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Components Analysis</title> <meta name="description" content="Search results for: Principal Components Analysis"> <meta name="keywords" content="Principal Components Analysis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Principal Components Analysis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 9743</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Principal Components Analysis</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9743</span> Observations about the Principal Components Analysis and Data Clustering Techniques in the Study of Medical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Cristina%20G.%20Dasc%C3%A2lu">Cristina G. Dascâlu</a>, <a href="https://publications.waset.org/search?q=Corina%20Dima%20Cozma"> Corina Dima Cozma</a>, <a href="https://publications.waset.org/search?q=Elena%20Carmen%20Cotrutz"> Elena Carmen Cotrutz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The medical data statistical analysis often requires the using of some special techniques, because of the particularities of these data. The principal components analysis and the data clustering are two statistical methods for data mining very useful in the medical field, the first one as a method to decrease the number of studied parameters, and the second one as a method to analyze the connections between diagnosis and the data about the patient-s condition. In this paper we investigate the implications obtained from a specific data analysis technique: the data clustering preceded by a selection of the most relevant parameters, made using the principal components analysis. Our assumption was that, using the principal components analysis before data clustering - in order to select and to classify only the most relevant parameters – the accuracy of clustering is improved, but the practical results showed the opposite fact: the clustering accuracy decreases, with a percentage approximately equal with the percentage of information loss reported by the principal components analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20clustering" title="Data clustering">Data clustering</a>, <a href="https://publications.waset.org/search?q=medical%20data" title=" medical data"> medical data</a>, <a href="https://publications.waset.org/search?q=principal%20components%0Aanalysis." title=" principal components analysis."> principal components analysis.</a> </p> <a href="https://publications.waset.org/15595/observations-about-the-principal-components-analysis-and-data-clustering-techniques-in-the-study-of-medical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15595/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15595/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15595/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15595/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15595/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15595/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15595/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15595/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15595/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15595/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15595.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1501</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9742</span> Principal Component Analysis for the Characterization in the Application of Some Soil Properties</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Kamolchanok%20Panishkan">Kamolchanok Panishkan</a>, <a href="https://publications.waset.org/search?q=Kanokporn%20Swangjang"> Kanokporn Swangjang</a>, <a href="https://publications.waset.org/search?q=Natdhera%20Sanmanee"> Natdhera Sanmanee</a>, <a href="https://publications.waset.org/search?q=Daoroong%20Sungthong"> Daoroong Sungthong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this research is to study principal component analysis for classification of 67 soil samples collected from different agricultural areas in the western part of Thailand. Six soil properties were measured on the soil samples and are used as original variables. Principal component analysis is applied to reduce the number of original variables. A model based on the first two principal components accounts for 72.24% of total variance. Score plots of first two principal components were used to map with agricultural areas divided into horticulture, field crops and wetland. The results showed some relationships between soil properties and agricultural areas. PCA was shown to be a useful tool for agricultural areas classification based on soil properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=soil%20organic%20matter" title="soil organic matter">soil organic matter</a>, <a href="https://publications.waset.org/search?q=soil%20properties" title=" soil properties"> soil properties</a>, <a href="https://publications.waset.org/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/search?q=principal%20components" title=" principal components"> principal components</a> </p> <a href="https://publications.waset.org/2959/principal-component-analysis-for-the-characterization-in-the-application-of-some-soil-properties" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2959/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2959/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2959/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2959/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2959/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2959/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2959/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2959/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2959/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2959/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2959.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4114</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9741</span> Similarity Measures and Weighted Fuzzy C-Mean Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Bainian%20Li">Bainian Li</a>, <a href="https://publications.waset.org/search?q=Kongsheng%20Zhang"> Kongsheng Zhang</a>, <a href="https://publications.waset.org/search?q=Jian%20Xu"> Jian Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper we study the fuzzy c-mean clustering algorithm combined with principal components method. Demonstratively analysis indicate that the new clustering method is well rather than some clustering algorithms. We also consider the validity of clustering method.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=FCM%20algorithm" title="FCM algorithm">FCM algorithm</a>, <a href="https://publications.waset.org/search?q=Principal%20Components%20Analysis" title=" Principal Components Analysis"> Principal Components Analysis</a>, <a href="https://publications.waset.org/search?q=Clustervalidity" title=" Clustervalidity"> Clustervalidity</a> </p> <a href="https://publications.waset.org/3579/similarity-measures-and-weighted-fuzzy-c-mean-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3579/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3579/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3579/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3579/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3579/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3579/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3579/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3579/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3579/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3579/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1724</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9740</span> Utilizing the Principal Component Analysis on Multispectral Aerial Imagery for Identification of Underlying Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Bosques-Perez">M. Bosques-Perez</a>, <a href="https://publications.waset.org/search?q=W.%20Izquierdo"> W. Izquierdo</a>, <a href="https://publications.waset.org/search?q=H.%20Martin"> H. Martin</a>, <a href="https://publications.waset.org/search?q=L.%20Deng"> L. Deng</a>, <a href="https://publications.waset.org/search?q=J.%20Rodriguez"> J. Rodriguez</a>, <a href="https://publications.waset.org/search?q=T.%20Yan"> T. Yan</a>, <a href="https://publications.waset.org/search?q=M.%20Cabrerizo"> M. Cabrerizo</a>, <a href="https://publications.waset.org/search?q=A.%20Barreto"> A. Barreto</a>, <a href="https://publications.waset.org/search?q=N.%20Rishe"> N. Rishe</a>, <a href="https://publications.waset.org/search?q=M.%20Adjouadi"> M. Adjouadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Big%20data" title="Big data">Big data</a>, <a href="https://publications.waset.org/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/search?q=multispectral" title=" multispectral"> multispectral</a>, <a href="https://publications.waset.org/search?q=principal%0D%0Acomponent%20analysis." title=" principal component analysis."> principal component analysis.</a> </p> <a href="https://publications.waset.org/10013776/utilizing-the-principal-component-analysis-on-multispectral-aerial-imagery-for-identification-of-underlying-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013776/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013776/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013776/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013776/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013776/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013776/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013776/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013776/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013776/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013776/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9739</span> Evolutionary Eigenspace Learning using CCIPCA and IPCA for Face Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ghazy%20M.R.