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discriminant analysis</title> <meta name="description" content="Search results for: linear discriminant analysis"> <meta name="keywords" content="linear discriminant analysis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/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="linear discriminant 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> 29827</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: linear discriminant analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29827</span> Measuring Multi-Class Linear Classifier for Image Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Susilawati%20Mohamad">Fatma Susilawati Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Abdul%20Manaf"> Azizah Abdul Manaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadhillah%20Ahmad"> Fadhillah Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Zarina%20Mohamad"> Zarina Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Suryani%20Wan%20Awang"> Wan Suryani Wan Awang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A simple and robust multi-class linear classifier is proposed and implemented. For a pair of classes of the linear boundary, a collection of segments of hyper planes created as perpendicular bisectors of line segments linking centroids of the classes or part of classes. Nearest Neighbor and Linear Discriminant Analysis are compared in the experiments to see the performances of each classifier in discriminating ripeness of oil palm. This paper proposes a multi-class linear classifier using Linear Discriminant Analysis (LDA) for image identification. Result proves that LDA is well capable in separating multi-class features for ripeness identification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-class" title="multi-class">multi-class</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20classifier" title=" linear classifier"> linear classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor" title=" nearest neighbor"> nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/51310/measuring-multi-class-linear-classifier-for-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51310.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">538</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">29826</span> Comparison of Linear Discriminant Analysis and Support Vector Machine Classifications for Electromyography Signals Acquired at Five Positions of Elbow Joint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amna%20Khan">Amna Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zareena%20Kausar"> Zareena Kausar</a>, <a href="https://publications.waset.org/abstracts/search?q=Saad%20Malik"> Saad Malik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bio Mechatronics has extended applications in the field of rehabilitation. It has been contributing since World War II in improving the applicability of prosthesis and assistive devices in real life scenarios. In this paper, classification accuracies have been compared for two classifiers against five positions of elbow. Electromyography (EMG) signals analysis have been acquired directly from skeletal muscles of human forearm for each of the three defined positions and at modified extreme positions of elbow flexion and extension using 8 electrode Myo armband sensor. Features were extracted from filtered EMG signals for each position. Performance of two classifiers, support vector machine (SVM) and linear discriminant analysis (LDA) has been compared by analyzing the classification accuracies. SVM illustrated classification accuracies between 90-96%, in contrast to 84-87% depicted by LDA for five defined positions of elbow keeping the number of samples and selected feature the same for both SVM and LDA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20accuracies" title="classification accuracies">classification accuracies</a>, <a href="https://publications.waset.org/abstracts/search?q=electromyography" title=" electromyography"> electromyography</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis%20%28LDA%29" title=" linear discriminant analysis (LDA)"> linear discriminant analysis (LDA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Myo%20armband%20sensor" title=" Myo armband sensor"> Myo armband sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/73619/comparison-of-linear-discriminant-analysis-and-support-vector-machine-classifications-for-electromyography-signals-acquired-at-five-positions-of-elbow-joint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73619.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">368</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">29825</span> Hybrid Approach for Face Recognition Combining Gabor Wavelet and Linear Discriminant Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A%3A%20Annis%20Fathima">A: Annis Fathima</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Vaidehi"> V. Vaidehi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ajitha"> S. Ajitha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20wavelet" title=" Gabor wavelet"> Gabor wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA" title=" LDA"> LDA</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN%20classifier" title=" k-NN classifier"> k-NN classifier</a> </p> <a href="https://publications.waset.org/abstracts/11196/hybrid-approach-for-face-recognition-combining-gabor-wavelet-and-linear-discriminant-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11196.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">467</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">29824</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">332</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">29823</span> Comparative Analysis of Spectral Estimation Methods for Brain-Computer Interfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rafik%20Djemili">Rafik Djemili</a>, <a href="https://publications.waset.org/abstracts/search?q=Hocine%20Bourouba"> Hocine Bourouba</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20Amara%20Korba"> M. C. Amara Korba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title="brain-computer interface">brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20imagery" title=" motor imagery"> motor imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/6971/comparative-analysis-of-spectral-estimation-methods-for-brain-computer-interfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6971.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">499</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">29822</span> Discriminant Analysis as a Function of Predictive Learning to Select Evolutionary Algorithms in Intelligent Transportation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jorge%20A.%20Ruiz-Vanoye">Jorge A. Ruiz-Vanoye</a>, <a href="https://publications.waset.org/abstracts/search?q=Ocotl%C3%A1n%20D%C3%ADaz-Parra"> Ocotlán Díaz-Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Alejandro%20Fuentes-Penna"> Alejandro Fuentes-Penna</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20V%C3%A9lez-D%C3%ADaz"> Daniel Vélez-Díaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Edith%20Olaco%20Garc%C3%ADa"> Edith Olaco García</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Intelligent%20Transportation%20Systems" title="Intelligent Transportation Systems">Intelligent Transportation Systems</a>, <a href="https://publications.waset.org/abstracts/search?q=data-mining%20techniques" title=" data-mining techniques"> data-mining techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/42737/discriminant-analysis-as-a-function-of-predictive-learning-to-select-evolutionary-algorithms-in-intelligent-transportation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42737.