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least squares algorithm</title> <meta name="description" content="Search results for: weighted least squares algorithm"> <meta name="keywords" content="weighted least squares algorithm"> <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" /> 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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="weighted least squares algorithm"> <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> 4360</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: weighted least squares algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4360</span> Genetic Algorithm to Construct and Enumerate 4脳4 Pan-Magic Squares</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younis%20R.%20Elhaddad">Younis R. Elhaddad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20Alshaari"> Mohamed A. Alshaari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since 2700 B.C the problem of constructing magic squares attracts many researchers. Magic squares one of most difficult challenges for mathematicians. In this work, we describe how to construct and enumerate Pan- magic squares using genetic algorithm, using new chromosome encoding technique. The results were promising within reasonable time. <p class="card-text"><strong>Keywords:</strong> <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=magic%20square" title=" magic square"> magic square</a>, <a href="https://publications.waset.org/abstracts/search?q=pan-magic%20square" title=" pan-magic square"> pan-magic square</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20intelligence" title=" computational intelligence"> computational intelligence</a> </p> <a href="https://publications.waset.org/abstracts/2917/genetic-algorithm-to-construct-and-enumerate-44-pan-magic-squares" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2917.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">576</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">4359</span> Variogram Fitting Based on the Wilcoxon Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20Al-Mofleh">Hazem Al-Mofleh</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Daniels"> John Daniels</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20McKean"> Joseph McKean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-linear%20wilcoxon" title="non-linear wilcoxon">non-linear wilcoxon</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimation" title=" robust estimation"> robust estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=variogram%20estimation" title=" variogram estimation"> variogram estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=wilcoxon%20norm" title=" wilcoxon norm"> wilcoxon norm</a> </p> <a href="https://publications.waset.org/abstracts/50377/variogram-fitting-based-on-the-wilcoxon-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50377.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">458</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">4358</span> Quadrature Mirror Filter Bank Design Using Population Based Stochastic Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ju-Hong%20Lee">Ju-Hong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ding-Chen%20Chung"> Ding-Chen Chung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper deals with the optimal design of two-channel linear-phase (LP) quadrature mirror filter (QMF) banks using a metaheuristic based optimization technique. Based on the theory of two-channel QMF banks using two recursive digital all-pass filters (DAFs), the design problem is appropriately formulated to result in an objective function which is a weighted sum of the group delay error of the designed QMF bank and the magnitude response error of the designed low-pass analysis filter. Through a frequency sampling and a weighted least squares approach, the optimization problem of the objective function can be solved by utilizing a particle swarm optimization algorithm. The resulting two-channel QMF banks can possess approximately LP response without magnitude distortion. Simulation results are presented for illustration and comparison. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quadrature%20mirror%20filter%20bank" title="quadrature mirror filter bank">quadrature mirror filter bank</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20all-pass%20filter" title=" digital all-pass filter"> digital all-pass filter</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20least%20squares%20algorithm" title=" weighted least squares algorithm"> weighted least squares algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/6051/quadrature-mirror-filter-bank-design-using-population-based-stochastic-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6051.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">521</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">4357</span> Recursive Doubly Complementary Filter Design Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ju-Hong%20Lee">Ju-Hong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ding-Chen%20Chung"> Ding-Chen Chung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the optimal design of recursive doubly complementary (DC) digital filter design using a metaheuristic based optimization technique. Based on the theory of DC digital filters using two recursive digital all-pass filters (DAFs), the design problem is appropriately formulated to result in an objective function which is a weighted sum of the phase response errors of the designed DAFs. To deal with the stability of the recursive DC filters during the design process, we can either impose some necessary constraints on the phases of the recursive DAFs. Through a frequency sampling and a weighted least squares approach, the optimization problem of the objective function can be solved by utilizing a population based stochastic optimization approach. The resulting DC digital filters can possess satisfactory frequency response. Simulation results are presented for illustration and comparison. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=doubly%20complementary" title="doubly complementary">doubly complementary</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20all-pass%20filter" title=" digital all-pass filter"> digital all-pass filter</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20least%20squares%20algorithm" title=" weighted least squares algorithm"> weighted least squares algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/12469/recursive-doubly-complementary-filter-design-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12469.