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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="multivariate analysis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 28021</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: multivariate analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28021</span> Multivariate Genome-Wide Association Studies for Identifying Additional Loci for Myopia </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiao%20Fan">Qiao Fan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaobo%20Guo"> Xiaobo Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Junxian%20Zhu"> Junxian Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaohu%20Ding"> Xiaohu Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Yu%20Cheng"> Ching-Yu Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Tien-Yin%20Wong"> Tien-Yin Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingguang%20He"> Mingguang He</a>, <a href="https://publications.waset.org/abstracts/search?q=Heping%20Zhang"> Heping Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xueqin%20Wang"> Xueqin Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A systematic, simultaneous analysis of multiple phenotypes in genome-wide association studies (GWASs) draws a great attention to integrate the signals from single phenotypes with increased power. However, lacking an interpretable and efficient multivariate GWAS analysis impede the application of such approach. In this study, we propose to decompose the multivariate model into a series of simple univariate models. This transformation illuminates what exactly the individual trait contributes to the significant signals from the multivariate analyses. By employing our approach in the analysis of three myopia-related endophenotypes from the Singapore Malay Eye Study (SIMES), we identify novel candidate loci which were successfully validated in an independent Guangzhou Twin Eye Study (GTES). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GWAS%20multivariate" title="GWAS multivariate">GWAS multivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20traits" title=" multiple traits"> multiple traits</a>, <a href="https://publications.waset.org/abstracts/search?q=myopia" title=" myopia"> myopia</a>, <a href="https://publications.waset.org/abstracts/search?q=association" title=" association"> association</a> </p> <a href="https://publications.waset.org/abstracts/79085/multivariate-genome-wide-association-studies-for-identifying-additional-loci-for-myopia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79085.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">224</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">28020</span> Multivariate Analysis of Spectroscopic Data for Agriculture Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asmaa%20M.%20Hussein">Asmaa M. Hussein</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20Wassal"> Amr Wassal</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Farouk%20Al-Sadek"> Ahmed Farouk Al-Sadek</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20F.%20Abd%20El-Rahman"> A. F. Abd El-Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brown%20rot%20disease" title="Brown rot disease">Brown rot disease</a>, <a href="https://publications.waset.org/abstracts/search?q=NIR%20spectroscopy" title=" NIR spectroscopy"> NIR spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=potato" title=" potato"> potato</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/127918/multivariate-analysis-of-spectroscopic-data-for-agriculture-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127918.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">190</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">28019</span> The Modality of Multivariate Skew Normal Mixture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bader%20Alruwaili">Bader Alruwaili</a>, <a href="https://publications.waset.org/abstracts/search?q=Surajit%20Ray"> Surajit Ray</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Finite mixtures are a flexible and powerful tool that can be used for univariate and multivariate distributions, and a wide range of research analysis has been conducted based on the multivariate normal mixture and multivariate of a t-mixture. Determining the number of modes is an important activity that, in turn, allows one to determine the number of homogeneous groups in a population. Our work currently being carried out relates to the study of the modality of the skew normal distribution in the univariate and multivariate cases. For the skew normal distribution, the aims are associated with studying the modality of the skew normal distribution and providing the ridgeline, the ridgeline elevation function, the $\Pi$ function, and the curvature function, and this will be conducive to an exploration of the number and location of mode when mixing the two components of skew normal distribution. The subsequent objective is to apply these results to the application of real world data sets, such as flow cytometry data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mode" title="mode">mode</a>, <a href="https://publications.waset.org/abstracts/search?q=modality" title=" modality"> modality</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20skew%20normal" title=" multivariate skew normal"> multivariate skew normal</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20mixture" title=" finite mixture"> finite mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20of%20mode" title=" number of mode"> number of mode</a> </p> <a href="https://publications.waset.org/abstracts/68912/the-modality-of-multivariate-skew-normal-mixture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68912.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">488</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">28018</span> Regression for Doubly Inflated Multivariate Poisson Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ishapathik%20Das">Ishapathik Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumen%20Sen"> Sumen Sen</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Rao%20Chaganty"> N. Rao Chaganty</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Sengupta"> Pooja Sengupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dependent multivariate count data occur in several research studies. These data can be modeled by a multivariate Poisson or Negative binomial distribution constructed using copulas. However, when some of the counts are inflated, that is, the number of observations in some cells are much larger than other cells, then the copula based multivariate Poisson (or Negative binomial) distribution may not fit well and it is not an appropriate statistical model for the data. There is a need to modify or adjust the multivariate distribution to account for the inflated frequencies. In this article, we consider the situation where the frequencies of two cells are higher compared to the other cells, and