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">472</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">29821</span> The Classification Performance in Parametric and Nonparametric Discriminant Analysis for a Class- Unbalanced Data of Diabetes Risk Groups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lily%20Ingsrisawang">Lily Ingsrisawang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tasanee%20Nacharoen"> Tasanee Nacharoen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=error%20rate" title="error rate">error rate</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap" title=" bootstrap"> bootstrap</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20risk%20groups" title=" diabetes risk groups"> diabetes risk groups</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors" title=" k-nearest neighbors "> k-nearest neighbors </a> </p> <a href="https://publications.waset.org/abstracts/23799/the-classification-performance-in-parametric-and-nonparametric-discriminant-analysis-for-a-class-unbalanced-data-of-diabetes-risk-groups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23799.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">435</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">29820</span> Design and Implementation of Generative Models for Odor Classification Using Electronic Nose</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kumar%20Shashvat">Kumar Shashvat</a>, <a href="https://publications.waset.org/abstracts/search?q=Amol%20P.%20Bhondekar"> Amol P. Bhondekar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=odor%20classification" title="odor classification">odor classification</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20models" title=" generative models"> generative models</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20bayes" title=" naive bayes"> naive bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/58224/design-and-implementation-of-generative-models-for-odor-classification-using-electronic-nose" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58224.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">387</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">29819</span> A Robust System for Foot Arch Type Classification from Static Foot Pressure Distribution Data Using Linear Discriminant Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Periyasamy">R. Periyasamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepak%20Joshi"> Deepak Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneh%20Anand"> Sneh Anand </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arch%20index" title="arch index">arch index</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20tool" title=" computational tool"> computational tool</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20foot%20pressure%20intensity%20image" title=" static foot pressure intensity image"> static foot pressure intensity image</a>, <a href="https://publications.waset.org/abstracts/search?q=foot%20pressure%20distribution" title=" foot pressure distribution"> foot pressure distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/13085/a-robust-system-for-foot-arch-type-classification-from-static-foot-pressure-distribution-data-using-linear-discriminant-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13085.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">499</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">29818</span> Image-Based (RBG) Technique for Estimating Phosphorus Levels of Different Crops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Ali">M. M. Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Al-%20Ani"> Ahmed Al- Ani</a>, <a href="https://publications.waset.org/abstracts/search?q=Derek%20Eamus"> Derek Eamus</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20K.%20Y.%20Tan"> Daniel K. Y. Tan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this glasshouse study, we developed the new image-based non-destructive technique for detecting leaf P status of different crops such as cotton, tomato and lettuce. Plants were allowed to grow on nutrient media containing different P concentrations, i.e. 0%, 50% and 100% of recommended P concentration (P0 = no P, L; P1 = 2.5 mL 10 L-1 of P and P2 = 5 mL 10 L-1 of P as NaH2PO4). After 10 weeks of growth, plants were harvested and data on leaf P contents were collected using the standard destructive laboratory method and at the same time leaf images were collected by a handheld crop image sensor. We calculated leaf area, leaf perimeter and RGB (red, green and blue) values of these images. This data was further used in the linear discriminant analysis (LDA) to estimate leaf P contents, which successfully classified these plants on the basis of leaf P contents. The data indicated that P deficiency in crop plants can be predicted using the image and morphological data. Our proposed non-destructive imaging method is precise in estimating P requirements of different crop species. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image-based%20techniques" title="image-based techniques">image-based techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=leaf%20area" title=" leaf area"> leaf area</a>, <a href="https://publications.waset.org/abstracts/search?q=leaf%20P%20contents" title=" leaf P contents"> leaf P contents</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/37572/image-based-rbg-technique-for-estimating-phosphorus-levels-of-different-crops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37572.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">29817</span> Impact Evaluation of Discriminant Analysis on Epidemic Protocol in Warships’s Scenarios</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davi%20Marinho%20de%20Araujo%20Falc%C3%A3o">Davi Marinho de Araujo Falcão</a>, <a href="https://publications.waset.org/abstracts/search?q=Ronaldo%20Moreira%20Salles"> Ronaldo Moreira Salles</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Henrique%20Maranh%C3%A3o"> Paulo Henrique Maranhão</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Disruption Tolerant Networks (DTN) are an evolution of Mobile Adhoc Networks (MANET) and work good in scenarioswhere nodes are sparsely distributed, with low density, intermittent connections and an end-to-end infrastructure is not possible to guarantee. Therefore, DTNs are recommended for high latency applications that can last from hours to days. The maritime scenario has mobility characteristics that contribute to a DTN network approach, but the concern with data security is also a relevant aspect in such scenarios. Continuing the previous work, which evaluated the performance of some DTN protocols (Epidemic, Spray and Wait, and Direct Delivery) in three warship scenarios and proposed the application of discriminant analysis, as a classification technique for secure connections, in the Epidemic protocol, thus, the current article proposes a new analysis of the directional discriminant function with opening angles smaller than 90 degrees, demonstrating that the increase in directivity influences the selection of a greater number of secure connections by the directional discriminant Epidemic protocol. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DTN" title="DTN">DTN</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=epidemic%20protocol" title=" epidemic protocol"> epidemic protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=tactical%20messages" title=" tactical messages"> tactical messages</a>, <a href="https://publications.waset.org/abstracts/search?q=warship%20scenario" title=" warship scenario"> warship scenario</a> </p> <a href="https://publications.waset.org/abstracts/141488/impact-evaluation-of-discriminant-analysis-on-epidemic-protocol-in-warshipss-scenarios" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141488.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">191</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">29816</span> Intrusion Detection System Using Linear Discriminant Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zyad%20Elkhadir">Zyad Elkhadir</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Chougdali"> Khalid Chougdali</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Benattou"> Mohammed Benattou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LDA" title="LDA">LDA</a>, <a href="https://publications.waset.org/abstracts/search?q=Pseudoinverse" title=" Pseudoinverse"> Pseudoinverse</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=IDS" title=" IDS"> IDS</a>, <a href="https://publications.waset.org/abstracts/search?q=NSL-KDD" title=" NSL-KDD"> NSL-KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=KDDcup99" title=" KDDcup99"> KDDcup99</a> </p> <a href="https://publications.waset.org/abstracts/37402/intrusion-detection-system-using-linear-discriminant-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37402.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">226</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">29815</span> Fetal Ilium as a Tool for Sex Determination: Discriminant Functional Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luv%20Sharma">Luv Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sex determination has been the most intriguing puzzle for forensic pathologists and anthropologists, for which efforts have been made for a long. Sexual dimorphism is well established in the adult pelvis, and it is known to provide the highest level of information about sexual dimorphism. This study was conducted to know whether this dimorphism exists in fetal bones or not. A total of 34 pairs of fetal pelvis bones (22 males and 12 Females), ages ranging from 4 months to full term, were collected from unidentified dead fetuses brought to the Department of Forensic Medicine for routine medicolegal autopsies to study for sexual dimorphism in the Department of Anatomy, Pt. BD Sharma PGIMS, Rohtak. Samples were divided into 2 age groups, and various metric parameters were recorded with the help of a digital vernier caliper. Data obtained was subjected to descriptive and discriminant functional analysis. Results of Descriptive and Discriminant Functional Analysis showed that sex determination can be done with 100% accuracy by using different combinations of parameters of fetal ilium. This study illustrates that sexual dimorphism exists from early fetal life after mid-pregnancy; it can be clearly established by discriminant functional analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilium" title="Ilium">Ilium</a>, <a href="https://publications.waset.org/abstracts/search?q=fetus" title=" fetus"> fetus</a>, <a href="https://publications.waset.org/abstracts/search?q=sex%20determination" title=" sex determination"> sex determination</a>, <a href="https://publications.waset.org/abstracts/search?q=morphometric" title=" morphometric"> morphometric</a> </p> <a href="https://publications.waset.org/abstracts/181447/fetal-ilium-as-a-tool-for-sex-determination-discriminant-functional-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181447.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">59</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">29814</span> Neighborhood Graph-Optimized Preserving Discriminant Analysis for Image Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoheng%20Tan">Xiaoheng Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianfang%20Li"> Xianfang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Guo"> Tan Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuchuan%20Liu"> Yuchuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhijun%20Yang"> Zhijun Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongye%20Li"> Hongye Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Fu"> Kai Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yufang%20Wu"> Yufang Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Heling%20Gong"> Heling Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets. <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=graph%20optimization%20local%20preserving%20projection" title=" graph optimization local preserving projection"> graph optimization local preserving projection</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=L%E2%82%82" title=" L₂"> L₂</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%82%81%20norm" title="₁ norm">₁ norm</a> </p> <a href="https://publications.waset.org/abstracts/129863/neighborhood-graph-optimized-preserving-discriminant-analysis-for-image-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129863.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">149</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">29813</span> Evalutaion of the Surface Water Quality Using the Water Quality Index and Discriminant Analysis Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lazhar%20Belkhiri">Lazhar Belkhiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ammar%20Tiri"> Ammar Tiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Mouni"> Lotfi Mouni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water resources present to the public order of the world a very important problem for the protection and management of water quality given the complexity of water quality data sets. In this study, the water quality index (WQI) and irrigation water quality index (IWQI) were calculated in order to evaluate the surface water quality for drinking and irrigation purposes based on nine hydrochemical parameters. In order to separate the variables that are the most responsible for the spatial differentiation, the discriminant analysis (DA) was applied. The results show that the surface water quality for drinking is poor quality and very poor quality based on WQI values, however, the values of IWQI reflect that this water is acceptable for irrigation with a restriction for sensitive plants. Consequently, the discriminant analysis DA method has shown that the following parameters pH, potassium, chloride, sulfate, and bicarbonate are significant discrimination between the different stations with the spatial variation of the surface water quality, therefore, the results obtained in this study provide very useful information to decision-makers <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=surface%20water%20quality" title="surface water quality">surface water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=drinking%20and%20irrigation%20purposes" title=" drinking and irrigation purposes"> drinking and irrigation purposes</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20index" title=" water quality index"> water quality index</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/176146/evalutaion-of-the-surface-water-quality-using-the-water-quality-index-and-discriminant-analysis-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176146.