%20Assassa">Ghazy M.R. Assassa</a>, <a href="https://publications.waset.org/search?q=Mona%20F.%20M.%20Mursi"> Mona F. M. Mursi</a>, <a href="https://publications.waset.org/search?q=Hatim%20A.%20Aboalsamh"> Hatim A. Aboalsamh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional principal components analysis (PCA) techniques for face recognition are based on batch-mode training using a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where within the framework of continuous learning, new training images are continuously added to the original set; this would trigger a costly continuous re-computation of the eigen space representation via repeating an entire batch-based training that includes the old and new images. Incremental PCA methods allow adding new images and updating the PCA representation. In this paper, two incremental PCA approaches, CCIPCA and IPCA, are examined and compared. Besides, different learning and testing strategies are proposed and applied to the two algorithms. The results suggest that batch PCA is inferior to both incremental approaches, and that all CCIPCAs are practically equivalent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Candid%20covariance-free%20incremental%20principal%0Acomponents%20analysis%20%28CCIPCA%29" title="Candid covariance-free incremental principal components analysis (CCIPCA)">Candid covariance-free incremental principal components analysis (CCIPCA)</a>, <a href="https://publications.waset.org/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/search?q=incremental%0Aprincipal%20components%20analysis%20%28IPCA%29." title=" incremental principal components analysis (IPCA)."> incremental principal components analysis (IPCA).</a> </p> <a href="https://publications.waset.org/12212/evolutionary-eigenspace-learning-using-ccipca-and-ipca-for-face-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12212/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12212/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12212/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12212/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12212/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12212/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12212/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12212/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12212/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12212/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12212.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1822</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9738</span> Principal Component Analysis using Singular Value Decomposition of Microarray Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dong%20Hoon%20Lim">Dong Hoon Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. Principal component analysis(PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. PCA, which can be implemented via a singular value decomposition(SVD), is useful for analysis of microarray data. For application of PCA using SVD we use the DNA microarray data for the small round blue cell tumors(SRBCT) of childhood by Khan et al.(2001). To decide the number of components which account for sufficient amount of information we draw scree plot. Biplot, a graphic display associated with PCA, reveals important features that exhibit relationship between variables and also the relationship of variables with observations.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Principal%20component%20analysis" title="Principal component analysis">Principal component analysis</a>, <a href="https://publications.waset.org/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a>, <a href="https://publications.waset.org/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/search?q=SRBCT" title=" SRBCT"> SRBCT</a> </p> <a href="https://publications.waset.org/16593/principal-component-analysis-using-singular-value-decomposition-of-microarray-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16593/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16593/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16593/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16593/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16593/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16593/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16593/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16593/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16593/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16593/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3250</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9737</span> Principal Component Regression in Noninvasive Pineapple Soluble Solids Content Assessment Based On Shortwave Near Infrared Spectrum</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=K.%20S.%20Chia">K. S. Chia</a>, <a href="https://publications.waset.org/search?q=H.%20Abdul%20Rahim"> H. Abdul Rahim</a>, <a href="https://publications.waset.org/search?q=R.%20Abdul%20Rahim"> R. Abdul Rahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Principal component regression (PCR) is a combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and implemented in the non-destructive assessment of the soluble solid content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation (RMSECV) of 0.8323 Brix° with principal components (PCs) of 14. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Pineapple" title="Pineapple">Pineapple</a>, <a href="https://publications.waset.org/search?q=Shortwave%20near%20infrared" title=" Shortwave near infrared"> Shortwave near infrared</a>, <a href="https://publications.waset.org/search?q=Principal%20component%20regression" title=" Principal component regression"> Principal component regression</a>, <a href="https://publications.waset.org/search?q=Non-invasive%20measurement%3B%20Soluble%20solids%20content" title=" Non-invasive measurement; Soluble solids content"> Non-invasive measurement; Soluble solids content</a> </p> <a href="https://publications.waset.org/13682/principal-component-regression-in-noninvasive-pineapple-soluble-solids-content-assessment-based-on-shortwave-near-infrared-spectrum" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13682/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13682/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13682/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13682/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13682/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13682/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13682/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13682/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13682/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13682/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13682.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2027</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9736</span> Multivariate Statistical Analysis of Decathlon Performance Results in Olympic Athletes (1988-2008)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jaebum%20Park">Jaebum Park</a>, <a href="https://publications.waset.org/search?q=Vladimir%20M.%20Zatsiorsky"> Vladimir M. Zatsiorsky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance results of the athletes competed in the 1988-2008 Olympic Games were analyzed (n = 166). The data were obtained from the IAAF official protocols. In the principal component analysis, the first three principal components explained 70% of the total variance. In the 1st principal component (with 43.1% of total variance explained) the largest factor loadings were for 100m (0.89), 400m (0.81), 110m hurdle run (0.76), and long jump (–0.72). This factor can be interpreted as the 'sprinting performance'. The loadings on the 2nd factor (15.3% of the total variance) presented a counter-intuitive throwing-jumping combination: the highest loadings were for throwing events (javelin throwing 0.76; shot put 0.74; and discus throwing 0.73) and also for jumping events (high jump 0.62; pole vaulting 0.58). On the 3rd factor (11.6% of total variance), the largest loading was for 1500 m running (0.88); all other loadings were below 0.4. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Decathlon" title="Decathlon">Decathlon</a>, <a href="https://publications.waset.org/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/search?q=Olympic%0D%0AGames" title=" Olympic Games"> Olympic Games</a>, <a href="https://publications.waset.org/search?q=multivariate%20statistical%20analysis." title=" multivariate statistical analysis."> multivariate statistical analysis.