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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="euclidean classifier"> <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> 422</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: euclidean classifier</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">422</span> Speed up Vector Median Filtering by Quasi Euclidean Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinai%20K.%20Singh">Vinai K. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images as for example colour images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean norms which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean norm is presented which is applied to vector median filtering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=euclidean%20norm" title="euclidean norm">euclidean norm</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi%20euclidean%20norm" title=" quasi euclidean norm"> quasi euclidean norm</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20median%20filtering" title=" vector median filtering"> vector median filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=applied%20mathematics" title=" applied mathematics"> applied mathematics</a> </p> <a href="https://publications.waset.org/abstracts/21942/speed-up-vector-median-filtering-by-quasi-euclidean-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21942.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">474</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">421</span> Triangular Geometric Feature for Offline Signature Verification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zuraidasahana%20Zulkarnain">Zuraidasahana Zulkarnain</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Shafry%20Mohd%20Rahim"> Mohd Shafry Mohd Rahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Anita%20Fairos%20Ismail"> Nor Anita Fairos Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Azhar%20M.%20Arsad"> Mohd Azhar M. Arsad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=euclidean%20classifier" title=" euclidean classifier"> euclidean classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=offline%20signature%20verification" title=" offline signature verification"> offline signature verification</a>, <a href="https://publications.waset.org/abstracts/search?q=voting-based%20classifier" title=" voting-based classifier"> voting-based classifier</a> </p> <a href="https://publications.waset.org/abstracts/45300/triangular-geometric-feature-for-offline-signature-verification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45300.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">378</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">420</span> Use of Gaussian-Euclidean Hybrid Function Based Artificial Immune System for Breast Cancer Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cuneyt%20Yucelbas">Cuneyt Yucelbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Seral%20Ozsen"> Seral Ozsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sule%20Yucelbas"> Sule Yucelbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Gulay%20Tezel"> Gulay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20immune%20system" title="artificial immune system">artificial immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20diagnosis" title=" breast cancer diagnosis"> breast cancer diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20function" title=" Euclidean function"> Euclidean function</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20function" title=" Gaussian function"> Gaussian function</a> </p> <a href="https://publications.waset.org/abstracts/5135/use-of-gaussian-euclidean-hybrid-function-based-artificial-immune-system-for-breast-cancer-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5135.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">419</span> Classification of Red, Green and Blue Values from Face Images Using k-NN Classifier to Predict the Skin or Non-Skin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Polat">Kemal Polat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=k-NN%20classifier" title="k-NN classifier">k-NN classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20or%20non-skin%20classification" title=" skin or non-skin classification"> skin or non-skin classification</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB%20values" title=" RGB values"> RGB values</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/86538/classification-of-red-green-and-blue-values-from-face-images-using-k-nn-classifier-to-predict-the-skin-or-non-skin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86538.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">248</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">418</span> SC-LSH: An Efficient Indexing Method for Approximate Similarity Search in High Dimensional Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanaa%20Chafik">Sanaa Chafik</a>, <a href="https://publications.waset.org/abstracts/search?q=Imane%20Daoudi"> Imane Daoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounim%20A.%20El%20Yacoubi"> Mounim A. El Yacoubi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20El%20Ouardi"> Hamid El Ouardi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. In this paper, we propose a new hashing algorithm to overcome the storage space problem and improve query time, while keeping a good accuracy as similar to that achieved by the original Euclidean LSH. The Experimental results on a real large-scale dataset show that the proposed approach achieves good performances and consumes less memory than the Euclidean LSH. