Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict...
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Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algorithms and methodologies for the accurate prediction employee turnover taking place in any organization. First of all preprocessing is done as a precautionary step as always before proceeding with the core part of the proposed work. New Intensive Optimized pca-Principal Component Analysis is used for feature selection and RFC-Random Forest Classifier is used for the classification purposes to classify accordingly to make the prediction more feasible. For classifying and predicting accurately, a methodology called Random Forest Classifier (RFC) classifier is deployed. The main objective of this work is to utilize Random Forest Classification methodology to break down fundamental purposes lying behind the worker turnover by making use of the information mining technique refer as Intensive Optimized pca for feature selection. Comparative study taking the proposed novel work with the existing is made for showing the efficiency of this work. The performance of this proposed method was found to perform better with improved yields of ROC, accuracy, precision, recall, and F1 score when compared to other existing methodologies.
The information applied technology of palmprint recognition is a biometric technology, it's based on the effective information on the palm (such as: palmprint) to identifies people. The palmprint is unique and cha...
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ISBN:
(纸本)9783038350118
The information applied technology of palmprint recognition is a biometric technology, it's based on the effective information on the palm (such as: palmprint) to identifies people. The palmprint is unique and characteristic, these are the identification of critical conditions. The feature extraction of palmprint image is a prerequisite for recognition, feature extraction algorithm depends on the quality of the recognition rate and efficiency. This paper presents a method of palmprint recognition algorithm based on Fisher linear discriminant analysis and improved pca algorithm. The experimental results show that, the recognition rate is improved.
3D animation technology is introduced into a virtual fashion show to design a new fashion show of show creation system in this paper. This system combines image processing and reconstruction technology and Matlab soft...
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ISBN:
(纸本)9783038352679
3D animation technology is introduced into a virtual fashion show to design a new fashion show of show creation system in this paper. This system combines image processing and reconstruction technology and Matlab software, the two methods and tools. Through the change of frame vector data, it realizes the reconstruction of image. In order to improve the speed of image capture and reconstruction, pca algorithm is adopted to reduce the dimensions of the vector data of each frame image. This system makes The three dimensional animation technology and performing choreography get together, which has achieved better artistic effect of show design, and has provided technical reference for the computer design of virtual fashion show in universities.
pca algorithm is a typical data dimensionality reduction method, which projects high-dimensional data to a lower-dimensional space to obtain a low-dimensional data set that can maximally represent these characteristic...
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ISBN:
(纸本)9781538662434
pca algorithm is a typical data dimensionality reduction method, which projects high-dimensional data to a lower-dimensional space to obtain a low-dimensional data set that can maximally represent these characteristics of the original data set. The pca algorithm can effectively achieve dimensionality reduction for high-dimensional data and is widely used in various fields. Aimed at the tedious calculation process of pca algorithm and the time-consuming of processing massive stream data, this paper proposes a distributed parallel dimensionality reduction algorithm that called DP-pca by improving the pca algorithm. Based on the theory of pca algorithm, DP-pca algorithm includes three parts of improvement research. Firstly, the original data set is preprocessed by using the "mean" method. Secondly, the solution process of correlation coefficient matrix is improved. Thirdly, this paper designs a distributed parallel dimensionality reduction scheme for DP-pca algorithm. In addition, this paper deploys DP-pca algorithm on Storm platform to realize parallelization of the algorithm, and tests the DP-pca algorithm. Experiments show that DP-pca algorithm improves computational efficiency and reduces the dimensionality reduction time, and improves the speedup ratio.
As one of the most important external insulation equipment in power system, insulators play an important role in transmission and distribution network. In this paper, qualitatively and quantitatively analysis of the c...
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ISBN:
(纸本)9781538684344
As one of the most important external insulation equipment in power system, insulators play an important role in transmission and distribution network. In this paper, qualitatively and quantitatively analysis of the contaminated elements on the surface of artificial contaminated insulators and natural contaminated insulators were carried out by Laser-Induced Breakdown Spectroscopy (LIBS). For artificial contaminated insulators, Na and Al elements can be used to characterize the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) levels respectively. For natural contaminated insulators, there are differences in spectral intensity of contaminated elements with different contamination years. And with the increase of the contamination years, the spectral intensity of the Si, Fe, Ca, Al, K and Na elements also have an increasing trend. Insulator samples under 4 contamination levels were classified based on the principal component analysis (pca) algorithm. Dimension reduction analysis was conducted by extracting 6 excitation spectra (Si, Fe, Ca, Al, K, Na) which can represent contamination information. The 2 principal components with the largest contribution rate were obtained and the principal component scores of each spectrum were calculated. The research results showed that the spectral sample points have obvious convergence phenomenon according to the contamination level of the insulators. Compared with the commonly used methods for judging contamination level, LIBS technology has the advantages of easy operation, no sample processing and high sensitivity. Therefore, the combination of LIBS technology and pca algorithm can save time and cost and improve the detection efficiency in the field of insulator contamination monitoring.