develop a doubly inflated multivariate Poisson distribution function using multivariate Gaussian copula. We also discuss procedures for regression on covariates for the doubly inflated multivariate count data. For illustrating the proposed methodologies, we present a real data containing bivariate count observations with inflations in two cells. Several models and linear predictors with log link functions are considered, and we discuss maximum likelihood estimation to estimate unknown parameters of the models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copula" title="copula">copula</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20copula" title=" Gaussian copula"> Gaussian copula</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20distributions" title=" multivariate distributions"> multivariate distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=inflated%20distributios" title=" inflated distributios"> inflated distributios</a> </p> <a href="https://publications.waset.org/abstracts/105114/regression-for-doubly-inflated-multivariate-poisson-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105114.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">156</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">28017</span> Effects of Video Games and Online Chat on Mathematics Performance in High School: An Approach of Multivariate Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lina%20Wu">Lina Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenyi%20Lu"> Wenyi Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Li"> Ye Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Regarding heavy video game players for boys and super online chat lovers for girls as a symbolic phrase in the current adolescent culture, this project of data analysis verifies the displacement effect on deteriorating mathematics performance. To evaluate correlation or regression coefficients between a factor of playing video games or chatting online and mathematics performance compared with other factors, we use multivariate analysis technique and take gender difference into account. We find the most important reason for the negative sign of the displacement effect on mathematics performance due to students’ poor academic background. Statistical analysis methods in this project could be applied to study internet users’ academic performance from the high school education to the college education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation%20coefficients" title="correlation coefficients">correlation coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=displacement%20effect" title=" displacement effect"> displacement effect</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis%20technique" title=" multivariate analysis technique"> multivariate analysis technique</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20coefficients" title=" regression coefficients"> regression coefficients</a> </p> <a href="https://publications.waset.org/abstracts/42703/effects-of-video-games-and-online-chat-on-mathematics-performance-in-high-school-an-approach-of-multivariate-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42703.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">363</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">28016</span> Model of Optimal Centroids Approach for Multivariate Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pham%20Van%20Nha">Pham Van Nha</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Cam%20Binh"> Le Cam Binh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20optimization" title="analysis of optimization">analysis of optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20based%20optimization" title=" artificial intelligence based optimization"> artificial intelligence based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20for%20learning%20and%20data%20analysis" title=" optimization for learning and data analysis"> optimization for learning and data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a> </p> <a href="https://publications.waset.org/abstracts/126058/model-of-optimal-centroids-approach-for-multivariate-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126058.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">208</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">28015</span> Discrimination Between Bacillus and Alicyclobacillus Isolates in Apple Juice by Fourier Transform Infrared Spectroscopy and Multivariate Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Murada%20Alholy">Murada Alholy</a>, <a href="https://publications.waset.org/abstracts/search?q=Mengshi%20Lin"> Mengshi Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20Alhaj"> Omar Alhaj</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Abugoush"> Mahmoud Abugoush</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Alicyclobacillus is a causative agent of spoilage in pasteurized and heat-treated apple juice products. Differentiating between this genus and the closely related Bacillus is crucially important. In this study, Fourier transform infrared spectroscopy (FT-IR) was used to identify and discriminate between four Alicyclobacillus strains and four Bacillus isolates inoculated individually into apple juice. Loading plots over the range of 1350 and 1700 cm-1 reflected the most distinctive biochemical features of Bacillus and Alicyclobacillus. Multivariate statistical methods (e.g. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA)) were used to analyze the spectral data. Distinctive separation of spectral samples was observed. This study demonstrates that FT-IR spectroscopy in combination with multivariate analysis could serve as a rapid and effective tool for fruit juice industry to differentiate between Bacillus and Alicyclobacillus and to distinguish between species belonging to these two genera. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alicyclobacillus" title="alicyclobacillus">alicyclobacillus</a>, <a href="https://publications.waset.org/abstracts/search?q=bacillus" title=" bacillus"> bacillus</a>, <a href="https://publications.waset.org/abstracts/search?q=FT-IR" title=" FT-IR"> FT-IR</a>, <a href="https://publications.waset.org/abstracts/search?q=spectroscopy" title=" spectroscopy"> spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a> </p> <a href="https://publications.waset.org/abstracts/29542/discrimination-between-bacillus-and-alicyclobacillus-isolates-in-apple-juice-by-fourier-transform-infrared-spectroscopy-and-multivariate-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29542.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">488</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">28014</span> A Non-parametric Clustering Approach for Multivariate Geostatistical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Francky%20Fouedjio">Francky Fouedjio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20data" title=" multivariate data"> multivariate data</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric" title=" non-parametric"> non-parametric</a> </p> <a href="https://publications.waset.org/abstracts/46870/a-non-parametric-clustering-approach-for-multivariate-geostatistical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46870.