</a> </p> <a href="https://publications.waset.org/13614/multivariate-statistical-analysis-of-decathlon-performance-results-in-olympic-athletes-1988-2008" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13614/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13614/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13614/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13614/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13614/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13614/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13614/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13614/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13614/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13614/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2811</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9735</span> Effects of Different Meteorological Variables on Reference Evapotranspiration Modeling: Application of Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Akinola%20Ikudayisi">Akinola Ikudayisi</a>, <a href="https://publications.waset.org/search?q=Josiah%20Adeyemo"> Josiah Adeyemo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The correct estimation of reference evapotranspiration (ETₒ) is required for effective irrigation water resources planning and management. However, there are some variables that must be considered while estimating and modeling ETₒ. This study therefore determines the multivariate analysis of correlated variables involved in the estimation and modeling of ETₒ at Vaalharts irrigation scheme (VIS) in South Africa using Principal Component Analysis (PCA) technique. Weather and meteorological data between 1994 and 2014 were obtained both from South African Weather Service (SAWS) and Agricultural Research Council (ARC) in South Africa for this study. Average monthly data of minimum and maximum temperature (°C), rainfall (mm), relative humidity (%), and wind speed (m/s) were the inputs to the PCA-based model, while ETₒ is the output. PCA technique was adopted to extract the most important information from the dataset and also to analyze the relationship between the five variables and ETₒ. This is to determine the most significant variables affecting ETₒ estimation at VIS. From the model performances, two principal components with a variance of 82.7% were retained after the eigenvector extraction. The results of the two principal components were compared and the model output shows that minimum temperature, maximum temperature and windspeed are the most important variables in ETₒ estimation and modeling at VIS. In order words, ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity are less important and cannot be used to provide enough information about ETₒ estimation at VIS. The outcome of this study has helped to reduce input variable dimensionality from five to the three most significant variables in ETₒ modelling at VIS, South Africa.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Irrigation" title="Irrigation">Irrigation</a>, <a href="https://publications.waset.org/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/search?q=reference%20evapotranspiration" title=" reference evapotranspiration"> reference evapotranspiration</a>, <a href="https://publications.waset.org/search?q=Vaalharts." title=" Vaalharts."> Vaalharts.</a> </p> <a href="https://publications.waset.org/10004766/effects-of-different-meteorological-variables-on-reference-evapotranspiration-modeling-application-of-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004766/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004766/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004766/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004766/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004766/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004766/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004766/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004766/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004766/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004766/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004766.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1061</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9734</span> Combined Feature Based Hyperspectral Image Classification Technique Using Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mrs.K.Kavitha">Mrs.K.Kavitha</a>, <a href="https://publications.waset.org/search?q=S.Arivazhagan"> S.Arivazhagan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Multi-class" title="Multi-class">Multi-class</a>, <a href="https://publications.waset.org/search?q=Run%20Length%20features" title=" Run Length features"> Run Length features</a>, <a href="https://publications.waset.org/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/search?q=ICA" title=" ICA"> ICA</a>, <a href="https://publications.waset.org/search?q=classification%20and%20Support%20Vector%20Machines." title=" classification and Support Vector Machines."> classification and Support Vector Machines.</a> </p> <a href="https://publications.waset.org/11395/combined-feature-based-hyperspectral-image-classification-technique-using-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11395/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11395/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11395/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11395/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11395/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11395/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11395/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11395/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11395/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11395/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11395.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1522</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9733</span> Adaptive Kernel Principal Analysis for Online Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mingtao%20Ding">Mingtao Ding</a>, <a href="https://publications.waset.org/search?q=Zheng%20Tian"> Zheng Tian</a>, <a href="https://publications.waset.org/search?q=Haixia%20Xu"> Haixia Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard KPCA.This formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=adaptive%20method" title="adaptive method">adaptive method</a>, <a href="https://publications.waset.org/search?q=kernel%20principal%20component%20analysis" title=" kernel principal component analysis"> kernel principal component analysis</a>, <a href="https://publications.waset.org/search?q=online%20extraction" title=" online extraction"> online extraction</a>, <a href="https://publications.waset.org/search?q=recursive%20algorithm" title=" recursive algorithm"> recursive algorithm</a> </p> <a href="https://publications.waset.org/5153/adaptive-kernel-principal-analysis-for-online-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5153/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5153/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5153/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5153/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5153/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5153/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5153/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5153/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5153/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5153/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5153.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1552</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9732</span> Face Recognition with PCA and KPCA using Elman Neural Network and SVM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hossein%20Esbati">Hossein Esbati</a>, <a href="https://publications.waset.org/search?q=Jalil%20Shirazi"> Jalil Shirazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, in order to categorize ORL database face pictures, principle Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) methods by using Elman neural network and Support Vector Machine (SVM) categorization methods are used. Elman network as a recurrent neural network is proposed for modeling storage systems and also it is used for reviewing the effect of using PCA numbers on system categorization precision rate and database pictures categorization time. Categorization stages are conducted with various components numbers and the obtained results of both Elman neural network categorization and support vector machine are compared. In optimum manner 97.41% recognition accuracy is obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Face%20recognition" title="Face recognition">Face recognition</a>, <a href="https://publications.waset.org/search?q=Principal%20Component%20Analysis" title=" Principal Component Analysis"> Principal Component Analysis</a>, <a href="https://publications.waset.org/search?q=Kernel%20Principal%20Component%20Analysis" title=" Kernel Principal Component Analysis"> Kernel Principal Component Analysis</a>, <a href="https://publications.waset.org/search?q=Neural%20network" title=" Neural network"> Neural network</a>, <a href="https://publications.waset.org/search?q=Support%0AVector%20Machine." title=" Support Vector Machine."> Support Vector Machine.</a> </p> <a href="https://publications.waset.org/3148/face-recognition-with-pca-and-kpca-using-elman-neural-network-and-svm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3148/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3148/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3148/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3148/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3148/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3148/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3148/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3148/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3148/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3148/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3148.