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">688</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">4356</span> Support Vector Regression with Weighted Least Absolute Deviations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=least%20absolute%20deviation" title="least absolute deviation">least absolute deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20programming" title=" quadratic programming"> quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=weight" title=" weight"> weight</a> </p> <a href="https://publications.waset.org/abstracts/23674/support-vector-regression-with-weighted-least-absolute-deviations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23674.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">527</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">4355</span> Hybrid Artificial Bee Colony and Least Squares Method for Rule-Based Systems Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahcene%20Habbi">Ahcene Habbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Boudouaoui"> Yassine Boudouaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses global optimization to learn fuzzy models, the second one hybridizes ABC and weighted least squares estimate method. The performances of the proposed ABC and ABC-LS fuzzy modeling strategies are evaluated on complex modeling problems and compared to other advanced modeling methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20design" title="automatic design">automatic design</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid" title=" hybrid"> hybrid</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20optimization" title=" swarm optimization"> swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/15603/hybrid-artificial-bee-colony-and-least-squares-method-for-rule-based-systems-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15603.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4354</span> A Ratio-Weighted Decision Tree Algorithm for Imbalance Dataset Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doyin%20Afolabi">Doyin Afolabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Adewole"> Phillip Adewole</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladipupo%20Sennaike"> Oladipupo Sennaike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20dataset" title=" imbalance dataset"> imbalance dataset</a> </p> <a href="https://publications.waset.org/abstracts/157609/a-ratio-weighted-decision-tree-algorithm-for-imbalance-dataset-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157609.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4353</span> Enhanced Weighted Centroid Localization Algorithm for Indoor Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Ni%C5%BEeti%C4%87%20Kosovi%C4%87">I. Ni啪eti膰 Kosovi膰</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Jagu%C5%A1t"> T. Jagu拧t</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lately, with the increasing number of location-based applications, demand for highly accurate and reliable indoor localization became urgent. This is a challenging problem, due to the measurement variance which is the consequence of various factors like obstacles, equipment properties and environmental changes in complex nature of indoor environments. In this paper we propose low-cost custom-setup infrastructure solution and localization algorithm based on the Weighted Centroid Localization (WCL) method. Localization accuracy is increased by several enhancements: calibration of RSSI values gained from wireless nodes, repetitive measurements of RSSI to exclude deviating values from the position estimation, and by considering orientation of the device according to the wireless nodes. We conducted several experiments to evaluate the proposed algorithm. High accuracy of ~1m was achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indoor%20environment" title="indoor environment">indoor environment</a>, <a href="https://publications.waset.org/abstracts/search?q=received%20signal%20strength%20indicator" title=" received signal strength indicator"> received signal strength indicator</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20centroid%20localization" title=" weighted centroid localization"> weighted centroid localization</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20localization" title=" wireless localization"> wireless localization</a> </p> <a href="https://publications.waset.org/abstracts/11878/enhanced-weighted-centroid-localization-algorithm-for-indoor-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11878.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">232</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">4352</span> Sparse Signal Restoration Algorithm Based on Piecewise Adaptive Backtracking Orthogonal Least Squares</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linyu%20Wang">Linyu Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahui%20Ma"> Jiahui Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianhong%20Xiang"> Jianhong Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanyu%20Jiang"> Hanyu Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> the traditional greedy compressed sensing algorithm needs to know the signal sparsity when recovering the signal, but the signal sparsity in the practical application can not be obtained as a priori information, and the recovery accuracy is low, which does not meet the needs of practical application. To solve this problem, this paper puts forward Piecewise adaptive backtracking orthogonal least squares algorithm. The algorithm is divided into two stages. In the first stage, the sparsity pre-estimation strategy is adopted, which can quickly approach the real sparsity and reduce time consumption. In the second stage iteration, the correction strategy and adaptive step size are used to accurately estimate the sparsity, and the backtracking idea is introduced to improve the accuracy of signal recovery. Through experimental simulation, the algorithm can accurately recover the estimated signal with fewer iterations when the sparsity is unknown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20algorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20method" title=" least square method"> least square method</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20reconstruction" title=" adaptive reconstruction"> adaptive reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/161616/sparse-signal-restoration-algorithm-based-on-piecewise-adaptive-backtracking-orthogonal-least-squares" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161616.