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">86</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">29812</span> Alcohol and Tobacco Influencing Prevalence of Hypertension among 15-54 Old Indian Men: An Application of Discriminant Analysis Using National Family Health Survey, 2015-16</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chander%20Shekhar">Chander Shekhar</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeetendra%20Yadav"> Jeetendra Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaziya%20Allarakha"> Shaziya Allarakha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hypertension has been described as an 'iceberg disease' as those who suffered are ignored and hence usually seek healthcare services at a very late stage. It is estimated that more than 2 million Indians are suffering from hypertensive heart disease that contributed to above 0.13 million deaths in 2016. The paper study aims to know the prevalence of Hypertension in India and its variation by socioeconomic backgrounds and to find out risk factors discriminating hypertension with special emphasis on consumption of tobacco and alcohol among men aged 15-54 years in India. The paper uses NFHS (2015-16) data. The paper used binary logistic regression and discriminant analysis to find significant predictors and discriminants of interest. The prevalence of hypertension was 16.5% in the study population. The results suggest that consumption of alcohol and tobacco are significant discriminant characteristics in carrying hypertension irrespective of what socioeconomic background characteristic he possesses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hypertention" title="hypertention">hypertention</a>, <a href="https://publications.waset.org/abstracts/search?q=alcohol" title=" alcohol"> alcohol</a>, <a href="https://publications.waset.org/abstracts/search?q=tobacco" title=" tobacco"> tobacco</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant" title=" discriminant"> discriminant</a> </p> <a href="https://publications.waset.org/abstracts/100762/alcohol-and-tobacco-influencing-prevalence-of-hypertension-among-15-54-old-indian-men-an-application-of-discriminant-analysis-using-national-family-health-survey-2015-16" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100762.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">147</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">29811</span> Performance Evaluation of Contemporary Classifiers for Automatic Detection of Epileptic EEG</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20E.%20Ch.%20Vidyasagar">K. E. Ch. Vidyasagar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Moghavvemi"> M. Moghavvemi</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20S.%20S.%20T.%20Prabhat"> T. S. S. T. Prabhat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN" title=" KNN"> KNN</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA" title=" LDA"> LDA</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a> </p> <a href="https://publications.waset.org/abstracts/14692/performance-evaluation-of-contemporary-classifiers-for-automatic-detection-of-epileptic-eeg" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14692.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">520</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">29810</span> Using Discriminant Analysis to Forecast Crime Rate in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20P.%20Popoola">O. P. Popoola</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20A.%20Alawode"> O. A. Alawode</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20O.%20Olayiwola"> M. O. Olayiwola</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Oladele"> A. M. Oladele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research work is based on using discriminant analysis to forecast crime rate in Nigeria between 1996 and 2008. The work is interested in how gender (male and female) relates to offences committed against the government, against other properties, disturbance in public places, murder/robbery offences and other offences. The data used was collected from the National Bureau of Statistics (NBS). SPSS, the statistical package was used to analyse the data. Time plot was plotted on all the 29 offences gotten from the raw data. Eigenvalues and Multivariate tests, Wilks’ Lambda, standardized canonical discriminant function coefficients and the predicted classifications were estimated. The research shows that the distribution of the scores from each function is standardized to have a mean O and a standard deviation of 1. The magnitudes of the coefficients indicate how strongly the discriminating variable affects the score. In the predicted group membership, 172 cases that were predicted to commit crime against Government group, 66 were correctly predicted and 106 were incorrectly predicted. After going through the predicted classifications, we found out that most groups numbers that were correctly predicted were less than those that were incorrectly predicted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discriminant%20analysis" title="discriminant analysis">discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=DA" title=" DA"> DA</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis%20of%20variance" title=" multivariate analysis of variance"> multivariate analysis of variance</a>, <a href="https://publications.waset.org/abstracts/search?q=MANOVA" title=" MANOVA"> MANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=canonical%20correlation" title=" canonical correlation"> canonical correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20Wilks%E2%80%99%20Lambda" title=" and Wilks’ Lambda "> and Wilks’ Lambda </a> </p> <a href="https://publications.waset.org/abstracts/26685/using-discriminant-analysis-to-forecast-crime-rate-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26685.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">29809</span> Predictive Analytics of Student Performance Determinants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahtab%20Davari">Mahtab Davari</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20Edward%20Okon"> Charles Edward Okon</a>, <a href="https://publications.waset.org/abstracts/search?q=Somayeh%20Aghanavesi"> Somayeh Aghanavesi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=student%20performance" title="student performance">student performance</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-validation" title=" cross-validation"> cross-validation</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/155072/predictive-analytics-of-student-performance-determinants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155072.