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1930</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9731</span> Extracting Single Trial Visual Evoked Potentials using Selective Eigen-Rate Principal Components</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Samraj%20Andrews">Samraj Andrews</a>, <a href="https://publications.waset.org/search?q=Ramaswamy%20Palaniappan"> Ramaswamy Palaniappan</a>, <a href="https://publications.waset.org/search?q=Nidal%20Kamel"> Nidal Kamel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In single trial analysis, when using Principal Component Analysis (PCA) to extract Visual Evoked Potential (VEP) signals, the selection of principal components (PCs) is an important issue. We propose a new method here that selects only the appropriate PCs. We denote the method as selective eigen-rate (SER). In the method, the VEP is reconstructed based on the rate of the eigen-values of the PCs. When this technique is applied on emulated VEP signals added with background electroencephalogram (EEG), with a focus on extracting the evoked P3 parameter, it is found to be feasible. The improvement in signal to noise ratio (SNR) is superior to two other existing methods of PC selection: Kaiser (KSR) and Residual Power (RP). Though another PC selection method, Spectral Power Ratio (SPR) gives a comparable SNR with high noise factors (i.e. EEGs), SER give more impressive results in such cases. Next, we applied SER method to real VEP signals to analyse the P3 responses for matched and non-matched stimuli. The P3 parameters extracted through our proposed SER method showed higher P3 response for matched stimulus, which confirms to the existing neuroscience knowledge. Single trial PCA using KSR and RP methods failed to indicate any difference for the stimuli. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Electroencephalogram" title="Electroencephalogram">Electroencephalogram</a>, <a href="https://publications.waset.org/search?q=P3" title=" P3"> P3</a>, <a href="https://publications.waset.org/search?q=Single%20trial%20VEP." title=" Single trial VEP."> Single trial VEP.</a> </p> <a href="https://publications.waset.org/7179/extracting-single-trial-visual-evoked-potentials-using-selective-eigen-rate-principal-components" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7179/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7179/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7179/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7179/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7179/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7179/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7179/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7179/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7179/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7179/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7179.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1641</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9730</span> Comparison of Power Generation Status of Photovoltaic Systems under Different Weather Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Zhaojun%20Wang">Zhaojun Wang</a>, <a href="https://publications.waset.org/search?q=Zongdi%20Sun"> Zongdi Sun</a>, <a href="https://publications.waset.org/search?q=Qinqin%20Cui"> Qinqin Cui</a>, <a href="https://publications.waset.org/search?q=Xingwan%20Ren"> Xingwan Ren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Based on multivariate statistical analysis theory, this paper uses the principal component analysis method, Mahalanobis distance analysis method and fitting method to establish the photovoltaic health model to evaluate the health of photovoltaic panels. First of all, according to weather conditions, the photovoltaic panel variable data are classified into five categories: sunny, cloudy, rainy, foggy, overcast. The health of photovoltaic panels in these five types of weather is studied. Secondly, a scatterplot of the relationship between the amount of electricity produced by each kind of weather and other variables was plotted. It was found that the amount of electricity generated by photovoltaic panels has a significant nonlinear relationship with time. The fitting method was used to fit the relationship between the amount of weather generated and the time, and the nonlinear equation was obtained. Then, using the principal component analysis method to analyze the independent variables under five kinds of weather conditions, according to the Kaiser-Meyer-Olkin test, it was found that three types of weather such as overcast, foggy, and sunny meet the conditions for factor analysis, while cloudy and rainy weather do not satisfy the conditions for factor analysis. Therefore, through the principal component analysis method, the main components of overcast weather are temperature, AQI, and pm2.5. The main component of foggy weather is temperature, and the main components of sunny weather are temperature, AQI, and pm2.5. Cloudy and rainy weather require analysis of all of their variables, namely temperature, AQI, pm2.5, solar radiation intensity and time. Finally, taking the variable values in sunny weather as observed values, taking the main components of cloudy, foggy, overcast and rainy weather as sample data, the Mahalanobis distances between observed value and these sample values are obtained. A comparative analysis was carried out to compare the degree of deviation of the Mahalanobis distance to determine the health of the photovoltaic panels under different weather conditions. It was found that the weather conditions in which the Mahalanobis distance fluctuations ranged from small to large were: foggy, cloudy, overcast and rainy.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fitting" title="Fitting">Fitting</a>, <a href="https://publications.waset.org/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/search?q=Mahalanobis%20distance" title=" Mahalanobis distance"> Mahalanobis distance</a>, <a href="https://publications.waset.org/search?q=SPSS" title=" SPSS"> SPSS</a>, <a href="https://publications.waset.org/search?q=MATLAB." title=" MATLAB."> MATLAB.</a> </p> <a href="https://publications.waset.org/10010134/comparison-of-power-generation-status-of-photovoltaic-systems-under-different-weather-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10010134/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10010134/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10010134/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10010134/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10010134/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10010134/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10010134/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10010134/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10010134/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10010134/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10010134.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">674</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9729</span> Finger Vein Recognition using PCA-based Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sepehr%20Damavandinejadmonfared">Sepehr Damavandinejadmonfared</a>, <a href="https://publications.waset.org/search?q=Ali%20Khalili%20Mobarakeh"> Ali Khalili Mobarakeh</a>, <a href="https://publications.waset.org/search?q=Mohsen%20Pashna">Mohsen Pashna</a>, <a href="https://publications.waset.org/search?q="></a>, <a href="https://publications.waset.org/search?q=Jiangping%20Gou%0D%0ASayedmehran%20Mirsafaie%20Rizi"> Jiangping Gou Sayedmehran Mirsafaie Rizi</a>, <a href="https://publications.waset.org/search?q=Saba%20Nazari"> Saba Nazari</a>, <a href="https://publications.waset.org/search?q=Shadi%20Mahmoodi%20Khaniabadi"> Shadi Mahmoodi Khaniabadi</a>, <a href="https://publications.waset.org/search?q=Mohamad%20Ali%20Bagheri"> Mohamad Ali Bagheri </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a novel algorithm is proposed to merit the accuracy of finger vein recognition. The performances of Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Kernel Entropy Component Analysis (KECA) in this algorithm are validated and compared with each other in order to determine which one is the most appropriate one in terms of finger vein recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Biometrics" title="Biometrics">Biometrics</a>, <a href="https://publications.waset.org/search?q=finger%20vein%20recognition" title=" finger vein recognition"> finger vein recognition</a>, <a href="https://publications.waset.org/search?q=PrincipalComponent%20Analysis%20%28PCA%29" title=" PrincipalComponent Analysis (PCA)"> PrincipalComponent Analysis (PCA)</a>, <a href="https://publications.waset.org/search?q=Kernel%20Principal%20Component%20Analysis%28KPCA%29" title=" Kernel Principal Component Analysis(KPCA)"> Kernel Principal Component Analysis(KPCA)</a>, <a href="https://publications.waset.org/search?q=Kernel%20Entropy%20Component%20Analysis%20%28KPCA%29." title=" Kernel Entropy Component Analysis (KPCA)."> Kernel Entropy Component Analysis (KPCA).