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">477</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">28013</span> Multivariate Statistical Process Monitoring of Base Metal Flotation Plant Using Dissimilarity Scale-Based Singular Spectrum Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syamala%20Krishnannair">Syamala Krishnannair</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A multivariate statistical process monitoring methodology using dissimilarity scale-based singular spectrum analysis (SSA) is proposed for the detection and diagnosis of process faults in the base metal flotation plant. Process faults are detected based on the multi-level decomposition of process signals by SSA using the dissimilarity structure of the process data and the subsequent monitoring of the multiscale signals using the unified monitoring index which combines T² with SPE. Contribution plots are used to identify the root causes of the process faults. The overall results indicated that the proposed technique outperformed the conventional multivariate techniques in the detection and diagnosis of the process faults in the flotation plant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20monitoring" title=" process monitoring"> process monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=dissimilarity%20scale" title=" dissimilarity scale"> dissimilarity scale</a> </p> <a href="https://publications.waset.org/abstracts/83263/multivariate-statistical-process-monitoring-of-base-metal-flotation-plant-using-dissimilarity-scale-based-singular-spectrum-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83263.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">209</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">28012</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">464</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">28011</span> Multi-scale Spatial and Unified Temporal Feature-fusion Network for Multivariate Time Series Anomaly Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Yang">Hang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jichao%20Li"> Jichao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kewei%20Yang"> Kewei Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianyang%20Lei"> Tianyang Lei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20system" title=" industrial system"> industrial system</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a> </p> <a href="https://publications.waset.org/abstracts/193205/multi-scale-spatial-and-unified-temporal-feature-fusion-network-for-multivariate-time-series-anomaly-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193205.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">14</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">28010</span> An AK-Chart for the Non-Normal Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Hau%20Liu">Chia-Hau Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tai-Yue%20Wang"> Tai-Yue Wang </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20control%20chart" title="multivariate control chart">multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=one-class%20classification%20method" title=" one-class classification method"> one-class classification method</a>, <a href="https://publications.waset.org/abstracts/search?q=non-normal%20data" title=" non-normal data"> non-normal data</a> </p> <a href="https://publications.waset.org/abstracts/7485/an-ak-chart-for-the-non-normal-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7485.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">422</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">28009</span> Irrigation Water Quality Evaluation Based on Multivariate Statistical Analysis: A Case Study of Jiaokou Irrigation District</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panpan%20Xu">Panpan Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Qiying%20Zhang"> Qiying Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Qian"> Hui Qian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Groundwater is main source of water supply in the Guanzhong Basin, China. To investigate the quality of groundwater for agricultural purposes in Jiaokou Irrigation District located in the east of the Guanzhong Basin, 141 groundwater samples were collected for analysis of major ions (K<sup>+</sup>, Na<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, SO<sub>4</sub><sup>2-</sup>, Cl<sup>-</sup>, HCO<sub>3</sub><sup>-</sup>, and CO<sub>3</sub><sup>2-</sup>), pH, and total dissolved solids (TDS). Sodium percentage (Na%), residual sodium carbonate (RSC), magnesium hazard (MH), and potential salinity (PS) were applied for irrigation water quality assessment. In addition, multivariate statistical techniques were used to identify the underlying hydrogeochemical processes. Results show that the content of TDS mainly depends on Cl<sup>-</sup>, Na<sup>+</sup>, Mg<sup>2+</sup>, and SO<sub>4</sub><sup>2-</sup>, and the HCO<sub>3</sub><sup>-</sup> content is generally high except for the eastern sand area. These are responsible for complex hydrogeochemical processes, such as dissolution of carbonate minerals (dolomite and calcite), gypsum, halite, and silicate minerals, the cation exchange, as well as evaporation and concentration. The average evaluation levels of Na%, RSC, MH, and PS for irrigation water quality are doubtful, good, unsuitable, and injurious to unsatisfactory, respectively. Therefore, it is necessary for decision makers to comprehensively consider the indicators and thus reasonably evaluate the irrigation water quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=irrigation%20water%20quality" title="irrigation water quality">irrigation water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20statistical%20analysis" title=" multivariate statistical analysis"> multivariate statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater" title=" groundwater"> groundwater</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrogeochemical%20process" title=" hydrogeochemical process"> hydrogeochemical process</a> </p> <a href="https://publications.waset.org/abstracts/130842/irrigation-water-quality-evaluation-based-on-multivariate-statistical-analysis-a-case-study-of-jiaokou-irrigation-district" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130842.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">141</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">28008</span> Applying Multivariate and Univariate Analysis of Variance on Socioeconomic, Health, and Security Variables in Jordan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20G.%20Khamis">Faisal G. Khamis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghaleb%20A.