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%20nearest%20neighbor%20search" title="approximate nearest neighbor search">approximate nearest neighbor search</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20image%20retrieval%20%28CBIR%29" title=" content based image retrieval (CBIR)"> content based image retrieval (CBIR)</a>, <a href="https://publications.waset.org/abstracts/search?q=curse%20of%20dimensionality" title=" curse of dimensionality"> curse of dimensionality</a>, <a href="https://publications.waset.org/abstracts/search?q=locality%20sensitive%20hashing" title=" locality sensitive hashing"> locality sensitive hashing</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20indexing" title=" multidimensional indexing"> multidimensional indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=scalability" title=" scalability"> scalability</a> </p> <a href="https://publications.waset.org/abstracts/12901/sc-lsh-an-efficient-indexing-method-for-approximate-similarity-search-in-high-dimensional-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12901.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">321</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">417</span> Parkinson’s Disease Detection Analysis through Machine Learning Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhtasim%20Shafi%20Kader">Muhtasim Shafi Kader</a>, <a href="https://publications.waset.org/abstracts/search?q=Fizar%20Ahmed"> Fizar Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Annesha%20Acharjee"> Annesha Acharjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used. <p class="card-text"><strong>Keywords:</strong> <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=adaptive%20boosting" title=" adaptive boosting"> adaptive boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=bagging%20classifier" title=" bagging classifier"> bagging classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20classifier" title=" random forest classifier"> random forest classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=XBG%20classifier" title=" XBG classifier"> XBG classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbor%20classifier" title=" k nearest neighbor classifier"> k nearest neighbor classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20classifier" title=" support vector classifier"> support vector classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20boosting%20classifier" title=" gradient boosting classifier"> gradient boosting classifier</a> </p> <a href="https://publications.waset.org/abstracts/148163/parkinsons-disease-detection-analysis-through-machine-learning-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148163.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">129</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">416</span> Use of Fractal Geometry in Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20M.%20Alkoot">Fuad M. Alkoot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main component of a machine learning system is the classifier. Classifiers are mathematical models that can perform classification tasks for a specific application area. Additionally, many classifiers are combined using any of the available methods to reduce the classifier error rate. The benefits gained from the combination of multiple classifier designs has motivated the development of diverse approaches to multiple classifiers. We aim to investigate using fractal geometry to develop an improved classifier combiner. Initially we experiment with measuring the fractal dimension of data and use the results in the development of a combiner strategy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20geometry" title="fractal geometry">fractal geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classifier" title=" classifier"> classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a> </p> <a href="https://publications.waset.org/abstracts/141274/use-of-fractal-geometry-in-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141274.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">216</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">415</span> Speaker Recognition Using LIRA Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nestor%20A.%20Garcia%20Fragoso">Nestor A. Garcia Fragoso</a>, <a href="https://publications.waset.org/abstracts/search?q=Tetyana%20Baydyk"> Tetyana Baydyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ernst%20Kussul"> Ernst Kussul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20learning" title="extreme learning">extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LIRA%20neural%20classifier" title=" LIRA neural classifier"> LIRA neural classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20identification" title=" speaker identification"> speaker identification</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20recognition" title=" voice recognition"> voice recognition</a> </p> <a href="https://publications.waset.org/abstracts/112384/speaker-recognition-using-lira-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112384.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">177</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">414</span> Comparing SVM and Naïve Bayes Classifier for Automatic Microaneurysm Detections </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Sopharak">A. Sopharak</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Uyyanonvara"> B. Uyyanonvara</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Barman"> S. Barman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diabetic%20retinopathy" title="diabetic retinopathy">diabetic retinopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=microaneurysm" title=" microaneurysm"> microaneurysm</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20Bayes%20classifier" title=" naive Bayes classifier"> naive Bayes classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20classifier" title=" SVM classifier"> SVM classifier</a> </p> <a href="https://publications.waset.org/abstracts/3939/comparing-svm-and-naive-bayes-classifier-for-automatic-microaneurysm-detections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3939.