</a> </p> <a href="https://publications.waset.org/9030/finger-vein-recognition-using-pca-based-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9030/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9030/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9030/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9030/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9030/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9030/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9030/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9030/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9030/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9030/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2680</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9728</span> The Robust Clustering with Reduction Dimension</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dyah%20E.%20Herwindiati">Dyah E. Herwindiati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A clustering is process to identify a homogeneous groups of object called as cluster. Clustering is one interesting topic on data mining. A group or class behaves similarly characteristics. This paper discusses a robust clustering process for data images with two reduction dimension approaches; i.e. the two dimensional principal component analysis (2DPCA) and principal component analysis (PCA). A standard approach to overcome this problem is dimension reduction, which transforms a high-dimensional data into a lower-dimensional space with limited loss of information. One of the most common forms of dimensionality reduction is the principal components analysis (PCA). The 2DPCA is often called a variant of principal component (PCA), the image matrices were directly treated as 2D matrices; they do not need to be transformed into a vector so that the covariance matrix of image can be constructed directly using the original image matrices. The decomposed classical covariance matrix is very sensitive to outlying observations. The objective of paper is to compare the performance of robust minimizing vector variance (MVV) in the two dimensional projection PCA (2DPCA) and the PCA for clustering on an arbitrary data image when outliers are hiden in the data set. The simulation aspects of robustness and the illustration of clustering images are discussed in the end of paper <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Breakdown%20point" title="Breakdown point">Breakdown point</a>, <a href="https://publications.waset.org/search?q=Consistency" title=" Consistency"> Consistency</a>, <a href="https://publications.waset.org/search?q=2DPCA" title=" 2DPCA"> 2DPCA</a>, <a href="https://publications.waset.org/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/search?q=Outlier" title=" Outlier"> Outlier</a>, <a href="https://publications.waset.org/search?q=Vector%20Variance" title=" Vector Variance"> Vector Variance</a> </p> <a href="https://publications.waset.org/14058/the-robust-clustering-with-reduction-dimension" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14058/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14058/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14058/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14058/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14058/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14058/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14058/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14058/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14058/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14058/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14058.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1697</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9727</span> Dynamical Analysis of Circadian Gene Expression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Carla%20Layana%20Luis%20Diambra">Carla Layana Luis Diambra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=circadian%20rhythms" title="circadian rhythms">circadian rhythms</a>, <a href="https://publications.waset.org/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/search?q=gene%20expression" title=" gene expression"> gene expression</a>, <a href="https://publications.waset.org/search?q=PCA." title=" PCA."> PCA.</a> </p> <a href="https://publications.waset.org/12666/dynamical-analysis-of-circadian-gene-expression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12666/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12666/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12666/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12666/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12666/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12666/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12666/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12666/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12666/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12666/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1592</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9726</span> Professional Management on Ecotourism and Conservation to Ensure the Future of Komodo National Park</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Daningsih%20Sulaeman">Daningsih Sulaeman</a>, <a href="https://publications.waset.org/search?q=Achmad%20Sjarmidi"> Achmad Sjarmidi</a>, <a href="https://publications.waset.org/search?q=Djoko%20T.%20Iskandar"> Djoko T. Iskandar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Komodo National Park can be associated with the implementation of ecotourism program. The result of Principal Components Analysis is synthesized, tested, and compared to the basic concept of ecotourism with some field adjustments. Principal aspects of professional management should involve ecotourism and wildlife welfare. The awareness should be focused on the future of the Natural Park as 7<sup>th</sup> Wonder Natural Heritage and its wildlife components, free from human wastes and beneficial to wildlife and local people. According to perceptions and expectations of visitors from various results of tourism programs, the visitor’s perceptions showed that the tourism management in Komodo National Park should pay more attention to visitor's satisfaction and expectation and gives positive impact directly to the ecosystem sustainability, local community and transparency to the conservation program.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=7th%20Wonders%20of%20Nature" title="7th Wonders of Nature">7th Wonders of Nature</a>, <a href="https://publications.waset.org/search?q=Ecotourism" title=" Ecotourism"> Ecotourism</a>, <a href="https://publications.waset.org/search?q=Komodo%20dragon" title=" Komodo dragon"> Komodo dragon</a>, <a href="https://publications.waset.org/search?q=visitor%E2%80%99s%20perceptions" title=" visitor’s perceptions"> visitor’s perceptions</a>, <a href="https://publications.waset.org/search?q=wildlife%20management." title=" wildlife management."> wildlife management.</a> </p> <a href="https://publications.waset.org/10010757/professional-management-on-ecotourism-and-conservation-to-ensure-the-future-of-komodo-national-park" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10010757/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10010757/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10010757/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10010757/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10010757/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10010757/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10010757/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10010757/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10010757/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10010757/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10010757.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1278</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9725</span> Classification of Defects by the SVM Method and the Principal Component Analysis (PCA)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Khelil">M. Khelil</a>, <a href="https://publications.waset.org/search?q=M.%20Boudraa"> M. Boudraa</a>, <a href="https://publications.waset.org/search?q=A.%20Kechida"> A. Kechida</a>, <a href="https://publications.waset.org/search?q=R.%20Drai"> R. Drai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analyses carried out on examples of detected defects echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect. This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis (PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the various algorithms proposed in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=NDT" title="NDT">NDT</a>, <a href="https://publications.waset.org/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/search?q=ultrasonics" title=" ultrasonics"> ultrasonics</a>, <a href="https://publications.waset.org/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/5278/classification-of-defects-by-the-svm-method-and-the-principal-component-analysis-pca" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5278/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5278/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5278/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5278/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5278/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5278/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5278/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5278/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5278/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5278/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5278.