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principal component analysis</title> <meta name="description" content="Search results for: kernel principal component analysis"> <meta name="keywords" content="kernel principal component 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 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kernel principal component analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29754</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/abstracts/search?q=Amir%20Hajian">Amir Hajian</a>, <a href="https://publications.waset.org/abstracts/search?q=Sepehr%20Damavandinejadmonfared"> Sepehr Damavandinejadmonfared</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=finger%20vein%20recognition" title=" finger vein recognition"> finger vein recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis%20%28PCA%29" title=" principal component analysis (PCA)"> principal component analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20principal%20component%20analysis%20%28KPCA%29" title=" kernel principal component analysis (KPCA)"> kernel principal component analysis (KPCA)</a> </p> <a href="https://publications.waset.org/abstracts/14476/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/abstracts/14476.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">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29753</span> Kernel-Based Double Nearest Proportion Feature Extraction for Hyperspectral Image Classiﬁcation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hung-Sheng%20Lin">Hung-Sheng Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Hsuan%20Li"> Cheng-Hsuan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20method" title=" kernel method"> kernel method</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20nearest%20proportion%20feature%20extraction" title=" double nearest proportion feature extraction"> double nearest proportion feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20double%20nearest%20feature%20extraction" title=" kernel double nearest feature extraction"> kernel double nearest feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/54639/kernel-based-double-nearest-proportion-feature-extraction-for-hyperspectral-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54639.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">344</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29752</span> A Time-Varying and Non-Stationary Convolution Spectral Mixture Kernel for Gaussian Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai%20Chen">Kai Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuguang%20Cui"> Shuguang Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Yin"> Feng Yin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process" title="Gaussian process">Gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20mixture" title=" spectral mixture"> spectral mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title=" non-stationary"> non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution" title=" convolution"> convolution</a> </p> <a href="https://publications.waset.org/abstracts/131675/a-time-varying-and-non-stationary-convolution-spectral-mixture-kernel-for-gaussian-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131675.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">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29751</span> Implementation of a Method of Crater Detection Using Principal Component Analysis in FPGA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Izuru%20Nomura">Izuru Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatsuya%20Takino"> Tatsuya Takino</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuji%20Kageyama"> Yuji Kageyama</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin%20Nagata"> Shin Nagata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroyuki%20Kamata"> Hiroyuki Kamata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a method of crater detection from the image of the lunar surface captured by the small space probe. We use the principal component analysis (PCA) to detect craters. Nevertheless, considering　severe environment of the space, it is impossible to use generic computer in practice. Accordingly, we have to implement the method in FPGA. This paper compares FPGA and generic computer by the processing time of a method of crater detection using principal component analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crater" title="crater">crater</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=eigenvector" title=" eigenvector"> eigenvector</a>, <a href="https://publications.waset.org/abstracts/search?q=strength%20value" title=" strength value"> strength value</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=processing%20time" title=" processing time "> processing time </a> </p> <a href="https://publications.waset.org/abstracts/19004/implementation-of-a-method-of-crater-detection-using-principal-component-analysis-in-fpga" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19004.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">554</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29750</span> Estimation of Functional Response Model by Supervised Functional Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyon%20I.%20Paek">Hyon I. Paek</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20Rim%20Kim"> Sang Rim Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyon%20A.%20Ryu"> Hyon A. Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In functional linear regression, one typical problem is to reduce dimension. Compared with multivariate linear regression, functional linear regression is regarded as an infinite-dimensional case, and the main task is to reduce dimensions of functional response and functional predictors. One common approach is to adapt functional principal component analysis (FPCA) on functional predictors and then use a few leading functional principal components (FPC) to predict the functional model. The leading FPCs estimated by the typical FPCA explain a major variation of the functional predictor, but these leading FPCs may not be mostly correlated with the functional response, so they may not be significant in the prediction for response. In this paper, we propose a supervised functional principal component analysis method for a functional response model with FPCs obtained by considering the correlation of the functional response. Our method would have a better prediction accuracy than the typical FPCA method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supervised" title="supervised">supervised</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20principal%20component%20analysis" title=" functional principal component analysis"> functional principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20response" title=" functional response"> functional response</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20linear%20regression" title=" functional linear regression"> functional linear regression</a> </p> <a href="https://publications.waset.org/abstracts/177071/estimation-of-functional-response-model-by-supervised-functional-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177071.