%20El-Refae"> Ghaleb A. El-Refae</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many researchers have studied socioeconomic, health, and security variables in the developed countries; however, very few studies used multivariate analysis in developing countries. The current study contributes to the scarce literature about the determinants of the variance in socioeconomic, health, and security factors. Questions raised were whether the independent variables (IVs) of governorate and year impact the socioeconomic, health, and security dependent variables (DVs) in Jordan, whether the marginal mean of each DV in each governorate and in each year is significant, which governorates are similar in difference means of each DV, and whether these DVs vary. The main objectives were to determine the source of variances in DVs, collectively and separately, testing which governorates are similar and which diverge for each DV. The research design was time series and cross-sectional analysis. The main hypotheses are that IVs affect DVs collectively and separately. Multivariate and univariate analyses of variance were carried out to test these hypotheses. The population of 12 governorates in Jordan and the available data of 15 years (2000–2015) accrued from several Jordanian statistical yearbooks. We investigated the effect of two factors of governorate and year on the four DVs of divorce rate, mortality rate, unemployment percentage, and crime rate. All DVs were transformed to multivariate normal distribution. We calculated descriptive statistics for each DV. Based on the multivariate analysis of variance, we found a significant effect in IVs on DVs with p < .001. Based on the univariate analysis, we found a significant effect of IVs on each DV with p < .001, except the effect of the year factor on unemployment was not significant with p = .642. The grand and marginal means of each DV in each governorate and each year were significant based on a 95% confidence interval. Most governorates are not similar in DVs with p < .001. We concluded that the two factors produce significant effects on DVs, collectively and separately. Based on these findings, the government can distribute its financial and physical resources to governorates more efficiently. By identifying the sources of variance that contribute to the variation in DVs, insights can help inform focused variation prevention efforts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANOVA" title="ANOVA">ANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=crime" title=" crime"> crime</a>, <a href="https://publications.waset.org/abstracts/search?q=divorce" title=" divorce"> divorce</a>, <a href="https://publications.waset.org/abstracts/search?q=governorate" title=" governorate"> governorate</a>, <a href="https://publications.waset.org/abstracts/search?q=hypothesis%20test" title=" hypothesis test"> hypothesis test</a>, <a href="https://publications.waset.org/abstracts/search?q=Jordan" title=" Jordan"> Jordan</a>, <a href="https://publications.waset.org/abstracts/search?q=MANOVA" title=" MANOVA"> MANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=means" title=" means"> means</a>, <a href="https://publications.waset.org/abstracts/search?q=mortality" title=" mortality"> mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=unemployment" title=" unemployment"> unemployment</a>, <a href="https://publications.waset.org/abstracts/search?q=year" title=" year"> year</a> </p> <a href="https://publications.waset.org/abstracts/85829/applying-multivariate-and-univariate-analysis-of-variance-on-socioeconomic-health-and-security-variables-in-jordan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85829.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">275</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">28007</span> The Moment of the Optimal Average Length of the Multivariate Exponentially Weighted Moving Average Control Chart for Equally Correlated Variables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edokpa%20Idemudia%20Waziri">Edokpa Idemudia Waziri</a>, <a href="https://publications.waset.org/abstracts/search?q=Salisu%20S.%20Umar"> Salisu S. Umar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Hotellng’s T^2 is a well-known statistic for detecting a shift in the mean vector of a multivariate normal distribution. Control charts based on T have been widely used in statistical process control for monitoring a multivariate process. Although it is a powerful tool, the T statistic is deficient when the shift to be detected in the mean vector of a multivariate process is small and consistent. The Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is one of the control statistics used to overcome the drawback of the Hotellng’s T statistic. In this paper, the probability distribution of the Average Run Length (ARL) of the MEWMA control chart when the quality characteristics exhibit substantial cross correlation and when the process is in-control and out-of-control was derived using the Markov Chain algorithm. The derivation of the probability functions and the moments of the run length distribution were also obtained and they were consistent with some existing results for the in-control and out-of-control situation. By simulation process, the procedure identified a class of ARL for the MEWMA control when the process is in-control and out-of-control. From our study, it was observed that the MEWMA scheme is quite adequate for detecting a small shift and a good way to improve the quality of goods and services in a multivariate situation. It was also observed that as the in-control average run length ARL0¬ or the number of variables (p) increases, the optimum value of the ARL0pt increases asymptotically and as the magnitude of the shift σ increases, the optimal ARLopt decreases. Finally, we use the example from the literature to illustrate our method and demonstrate its efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain" title=" markov chain"> markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20exponentially%20weighted%20moving%20average" title=" multivariate exponentially weighted moving average"> multivariate exponentially weighted moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20smoothing%20parameter" title=" optimal smoothing parameter"> optimal smoothing parameter</a> </p> <a href="https://publications.waset.org/abstracts/42648/the-moment-of-the-optimal-average-length-of-the-multivariate-exponentially-weighted-moving-average-control-chart-for-equally-correlated-variables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42648.