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2002</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9724</span> A Multivariate Statistical Approach for Water Quality Assessment of River Hindon, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nida%20Rizvi">Nida Rizvi</a>, <a href="https://publications.waset.org/search?q=Deeksha%20Katyal"> Deeksha Katyal</a>, <a href="https://publications.waset.org/search?q=Varun%20Joshi"> Varun Joshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River Hindon is an important river catering the demand of highly populated rural and industrial cluster of western Uttar Pradesh, India. Water quality of river Hindon is deteriorating at an alarming rate due to various industrial, municipal and agricultural activities. The present study aimed at identifying the pollution sources and quantifying the degree to which these sources are responsible for the deteriorating water quality of the river. Various water quality parameters, like pH, temperature, electrical conductivity, total dissolved solids, total hardness, calcium, chloride, nitrate, sulphate, biological oxygen demand, chemical oxygen demand, and total alkalinity were assessed. Water quality data obtained from eight study sites for one year has been subjected to the two multivariate techniques, namely, principal component analysis and cluster analysis. Principal component analysis was applied with the aim to find out spatial variability and to identify the sources responsible for the water quality of the river. Three Varifactors were obtained after varimax rotation of initial principal components using principal component analysis. Cluster analysis was carried out to classify sampling stations of certain similarity, which grouped eight different sites into two clusters. The study reveals that the anthropogenic influence (municipal, industrial, waste water and agricultural runoff) was the major source of river water pollution. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and elucidation of multifaceted data sets, recognition of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cluster%20analysis" title="Cluster analysis">Cluster analysis</a>, <a href="https://publications.waset.org/search?q=multivariate%20statistical%20technique" title=" multivariate statistical technique"> multivariate statistical technique</a>, <a href="https://publications.waset.org/search?q=river%20Hindon" title=" river Hindon"> river Hindon</a>, <a href="https://publications.waset.org/search?q=water%20Quality." title=" water Quality."> water Quality.</a> </p> <a href="https://publications.waset.org/10003212/a-multivariate-statistical-approach-for-water-quality-assessment-of-river-hindon-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003212/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003212/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003212/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003212/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003212/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003212/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003212/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003212/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003212/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003212/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003212.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3813</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9723</span> Solution of S3 Problem of Deformation Mechanics for a Definite Condition and Resulting Modifications of Important Failure Theories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ranajay%20Bhowmick">Ranajay Bhowmick</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Analysis of stresses for an infinitesimal tetrahedron leads to a situation where we obtain a cubic equation consisting of three stress invariants. This cubic equation, when solved for a definite condition, gives the principal stresses directly without requiring any cumbersome and time-consuming trial and error methods or iterative numerical procedures. Since the failure criterion of different materials are generally expressed as functions of principal stresses, an attempt has been made in this study to incorporate the solutions of the cubic equation in the form of principal stresses, obtained for a definite condition, into some of the established failure theories to determine their modified descriptions. It has been observed that the failure theories can be represented using the quadratic stress invariant and the orientation of the principal plane.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cubic%20equation" title="Cubic equation">Cubic equation</a>, <a href="https://publications.waset.org/search?q=stress%20invariant" title=" stress invariant"> stress invariant</a>, <a href="https://publications.waset.org/search?q=trigonometric" title=" trigonometric"> trigonometric</a>, <a href="https://publications.waset.org/search?q=explicit%20solution" title=" explicit solution"> explicit solution</a>, <a href="https://publications.waset.org/search?q=principal%20stress" title=" principal stress"> principal stress</a>, <a href="https://publications.waset.org/search?q=failure%20criterion." title=" failure criterion."> failure criterion.</a> </p> <a href="https://publications.waset.org/10011636/solution-of-s3-problem-of-deformation-mechanics-for-a-definite-condition-and-resulting-modifications-of-important-failure-theories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011636/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011636/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011636/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011636/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011636/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011636/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011636/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011636/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011636/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011636/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9722</span> Application of Multi-Dimensional Principal Component Analysis to Medical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Naoki%20Yamamoto">Naoki Yamamoto</a>, <a href="https://publications.waset.org/search?q=Jun%20Murakami"> Jun Murakami</a>, <a href="https://publications.waset.org/search?q=Chiharu%20Okuma"> Chiharu Okuma</a>, <a href="https://publications.waset.org/search?q=Yutaro%20Shigeto"> Yutaro Shigeto</a>, <a href="https://publications.waset.org/search?q=Satoko%20Saito"> Satoko Saito</a>, <a href="https://publications.waset.org/search?q=Takashi%20Izumi"> Takashi Izumi</a>, <a href="https://publications.waset.org/search?q=Nozomi%20Hayashida"> Nozomi Hayashida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multi-dimensional principal component analysis (PCA) is the extension of the PCA, which is used widely as the dimensionality reduction technique in multivariate data analysis, to handle multi-dimensional data. To calculate the PCA the singular value decomposition (SVD) is commonly employed by the reason of its numerical stability. The multi-dimensional PCA can be calculated by using the higher-order SVD (HOSVD), which is proposed by Lathauwer et al., similarly with the case of ordinary PCA. In this paper, we apply the multi-dimensional PCA to the multi-dimensional medical data including the functional independence measure (FIM) score, and describe the results of experimental analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=multi-dimensional%20principal%20component%20analysis" title="multi-dimensional principal component analysis">multi-dimensional principal component analysis</a>, <a href="https://publications.waset.org/search?q=higher-order%20SVD%20%28HOSVD%29" title=" higher-order SVD (HOSVD)"> higher-order SVD (HOSVD)</a>, <a href="https://publications.waset.org/search?q=functional%20independence%20measure%20%28FIM%29" title=" functional independence measure (FIM)"> functional independence measure (FIM)</a>, <a href="https://publications.waset.org/search?q=medical%20data" title=" medical data"> medical data</a>, <a href="https://publications.waset.org/search?q=tensor%20decomposition" title=" tensor decomposition"> tensor decomposition</a> </p> <a href="https://publications.waset.org/5605/application-of-multi-dimensional-principal-component-analysis-to-medical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5605/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5605/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5605/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5605/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5605/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5605/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5605/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5605/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5605/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5605/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2502</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9721</span> Non-negative Principal Component Analysis for Face Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Zhang%20Yan">Zhang Yan</a>, <a href="https://publications.waset.org/search?q=Yu%20Bin"> Yu Bin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Principle component analysis is often combined with the state-of-art classification algorithms to recognize human faces. However, principle component analysis can only capture these features contributing