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">126</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">29808</span> A Neurofeedback Learning Model Using Time-Frequency Analysis for Volleyball Performance Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Yousefi">Hamed Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnaz%20Mohammadi"> Farnaz Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Niloufar%20Mirian"> Niloufar Mirian</a>, <a href="https://publications.waset.org/abstracts/search?q=Navid%20Amini"> Navid Amini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Investigating possible capacities of visual functions where adapted mechanisms can enhance the capability of sports trainees is a promising area of research, not only from the cognitive viewpoint but also in terms of unlimited applications in sports training. In this paper, the visual evoked potential (VEP) and event-related potential (ERP) signals of amateur and trained volleyball players in a pilot study were processed. Two groups of amateur and trained subjects are asked to imagine themselves in the state of receiving a ball while they are shown a simulated volleyball field. The proposed method is based on a set of time-frequency features using algorithms such as Gabor filter, continuous wavelet transform, and a multi-stage wavelet decomposition that are extracted from VEP signals that can be indicative of being amateur or trained. The linear discriminant classifier achieves the accuracy, sensitivity, and specificity of 100% when the average of the repetitions of the signal corresponding to the task is used. The main purpose of this study is to investigate the feasibility of a fast, robust, and reliable feature/model determination as a neurofeedback parameter to be utilized for improving the volleyball players’ performance. The proposed measure has potential applications in brain-computer interface technology where a real-time biomarker is needed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visual%20evoked%20potential" title="visual evoked potential">visual evoked potential</a>, <a href="https://publications.waset.org/abstracts/search?q=time-frequency%20feature%20extraction" title=" time-frequency feature extraction"> time-frequency feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=short-time%20Fourier%20transform" title=" short-time Fourier transform"> short-time Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=event-related%20spectrum%20potential%20classification" title=" event-related spectrum potential classification"> event-related spectrum potential classification</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/134580/a-neurofeedback-learning-model-using-time-frequency-analysis-for-volleyball-performance-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134580.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">138</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">29807</span> A Proposed Optimized and Efficient Intrusion Detection System for Wireless Sensor Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulaziz%20Alsadhan">Abdulaziz Alsadhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Khan"> Naveed Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Particle%20Swarm%20Optimization%20%28PSO%29" title="Particle Swarm Optimization (PSO)">Particle Swarm Optimization (PSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=Principle%20Component%20Analysis%20%28PCA%29" title=" Principle Component Analysis (PCA)"> Principle Component Analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Linear%20Discriminant%20Analysis%20%28LDA%29" title=" Linear Discriminant Analysis (LDA)"> Linear Discriminant Analysis (LDA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Local%20Binary%20Pattern%20%28LBP%29" title=" Local Binary Pattern (LBP)"> Local Binary Pattern (LBP)</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine%20%28SVM%29" title=" Support Vector Machine (SVM)"> Support Vector Machine (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Multilayer%20Perceptron%20%28MLP%29" title=" Multilayer Perceptron (MLP)"> Multilayer Perceptron (MLP)</a> </p> <a href="https://publications.waset.org/abstracts/1787/a-proposed-optimized-and-efficient-intrusion-detection-system-for-wireless-sensor-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1787.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">367</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">29806</span> Diet and Exercise Intervention and Bio–Atherogenic Markers for Obesity Classes of Black South Africans with Type 2 Diabetes Mellitus Using Discriminant Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oladele%20V.%20Adeniyi">Oladele V. Adeniyi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Longo-Mbenza"> B. Longo-Mbenza</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20T.%20Goon"> Daniel T. Goon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Lipids are often low or in the normal ranges and controversial in the atherogenesis among Black Africans. The effect of the severity of obesity on some traditional and novel cardiovascular disease risk factors is unclear before and after a diet and exercise maintenance programme among obese black South Africans with type 2 diabetes mellitus (T2DM). Therefore, this study aimed to identify the risk factors to discriminate obesity classes among patients with T2DM before and after a diet and exercise programme. Methods: This interventional cohort of Black South Africans with T2DM was followed by a very – low calorie diet and exercise programme in Mthatha, between August and November 2013. Gender, age, and the levels of body mass index (BMI), blood pressure, monthly income, daily frequency of meals, blood random plasma glucose (RPG), serum creatinine, total cholesterol (TC), triglycerides (TG), LDL –C, HDL – C, Non-HDL, ratios of TC/HDL, TG/HDL, and LDL/HDL were recorded. Univariate analysis (ANOVA) and multivariate discriminant analysis were performed to separate obesity classes: normal weight (BMI = 18.5 – 24.9 kg/m2), overweight (BMI = 25 – 29.9 kg/m2), obesity Class 1 (BMI = 30 – 34.9 kg/m2), obesity Class 2 (BMI = 35 – 39.9 kg/m2), and obesity Class 3 (BMI ≥ 40 kg/m2). Results: At the baseline (1st Month September), all 327 patients were overweight/obese: 19.6% overweight, 42.8% obese class 1, 22.3% obese class 2, and 15.3% obese class 3. In discriminant analysis, only systolic blood pressure (SBP with positive association) and LDL/HDL ratio (negative association) significantly separated increasing obesity classes. At the post – evaluation (3rd Month November), out of all 327 patients, 19.9%, 19.3%, 37.6%, 15%, and 8.3% had normal weight, overweight, obesity class 1, obesity class 2, and obesity class 3, respectively. There was a significant negative association between serum creatinine and increase in BMI. In discriminant analysis, only age (positive association), SBP (U – shaped relationship), monthly income (inverted U – shaped association), daily frequency of meals (positive association), and LDL/HDL ratio (positive association) classified significantly increasing obesity classes. Conclusion: There is an epidemic of diabesity (Obesity + T2DM) in this Black South Africans with some weight loss. Further studies are needed to understand positive or negative linear correlations and paradoxical curvilinear correlations between these markers and increase in BMI among black South African T2DM patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=atherogenic%20dyslipidaemia" title="atherogenic dyslipidaemia">atherogenic dyslipidaemia</a>, <a href="https://publications.waset.org/abstracts/search?q=dietary%20interventions" title=" dietary interventions"> dietary interventions</a>, <a href="https://publications.waset.org/abstracts/search?q=obesity" title=" obesity"> obesity</a>, <a href="https://publications.waset.org/abstracts/search?q=south%20africans" title=" south africans"> south africans</a> </p> <a href="https://publications.waset.org/abstracts/27698/diet-and-exercise-intervention-and-bio-atherogenic-markers-for-obesity-classes-of-black-south-africans-with-type-2-diabetes-mellitus-using-discriminant-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27698.