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">328</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">413</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">412</span> Refutation of Imre Hermann's Allegation: János Bolyai Was Not Insane</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ol%C3%A1h%20G%C3%A1l%20R%C3%B3bert">Oláh Gál Róbert</a>, <a href="https://publications.waset.org/abstracts/search?q=Veress%20B%C3%A1gyi%20Ibolya"> Veress Bágyi Ibolya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The scientific public has relatively little knowledge about the Hungarian János Bolyai, one of the greatest thinkers of all times. Few people know that apart from being the founder of the non-Euclidean geometry he was also interested in sociology, philosophy, epistemology and linguistics. According to the renowned Hungarian psychoanalytic Imre Hermann, who lives in France, János Bolyai was mentally deranged. However, this is incorrect. The present article intends to prove that he was completely sane until the moment of his death. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imre%20Hermann" title="Imre Hermann">Imre Hermann</a>, <a href="https://publications.waset.org/abstracts/search?q=insane" title=" insane"> insane</a>, <a href="https://publications.waset.org/abstracts/search?q=J%C3%A1nos%20Bolyai" title=" János Bolyai"> János Bolyai</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics" title=" mathematics"> mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Euclidean%20geometry" title=" non-Euclidean geometry"> non-Euclidean geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=psyphoanalytic" title=" psyphoanalytic"> psyphoanalytic</a> </p> <a href="https://publications.waset.org/abstracts/35797/refutation-of-imre-hermanns-allegation-janos-bolyai-was-not-insane" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35797.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">491</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">411</span> An Adaptive Oversampling Technique for Imbalanced Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaukat%20Ali%20Shahee">Shaukat Ali Shahee</a>, <a href="https://publications.waset.org/abstracts/search?q=Usha%20Ananthakumar"> Usha Ananthakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets. <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=imbalanced%20dataset" title=" imbalanced dataset"> imbalanced dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=Lowner-John%20ellipsoid" title=" Lowner-John ellipsoid"> Lowner-John ellipsoid</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20based%20clustering" title=" model based clustering"> model based clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=oversampling" title=" oversampling"> oversampling</a> </p> <a href="https://publications.waset.org/abstracts/83833/an-adaptive-oversampling-technique-for-imbalanced-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83833.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">418</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">410</span> Vector-Based Analysis in Cognitive Linguistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chuluundorj%20Begz">Chuluundorj Begz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the dynamic, psycho-cognitive approach to study of human verbal thinking on the basis of typologically different languages /as a Mongolian, English and Russian/. Topological equivalence in verbal communication serves as a basis of Universality of mental structures and therefore deep structures. Mechanism of verbal thinking consisted at the deep level of basic concepts, rules for integration and classification, neural networks of vocabulary. In neuro cognitive study of language, neural architecture and neuro psychological mechanism of verbal cognition are basis of a vector-based modeling. Verbal perception and interpretation of the infinite set of meanings and propositions in mental continuum can be modeled by applying tensor methods. Euclidean and non-Euclidean spaces are applied for a description of human semantic vocabulary and high order structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20spaces" title="Euclidean spaces">Euclidean spaces</a>, <a href="https://publications.waset.org/abstracts/search?q=isomorphism%20and%20homomorphism" title=" isomorphism and homomorphism"> isomorphism and homomorphism</a>, <a href="https://publications.waset.org/abstracts/search?q=mental%20lexicon" title=" mental lexicon"> mental lexicon</a>, <a href="https://publications.waset.org/abstracts/search?q=mental%20mapping" title=" mental mapping"> mental mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20memory" title=" semantic memory"> semantic memory</a>, <a href="https://publications.waset.org/abstracts/search?q=verbal%20cognition" title=" verbal cognition"> verbal cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20space" title=" vector space"> vector space</a> </p> <a href="https://publications.waset.org/abstracts/22970/vector-based-analysis-in-cognitive-linguistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22970.