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OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT
Published 2023“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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2
A region-based Principal Component Analysis (PCA) technique for medical image compression
Published 2022“…The research has successfully developed an improved region-based compression scheme for medical images where lossy and lossless compression is implemented in one PCA architecture. …”
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3
Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224)
“…The developed algorithm weighted the n objects contribution in explaining the separation between groups. …”
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4
Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models
Published 2023“…Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. …”
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Algorithm for rapid identification of flavonoids classes
Published 2017“…Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was utilized. The results exhibited that few significant wavenumber range provides the identification and characterization of the flavonoids classes based on PCA algorithm. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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7
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40…”
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8
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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9
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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10
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2023“…The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.…”
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11
Disposable Biomimetic Array Sensor Strip Coupled With Chemometric Algorithm For Quality Assessment Of Orthosiphon Stamineus Benth Samples
Published 2006“…A disposable screen printed array sensor strip based on self-plasticized methacrylate acrylate PVC blend lipid membranes combined with chemometric algorithm has been developed and applied for qualitative and quantitative analysis of O.stamineus samples. …”
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12
Characterization And Classification Of Bioactive Compound In Natural Products By FTIR And Multivariate Data Analysis
Published 2018“…Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was performed. The results showed that the flavonoids classes were identified according to spectral region assigned using the PCA algorithm based on the FTIR spectrum of the samples. …”
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Monograph -
13
Raspberry Pi-Based Finger Vein Recognition System Using PCANet
Published 2018“…A comparison between PCANet and PCA shows that PCANet is outperform under limited training samples, with an increase of 21.3% than that of PCA. …”
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Monograph -
14
Optimized clustering with modified K-means algorithm
Published 2021“…In dealing with correlated variables, PCA was embedded in the proposed algorithm. The developed algorithms were tested on uncorrelated and correlated simulated data sets, generated under various conditions. …”
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15
Natural image noise level estimation based on local statistics for blind noise reduction
Published 2018“…Consequently, we need to estimate the noise level from the selected patches using principal component analysis (PCA). For blind denoising applications, the proposed denoising algorithm integrates the undecimated wavelet-based denoising algorithms and PCA to develop the subjective and objective qualities of the observed image, which result from filtering processes. …”
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Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data
Published 2022“…The RMD-MRCD-PCA is developed by incorporating the Principal Component Analysis (PCA) in the MRCD algorithm whereby this robust approach shrinks the covariance matrix to make it invertible and thus, can be employed to compute the RMD for high dimensional data. …”
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Development of algorithm to characterize flavonoids classes
Published 2017“…Development of algorithm based on PCA for the analysis and identification of flavonoids classes based on FTIR spectrum is introduced. …”
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Proceeding Paper -
18
Application of multivariate data analysis for rapid identification of flavonoids classes
Published 2016“…Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was performed. The results showed that the flavonoids classes were identified according to spectral region assigned using the PCA algorithm based on the FTIR spectrum of the samples. …”
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Proceeding Paper -
19
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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20
Algorithm of face recognition by principal component analysis
Published 2002“…A face recognition algorithm based on Principal Component Analysis (PCA) has been developed and tested for computer vision applications. …”
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