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">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29749</span> On the Cluster of the Families of Hybrid Polynomial Kernels in Kernel Density Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benson%20Ade%20Eniola%20Afere">Benson Ade Eniola Afere</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the years, kernel density estimation has been extensively studied within the context of nonparametric density estimation. The fundamental components of kernel density estimation are the kernel function and the bandwidth. While the mathematical exploration of the kernel component has been relatively limited, its selection and development remain crucial. The Mean Integrated Squared Error (MISE), serving as a measure of discrepancy, provides a robust framework for assessing the effectiveness of any kernel function. A kernel function with a lower MISE is generally considered to perform better than one with a higher MISE. Hence, the primary aim of this article is to create kernels that exhibit significantly reduced MISE when compared to existing classical kernels. Consequently, this article introduces a cluster of hybrid polynomial kernel families. The construction of these proposed kernel functions is carried out heuristically by combining two kernels from the classical polynomial kernel family using probability axioms. We delve into the analysis of error propagation within these kernels. To assess their performance, simulation experiments, and real-life datasets are employed. The obtained results demonstrate that the proposed hybrid kernels surpass their classical kernel counterparts in terms of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classical%20polynomial%20kernels" title="classical polynomial kernels">classical polynomial kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20of%20families" title=" cluster of families"> cluster of families</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20error" title=" global error"> global error</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20Kernels" title=" hybrid Kernels"> hybrid Kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20density%20estimation" title=" Kernel density estimation"> Kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/171468/on-the-cluster-of-the-families-of-hybrid-polynomial-kernels-in-kernel-density-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171468.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">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29748</span> Differentiation between Different Rangeland Sites Using Principal Component Analysis in Semi-Arid Areas of Sudan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nancy%20Ibrahim%20Abdalla">Nancy Ibrahim Abdalla</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelaziz%20Karamalla%20Gaiballa"> Abdelaziz Karamalla Gaiballa </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rangelands in semi-arid areas provide a good source for feeding huge numbers of animals and serving environmental, economic and social importance; therefore, these areas are considered economically very important for the pastoral sector in Sudan. This paper investigates the means of differentiating between different rangelands sites according to soil types using principal component analysis to assist in monitoring and assessment purposes. Three rangeland sites were identified in the study area as flat sandy sites, sand dune site, and hard clay site. Principal component analysis (PCA) was used to reduce the number of factors needed to distinguish between rangeland sites and produce a new set of data including the most useful spectral information to run satellite image processing. It was performed using selected types of data (two vegetation indices, topographic data and vegetation surface reflectance within the three bands of MODIS data). Analysis with PCA indicated that there is a relatively high correspondence between vegetation and soil of the total variance in the data set. The results showed that the use of the principal component analysis (PCA) with the selected variables showed a high difference, reflected in the variance and eigenvalues and it can be used for differentiation between different range sites. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title="principal component analysis">principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=rangeland%20sites" title=" rangeland sites"> rangeland sites</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-arid%20areas" title=" semi-arid areas"> semi-arid areas</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20types" title=" soil types"> soil types</a> </p> <a href="https://publications.waset.org/abstracts/99240/differentiation-between-different-rangeland-sites-using-principal-component-analysis-in-semi-arid-areas-of-sudan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99240.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">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29747</span> Correlation between Electromyographic and Textural Parameters for Different Textured Indian Foods Using Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Rustagi">S. Rustagi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20S.%20Sodhi"> N. S. Sodhi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Dhillon"> B. Dhillon</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kaur"> T. Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this study was to check whether there is any relationship between electromyographic (EMG) and textural parameters during food texture evaluation. In this study, a total of eighteen mastication variables were measured for entire mastication, per chew mastication and three different stages of mastication (viz. early, middle and late) by EMG for five different foods using eight human subjects. Cluster analysis was used to reduce the number of mastication variables from 18 to 5, so that principal component analysis (PCA) could be applied on them. The PCA further resulted in two meaningful principal components. The principal component scores for each food were measured and correlated with five textural parameters (viz. hardness, cohesiveness, chewiness, gumminess and adhesiveness). Correlation coefficients were found to be statistically significant (p < 0.10) for cohesiveness and adhesiveness while if we reduce the significance level (p < 0.20) then chewiness also showed correlation with mastication parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromyography" title="electromyography">electromyography</a>, <a href="https://publications.waset.org/abstracts/search?q=mastication" title=" mastication"> mastication</a>, <a href="https://publications.waset.org/abstracts/search?q=sensory" title=" sensory"> sensory</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a> </p> <a href="https://publications.waset.org/abstracts/85029/correlation-between-electromyographic-and-textural-parameters-for-different-textured-indian-foods-using-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85029.