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">519</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">409</span> Teaching Non-Euclidean Geometries to Learn Euclidean One: An Experimental Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Benvenuti">Silvia Benvenuti</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandra%20Cardinali"> Alessandra Cardinali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, for instance, in relation to the Covid 19 pandemic and the evidence of climate change, it is becoming quite clear that the development of a young kid into an adult citizen requires a solid scientific background. Citizens are required to exert logical thinking and know the methods of science in order to adapt, understand, and develop as persons. Mathematics sits at the core of these required skills: learning the axiomatic method is fundamental to understand how hard sciences work and helps in consolidating logical thinking, which will be useful for the entire life of a student. At the same time, research shows that the axiomatic study of geometry is a problematic topic for students, even for those with interest in mathematics. With this in mind, the main goals of the research work we will describe are: (1) to show whether non-Euclidean geometries can be a tool to allow students to consolidate the knowledge of Euclidean geometries by developing it in a critical way; (2) to promote the understanding of the modern axiomatic method in geometry; (3) to give students a new perspective on mathematics so that they can see it as a creative activity and a widely discussed topic with a historical background. One of the main issues related to the state-of-the-art in this topic is the shortage of experimental studies with students. For this reason, our aim is to show further experimental evidence of the potential benefits of teaching non-Euclidean geometries at high school, based on data collected from a study started in 2005 in the frame of the Italian National Piano Lauree Scientifiche, continued by a teacher training organized in September 2018, perfected in a pilot study that involved 77 high school students during the school years 2018-2019 and 2019-2020. and finally implemented through an experimental study conducted in 2020-21 with 87 high school students. Our study shows that there is potential for further research to challenge current conceptions of the school mathematics curriculum and of the capabilities of high school mathematics students. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Non-Euclidean%20geometries" title="Non-Euclidean geometries">Non-Euclidean geometries</a>, <a href="https://publications.waset.org/abstracts/search?q=beliefs%20about%20mathematics" title=" beliefs about mathematics"> beliefs about mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=questionnaires" title=" questionnaires"> questionnaires</a>, <a href="https://publications.waset.org/abstracts/search?q=modern%20axiomatic%20method" title=" modern axiomatic method"> modern axiomatic method</a> </p> <a href="https://publications.waset.org/abstracts/156531/teaching-non-euclidean-geometries-to-learn-euclidean-one-an-experimental-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156531.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">408</span> Iris Recognition Based on the Low Order Norms of Gradient Components</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iman%20A.%20Saad">Iman A. Saad</a>, <a href="https://publications.waset.org/abstracts/search?q=Loay%20E.%20George"> Loay E. George</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title="iris recognition">iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=contrast%20stretching" title=" contrast stretching"> contrast stretching</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20features" title=" gradient features"> gradient features</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20features" title=" texture features"> texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20metric" title=" Euclidean metric"> Euclidean metric</a> </p> <a href="https://publications.waset.org/abstracts/13277/iris-recognition-based-on-the-low-order-norms-of-gradient-components" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13277.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">334</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">407</span> A Comparative Study of k-NN and MLP-NN Classifiers Using GA-kNN Based Feature Selection Method for Wood Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Uswah%20Khairuddin">Uswah Khairuddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubiyah%20Yusof"> Rubiyah Yusof</a>, <a href="https://publications.waset.org/abstracts/search?q=Nenny%20Ruthfalydia%20Rosli"> Nenny Ruthfalydia Rosli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=wood%20recognition%20system" title=" wood recognition system "> wood recognition system </a> </p> <a href="https://publications.waset.org/abstracts/25573/a-comparative-study-of-k-nn-and-mlp-nn-classifiers-using-ga-knn-based-feature-selection-method-for-wood-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25573.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">545</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">406</span> Algorithms for Fast Computation of Pan Matrix Profiles of Time Series Under Unnormalized Euclidean Distances</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jing%20Zhang">Jing Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Nikovski"> Daniel Nikovski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an approximation algorithm called LINKUMP to compute the Pan Matrix Profile (PMP) under the unnormalized l∞ distance (useful for value-based similarity search) using double-ended queue and linear interpolation. The algorithm has comparable time/space complexities as the state-of-the-art algorithm for typical PMP computation under the normalized l₂ distance (useful for shape-based similarity search). We validate its efficiency and effectiveness through extensive numerical experiments and a real-world anomaly detection application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pan%20matrix%20profile" title="pan matrix profile">pan matrix profile</a>, <a href="https://publications.waset.org/abstracts/search?q=unnormalized%20euclidean%20distance" title=" unnormalized euclidean distance"> unnormalized euclidean distance</a>, <a href="https://publications.waset.org/abstracts/search?q=double-ended%20queue" title=" double-ended queue"> double-ended queue</a>, <a href="https://publications.waset.org/abstracts/search?q=discord%20discovery" title=" discord discovery"> discord discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a> </p> <a href="https://publications.waset.org/abstracts/144363/algorithms-for-fast-computation-of-pan-matrix-profiles-of-time-series-under-unnormalized-euclidean-distances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144363.