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">367</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">29805</span> Reliability Prediction of Tires Using Linear Mixed-Effects Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Myung%20Hwan%20Na">Myung Hwan Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-%20Chun%20Song"> Ho- Chun Song</a>, <a href="https://publications.waset.org/abstracts/search?q=EunHee%20Hong"> EunHee Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=tires" title=" tires"> tires</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20data" title=" field data"> field data</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed-effects%20model" title=" linear mixed-effects model"> linear mixed-effects model</a> </p> <a href="https://publications.waset.org/abstracts/37815/reliability-prediction-of-tires-using-linear-mixed-effects-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37815.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">564</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">29804</span> Discrimination of Bio-Analytes by Using Two-Dimensional Nano Sensor Array</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Behera">P. Behera</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20K.%20Singh"> K. K. Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20K.%20Saini"> D. K. Saini</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20De"> M. De</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Implementation of 2D materials in the detection of bio analytes is highly advantageous in the field of sensing because of its high surface to volume ratio. We have designed our sensor array with different cationic two-dimensional MoS₂, where surface modification was achieved by cationic thiol ligands with different functionality. Green fluorescent protein (GFP) was chosen as signal transducers for its biocompatibility and anionic nature, which can bind to the cationic MoS₂ surface easily, followed by fluorescence quenching. The addition of bio-analyte to the sensor can decomplex the cationic MoS₂ and GFP conjugates, followed by the regeneration of GFP fluorescence. The fluorescence response pattern belongs to various analytes collected and transformed to linear discriminant analysis (LDA) for classification. At first, 15 different proteins having wide range of molecular weight and isoelectric points were successfully discriminated at 50 nM with detection limit of 1 nM. The sensor system was also executed in biofluids such as serum, where 10 different proteins at 2.5 μM were well separated. After successful discrimination of protein analytes, the sensor array was implemented for bacteria sensing. Six different bacteria were successfully classified at OD = 0.05 with a detection limit corresponding to OD = 0.005. The optimized sensor array was able to classify uropathogens from non-uropathogens in urine medium. Further, the technique was applied for discrimination of bacteria possessing resistance to different types and amounts of drugs. We found out the mechanism of sensing through optical and electrodynamic studies, which indicates the interaction between bacteria with the sensor system was mainly due to electrostatic force of interactions, but the separation of native bacteria from their drug resistant variant was due to Van der Waals forces. There are two ways bacteria can be detected, i.e., through bacterial cells and lysates. The bacterial lysates contain intracellular information and also safe to analysis as it does not contain live cells. Lysates of different drug resistant bacteria were patterned effectively from the native strain. From unknown sample analysis, we found that discrimination of bacterial cells is more sensitive than that of lysates. But the analyst can prefer bacterial lysates over live cells for safer analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=array-based%20sensing" title="array-based sensing">array-based sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20resistant%20bacteria" title=" drug resistant bacteria"> drug resistant bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=two-dimensional%20MoS%E2%82%82" title=" two-dimensional MoS₂"> two-dimensional MoS₂</a> </p> <a href="https://publications.waset.org/abstracts/133539/discrimination-of-bio-analytes-by-using-two-dimensional-nano-sensor-array" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133539.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">143</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">29803</span> Identification and Classification of Fiber-Fortified Semolina by Near-Infrared Spectroscopy (NIR)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amanda%20T.%20Badar%C3%B3">Amanda T. Badaró</a>, <a href="https://publications.waset.org/abstracts/search?q=Douglas%20F.%20Barbin"> Douglas F. Barbin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20T.%20Garcia"> Sofia T. Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Teresa%20P.%20S.%20Clerici"> Maria Teresa P. S. Clerici</a>, <a href="https://publications.waset.org/abstracts/search?q=Amanda%20R.%20Ferreira"> Amanda R. Ferreira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chemometrics" title="Chemometrics">Chemometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=fiber" title=" fiber"> fiber</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=near-infrared%20spectroscopy" title=" near-infrared spectroscopy"> near-infrared spectroscopy</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=semolina" title=" semolina"> semolina</a> </p> <a href="https://publications.waset.org/abstracts/81774/identification-and-classification-of-fiber-fortified-semolina-by-near-infrared-spectroscopy-nir" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81774.