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">422</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">28006</span> Neutral Heavy Scalar Searches via Standard Model Gauge Boson Decays at the Large Hadron Electron Collider with Multivariate Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luigi%20Delle%20Rose">Luigi Delle Rose</a>, <a href="https://publications.waset.org/abstracts/search?q=Oliver%20Fischer"> Oliver Fischer</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Hammad"> Ahmed Hammad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we study the prospects of the proposed Large Hadron electron Collider (LHeC) in the search for heavy neutral scalar particles. We consider a minimal model with one additional complex scalar singlet that interacts with the Standard Model (SM) via mixing with the Higgs doublet, giving rise to an SM-like Higgs boson and a heavy scalar particle. Both scalar particles are produced via vector boson fusion and can be tested via their decays into pairs of SM particles, analogously to the SM Higgs boson. Using multivariate techniques, we show that the LHeC is sensitive to heavy scalars with masses between 200 and 800 GeV down to scalar mixing of order 0.01. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beyond%20the%20standard%20model" title="beyond the standard model">beyond the standard model</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20hadron%20electron%20collider" title=" large hadron electron collider"> large hadron electron collider</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis" title=" multivariate analysis"> multivariate analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=scalar%20singlet" title=" scalar singlet"> scalar singlet</a> </p> <a href="https://publications.waset.org/abstracts/102214/neutral-heavy-scalar-searches-via-standard-model-gauge-boson-decays-at-the-large-hadron-electron-collider-with-multivariate-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102214.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">28005</span> A Cohort and Empirical Based Multivariate Mortality Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeffrey%20Tzu-Hao%20Tsai">Jeffrey Tzu-Hao Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi-Shan%20Wong"> Yi-Shan Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article proposes a cohort-age-period (CAP) model to characterize multi-population mortality processes using cohort, age, and period variables. Distinct from the factor-based Lee-Carter-type decomposition mortality model, this approach is empirically based and includes the age, period, and cohort variables into the equation system. The model not only provides a fruitful intuition for explaining multivariate mortality change rates but also has a better performance in forecasting future patterns. Using the US and the UK mortality data and performing ten-year out-of-sample tests, our approach shows smaller mean square errors in both countries compared to the models in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=longevity%20risk" title="longevity risk">longevity risk</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20mortality%20model" title=" stochastic mortality model"> stochastic mortality model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20mortality%20rate" title=" multivariate mortality rate"> multivariate mortality rate</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a> </p> <a href="https://publications.waset.org/abstracts/182234/a-cohort-and-empirical-based-multivariate-mortality-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182234.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">53</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">28004</span> Introduction of Robust Multivariate Process Capability Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behrooz%20Khalilloo">Behrooz Khalilloo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Shahriari"> Hamid Shahriari</a>, <a href="https://publications.waset.org/abstracts/search?q=Emad%20Roghanian"> Emad Roghanian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process capability indices (PCIs) are important concepts of statistical quality control and measure the capability of processes and how much processes are meeting certain specifications. An important issue in statistical quality control is parameter estimation. Under the assumption of multivariate normality, the distribution parameters, mean vector and variance-covariance matrix must be estimated, when they are unknown. Classic estimation methods like method of moment estimation (MME) or maximum likelihood estimation (MLE) makes good estimation of the population parameters when data are not contaminated. But when outliers exist in the data, MME and MLE make weak estimators of the population parameters. So we need some estimators which have good estimation in the presence of outliers. In this work robust M-estimators for estimating these parameters are used and based on robust parameter estimators, robust process capability indices are introduced. The performances of these robust estimators in the presence of outliers and their effects on process capability indices are evaluated by real and simulated multivariate data. The results indicate that the proposed robust capability indices perform much better than the existing process capability indices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20process%20capability%20indices" title="multivariate process capability indices">multivariate process capability indices</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20M-estimator" title=" robust M-estimator"> robust M-estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20quality%20control" title=" multivariate quality control"> multivariate quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20quality%20control" title=" statistical quality control"> statistical quality control</a> </p> <a href="https://publications.waset.org/abstracts/81586/introduction-of-robust-multivariate-process-capability-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81586.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">283</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">28003</span> Simultaneous Determination of Six Characterizing/Quality Parameters of Biodiesels via 1H NMR and Multivariate Calibration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gustavo%20G.%20Shimamoto">Gustavo G. Shimamoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthieu%20Tubino"> Matthieu Tubino</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The characterization and the quality of biodiesel samples are checked by determining several parameters. Considering a large number of analysis to be performed, as well as the disadvantages of the use of toxic solvents and waste generation, multivariate calibration is suggested to reduce the number of tests. In this work, hydrogen nuclear magnetic resonance (1H NMR) spectra were used to build multivariate models, from partial least squares (PLS) regression, in order to determine simultaneously six important characterizing and/or quality parameters of biodiesels: density at 20 ºC, kinematic viscosity at 40 ºC, iodine value, acid number, oxidative stability, and water content. Biodiesels from twelve different oils sources were used in this study: babassu, brown flaxseed, canola, corn, cottonseed, macauba almond, microalgae, palm kernel, residual frying, sesame, soybean, and sunflower. 