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">247</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">405</span> Using Machine Learning to Build a Real-Time COVID-19 Mask Safety Monitor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yash%20Jain">Yash Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=datasets" title="datasets">datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=classifier" title=" classifier"> classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=mask-detection" title=" mask-detection"> mask-detection</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time" title=" real-time"> real-time</a>, <a href="https://publications.waset.org/abstracts/search?q=TinyYoloV3" title=" TinyYoloV3"> TinyYoloV3</a>, <a href="https://publications.waset.org/abstracts/search?q=two-stage%20neural%20network%20classifier" title=" two-stage neural network classifier"> two-stage neural network classifier</a> </p> <a href="https://publications.waset.org/abstracts/137207/using-machine-learning-to-build-a-real-time-covid-19-mask-safety-monitor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137207.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">161</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">404</span> Decision Trees Constructing Based on K-Means Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Loai%20Abdallah">Loai Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Yousef"> Malik Yousef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ensemble%20clustering" title="ensemble clustering">ensemble clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=K%20nearest%20neighbors" title=" K nearest neighbors"> K nearest neighbors</a> </p> <a href="https://publications.waset.org/abstracts/89656/decision-trees-constructing-based-on-k-means-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89656.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">190</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">403</span> The Diminished Online Persona: A Semantic Change of Chinese Classifier Mei on Weibo</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hui%20Shi">Hui Shi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates a newly emerged usage of Chinese numeral classifier mei (枚) in the cyberspace. In modern Chinese grammar, mei as a classifier should occupy the pre-nominal position, and its valid accompanying nouns are restricted to small, flat, fragile inanimate objects rather than humans. To examine the semantic change of mei, two types of data from Weibo.com were collected. First, 500 mei-included Weibo posts constructed a corpus for analyzing this classifier's word order distribution (post-nominal or pre-nominal) as well as its accompanying nouns' semantics (inanimate or human). Second, considering that mei accompanies a remarkable number of human nouns in the first corpus, the second corpus is composed of mei-involved Weibo IDs from users located in first and third-tier cities (n=8 respectively). The findings show that in the cyber community, mei frequently classifies human-related neologisms at the archaic post-normal position. Besides, the 23 to 29-year-old females as well as Weibo users from third-tier cities are the major populations who adopt mei in their user IDs for self-description and identity expression. This paper argues that the creative usage of mei gains popularity in the Chinese internet due to a humor effect. The marked word order switch and semantic misapplication combined to trigger incongruity and jocularity. This study has significance for research on Chinese cyber neologism. It may also lay a foundation for further studies on Chinese classifier change and Chinese internet communication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20classifier" title="Chinese classifier">Chinese classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=humor" title=" humor"> humor</a>, <a href="https://publications.waset.org/abstracts/search?q=neologism" title=" neologism"> neologism</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20change" title=" semantic change"> semantic change</a> </p> <a href="https://publications.waset.org/abstracts/95249/the-diminished-online-persona-a-semantic-change-of-chinese-classifier-mei-on-weibo" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95249.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">253</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">402</span> Breast Cancer Survivability Prediction via Classifier Ensemble</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Al-Badrashiny">Mohamed Al-Badrashiny</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelghani%20Bellaachia"> Abdelghani Bellaachia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classifier%20ensemble" title="classifier ensemble">classifier ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20survivability" title=" breast cancer survivability"> breast cancer survivability</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=SEER" title=" SEER"> SEER</a> </p> <a href="https://publications.waset.org/abstracts/42621/breast-cancer-survivability-prediction-via-classifier-ensemble" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42621.