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">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29746</span> On the Estimation of Crime Rate in the Southwest of Nigeria: Principal Component Analysis Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kayode%20Balogun">Kayode Balogun</a>, <a href="https://publications.waset.org/abstracts/search?q=Femi%20Ayoola"> Femi Ayoola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Crime is at alarming rate in this part of world and there are many factors that are contributing to this antisocietal behaviour both among the youths and old. In this work, principal component analysis (PCA) was used as a tool to reduce the dimensionality and to really know those variables that were crime prone in the study region. Data were collected on twenty-eight crime variables from National Bureau of Statistics (NBS) databank for a period of fifteen years, while retaining as much of the information as possible. We use PCA in this study to know the number of major variables and contributors to the crime in the Southwest Nigeria. The results of our analysis revealed that there were eight principal variables have been retained using the Scree plot and Loading plot which implies an eight-equation solution will be appropriate for the data. The eight components explained 93.81% of the total variation in the data set. We also found that the highest and commonly committed crimes in the Southwestern Nigeria were: Assault, Grievous Harm and Wounding, theft/stealing, burglary, house breaking, false pretence, unlawful arms possession and breach of public peace. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crime%20rates" title="crime rates">crime rates</a>, <a href="https://publications.waset.org/abstracts/search?q=data" title=" data"> data</a>, <a href="https://publications.waset.org/abstracts/search?q=Southwest%20Nigeria" title=" Southwest Nigeria"> Southwest Nigeria</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=variables" title=" variables"> variables</a> </p> <a href="https://publications.waset.org/abstracts/27078/on-the-estimation-of-crime-rate-in-the-southwest-of-nigeria-principal-component-analysis-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27078.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">444</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29745</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/abstracts/search?q=Marcos%20Bosques-Perez">Marcos Bosques-Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Walter%20Izquierdo"> Walter Izquierdo</a>, <a href="https://publications.waset.org/abstracts/search?q=Harold%20Martin"> Harold Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=Liangdon%20Deng"> Liangdon Deng</a>, <a href="https://publications.waset.org/abstracts/search?q=Josue%20Rodriguez"> Josue Rodriguez</a>, <a href="https://publications.waset.org/abstracts/search?q=Thony%20Yan"> Thony Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mercedes%20Cabrerizo"> Mercedes Cabrerizo</a>, <a href="https://publications.waset.org/abstracts/search?q=Armando%20Barreto"> Armando Barreto</a>, <a href="https://publications.waset.org/abstracts/search?q=Naphtali%20Rishe"> Naphtali Rishe</a>, <a href="https://publications.waset.org/abstracts/search?q=Malek%20Adjouadi"> Malek Adjouadi</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral" title=" multispectral"> multispectral</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/170875/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/abstracts/170875.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">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29744</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/abstracts/search?q=A.%20Brunel">A. Brunel</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kernevez"> A. Kernevez</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Leclere"> F. Leclere</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=wine" title="wine">wine</a>, <a href="https://publications.waset.org/abstracts/search?q=grape" title=" grape"> grape</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20conditions" title=" weather conditions"> weather conditions</a>, <a href="https://publications.waset.org/abstracts/search?q=rating" title=" rating"> rating</a>, <a href="https://publications.waset.org/abstracts/search?q=climate" title=" climate"> climate</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=metric%20analysis" title=" metric analysis"> metric analysis</a> </p> <a href="https://publications.waset.org/abstracts/44241/quantitative-ranking-evaluation-of-wine-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44241.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">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29743</span> Evaluation of Yield and Yield Components of Malaysian Palm Oil Board-Senegal Oil Palm Germplasm Using Multivariate Tools </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khin%20Aye%20Myint">Khin Aye Myint</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Rafii%20Yusop"> Mohd Rafii Yusop</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Yusoff%20Abd%20Samad"> Mohd Yusoff Abd Samad</a>, <a href="https://publications.waset.org/abstracts/search?q=Shairul%20Izan%20Ramlee"> Shairul Izan Ramlee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Din%20Amiruddin"> Mohd Din Amiruddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Zulkifli%20Yaakub"> Zulkifli Yaakub</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The narrow base of genetic is the main obstacle of breeding and genetic improvement in oil palm industry. In order to broaden the genetic bases, the Malaysian Palm Oil Board has been extensively collected wild germplasm from its original area of 11 African countries which are Nigeria, Senegal, Gambia, Guinea, Sierra Leone, Ghana, Cameroon, Zaire, Angola, Madagascar, and Tanzania. The germplasm collections were established and maintained as a field gene bank in Malaysian Palm Oil Board (MPOB) Research Station in Kluang, Johor, Malaysia to conserve a wide range of oil palm genetic resources for genetic improvement of Malaysian oil palm industry. Therefore, assessing the performance and genetic diversity of the wild materials is very important for understanding the genetic structure of natural oil palm population and to explore genetic resources. Principal component analysis (PCA) and Cluster analysis are very efficient multivariate tools in the evaluation of genetic variation of germplasm and have been applied in many crops. In this study, eight populations of MPOB-Senegal oil palm germplasm were studied to explore the genetic variation pattern using PCA and cluster analysis. A total of 20 yield and yield component traits were used to analyze PCA and Ward’s clustering using SAS 9.4 version software. The first four principal components which have eigenvalue >1 accounted for 93% of total variation with the value of 44%, 19%, 18% and 12% respectively for each principal component. PC1 showed highest positive correlation with fresh fruit bunch (0.315), bunch number (0.321), oil yield (0.317), kernel yield (0.326), total economic product (0.324), and total oil (0.324) while PC 2 has the largest positive association with oil to wet mesocarp (0.397) and oil to fruit (0.458). The oil palm population were grouped into four distinct clusters based on 20 evaluated traits, this imply that high genetic variation existed in among the germplasm. Cluster 1 contains two populations which are SEN 12 and SEN 10, while cluster 2 has only one population of SEN 3. Cluster 3 consists of three populations which are SEN 4, SEN 6, and SEN 7 while SEN 2 and SEN 5 were grouped in cluster 4. Cluster 4 showed the highest mean value of fresh fruit bunch, bunch number, oil yield, kernel yield, total economic product, and total oil and Cluster 1 was characterized by high oil to wet mesocarp, and oil to fruit. The desired traits that have the largest positive correlation on extracted PCs could be utilized for the improvement of oil palm breeding program. The populations from different clusters with the highest cluster means could be used for hybridization. The information from this study can be utilized for effective conservation and selection of the MPOB-Senegal oil palm germplasm for the future breeding program. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cluster%20analysis" title="cluster analysis">cluster analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20variability" title=" genetic variability"> genetic variability</a>, <a href="https://publications.waset.org/abstracts/search?q=germplasm" title=" germplasm"> germplasm</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20palm" title=" oil palm"> oil palm</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/98332/evaluation-of-yield-and-yield-components-of-malaysian-palm-oil-board-senegal-oil-palm-germplasm-using-multivariate-tools" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98332.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">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29742</span> Detection of Cardiac Arrhythmia Using Principal Component Analysis and Xgboost Model </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujay%20Kotwale">Sujay Kotwale</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramasubba%20Reddy%20M."> Ramasubba Reddy M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiac%20arrhythmia" title="cardiac arrhythmia">cardiac arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBoost" title=" XGBoost"> XGBoost</a> </p> <a href="https://publications.waset.org/abstracts/126916/detection-of-cardiac-arrhythmia-using-principal-component-analysis-and-xgboost-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126916.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">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29741</span> Identifying Missing Component in the Bechdel Test Using Principal Component Analysis Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raghav%20Lakhotia">Raghav Lakhotia</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Kanth%20Nagesh"> Chandra Kanth Nagesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Krishna%20Madgula"> Krishna Madgula</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A lot has been said and discussed regarding the rationale and significance of the Bechdel Score. It became a digital sensation in 2013, when Swedish cinemas began to showcase the Bechdel test score of a film alongside its rating. The test has drawn criticism from experts and the film fraternity regarding its use to rate the female presence in a movie. The pundits believe that the score is too simplified and the underlying criteria of a film to pass the test must include 1) at least two women, 2) who have at least one dialogue, 3) about something other than a man, is egregious. In this research, we have considered a few more parameters which highlight how we represent females in film, like the number of female dialogues in a movie, dialogue genre, and part of speech tags in the dialogue. The parameters were missing in the existing criteria to calculate the Bechdel score. The research aims to analyze 342 movies scripts to test a hypothesis if these extra parameters, above with the current Bechdel criteria, are significant in calculating the female representation score. The result of the Principal Component Analysis method concludes that the female dialogue content is a key component and should be considered while measuring the representation of women in a work of fiction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bechdel%20test" title="Bechdel test">Bechdel test</a>, <a href="https://publications.waset.org/abstracts/search?q=dialogue%20genre" title=" dialogue genre"> dialogue genre</a>, <a href="https://publications.waset.org/abstracts/search?q=parts%20of%20speech%20tags" title=" parts of speech tags"> parts of speech tags</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/105826/identifying-missing-component-in-the-bechdel-test-using-principal-component-analysis-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105826.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">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29740</span> Application of Principal Component Analysis and Ordered Logit Model in Diabetic Kidney Disease Progression in People with Type 2 Diabetes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mequanent%20Wale%20Mekonen">Mequanent Wale Mekonen</a>, <a href="https://publications.waset.org/abstracts/search?q=Edoardo%20Otranto"> Edoardo Otranto</a>, <a href="https://publications.waset.org/abstracts/search?q=Angela%20Alibrandi"> Angela Alibrandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetic kidney disease is one of the main microvascular complications caused by diabetes. Several clinical and biochemical variables are reported to be associated with diabetic kidney disease in people with type 2 diabetes. However, their interrelations could distort the effect estimation of these variables for the disease's progression. The objective of the study is to determine how the biochemical and clinical variables in people with type 2 diabetes are interrelated with each other and their effects on kidney disease progression through advanced statistical methods. First, principal component analysis was used to explore how the biochemical and clinical variables intercorrelate with each other, which helped us reduce a set of correlated biochemical variables to a smaller number of uncorrelated variables. Then, ordered logit regression models (cumulative, stage, and adjacent) were employed to assess the effect of biochemical and clinical variables on the order-level response variable (progression of kidney function) by considering the proportionality assumption for more robust effect estimation. This retrospective cross-sectional study retrieved data from a type 2 diabetic cohort in a polyclinic hospital at the University of Messina, Italy. The principal component analysis yielded three uncorrelated components. These are principal component 1, with negative loading of glycosylated haemoglobin, glycemia, and creatinine; principal component 2, with negative loading of total cholesterol and low-density lipoprotein; and principal component 3, with negative loading of high-density lipoprotein and a positive load of triglycerides. The ordered logit models (cumulative, stage, and adjacent) showed that the first component (glycosylated haemoglobin, glycemia, and creatinine) had a significant