1H NMR reflects the structures of the compounds present in biodiesel samples and showed suitable correlations with the six parameters. The PLS models were constructed with latent variables between 5 and 7, the obtained values of r(cal) and r(val) were greater than 0.994 and 0.989, respectively. In addition, the models were considered suitable to predict all the six parameters for external samples, taking into account the analytical speed to perform it. Thus, the alliance between 1H NMR and PLS showed to be appropriate to characterize and evaluate the quality of biodiesels, reducing significantly analysis time, the consumption of reagents/solvents, and waste generation. Therefore, the proposed methods can be considered to adhere to the principles of green chemistry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biodiesel" title="biodiesel">biodiesel</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20calibration" title=" multivariate calibration"> multivariate calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=nuclear%20magnetic%20resonance" title=" nuclear magnetic resonance"> nuclear magnetic resonance</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20parameters" title=" quality parameters"> quality parameters</a> </p> <a href="https://publications.waset.org/abstracts/33348/simultaneous-determination-of-six-characterizingquality-parameters-of-biodiesels-via-1h-nmr-and-multivariate-calibration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33348.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">539</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">28002</span> Prediction of Marine Ecosystem Changes Based on the Integrated Analysis of Multivariate Data Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prozorkevitch%20D.">Prozorkevitch D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mishurov%20A."> Mishurov A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sokolov%20K."> Sokolov K.</a>, <a href="https://publications.waset.org/abstracts/search?q=Karsakov%20L."> Karsakov L.</a>, <a href="https://publications.waset.org/abstracts/search?q=Pestrikova%20L."> Pestrikova L.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The current body of knowledge about the marine environment and the dynamics of marine ecosystems includes a huge amount of heterogeneous data collected over decades. It generally includes a wide range of hydrological, biological and fishery data. Marine researchers collect these data and analyze how and why the ecosystem changes from past to present. Based on these historical records and linkages between the processes it is possible to predict future changes. Multivariate analysis of trends and their interconnection in the marine ecosystem may be used as an instrument for predicting further ecosystem evolution. A wide range of information about the components of the marine ecosystem for more than 50 years needs to be used to investigate how these arrays can help to predict the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=barents%20sea%20ecosystem" title="barents sea ecosystem">barents sea ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=abiotic" title=" abiotic"> abiotic</a>, <a href="https://publications.waset.org/abstracts/search?q=biotic" title=" biotic"> biotic</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20sets" title=" data sets"> data sets</a>, <a href="https://publications.waset.org/abstracts/search?q=trends" title=" trends"> trends</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/148270/prediction-of-marine-ecosystem-changes-based-on-the-integrated-analysis-of-multivariate-data-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148270.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">116</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">28001</span> On the Bootstrap P-Value Method in Identifying out of Control Signals in Multivariate Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Ikpotokin">O. Ikpotokin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20control%20limit" title="bootstrap control limit">bootstrap control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value%20method" title=" p-value method"> p-value method</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-control%20signals" title=" out-of-control signals"> out-of-control signals</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value" title=" p-value"> p-value</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20characteristics" title=" quality characteristics"> quality characteristics</a> </p> <a href="https://publications.waset.org/abstracts/77853/on-the-bootstrap-p-value-method-in-identifying-out-of-control-signals-in-multivariate-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77853.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">347</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">28000</span> The Use of Boosted Multivariate Trees in Medical Decision-Making for Repeated Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebru%20Turgal">Ebru Turgal</a>, <a href="https://publications.waset.org/abstracts/search?q=Beyza%20Doganay%20Erdogan"> Beyza Doganay Erdogan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boosted%20multivariate%20trees" title="boosted multivariate trees">boosted multivariate trees</a>, <a href="https://publications.waset.org/abstracts/search?q=longitudinal%20data" title=" longitudinal data"> longitudinal data</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20regression%20tree" title=" multivariate regression tree"> multivariate regression tree</a>, <a href="https://publications.waset.org/abstracts/search?q=panel%20data" title=" panel data"> panel data</a> </p> <a href="https://publications.waset.org/abstracts/87009/the-use-of-boosted-multivariate-trees-in-medical-decision-making-for-repeated-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87009.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">203</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">27999</span> Analyzing the Influence of Hydrometeorlogical Extremes, Geological Setting, and Social Demographic on Public Health</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irfan%20Ahmad%20Afip">Irfan Ahmad Afip</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geographical%20information%20system" title="geographical information system">geographical information system</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrometeorological" title=" hydrometeorological"> hydrometeorological</a>, <a href="https://publications.waset.org/abstracts/search?q=leptospirosis" title=" leptospirosis"> leptospirosis</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20regression" title=" multivariate regression"> multivariate regression</a> </p> <a href="https://publications.waset.org/abstracts/121967/analyzing-the-influence-of-hydrometeorlogical-extremes-geological-setting-and-social-demographic-on-public-health" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121967.