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">328</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">401</span> Multi-Sensor Target Tracking Using Ensemble Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhekisipho%20Twala">Bhekisipho Twala</a>, <a href="https://publications.waset.org/abstracts/search?q=Mantepu%20Masetshaba"> Mantepu Masetshaba</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramapulana%20Nkoana"> Ramapulana Nkoana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single%20classifier" title="single classifier">single classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20learning" title=" ensemble learning"> ensemble learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-target%20tracking" title=" multi-target tracking"> multi-target tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20classifiers" title=" multiple classifiers"> multiple classifiers</a> </p> <a href="https://publications.waset.org/abstracts/140816/multi-sensor-target-tracking-using-ensemble-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140816.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">268</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">400</span> Using Classifiers to Predict Student Outcome at Higher Institute of Telecommunication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20M.%20Alkoot">Fuad M. Alkoot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We aim at highlighting the benefits of classifier systems especially in supporting educational management decisions. The paper aims at using classifiers in an educational application where an outcome is predicted based on given input parameters that represent various conditions at the institute. We present a classifier system that is designed using a limited training set with data for only one semester. The achieved system is able to reach at previously known outcomes accurately. It is also tested on new input parameters representing variations of input conditions to see its prediction on the possible outcome value. Given the supervised expectation of the outcome for the new input we find the system is able to predict the correct outcome. Experiments were conducted on one semester data from two departments only, Switching and Mathematics. Future work on other departments with larger training sets and wider input variations will show additional benefits of classifier systems in supporting the management decisions at an educational institute. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=classifier%20design" title=" classifier design"> classifier design</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20management" title=" educational management"> educational management</a>, <a href="https://publications.waset.org/abstracts/search?q=outcome%20estimation" title=" outcome estimation"> outcome estimation</a> </p> <a href="https://publications.waset.org/abstracts/50309/using-classifiers-to-predict-student-outcome-at-higher-institute-of-telecommunication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50309.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">278</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">399</span> Random Subspace Neural Classifier for Meteor Recognition in the Night Sky </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Vera">Carlos Vera</a>, <a href="https://publications.waset.org/abstracts/search?q=Tetyana%20Baydyk"> Tetyana Baydyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ernst%20Kussul"> Ernst Kussul</a>, <a href="https://publications.waset.org/abstracts/search?q=Graciela%20Velasco"> Graciela Velasco</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Aparicio"> Miguel Aparicio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contour%20orientation%20histogram" title="contour orientation histogram">contour orientation histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=meteors" title=" meteors"> meteors</a>, <a href="https://publications.waset.org/abstracts/search?q=night%20sky" title=" night sky"> night sky</a>, <a href="https://publications.waset.org/abstracts/search?q=RSC%20neural%20classifier" title=" RSC neural classifier"> RSC neural classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=stars" title=" stars "> stars </a> </p> <a href="https://publications.waset.org/abstracts/136153/random-subspace-neural-classifier-for-meteor-recognition-in-the-night-sky" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136153.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">398</span> A Reliable Multi-Type Vehicle Classification System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20S.%20Moussa">Ghada S. Moussa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20classification" title="vehicle classification">vehicle classification</a>, <a href="https://publications.waset.org/abstracts/search?q=bag-of-words%20technique" title=" bag-of-words technique"> bag-of-words technique</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20classifier" title=" SVM classifier"> SVM classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA%20classifier" title=" LDA classifier"> LDA classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN%20classifier" title=" KNN classifier"> KNN classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT%20algorithm" title=" SIFT algorithm"> SIFT algorithm</a> </p> <a href="https://publications.waset.org/abstracts/7262/a-reliable-multi-type-vehicle-classification-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7262.