effect on the progression of kidney disease. For instance, the cumulative odds model indicated that the first principal component (linear combination of glycosylated haemoglobin, glycemia, and creatinine) had a strong and significant effect on the progression of kidney disease, with an effect or odds ratio of 0.423 (P value = 0.000). However, this effect was inconsistent across levels of kidney disease because the first principal component did not meet the proportionality assumption. To address the proportionality problem and provide robust effect estimates, alternative ordered logit models, such as the partial cumulative odds model, the partial adjacent category model, and the partial continuation ratio model, were used. These models suggested that clinical variables such as age, sex, body mass index, medication (metformin), and biochemical variables such as glycosylated haemoglobin, glycemia, and creatinine have a significant effect on the progression of kidney disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diabetic%20kidney%20disease" title="diabetic kidney disease">diabetic kidney disease</a>, <a href="https://publications.waset.org/abstracts/search?q=ordered%20logit%20model" title=" ordered logit model"> ordered logit model</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=type%202%20diabetes" title=" type 2 diabetes"> type 2 diabetes</a> </p> <a href="https://publications.waset.org/abstracts/186851/application-of-principal-component-analysis-and-ordered-logit-model-in-diabetic-kidney-disease-progression-in-people-with-type-2-diabetes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186851.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">39</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29739</span> A Study on the Performance of 2-PC-D Classification Model </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Aini%20Abdul%20Wahab">Nurul Aini Abdul Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Syamim%20Halidin"> Nor Syamim Halidin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayidatina%20Aisah%20Masnan"> Sayidatina Aisah Masnan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nur%20Izzati%20Romli"> Nur Izzati Romli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many applications of principle component method for reducing the large set of variables in various fields. Fisher’s Discriminant function is also a popular tool for classification. In this research, the researcher focuses on studying the performance of Principle Component-Fisher’s Discriminant function in helping to classify rice kernels to their defined classes. The data were collected on the smells or odour of the rice kernel using odour-detection sensor, Cyranose. 32 variables were captured by this electronic nose (e-nose). The objective of this research is to measure how well a combination model, between principle component and linear discriminant, to be as a classification model. Principle component method was used to reduce all 32 variables to a smaller and manageable set of components. Then, the reduced components were used to develop the Fisher’s Discriminant function. In this research, there are 4 defined classes of rice kernel which are Aromatic, Brown, Ordinary and Others. Based on the output from principle component method, the 32 variables were reduced to only 2 components. Based on the output of classification table from the discriminant analysis, 40.76% from the total observations were correctly classified into their classes by the PC-Discriminant function. Indirectly, it gives an idea that the classification model developed has committed to more than 50% of misclassifying the observations. As a conclusion, the Fisher’s Discriminant function that was built on a 2-component from PCA (2-PC-D) is not satisfying to classify the rice kernels into its defined classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20model" title="classification model">classification model</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant%20function" title=" discriminant function"> discriminant function</a>, <a href="https://publications.waset.org/abstracts/search?q=principle%20component%20analysis" title=" principle component analysis"> principle component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20reduction" title=" variable reduction"> variable reduction</a> </p> <a href="https://publications.waset.org/abstracts/66321/a-study-on-the-performance-of-2-pc-d-classification-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66321.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">331</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29738</span> Sparse Principal Component Analysis: A Least Squares Approximation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Merola">Giovanni Merola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse Principal Components Analysis aims to find principal components with few non-zero loadings. We derive such sparse solutions by adding a genuine sparsity requirement to the original Principal Components Analysis (PCA) objective function. This approach differs from others because it preserves PCA's original optimality: uncorrelatedness of the components and least squares approximation of the data. To identify the best subset of non-zero loadings we propose a branch-and-bound search and an iterative elimination algorithm. This last algorithm finds sparse solutions with large loadings and can be run without specifying the cardinality of the loadings and the number of components to compute in advance. We give thorough comparisons with the existing sparse PCA methods and several examples on real datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SPCA" title="SPCA">SPCA</a>, <a href="https://publications.waset.org/abstracts/search?q=uncorrelated%20components" title=" uncorrelated components"> uncorrelated components</a>, <a href="https://publications.waset.org/abstracts/search?q=branch-and-bound" title=" branch-and-bound"> branch-and-bound</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20elimination" title=" backward elimination"> backward elimination</a> </p> <a href="https://publications.waset.org/abstracts/14630/sparse-principal-component-analysis-a-least-squares-approximation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14630.