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">212</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">29802</span> Bioinformatic Approaches in Population Genetics and Phylogenetic Studies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masoud%20Sheidai">Masoud Sheidai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biologists with a special field of population genetics and phylogeny have different research tasks such as populations’ genetic variability and divergence, species relatedness, the evolution of genetic and morphological characters, and identification of DNA SNPs with adaptive potential. To tackle these problems and reach a concise conclusion, they must use the proper and efficient statistical and bioinformatic methods as well as suitable genetic and morphological characteristics. In recent years application of different bioinformatic and statistical methods, which are based on various well-documented assumptions, are the proper analytical tools in the hands of researchers. The species delineation is usually carried out with the use of different clustering methods like K-means clustering based on proper distance measures according to the studied features of organisms. A well-defined species are assumed to be separated from the other taxa by molecular barcodes. The species relationships are studied by using molecular markers, which are analyzed by different analytical methods like multidimensional scaling (MDS) and principal coordinate analysis (PCoA). The species population structuring and genetic divergence are usually investigated by PCoA and PCA methods and a network diagram. These are based on bootstrapping of data. The Association of different genes and DNA sequences to ecological and geographical variables is determined by LFMM (Latent factor mixed model) and redundancy analysis (RDA), which are based on Bayesian and distance methods. Molecular and morphological differentiating characters in the studied species may be identified by linear discriminant analysis (DA) and discriminant analysis of principal components (DAPC). We shall illustrate these methods and related conclusions by giving examples from different edible and medicinal plant species. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GWAS%20analysis" title="GWAS analysis">GWAS analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=K-Means%20clustering" title=" K-Means clustering"> K-Means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=LFMM" title=" LFMM"> LFMM</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20scaling" title=" multidimensional scaling"> multidimensional scaling</a>, <a href="https://publications.waset.org/abstracts/search?q=redundancy%20analysis" title=" redundancy analysis"> redundancy analysis</a> </p> <a href="https://publications.waset.org/abstracts/149154/bioinformatic-approaches-in-population-genetics-and-phylogenetic-studies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149154.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">124</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">29801</span> Sensitivity Analysis in Fuzzy Linear Programming Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Nasseri">S. H. Nasseri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ebrahimnejad"> A. Ebrahimnejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuzzy set theory has been applied to many fields, such as operations research, control theory, and management sciences. In this paper, we consider two classes of fuzzy linear programming (FLP) problems: Fuzzy number linear programming and linear programming with trapezoidal fuzzy variables problems. We state our recently established results and develop fuzzy primal simplex algorithms for solving these problems. Finally, we give illustrative examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20linear%20programming" title="fuzzy linear programming">fuzzy linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20numbers" title=" fuzzy numbers"> fuzzy numbers</a>, <a href="https://publications.waset.org/abstracts/search?q=duality" title=" duality"> duality</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a> </p> <a href="https://publications.waset.org/abstracts/16916/sensitivity-analysis-in-fuzzy-linear-programming-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16916.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">565</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">29800</span> Improvement of the Melon (Cucumis melo L.) through Genetic Gain and Discriminant Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Naroui%20Rad">M. R. Naroui Rad</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Fanaei"> H. Fanaei</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ghalandarzehi"> A. Ghalandarzehi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To find out the yield of melon, the traits are vital. This research was performed with the objective to assess the impact of nine different morphological traits on the production of 20 melon landraces in the sistan weather region. For all the traits genetic variation was noted. Minimum genetical variance (9.66) along with high genetic interaction with the environment led to low heritability (0.24) of the yield. The broad sense heritability of the traits that were included into the differentiating model was more than it was in the production. In this study, the five selected traits, number of fruit, fruit weight, fruit width, flesh diameter and plant yield can differentiate the genotypes with high or low production. This demonstrated the significance of these 5 traits in plant breeding programs. Discriminant function of these 5 traits, particularly, the weight of the fruit, in case of the current outputs was employed as an all-inclusive parameter for pointing out landraces with the highest yield. 75% of variation in yield can be explained with this index, and the weight of fruit also has substantial relation with the total production (r=0.72**). This factor can be highly beneficial in case of future breeding program selections. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=melon" title="melon">melon</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminant%20analysis" title=" discriminant analysis"> discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20components" title=" genetic components"> genetic components</a>, <a href="https://publications.waset.org/abstracts/search?q=yield" title=" yield"> yield</a>, <a href="https://publications.waset.org/abstracts/search?q=selection" title=" selection"> selection</a> </p> <a href="https://publications.waset.org/abstracts/48563/improvement-of-the-melon-cucumis-melo-l-through-genetic-gain-and-discriminant-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48563.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">333</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">29799</span> Application of Combined Cluster and Discriminant Analysis to Make the Operation of Monitoring Networks More Economical</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Norbert%20Magyar">Norbert Magyar</a>, <a href="https://publications.waset.org/abstracts/search?q=Jozsef%20Kovacs"> Jozsef Kovacs</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Tanos"> Peter Tanos</a>, <a href="https://publications.waset.org/abstracts/search?q=Balazs%20Trasy"> Balazs Trasy</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamas%20Garamhegyi"> Tamas Garamhegyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Istvan%20Gabor%20Hatvani"> Istvan Gabor Hatvani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water is one of the most important common resources, and as a result of urbanization, agriculture, and industry it is becoming more and more exposed to potential pollutants. The prevention of the deterioration of water quality is a crucial role for environmental scientist. To achieve this aim, the operation of monitoring networks is necessary. In general, these networks have to meet many important requirements, such as representativeness and cost efficiency. However, existing monitoring networks often include sampling sites which are unnecessary. With the elimination of these sites the monitoring network can be optimized, and it can operate more economically. The aim of this study is to illustrate the applicability of the CCDA (Combined Cluster and Discriminant Analysis) to the field of water quality monitoring and optimize the monitoring networks of a