Bearing faults are the main contributors to the failures of induction motors,which are widely used in industrial *** this paper,8 embedded coils are firstly proposed for air gap *** the 8 coils,6 differential signals ...
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ISBN:
(纸本)9781538629185
Bearing faults are the main contributors to the failures of induction motors,which are widely used in industrial *** this paper,8 embedded coils are firstly proposed for air gap *** the 8 coils,6 differential signals are generated,which are sensitive indicators to the bearing ***,pca is introduced for fault *** with an oil pump fault detection are carried out to verify our *** results convince the validity and effectiveness of the embedded coils and pca based method.
Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. Although the current rainfall monitoring system in China has a dense station network and hig...
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Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. Although the current rainfall monitoring system in China has a dense station network and high-precision rainfall data, the time series is short. In contrast, historical rainfall data have a longer sample time series but lower precision. This study introduced a pca algorithm to reconstruct historical rainfall data. Based on the temporal and spatial characteristics of rainfall extracted from high-resolution rainfall data over the past decade, historical (6 h intervals) rainfall spatial data were reconstructed into high-resolution (1 h intervals) spatial data to satisfy the requirements of the urban flood risk analysis. The results showed that the average error between the reconstructed data and measured values in the high-value area was within 15% and in the low-value area was within 20%, representing decreases of approximately 65% and 40%, respectively, compared to traditional interpolation data. The reconstructed historical spatial rainfall data conformed to the temporal and spatial distribution characteristics of rainfall, improved the granularity of rainfall spatial data, and enabled the effective and reasonable extraction and summary of the fine temporal and spatial distribution characteristics of rainfall.
Principal Component Principles (pca) based algorithm to extract cracks in concrete bridge decks for the purpose of automating inspection is presented. pca will be used to identify clusters using a database of bridge i...
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Principal Component Principles (pca) based algorithm to extract cracks in concrete bridge decks for the purpose of automating inspection is presented. pca will be used to identify clusters using a database of bridge images. Results from three different pca approaches are presented in this work. The first approach employs pca by itself on raw data. In the second approach, a linear structure modeling is implemented prior to pca processing in an effort to enhance the results since cracks can be detected as linear structures. Several convolution-processes with masks designed to identify linear structure in the data are used. In both cases, attempts to detect cracks in a global framework were used. The third approach, on the other hand, used local information (neighborhoods) instead of global. That is, each image is segmented into small blocks where each block is processed as an individual entity. Experimental results show enhancement in the local detection with linear modeling over the global. (C) 2006 Elsevier Ltd. All rights reserved.
With the rising demand for non-invasive, continuous glucose monitoring systems, this work focuses on developing an accurate and efficient solution using a QuadRing-based resonator sensor. The ability to detect blood g...
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With the rising demand for non-invasive, continuous glucose monitoring systems, this work focuses on developing an accurate and efficient solution using a QuadRing-based resonator sensor. The ability to detect blood glucose levels without invasive techniques is crucial for managing diabetes and other health conditions. While several glucose sensors exist, many suffer from lower sensitivity, inaccurate readings due to environmental interference, or complex calibration requirements. Thus, the primary objective of this study is to design and test a QuadRing-based RF resonator sensor capable of detecting glucose levels with high sensitivity and accuracy using return loss shifts and dielectric characterization. Scattering parameters (S11) were measured, and a Principal Component Analysis (pca) model was applied to improve the accuracy of glucose detection across various concentrations. Fabrication and return loss measurements were performed on physical phantoms, and the sensor's performance was evaluated against simulated and measured results in an air medium. The sensor exhibited a relative sensitivity of 8.18%, with a high-quality factor (Q >= 10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q \ge 10$$\end{document}) ranging from 12.40\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$12.40$$\end{document} at 70 mg/dL to 11.27\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$11.27$$\end{document} at 130 mg/dL, demonstrating its ability t
Four pca algorithms, namely NIPALS, the power method (POWER), singular value decomposition (SVD) and eigenvalue decomposition (EVD), and their kernel versions are systematically applied to three NIR data sets from the...
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Four pca algorithms, namely NIPALS, the power method (POWER), singular value decomposition (SVD) and eigenvalue decomposition (EVD), and their kernel versions are systematically applied to three NIR data sets from the pharmaceutical industry. Cross-validation is used to determine the number of PC factors needed as the input for linear discriminant analysis (LDA). LDA with pca as the dimension reduction method successfully classifies all three data sets. The kernel algorithms are faster than their corresponding classic algorithms. Of the four classic algorithms, SVD is the fastest. When only the first few PCs are desired, the kernel-POWER method is the fastest of all the algorithms, When all PCs are required, EVD is the most efficient of the four kernel algorithms. When cross-validation is applied, kernel-EVD greatly reduces the elapsed time compared to the classic algorithms. To further speed up cross-validation, two matrix updating methods are proposed. Compared to the normal cross-validation procedure, the first method slightly improves the speed of cross-validation by using the normal kernel-EVD. The second method greatly speeds up cross-validation, but needs a modified kernel-EVD algorithm.
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