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">115</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">27998</span> Landslide Susceptibility Mapping: A Comparison between Logistic Regression and Multivariate Adaptive Regression Spline Models in the Municipality of Oudka, Northern of Morocco</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benchelha">S. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20C.%20Aoudjehane"> H. C. Aoudjehane</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hakdaoui"> M. Hakdaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20El%20Hamdouni"> R. El Hamdouni</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mansouri"> H. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Benchelha"> T. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Layelmam"> M. Layelmam</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Alaoui"> M. Alaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslide%20susceptibility%20mapping" title="landslide susceptibility mapping">landslide susceptibility mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20logistic" title=" regression logistic"> regression logistic</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20adaptive%20regression%20spline" title=" multivariate adaptive regression spline"> multivariate adaptive regression spline</a>, <a href="https://publications.waset.org/abstracts/search?q=Oudka" title=" Oudka"> Oudka</a>, <a href="https://publications.waset.org/abstracts/search?q=Taounate" title=" Taounate"> Taounate</a> </p> <a href="https://publications.waset.org/abstracts/107250/landslide-susceptibility-mapping-a-comparison-between-logistic-regression-and-multivariate-adaptive-regression-spline-models-in-the-municipality-of-oudka-northern-of-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107250.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">188</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">27997</span> The Factors Predicting Credibility of News in Social Media in Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ekapon%20Thienthaworn">Ekapon Thienthaworn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to study the reliability of the forecasting factor in social media by using survey research methods with questionnaires. The sampling is the group of undergraduate students in Bangkok. A multiple-step random number of 400 persons, data analysis are descriptive statistics with multivariate regression analysis. The research found the average of the overall trust at the intermediate level for reading the news in social media and the results of the multivariate regression analysis to find out the factors that forecast credibility of the media found the only content that has the power to forecast reliability of undergraduate students in Bangkok to reading the news on social media at the significance level.at 0.05.These can be factors with forecasts reliability of news in social media by a variable that has the highest influence factor of the media content and the speed is also important for reliability of the news. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credibility%20of%20news" title="credibility of news">credibility of news</a>, <a href="https://publications.waset.org/abstracts/search?q=behaviors%20and%20attitudes" title=" behaviors and attitudes"> behaviors and attitudes</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20board" title=" web board"> web board</a> </p> <a href="https://publications.waset.org/abstracts/55587/the-factors-predicting-credibility-of-news-in-social-media-in-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55587.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">468</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">27996</span> The Use of Multivariate Statistical and GIS for Characterization Groundwater Quality in Laghouat Region, Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rouighi%20Mustapha">Rouighi Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Bouzid%20Laghaa%20Souad"> Bouzid Laghaa Souad</a>, <a href="https://publications.waset.org/abstracts/search?q=Rouighi%20Tahar"> Rouighi Tahar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to rain Shortage and the increase of population in the last years, wells excavation and groundwater use for different purposes had been increased without any planning. This is a great challenge for our country. Moreover, this scarcity of water resources in this region is unfortunately combined with rapid fresh water resources quality deterioration, due to salinity and contamination processes. Therefore, it is necessary to conduct the studies about groundwater quality in Algeria. In this work consists in the identification of the factors which influence the water quality parameters in Laghouat region by using statistical analysis Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and geographic information system (GIS) in an attempt to discriminate the sources of the variation of water quality variations. The results of PCA technique indicate that variables responsible for water quality composition are mainly related to soluble salts variables; natural processes and the nature of the rock which modifies significantly the water chemistry. Inferred from the positive correlation between K+ and NO3-, NO3- is believed to be human induced rather than naturally originated. In this study, the multivariate statistical analysis and GIS allows the hydrogeologist to have supplementary tools in the characterization and evaluating of aquifers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cluster" title="cluster">cluster</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis" title=" analysis"> analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater" title=" groundwater"> groundwater</a>, <a href="https://publications.waset.org/abstracts/search?q=laghouat" title=" laghouat"> laghouat</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a> </p> <a href="https://publications.waset.org/abstracts/38669/the-use-of-multivariate-statistical-and-gis-for-characterization-groundwater-quality-in-laghouat-region-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38669.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">323</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">27995</span> Classification of Generative Adversarial Network Generated Multivariate Time Series Data Featuring Transformer-Based Deep Learning Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thrivikraman%20Aswathi">Thrivikraman Aswathi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Advaith"> S. Advaith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20time%20series" title=" multivariate time series"> multivariate time series</a> </p> <a href="https://publications.waset.org/abstracts/157460/classification-of-generative-adversarial-network-generated-multivariate-time-series-data-featuring-transformer-based-deep-learning-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157460.