river (the Danube), a wetland-lake system (Kis-Balaton & Lake Balaton), and two surface-subsurface water systems on the watershed of Lake Neusiedl/Lake Fertő and on the Szigetköz area over a period of approximately two decades. CCDA combines two multivariate data analysis methods: hierarchical cluster analysis and linear discriminant analysis. Its goal is to determine homogeneous groups of observations, in our case sampling sites, by comparing the goodness of preconceived classifications obtained from hierarchical cluster analysis with random classifications. The main idea behind CCDA is that if the ratio of correctly classified cases for a grouping is higher than at least 95% of the ratios for the random classifications, then at the level of significance (α=0.05) the given sampling sites don’t form a homogeneous group. Due to the fact that the sampling on the Lake Neusiedl/Lake Fertő was conducted at the same time at all sampling sites, it was possible to visualize the differences between the sampling sites belonging to the same or different groups on scatterplots. Based on the results, the monitoring network of the Danube yields redundant information over certain sections, so that of 12 sampling sites, 3 could be eliminated without loss of information. In the case of the wetland (Kis-Balaton) one pair of sampling sites out of 12, and in the case of Lake Balaton, 5 out of 10 could be discarded. For the groundwater system of the catchment area of Lake Neusiedl/Lake Fertő all 50 monitoring wells are necessary, there is no redundant information in the system. The number of the sampling sites on the Lake Neusiedl/Lake Fertő can decrease to approximately the half of the original number of the sites. Furthermore, neighbouring sampling sites were compared pairwise using CCDA and the results were plotted on diagrams or isoline maps showing the location of the greatest differences. These results can help researchers decide where to place new sampling sites. The application of CCDA proved to be a useful tool in the optimization of the monitoring networks regarding different types of water bodies. Based on the results obtained, the monitoring networks can be operated more economically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combined%20cluster%20and%20discriminant%20analysis" title="combined cluster and discriminant analysis">combined cluster and discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20efficiency" title=" cost efficiency"> cost efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring%20network%20optimization" title=" monitoring network optimization"> monitoring network optimization</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/59634/application-of-combined-cluster-and-discriminant-analysis-to-make-the-operation-of-monitoring-networks-more-economical" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59634.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">348</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">29798</span> Distinguishing between Bacterial and Viral Infections Based on Peripheral Human Blood Tests Using Infrared Microscopy and Multivariate Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Agbaria">H. Agbaria</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Salman"> A. Salman</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Huleihel"> M. Huleihel</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Beck"> G. Beck</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20H.%20Rich"> D. H. Rich</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mordechai"> S. Mordechai</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Kapelushnik"> J. Kapelushnik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Viral and bacterial infections are responsible for variety of diseases. These infections have similar symptoms like fever, sneezing, inflammation, vomiting, diarrhea and fatigue. Thus, physicians may encounter difficulties in distinguishing between viral and bacterial infections based on these symptoms. Bacterial infections differ from viral infections in many other important respects regarding the response to various medications and the structure of the organisms. In many cases, it is difficult to know the origin of the infection. The physician orders a blood, urine test, or 'culture test' of tissue to diagnose the infection type when it is necessary. Using these methods, the time that elapses between the receipt of patient material and the presentation of the test results to the clinician is typically too long ( > 24 hours). This time is crucial in many cases for saving the life of the patient and for planning the right medical treatment. Thus, rapid identification of bacterial and viral infections in the lab is of great importance for effective treatment especially in cases of emergency. Blood was collected from 50 patients with confirmed viral infection and 50 with confirmed bacterial infection. White blood cells (WBCs) and plasma were isolated and deposited on a zinc selenide slide, dried and measured under a Fourier transform infrared (FTIR) microscope to obtain their infrared absorption spectra. The acquired spectra of WBCs and plasma were analyzed in order to differentiate between the two types of infections. In this study, the potential of FTIR microscopy in tandem with multivariate analysis was evaluated for the identification of the agent that causes the human infection. The method was used to identify the infectious agent type as either bacterial or viral, based on an analysis of the blood components [i.e., white blood cells (WBC) and plasma] using their infrared vibrational spectra. The time required for the analysis and evaluation after obtaining the blood sample was less than one hour. In the analysis, minute spectral differences in several bands of the FTIR spectra of WBCs were observed between groups of samples with viral and bacterial infections. By employing the techniques of feature extraction with linear discriminant analysis (LDA), a sensitivity of ~92 % and a specificity of ~86 % for an infection type diagnosis was achieved. The present preliminary study suggests that FTIR spectroscopy of WBCs is a potentially feasible and efficient tool for the diagnosis of the infection type. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=viral%20infection" title="viral infection">viral infection</a>, <a href="https://publications.waset.org/abstracts/search?q=bacterial%20infection" title=" bacterial infection"> bacterial infection</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=plasma" title=" plasma"> plasma</a>, <a href="https://publications.waset.org/abstracts/search?q=white%20blood%20cells" title=" white blood cells"> white blood cells</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared%20spectroscopy" title=" infrared spectroscopy"> infrared spectroscopy</a> </p> <a href="https://publications.waset.org/abstracts/68593/distinguishing-between-bacterial-and-viral-infections-based-on-peripheral-human-blood-tests-using-infrared-microscopy-and-multivariate-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68593.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> <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=linear%20discriminant%20analysis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis&page=3">3</a></li> <li class="page-item"><a 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