to the global characteristics of data because it is a global feature selection algorithm. It misses those features contributing to the local characteristics of data because each principal component only contains some levels of global characteristics of data. In this study, we present a novel face recognition approach using non-negative principal component analysis which is added with the constraint of non-negative to improve data locality and contribute to elucidating latent data structures. Experiments are performed on the Cambridge ORL face database. We demonstrate the strong performances of the algorithm in recognizing human faces in comparison with PCA and NREMF approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/search?q=non-negativeprinciple%20component%20analysis%20%28NPCA%29" title=" non-negativeprinciple component analysis (NPCA)"> non-negativeprinciple component analysis (NPCA)</a> </p> <a href="https://publications.waset.org/14158/non-negative-principal-component-analysis-for-face-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14158/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14158/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14158/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14158/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14158/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14158/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14158/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14158/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14158/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14158/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14158.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1695</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9720</span> Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Amir%20Hajian">Amir Hajian</a>, <a href="https://publications.waset.org/search?q=Sepehr%20Damavandinejadmonfared"> Sepehr Damavandinejadmonfared</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as there are several kernel functions which can be used within PCA-based algorithms. In this paper, however, another side of PCA-based algorithms -particularly KPCA- is investigated. The aspect of dimension of feature vector in PCA-based algorithms is of importance especially when it comes to the real-world applications and usage of such algorithms. It means that a fixed dimension of feature vector has to be set to reduce the dimension of the input and output data and extract the features from them. Then a classifier is performed to classify the data and make the final decision. We analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in this paper and investigate the optimal feature extraction dimension in finger vein recognition using KPCA.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Biometrics" title="Biometrics">Biometrics</a>, <a href="https://publications.waset.org/search?q=finger%20vein%20recognition" title=" finger vein recognition"> finger vein recognition</a>, <a href="https://publications.waset.org/search?q=Principal%0D%0AComponent%20Analysis%20%28PCA%29" title=" Principal Component Analysis (PCA)"> Principal Component Analysis (PCA)</a>, <a href="https://publications.waset.org/search?q=Kernel%20Principal%20Component%20Analysis%0D%0A%28KPCA%29." title=" Kernel Principal Component Analysis (KPCA)."> Kernel Principal Component Analysis (KPCA).</a> </p> <a href="https://publications.waset.org/9999486/optimal-feature-extraction-dimension-in-finger-vein-recognition-using-kernel-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999486/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999486/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999486/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999486/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999486/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999486/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999486/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999486/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999486/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999486/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999486.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1962</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9719</span> Health Assessment of Electronic Products using Mahalanobis Distance and Projection Pursuit Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sachin%20Kumar">Sachin Kumar</a>, <a href="https://publications.waset.org/search?q=Vasilis%20Sotiris"> Vasilis Sotiris</a>, <a href="https://publications.waset.org/search?q=Michael%20Pecht"> Michael Pecht</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysis. These methods have been used to detect deviations in system performance from normal operation and for critical parameter isolation in multivariate environments. The study evaluates the detection capability of each approach on a set of test data with known faults against a baseline set of data representative of such “healthy" systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Mahalanobis%20distance" title="Mahalanobis distance">Mahalanobis distance</a>, <a href="https://publications.waset.org/search?q=Principle%20components" title=" Principle components"> Principle components</a>, <a href="https://publications.waset.org/search?q=Projection%20pursuit" title="Projection pursuit">Projection pursuit</a>, <a href="https://publications.waset.org/search?q=Health%20assessment" title=" Health assessment"> Health assessment</a>, <a href="https://publications.waset.org/search?q=Anomaly." title=" Anomaly."> Anomaly.</a> </p> <a href="https://publications.waset.org/8544/health-assessment-of-electronic-products-using-mahalanobis-distance-and-projection-pursuit-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8544/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8544/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8544/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8544/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8544/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8544/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8544/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8544/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8544/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8544/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8544.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1681</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9718</span> Application of Artificial Neural Network for Predicting Maintainability Using Object-Oriented Metrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=K.%20K.%20Aggarwal">K. K. Aggarwal</a>, <a href="https://publications.waset.org/search?q=Yogesh%20Singh"> Yogesh Singh</a>, <a href="https://publications.waset.org/search?q=Arvinder%20Kaur"> Arvinder Kaur</a>, <a href="https://publications.waset.org/search?q=Ruchika%20Malhotra"> Ruchika Malhotra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. One such technique is Artificial Neural Network (ANN). This paper examined the application of ANN for software quality prediction using Object- Oriented (OO) metrics. Quality estimation includes estimating maintainability of software. The dependent variable in our study was maintenance effort. The independent variables were principal components of eight OO metrics. The results showed that the Mean Absolute Relative Error (MARE) was 0.265 of ANN model. Thus we found that ANN method was useful in constructing software quality model.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Software%20quality" title="Software quality">Software quality</a>, <a href="https://publications.waset.org/search?q=Measurement" title=" Measurement"> Measurement</a>, <a href="https://publications.waset.org/search?q=Metrics" title=" Metrics"> Metrics</a>, <a href="https://publications.waset.org/search?q=Artificial%20neural%20network" title=" Artificial neural network"> Artificial neural network</a>, <a href="https://publications.waset.org/search?q=Coupling" title=" Coupling"> Coupling</a>, <a href="https://publications.waset.org/search?q=Cohesion" title=" Cohesion"> Cohesion</a>, <a href="https://publications.waset.org/search?q=Inheritance" title=" Inheritance"> Inheritance</a>, <a href="https://publications.waset.org/search?q=Principal%20component%20analysis." title=" Principal component analysis."> Principal component analysis.</a> </p> <a href="https://publications.waset.org/3096/application-of-artificial-neural-network-for-predicting-maintainability-using-object-oriented-metrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3096/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3096/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3096/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3096/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3096/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3096/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3096/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3096/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3096/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3096/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3096.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2573</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9717</span> Quantitative Ranking Evaluation of Wine Quality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Brunel">A. Brunel</a>, <a href="https://publications.waset.org/search?q=A.