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">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29737</span> Principal Component Analysis of Body Weight and Morphometric Traits of New Zealand Rabbits Raised under Semi-Arid Condition in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Abayomi%20Rotimi">Emmanuel Abayomi Rotimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Context: Rabbits production plays important role in increasing animal protein supply in Nigeria. Rabbit production provides a cheap, affordable, and healthy source of meat. The growth of animals involves an increase in body weight, which can change the conformation of various parts of the body. Live weight and linear measurements are indicators of growth rate in rabbits and other farm animals. Aims: This study aimed to define the body dimensions of New Zealand rabbits and also to investigate the morphometric traits variables that contribute to body conformation by the use of principal component analysis (PCA). Methods: Data were obtained from 80 New Zealand rabbits (40 bucks and 40 does) raised in Livestock Teaching and Research Farm, Federal University Dutsinma. Data were taken on body weight (BWT), body length (BL), ear length (EL), tail length (TL), heart girth (HG) and abdominal circumference (AC). Data collected were subjected to multivariate analysis using SPSS 20.0 statistical package. Key results: The descriptive statistics showed that the mean BWT, BL, EL, TL, HG, and AC were 0.91kg, 27.34cm, 10.24cm, 8.35cm, 19.55cm and 21.30cm respectively. Sex showed significant (P<0.05) effect on all the variables examined, with higher values recorded for does. The phenotypic correlation coefficient values (r) between the morphometric traits were all positive and ranged from r = 0.406 (between EL and BL) to r = 0.909 (between AC and HG). HG is the most correlated with BWT (r = 0.786). The principal component analysis with variance maximizing orthogonal rotation was used to extract the components. Two principal components (PCs) from the factor analysis of morphometric traits explained about 80.42% of the total variance. PC1 accounted for 64.46% while PC2 accounted for 15.97% of the total variances. Three variables, representing body conformation, loaded highest in PC1. PC1 had the highest contribution (64.46%) to the total variance, and it is regarded as body conformation traits. Conclusions: This component could be used as selection criteria for improving body weight of rabbits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conformation" title="conformation">conformation</a>, <a href="https://publications.waset.org/abstracts/search?q=multicollinearity" title=" multicollinearity"> multicollinearity</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate" title=" multivariate"> multivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=rabbits%20and%20principal%20component%20analysis" title=" rabbits and principal component analysis"> rabbits and principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/110184/principal-component-analysis-of-body-weight-and-morphometric-traits-of-new-zealand-rabbits-raised-under-semi-arid-condition-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110184.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">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29736</span> Modeling Factors Affecting Fertility Transition in Africa: Case of Kenya</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dennis%20Okora%20Amima%20Ondieki">Dennis Okora Amima Ondieki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fertility transition has been identified to be affected by numerous factors. This research aimed to investigate the most real factors affecting fertility transition in Kenya. These factors were firstly extracted from the literature convened into demographic features, social, and economic features, social-cultural features, reproductive features and modernization features. All these factors had 23 factors identified for this study. The data for this study was from the Kenya Demographic and Health Surveys (KDHS) conducted in 1999-2003 and 2003-2008/9. The data was continuous, and it involved the mean birth order for the ten periods. Principal component analysis (PCA) was utilized using 23 factors. Principal component analysis conveyed religion, region, education and marital status as the real factors. PC scores were calculated for every point. The identified principal components were utilized as forecasters in the multiple regression model, with the fertility level as the response variable. The four components were found to be affecting fertility transition differently. It was found that fertility is affected positively by factors of region and marital and negatively by factors of religion and education. These four factors can be considered in the planning policy in Kenya and Africa at large. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fertility%20transition" title="fertility transition">fertility transition</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenya%20demographic%20health%20survey" title=" Kenya demographic health survey"> Kenya demographic health survey</a>, <a href="https://publications.waset.org/abstracts/search?q=birth%20order" title=" birth order"> birth order</a> </p> <a href="https://publications.waset.org/abstracts/177555/modeling-factors-affecting-fertility-transition-in-africa-case-of-kenya" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177555.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">99</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29735</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/abstracts/search?q=Akinola%20Ikudayisi">Akinola Ikudayisi</a>, <a href="https://publications.waset.org/abstracts/search?q=Josiah%20Adeyemo"> Josiah Adeyemo</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=irrigation" title="irrigation">irrigation</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20evapotranspiration" title=" reference evapotranspiration"> reference evapotranspiration</a>, <a href="https://publications.waset.org/abstracts/search?q=Vaalharts" title=" Vaalharts"> Vaalharts</a> </p> <a href="https://publications.waset.org/abstracts/44193/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/abstracts/44193.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">258</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29734</span> Online Prediction of Nonlinear Signal Processing Problems Based Kernel Adaptive Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamza%20Nejib">Hamza Nejib</a>, <a href="https://publications.waset.org/abstracts/search?q=Okba%20Taouali"> Okba Taouali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20prediction" title="online prediction">online prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=KAF" title=" KAF"> KAF</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=RKHS" title=" RKHS"> RKHS</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20methods" title=" Kernel methods"> Kernel methods</a>, <a href="https://publications.waset.org/abstracts/search?q=KRLS" title=" KRLS"> KRLS</a>, <a href="https://publications.waset.org/abstracts/search?q=KLMS" title=" KLMS"> KLMS</a> </p> <a href="https://publications.waset.org/abstracts/63627/online-prediction-of-nonlinear-signal-processing-problems-based-kernel-adaptive-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63627.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">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29733</span> Application of FT-NIR Spectroscopy and Electronic Nose in On-line Monitoring of Dough Proofing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madhuresh%20Dwivedi">Madhuresh Dwivedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Navneet%20Singh%20Deora"> Navneet Singh Deora</a>, <a href="https://publications.waset.org/abstracts/search?q=Aastha%20Deswal"> Aastha Deswal</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20N.%20Mishra"> H. N. Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> FT-NIR spectroscopy and electronic nose was used to study the kinetics of dough proofing. Spectroscopy was conducted with an optic probe in the diffuse reflectance mode. The dough leavening was carried out at different temperatures (25 and 35°C) and constant RH (80%). Spectra were collected in the range of wave numbers from 12,000 to 4,000 cm-1 directly on the samples, every 5 min during proofing, up to 2 hours. NIR spectra were corrected for scatter effect and second order derivatization was done to transform the spectra. Principal component analysis (PCA) was applied for the leavening process and process kinetics was calculated. PCA was performed on data set and loadings were calculated. For leavening, four absorption zones (8,950-8,850, 7,200-6,800, 5,250-5,150 and 4,700-4,250 cm-1) were involved in describing the process. Simultaneously electronic nose was also used for understanding the development of odour compounds during fermentation. The electronic nose was able to differential the sample on the basis of aroma generation at different time during fermentation. In order to rapidly differentiate samples based on odor, a Principal component analysis is performed and successfully demonstrated in this study. The result suggests that electronic nose and FT-NIR spectroscopy can be utilized for the online quality control of the fermentation process during leavening of bread dough. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FT-NIR" title="FT-NIR">FT-NIR</a>, <a href="https://publications.waset.org/abstracts/search?q=dough" title=" dough"> dough</a>, <a href="https://publications.waset.org/abstracts/search?q=e-nose" title=" e-nose"> e-nose</a>, <a href="https://publications.waset.org/abstracts/search?q=proofing" title=" proofing"> proofing</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/6309/application-of-ft-nir-spectroscopy-and-electronic-nose-in-on-line-monitoring-of-dough-proofing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6309.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">390</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29732</span> A Formal Verification Approach for Linux Kernel Designing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zi%20Wang">Zi Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinlei%20He"> Xinlei He</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianghua%20Lv"> Jianghua Lv</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuqing%20Lan"> Yuqing Lan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Kernel though widely used, is complicated. Errors caused by some bugs are often costly. Statically, more than half of the mistakes occur in the design phase. Thus, we introduce a modeling method, KMVM (Linux Kernel Modeling and verification Method), based on type theory for proper designation and correct exploitation of the Kernel. In the model, the Kernel is separated into six levels: subsystem, dentry, file, struct, func, and base. Each level is treated as a type. The types are specified in the structure and relationship. At the same time, we use a demanding path to express the function to be implemented. The correctness of the design is verified by recursively checking the type relationship and type existence. The method has been applied to verify the OPEN business of VFS (virtual file system) in Linux Kernel. Also, we have designed and developed a set of security communication mechanisms in the Kernel with verification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=formal%20approach" title="formal approach">formal approach</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20theory" title=" type theory"> type theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Linux%20Kernel" title=" Linux Kernel"> Linux Kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20program" title=" software program"> software program</a> </p> <a href="https://publications.waset.org/abstracts/155062/a-formal-verification-approach-for-linux-kernel-designing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155062.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">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29731</span> Principal Component Analysis Applied to the Electric Power Systems – Practical Guide; Practical Guide for Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Morales">John Morales</a>, <a href="https://publications.waset.org/abstracts/search?q=Eduardo%20Ordu%C3%B1a"> Eduardo Orduña</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently the Principal Component Analysis (PCA) theory has been used to develop algorithms regarding to Electric Power Systems (EPS). In this context, this paper presents a practical tutorial of this technique detailed their concept, on-line and off-line mathematical foundations, which are necessary and desirables in EPS algorithms. Thus, features of their eigenvectors which are very useful to real-time process are explained, showing how it is possible to select these parameters through a direct optimization. On the other hand, in this work in order to show the application of PCA to off-line and on-line signals, an example step to step using Matlab commands is presented. Finally, a list of different approaches using PCA is presented, and some works which could be analyzed using this tutorial are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=practical%20guide%3B%20on-line%3B%20off-line" title="practical guide; on-line; off-line">practical guide; on-line; off-line</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=faults" title=" faults"> faults</a> </p> <a href="https://publications.waset.org/abstracts/34859/principal-component-analysis-applied-to-the-electric-power-systems-practical-guide-practical-guide-for-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34859.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">563</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29730</span> Quantum Kernel Based Regressor for Prediction of Non-Markovianity of Open Quantum Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Tancara">Diego Tancara</a>, <a href="https://publications.waset.org/abstracts/search?q=Raul%20Coto"> Raul Coto</a>, <a href="https://publications.waset.org/abstracts/search?q=Ariel%20Norambuena"> Ariel Norambuena</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoseein%20T.%20Dinani"> Hoseein T. Dinani</a>, <a href="https://publications.waset.org/abstracts/search?q=Felipe%20Fanchini"> Felipe Fanchini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlapping between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlapping between quantum states. We observe a good performance of the models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantum" title="quantum">quantum</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel" title=" kernel"> kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=non-markovianity" title=" non-markovianity"> non-markovianity</a> </p> <a href="https://publications.waset.org/abstracts/165769/quantum-kernel-based-regressor-for-prediction-of-non-markovianity-of-open-quantum-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165769.