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">358</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">397</span> Cardiokey: A Binary and Multi-Class Machine Learning Approach to Identify Individuals Using Electrocardiographic Signals on Wearable Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Chami">S. Chami</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Chauvin"> J. Chauvin</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Demarest"> T. Demarest</a>, <a href="https://publications.waset.org/abstracts/search?q=Stan%20Ng"> Stan Ng</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Straus"> M. Straus</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Jahner"> W. Jahner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiographic" title=" electrocardiographic"> electrocardiographic</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=signals%20processing" title=" signals processing"> signals processing</a> </p> <a href="https://publications.waset.org/abstracts/114879/cardiokey-a-binary-and-multi-class-machine-learning-approach-to-identify-individuals-using-electrocardiographic-signals-on-wearable-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">396</span> Bundle Block Detection Using Spectral Coherence and Levenberg Marquardt Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Padmavathi">K. Padmavathi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Sri%20Ramakrishna"> K. Sri Ramakrishna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study describes a procedure for the detection of Left and Right Bundle Branch Block (LBBB and RBBB) ECG patterns using spectral Coherence(SC) technique and LM Neural Network. The Coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. The QT variations of Bundle Blocks are observed in lead V1 of ECG. Spectral Coherence technique uses Welch method for calculating PSD. For the detection of normal and Bundle block beats, SC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 99.5 percent. The data was collected from MIT-BIH Arrhythmia database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bundle%20block" title="bundle block">bundle block</a>, <a href="https://publications.waset.org/abstracts/search?q=SC" title=" SC"> SC</a>, <a href="https://publications.waset.org/abstracts/search?q=LMNN%20classifier" title=" LMNN classifier"> LMNN classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=welch%20method" title=" welch method"> welch method</a>, <a href="https://publications.waset.org/abstracts/search?q=PSD" title=" PSD"> PSD</a>, <a href="https://publications.waset.org/abstracts/search?q=MIT-BIH" title=" MIT-BIH"> MIT-BIH</a>, <a href="https://publications.waset.org/abstracts/search?q=arrhythmia%20database" title=" arrhythmia database"> arrhythmia database</a> </p> <a href="https://publications.waset.org/abstracts/17530/bundle-block-detection-using-spectral-coherence-and-levenberg-marquardt-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17530.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">281</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">395</span> Sentiment Analysis of Ensemble-Based Classifiers for E-Mail Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muthukumarasamy%20Govindarajan">Muthukumarasamy Govindarajan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=arcing" title=" arcing"> arcing</a>, <a href="https://publications.waset.org/abstracts/search?q=bagging" title=" bagging"> bagging</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</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=sentiment%20mining" title=" sentiment mining"> sentiment mining</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/112240/sentiment-analysis-of-ensemble-based-classifiers-for-e-mail-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112240.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">394</span> Variable Tree Structure QR Decomposition-M Algorithm (QRD-M) in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Hyun%20Ro">Jae-Hyun Ro</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong-Kwang%20Kim"> Jong-Kwang Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Hee%20Kang"> Chang-Hee Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyoung-Kyu%20Song"> Hyoung-Kyu Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, QR decomposition-M algorithm (QRD-M) has suboptimal error performance. However, the QRD-M has still high complexity due to many calculations at each layer in tree structure. To reduce the complexity of the QRD-M, proposed QRD-M modifies existing tree structure by eliminating unnecessary candidates at almost whole layers. The method of the elimination is discarding the candidates which have accumulated squared Euclidean distances larger than calculated threshold. The simulation results show that the proposed QRD-M has same bit error rate (BER) performance with lower complexity than the conventional QRD-M. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complexity" title="complexity">complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=MIMO-OFDM" title=" MIMO-OFDM"> MIMO-OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=QRD-M" title=" QRD-M"> QRD-M</a>, <a href="https://publications.waset.org/abstracts/search?q=squared%20Euclidean%20distance" title=" squared Euclidean distance"> squared Euclidean distance</a> </p> <a href="https://publications.waset.org/abstracts/52299/variable-tree-structure-qr-decomposition-m-algorithm-qrd-m-in-multiple-input-multiple-output-orthogonal-frequency-division-multiplexing-mimo-ofdm-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52299.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">393</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> <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=euclidean%20classifier&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=euclidean%20classifier&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=euclidean%20classifier&page=4">4</a></li> <li class="page-item"><a class="page-link" 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