Principal Component Analysis based Feature Vector Extraction

Murali, M.

doi:10.17485/ijst/2015/v8i35/77760

Cited by 10 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A simple, non-parametric technique called PCA can be used to eliminate relevant information from large datasets and reduce the number of dimensions without sacrificing information [22]. It calculates the distance between two objects using the Euclidean distance concept.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Signature verification based on proposed fast hyper deep neural network

Hashim,

Mohsin,

Alkhayyat

2024

IJ-AI

View full text Add to dashboard Cite

<span>Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearance-based features, gray-level co-occurrence matrix (GLCM) as texture-features, and fast Fourier transform (FFT) as frequency-features are extracted from signature images in order to build a hybrid feature vector for each image. Finally, to classify signature features, we have designed a proposed fast hyper deep neural network (FHDNN) architecture. Two different datasets are used to evaluate our model these are SigComp2011, and CEDAR datasets. The results collected demonstrate that the suggested model can operate with accuracy equal to 100%, outperforming several of its predecessors. In the terms of (precision, recall, and F-score) it gives a very good results for both datasets and exceeds (1.00, 0.487, and 0.655 respectively) on Sigcomp2011 dataset and (1.00, 0.507, and 0.672 respectively) on CEDAR dataset.</span>

show abstract

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Signature verification based on proposed fast hyper deep neural network

Hashim,

Mohsin,

Alkhayyat

2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Principal component analysis (Hidayat et al, 2011;Lhazmir et al, 2017;Murali, 2015) is a feature extraction method utilized for decreasing the features and dimensions for improving the computational ability of the model. The data set usually comprises several associated variables, signifying possibilities of various redundant variations.…”

Section: Feature Extraction -Principal Components Analysis (Pca)mentioning

confidence: 99%

Intrusion Detection System for IoE-Based Medical Networks

Lakhotia

Dwivedi

Sharma

et al. 2023

Journal of Database Management

View full text Add to dashboard Cite

Internet of everything (IoE) has the power of reforming the healthcare sector - various medical devices, hardware, and software applications that are interconnected, tendering a massive volume of data. The huge interconnected medical-based network is prone to significant malicious attacks that can modify the medical data being communicated and transferred. IoE permits dynamic two-way communication and empowers the network with intellect, sophisticated data handling, caching, and allocation mechanisms. In this paper, an improvement in the conventional variable-sized detector generation for healthcare - IVD-IMT algorithm under Artificial Immune System (AIS) based Intrusion Detection System (IDS) capable of handling enormous data generated by the IoE medical network is proposed. Algorithm efficiency is dependent on two performance metrics - detection rate and false alarm rate. The input parameters were tuned using synthetic datasets and then tested over the NSL-KDD dataset. The research lays emphasis on lowering the false alarm rate without compromising on the detection rate.

show abstract

“…The principal reason behind using PCA is to decrease the dimension of space complexity. The maximum number of principal components is the number of variables in the original space [18]. The linear transformation maps the original ndimensional space into an m-dimensional feature subspace.…”

Section: Principal Component Analysismentioning

confidence: 99%

Towards Face Recognition Using Eigenface

Bhuiyan¹

2016

ijacsa

View full text Add to dashboard Cite

Abstract-This paper presents a face recognition system employing eigenface-based approach. The principal objective of this research is to extract feature vectors from images and to reduce the dimension of information. The method is implemented on frontal view facial images of persons to explore a twodimensional representation of facial images. The system is organized with RMS (Root Mean Square) contrast scaling technique employed for pre-processing the images to adjust with poor lighting conditions. Experiments have been conducted using Carnegie Mellon University database of human faces and University of Essex Computer Vision Research Projects dataset. Experimental results indicate that the proposed eigenface-based approach can classify the faces with accuracy more than 80% in all cases.

show abstract

Principal Component Analysis based Feature Vector Extraction

Cited by 10 publications

References 8 publications

Signature verification based on proposed fast hyper deep neural network

Signature verification based on proposed fast hyper deep neural network

Intrusion Detection System for IoE-Based Medical Networks

Towards Face Recognition Using Eigenface

Contact Info

Product

Resources

About