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">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29729</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/abstracts/search?q=Zhaojun%20Wang">Zhaojun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zongdi%20Sun"> Zongdi Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Qinqin%20Cui"> Qinqin Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingwan%20Ren"> Xingwan Ren</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fitting" title="fitting">fitting</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title=" Mahalanobis distance"> Mahalanobis distance</a>, <a href="https://publications.waset.org/abstracts/search?q=SPSS" title=" SPSS"> SPSS</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/97522/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/abstracts/97522.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">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29728</span> QSRR Analysis of 17-Picolyl and 17-Picolinylidene Androstane Derivatives Based on Partial Least Squares and Principal Component Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Podunavac-Kuzmanovi%C4%87">Sanja Podunavac-Kuzmanović</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Kova%C4%8Devi%C4%87"> Strahinja Kovačević</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20Jevri%C4%87"> Lidija Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Evgenija%20Djurendi%C4%87"> Evgenija Djurendić</a>, <a href="https://publications.waset.org/abstracts/search?q=Jovana%20Ajdukovi%C4%87"> Jovana Ajduković</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are several methods for determination of the lipophilicity of biologically active compounds, however chromatography has been shown as a very suitable method for this purpose. Chromatographic (C18-RP-HPLC) analysis of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives was carried out. The obtained retention indices (logk, methanol (90%) / water (10%)) were correlated with calculated physicochemical and lipophilicity descriptors. The QSRR analysis was carried out applying principal component regression (PCR) and partial least squares regression (PLS). The PCR and PLS model were selected on the basis of the highest variance and the lowest root mean square error of cross-validation. The obtained PCR and PLS model successfully correlate the calculated molecular descriptors with logk parameter indicating the significance of the lipophilicity of compounds in chromatographic process. On the basis of the obtained results it can be concluded that the obtained logk parameters of the analyzed androstane derivatives can be considered as their chromatographic lipophilicity. These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1105. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=androstane%20derivatives" title="androstane derivatives">androstane derivatives</a>, <a href="https://publications.waset.org/abstracts/search?q=chromatography" title=" chromatography"> chromatography</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20structure" title=" molecular structure"> molecular structure</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20regression" title=" principal component regression"> principal component regression</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20least%20squares%20regression" title=" partial least squares regression"> partial least squares regression</a> </p> <a href="https://publications.waset.org/abstracts/38073/qsrr-analysis-of-17-picolyl-and-17-picolinylidene-androstane-derivatives-based-on-partial-least-squares-and-principal-component-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38073.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">276</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29727</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/abstracts/search?q=Nida%20Rizvi">Nida Rizvi</a>, <a href="https://publications.waset.org/abstracts/search?q=Deeksha%20Katyal"> Deeksha Katyal</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=cluster%20analysis" title="cluster analysis">cluster analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20statistical%20techniques" title=" multivariate statistical techniques"> multivariate statistical techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=river%20Hindon" title=" river Hindon"> river Hindon</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality" title=" water quality"> water quality</a> </p> <a href="https://publications.waset.org/abstracts/35271/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/abstracts/35271.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">465</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29726</span> Statistical Model of Water Quality in Estero El Macho, Machala-El Oro</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rafael%20Zhindon%20Almeida">Rafael Zhindon Almeida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20modeling" title="statistical modeling">statistical modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality" title=" water quality"> water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20linear%20regression" title=" multiple linear regression"> multiple linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20components" title=" principal components"> principal components</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20models" title=" statistical models"> statistical models</a> </p> <a href="https://publications.waset.org/abstracts/176758/statistical-model-of-water-quality-in-estero-el-macho-machala-el-oro" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176758.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">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29725</span> Fuzzy-Machine Learning Models for the Prediction of Fire Outbreak: A Comparative Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Uduak%20Umoh">Uduak Umoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Imo%20Eyoh"> Imo Eyoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmauel%20Nyoho"> Emmauel Nyoho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Machine%20Learning%20Algorithms" title="Machine Learning Algorithms ">Machine Learning Algorithms </a>, <a href="https://publications.waset.org/abstracts/search?q=Interval%20Type-2%20Fuzzy%20Logic" title=" Interval Type-2 Fuzzy Logic"> Interval Type-2 Fuzzy Logic</a>, <a href="https://publications.waset.org/abstracts/search?q=Fire%20Outbreak" title=" Fire Outbreak"> Fire Outbreak</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine" title=" Support Vector Machine"> Support Vector Machine</a>, <a href="https://publications.waset.org/abstracts/search?q=K-Nearest%20Neighbour" title=" K-Nearest Neighbour"> K-Nearest Neighbour</a>, <a href="https://publications.waset.org/abstracts/search?q=Principal%20Component%20Analysis" title=" Principal Component Analysis "> Principal Component Analysis </a> </p> <a href="https://publications.waset.org/abstracts/128079/fuzzy-machine-learning-models-for-the-prediction-of-fire-outbreak-a-comparative-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128079.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">182</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=kernel%20principal%20component%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" 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