysis. However, it is experimentally discovered that the performance of IKPCA is worse than that of other algorithms. To improve the IKPCA algorithm, interval ensemble kernel principal component analysis (IEKPCA) is proposed. By optimizing the width parameters of the Gaussian kernel function, IEKPCA yields better performances. Ensemble learning is incorporated in the IEKPCA algorithm to build submodels with different width parameters. However, the multiple submodels will yield a large number of results, which will complicate the algorithm. To simplify the algorithm, a Bayesian decision is used to convert the result into fault probability. The final result is obtained via a weighting strategy. To verify the method, IEKPCA is applied to the Tennessee Eastman (TE) process. The false alarm rate, fault detection rate, accuracy, and other indicators used in the IEKPCA are compared with those of other algorithms. The results show that the IEKPCA improves the accuracy of uncertain nonlinear process monitoring.

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Multi-channel Raman Spectral Reconstruction Based on Gaussian Kernel Principal Component Analysis

ACTA PHOTONICA SINICA ◽

10.3788/gzxb20204903.0330001 ◽

2020 ◽

Vol 49 (3) ◽

pp. 330001-330001

Author(s):

王昕 Xin WANG ◽

康哲铭 Zhe-ming KANG ◽

刘龙 Long LIU ◽

范贤光 Xian-guang FAN

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Gaussian Kernel ◽

Spectral Reconstruction ◽

Raman Spectral

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Visualization of classified data with kernel principal component analysis

10.31227/osf.io/cbfxu ◽

2018 ◽

Author(s):

Toni Bakhtiar

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Feature Space ◽

Original Data ◽

Component Analysis ◽

Kernel Functions ◽

Kernel Principal Component Analysis ◽

Gaussian Kernel ◽

Kernel Pca ◽

Study Results

Kernel Principal Component Analysis (Kernel PCA) is a generalization of the ordinary PCA which allows mapping the original data into a high-dimensional feature space. The mapping is expected to address the issues of nonlinearity among variables and separation among classes in the original data space. The key problem in the use of kernel PCA is the parameter estimation used in kernel functions that so far has not had quite obvious guidance, where the parameter selection mainly depends on the objectivity of the research. This study exploited the use of Gaussian kernel function and focused on the ability of kernel PCA in visualizing the separation of the classified data. Assessments were undertaken based on misclassification obtained by Fisher Discriminant Linear Analysis of the first two principal components. This study results suggest for the visualization of kernel PCA by selecting the parameter in the interval between the closest and the furthest distances among the objects of original data is better than that of ordinary PCA.

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Face recognition based on Second Generation of Curvelet Transform and Kernel Principal Component Analysis

2011 4th International Congress on Image and Signal Processing ◽

10.1109/cisp.2011.6100408 ◽

2011 ◽

Cited By ~ 1

Author(s):

Peipei Shi ◽

Xuebin Li

Keyword(s):

Principal Component Analysis ◽

Face Recognition ◽

Second Generation ◽

Curvelet Transform ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis

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Multi Core DNN based IDS for Botnet Attacks using KPCA Reduction Techniques

10.21203/rs.3.rs-795895/v1 ◽

2021 ◽

Author(s):

Sharmila B S ◽

Rohini Nagapadma

Keyword(s):

Detection System ◽

Research Work ◽

Principal Component ◽

Kernel Principal Component Analysis ◽

The Internet ◽

Reduction Techniques ◽

Private Data ◽

Learning Techniques ◽

Iot Devices ◽

The Internet Of Things

Abstract Research on network security has recently acquired attention in the field of the Internet of Things. In the context of security, most of the IoT devices with the internet are connected directly which results in the exploitation of private data. Nowadays, the fraudster will release novel attacks very frequently especially for IoT devices. As a result, the traditional sophisticated Intrusion Detection System (IDS) model is not suitable for the identification of vulnerabilities in IoT devices. In our research work, we propose MCDNN for IDS. MCDNN is Multi Core DNN with having parallel optimizer. Rather than a traditional dataset, this paper experiment is conducted on the BoTIoT dataset. Since IoT devices generate a huge volume of data, this work focuses on reducing huge datasets using Kernel Principal Component Analysis(KPCA) reduction technique with optimizer parallelly. To decrease false alarm rate and maintaining less computational power multi-core is introduced in our research work. This helps identification of vulnerabilities in IoT devices using deep learning techniques faster. Experimental results indicate that designing MCDNN based IDS with different optimizers parallelly achieved higher performance than those of other techniques.

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Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach

Expert Systems with Applications ◽

10.1016/j.eswa.2017.06.038 ◽

2017 ◽

Vol 88 ◽

pp. 152-164 ◽

Cited By ~ 24

Author(s):

Deepak Ranjan Nayak ◽

Ratnakar Dash ◽

Banshidhar Majhi ◽

Vijendra Prasad

Keyword(s):

Neural Network ◽

Detection System ◽

Probabilistic Neural Network ◽

Curvelet Transform ◽

System A ◽

Pathological Brain Detection ◽

Fast Discrete Curvelet Transform

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Off-line Odia Handwritten Character Recognition: an Axis Constellation Model Based Research

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1163.0789s219 ◽

2019 ◽

Vol 8 (9S2) ◽

pp. 788-793

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Character Recognition ◽

Simulation Analysis ◽

Recognition Rate ◽

Principal Component ◽

Gaussian Kernel ◽

Handwritten Character Recognition ◽

Research Institutes ◽

Handwritten Character

Handwritten Character Recognition is most challenging area of research, in which for various aspects a little enhancement can be always achieved. It is due to the irregularity of writing and shapes of different class user’s orientation affects the recognition rate. In this paper we have taken the complexity of Odia handwritten character recognition and successfully resolve with Principal Component Analysis (PCA). Here we had adopted a model in which the importance of symmetric axis chords in recognition of unconstrained handwritten characters is established. This symmetric axis chords are drawn along both row-wise and column-wise among the points one end to other. In addition to we have calculated the statistical feature as Euclidian distance, Hamilton distance which drawn from the midpoint of the symmetric chord to nearest pixel of the character. Apart from it we have also reported the angular values from the centroid of the image to the character pixel. This empirical model also harnessed the PCA over the feature set and perform the dimension reduction to the feature set which later termed as the key feature set. A certain series of experiment was carried on for the proper implementation of proposed technique, henceforth we have taken the standard Handwritten Database from various research institutes. Lastly on simulation analysis Radial Basis Function Neural Network (RBFNN) has been reported as to achieve high recognition rate through Gaussian kernel and a comparison among them has also reported here with.

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Probabilistic Neural Network based Approach for Handwritten Character Recognition

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2010.1029 ◽

2010 ◽

pp. 105-109

Author(s):

V.N. Manjunath Aradhya ◽

S. K. Niranjan ◽

G. Hemantha Kumar

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Character Recognition ◽

Probabilistic Neural Network ◽

Principal Component ◽

Recognition System ◽

Indian Language ◽

South Indian ◽

Extraction Scheme ◽

Standard Techniques

In this paper, recognition system for totally unconstrained handwritten characters for south Indian language of Kannada is proposed. The proposed feature extraction technique is based on Fourier Transform and well known Principal Component Analysis (PCA). The system trains the appropriate frequency band images followed by PCA feature extraction scheme. For subsequent classification technique, Probabilistic Neural Network (PNN) is used. The proposed system is tested on large database containing Kannada characters and also tested on standard COIL-20 object database and the results were found to be better compared to standard techniques.

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