%20Kernevez"> A. Kernevez</a>, <a href="https://publications.waset.org/search?q=F.%20Leclere"> F. Leclere</a>, <a href="https://publications.waset.org/search?q=J.%20Trenteseaux"> J. Trenteseaux</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, wine quality is only evaluated by wine experts with their own different personal tastes, even if they may agree on some common features. So producers do not have any unbiased way to independently assess the quality of their products. A tool is here proposed to evaluate wine quality by an objective ranking based upon the variables entering wine elaboration, and analysed through principal component analysis (PCA) method. Actual climatic data are compared by measuring the relative distance between each considered wine, out of which the general ranking is performed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Wine" title="Wine">Wine</a>, <a href="https://publications.waset.org/search?q=grape" title=" grape"> grape</a>, <a href="https://publications.waset.org/search?q=vine" title=" vine"> vine</a>, <a href="https://publications.waset.org/search?q=weather%20conditions" title=" weather conditions"> weather conditions</a>, <a href="https://publications.waset.org/search?q=rating" title=" rating"> rating</a>, <a href="https://publications.waset.org/search?q=climate" title=" climate"> climate</a>, <a href="https://publications.waset.org/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/search?q=metric%20analysis." title=" metric analysis."> metric analysis.</a> </p> <a href="https://publications.waset.org/10003864/quantitative-ranking-evaluation-of-wine-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003864/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003864/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003864/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003864/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003864/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003864/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003864/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003864/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003864/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003864/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2132</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9716</span> Developing New Media Credibility Scale: A Multidimensional Perspective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hanaa%20Farouk%20Saleh">Hanaa Farouk Saleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purposes of this study are to develop a scale that reflects emerging theoretical understandings of new media credibility, based on the evolution of credibility studies in western researches, identification of the determinants of credibility in the media and its components by comparing traditional and new media credibility scales and building accumulative scale to test new media credibility. This approach was built on western researches using conceptualizations of media credibility, which focuses on four principal components: Source (journalist), message (article), medium (newspaper, radio, TV, web, etc.), and organization (owner of the medium), and adding user and cultural context as key components to assess new media credibility in particular. This study’s value lies in its contribution to the conceptualization and development of new media credibility through the creation of a theoretical measurement tool. Future studies should explore this scale to test new media credibility, which represents a promising new approach in the efforts to define and measure credibility of all media types. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Credibility%20scale" title="Credibility scale">Credibility scale</a>, <a href="https://publications.waset.org/search?q=media%20credibility%20components" title=" media credibility components"> media credibility components</a>, <a href="https://publications.waset.org/search?q=new%0D%0Amedia%20credibility%20scale" title=" new media credibility scale"> new media credibility scale</a>, <a href="https://publications.waset.org/search?q=scale%20development." title=" scale development."> scale development.</a> </p> <a href="https://publications.waset.org/10004537/developing-new-media-credibility-scale-a-multidimensional-perspective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004537/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004537/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004537/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004537/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004537/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004537/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004537/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004537/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004537/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004537/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004537.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2880</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9715</span> A Critical Survey of Reusability Aspects for Component-Based Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Arun%20Sharma">Arun Sharma</a>, <a href="https://publications.waset.org/search?q=Rajesh%20Kumar"> Rajesh Kumar</a>, <a href="https://publications.waset.org/search?q=P.%20S.%20Grover"> P. S. Grover</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The last decade has shown that object-oriented concept by itself is not that powerful to cope with the rapidly changing requirements of ongoing applications. Component-based systems achieve flexibility by clearly separating the stable parts of systems (i.e. the components) from the specification of their composition. In order to realize the reuse of components effectively in CBSD, it is required to measure the reusability of components. However, due to the black-box nature of components where the source code of these components are not available, it is difficult to use conventional metrics in Component-based Development as these metrics require analysis of source codes. In this paper, we survey few existing component-based reusability metrics. These metrics give a border view of component-s understandability, adaptability, and portability. It also describes the analysis, in terms of quality factors related to reusability, contained in an approach that aids significantly in assessing existing components for reusability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Components" title="Components">Components</a>, <a href="https://publications.waset.org/search?q=Customizability" title=" Customizability"> Customizability</a>, <a href="https://publications.waset.org/search?q=Reusability" title=" Reusability"> Reusability</a>, <a href="https://publications.waset.org/search?q=and%0AObservability." title=" and Observability."> and Observability.</a> </p> <a href="https://publications.waset.org/3086/a-critical-survey-of-reusability-aspects-for-component-based-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3086/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3086/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3086/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3086/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3086/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3086/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3086/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3086/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3086/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3086/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3086.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2469</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9714</span> Using PFA in Feature Analysis and Selection for H.264 Adaptation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nora%20A.%20Naguib">Nora A. Naguib</a>, <a href="https://publications.waset.org/search?q=Ahmed%20E.%20Hussein"> Ahmed E. Hussein</a>, <a href="https://publications.waset.org/search?q=Hesham%20A.%20Keshk"> Hesham A. Keshk</a>, <a href="https://publications.waset.org/search?q=Mohamed%20I.%20El-Adawy"> Mohamed I. El-Adawy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Classification of video sequences based on their contents is a vital process for adaptation techniques. It helps decide which adaptation technique best fits the resource reduction requested by the client. In this paper we used the principal feature analysis algorithm to select a reduced subset of video features. The main idea is to select only one feature from each class based on the similarities between the features within that class. Our results showed that using this feature reduction technique the source video features can be completely omitted from future classification of video sequences.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Adaptation" title="Adaptation">Adaptation</a>, <a href="https://publications.waset.org/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/search?q=H.264" title=" H.264"> H.264</a>, <a href="https://publications.waset.org/search?q=Principal%20Feature%20Analysis%20%28PFA%29" title=" Principal Feature Analysis (PFA)"> Principal Feature Analysis (PFA)</a> </p> <a href="https://publications.waset.org/1982/using-pfa-in-feature-analysis-and-selection-for-h264-adaptation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1982/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a 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