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">27994</span> Multivariate Analytical Insights into Spatial and Temporal Variation in Water Quality of a Major Drinking Water Reservoir</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azadeh%20Golshan">Azadeh Golshan</a>, <a href="https://publications.waset.org/abstracts/search?q=Craig%20Evans"> Craig Evans</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Geary"> Phillip Geary</a>, <a href="https://publications.waset.org/abstracts/search?q=Abigail%20Morrow"> Abigail Morrow</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoe%20Rogers"> Zoe Rogers</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcel%20Maeder"> Marcel Maeder</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 22 physicochemical variables have been determined in water samples collected weekly from January to December in 2013 from three sampling stations located within a major drinking water reservoir. Classical Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis was used to investigate the environmental factors associated with the physico-chemical variability of the water samples at each of the sampling stations. Matrix augmentation MCR-ALS (MA-MCR-ALS) was also applied, and the two sets of results were compared for interpretative clarity. Links between these factors, reservoir inflows and catchment land-uses were investigated and interpreted in relation to chemical composition of the water and their resolved geographical distribution profiles. The results suggested that the major factors affecting reservoir water quality were those associated with agricultural runoff, with evidence of influence on algal photosynthesis within the water column. Water quality variability within the reservoir was also found to be strongly linked to physical parameters such as water temperature and the occurrence of thermal stratification. The two methods applied (MCR-ALS and MA-MCR-ALS) led to similar conclusions; however, MA-MCR-ALS appeared to provide results more amenable to interpretation of temporal and geological variation than those obtained through classical MCR-ALS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drinking%20water%20reservoir" title="drinking water reservoir">drinking water reservoir</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis" title=" multivariate analysis"> multivariate analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=physico-chemical%20parameters" title=" physico-chemical parameters"> physico-chemical parameters</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/81651/multivariate-analytical-insights-into-spatial-and-temporal-variation-in-water-quality-of-a-major-drinking-water-reservoir" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81651.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">291</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">27993</span> Prediction of Slaughter Body Weight in Rabbits: Multivariate Approach through Path Coefficient and Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20A.%20Bindu">K. A. Bindu</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20V.%20Raja"> T. V. Raja</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20M.%20Rojan"> P. M. Rojan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Siby"> A. Siby</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The multivariate path coefficient approach was employed to study the effects of various production and reproduction traits on the slaughter body weight of rabbits. Information on 562 rabbits maintained at the university rabbit farm attached to the Centre for Advanced Studies in Animal Genetics, and Breeding, Kerala Veterinary and Animal Sciences University, Kerala State, India was utilized. The manifest variables used in the study were age and weight of dam, birth weight, litter size at birth and weaning, weight at first, second and third months. The linear multiple regression analysis was performed by keeping the slaughter weight as the dependent variable and the remaining as independent variables. The model explained 48.60 percentage of the total variation present in the market weight of the rabbits. Even though the model used was significant, the standardized beta coefficients for the independent variables viz., age and weight of the dam, birth weight and litter sizes at birth and weaning were less than one indicating their negligible influence on the slaughter weight. However, the standardized beta coefficient of the second-month body weight was maximum followed by the first-month weight indicating their major role on the market weight. All the other factors influence indirectly only through these two variables. Hence it was concluded that the slaughter body weight can be predicted using the first and second-month body weights. The principal components were also developed so as to achieve more accuracy in the prediction of market weight of rabbits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=component%20analysis" title="component analysis">component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate" title=" multivariate"> multivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=slaughter" title=" slaughter"> slaughter</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/104373/prediction-of-slaughter-body-weight-in-rabbits-multivariate-approach-through-path-coefficient-and-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104373.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">165</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">27992</span> Application of Deep Learning in Top Pair and Single Top Quark Production at the Large Hadron Collider</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ijaz%20Ahmed">Ijaz Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Anwar%20Zada"> Anwar Zada</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Waqas"> Muhammad Waqas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20U.%20Ashraf"> M. U. Ashraf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=top%20tagger" title="top tagger">top tagger</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate" title=" multivariate"> multivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LHC" title=" LHC"> LHC</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20top" title=" single top"> single top</a> </p> <a href="https://publications.waset.org/abstracts/156965/application-of-deep-learning-in-top-pair-and-single-top-quark-production-at-the-large-hadron-collider" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156965.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">111</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=multivariate%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis&page=4">4</a></li> <li class="page-item"><a class="page-link" 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