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">148</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">4351</span> Influence of Parameters of Modeling and Data Distribution for Optimal Condition on Locally Weighted Projection Regression Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi">Farhad Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Mollakazemi"> Mohammad Javad Mollakazemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aref%20Ghafouri"> Aref Ghafouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=local%20nonlinear%20estimation" title="local nonlinear estimation">local nonlinear estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=LWPR%20algorithm" title=" LWPR algorithm"> LWPR algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20training%20method" title=" online training method"> online training method</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20weighted%20projection%20regression%20method" title=" locally weighted projection regression method"> locally weighted projection regression method</a> </p> <a href="https://publications.waset.org/abstracts/14554/influence-of-parameters-of-modeling-and-data-distribution-for-optimal-condition-on-locally-weighted-projection-regression-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14554.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">502</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">4350</span> Orthogonal Regression for Nonparametric Estimation of Errors-In-Variables Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anastasiia%20Yu.%20Timofeeva">Anastasiia Yu. Timofeeva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grade%20point%20average" title="grade point average">grade point average</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20regression" title=" orthogonal regression"> orthogonal regression</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20regression%20spline" title=" penalized regression spline"> penalized regression spline</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20weighted%20regression" title=" locally weighted regression"> locally weighted regression</a> </p> <a href="https://publications.waset.org/abstracts/11927/orthogonal-regression-for-nonparametric-estimation-of-errors-in-variables-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11927.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">416</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">4349</span> Multi-Objective Variable Neighborhood Search Algorithm to Solving Scheduling Problem with Transportation Times</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Khalili">Majid Khalili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective variable neighborhood algorithm (MOVNS). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOVNS provides sound performance comparing with other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=no-wait%20hybrid%20flowshop%20scheduling%3B%20multi-objective%20variable%20neighborhood%20algorithm%3B%20makespan%3B%20total%20weighted%20tardiness" title="no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness">no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness</a> </p> <a href="https://publications.waset.org/abstracts/15098/multi-objective-variable-neighborhood-search-algorithm-to-solving-scheduling-problem-with-transportation-times" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15098.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">4348</span> Bag of Words Representation Based on Weighting Useful Visual Words</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Abdedayem">Fatma Abdedayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most effective and efficient methods in image categorization are almost based on bag-of-words (BOW) which presents image by a histogram of occurrence of visual words. In this paper, we propose a novel extension to this method. Firstly, we extract features in multi-scales by applying a color local descriptor named opponent-SIFT. Secondly, in order to represent image we use Spatial Pyramid Representation (SPR) and an extension to the BOW method which based on weighting visual words. Typically, the visual words are weighted during histogram assignment by computing the ratio of their occurrences in the image to the occurrences in the background. Finally, according to classical BOW retrieval framework, only a few words of the vocabulary is useful for image representation. Therefore, we select the useful weighted visual words that respect the threshold value. Experimentally, the algorithm is tested by using different image classes of PASCAL VOC 2007 and is compared against the classical bag-of-visual-words algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BOW" title="BOW">BOW</a>, <a href="https://publications.waset.org/abstracts/search?q=useful%20visual%20words" title=" useful visual words"> useful visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20visual%20words" title=" weighted visual words"> weighted visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20visual%20words" title=" bag of visual words"> bag of visual words</a> </p> <a href="https://publications.waset.org/abstracts/14009/bag-of-words-representation-based-on-weighting-useful-visual-words" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14009.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">436</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">4347</span> Notes on Frames in Weighted Hardy Spaces and Generalized Weighted Composition Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shams%20Alyusof">Shams Alyusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is to enrich the studies of the frames due to their prominent role in pure mathematics as well as in applied mathematics and many applications in computer science and engineering. Recently, there are remarkable studies of operators that preserve frames on some spaces, and this research could be considered as an extension of such studies. Indeed, this paper is to we characterize weighted composition operators that preserve frames in weighted Hardy spaces on the open unit disk. Moreover, it shows that this characterization does not apply to generalized weighted composition operators on such spaces. Nevertheless, this study could be extended to provide more specific characterizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frames" title="frames">frames</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20weighted%20composition%20operators" title=" generalized weighted composition operators"> generalized weighted composition operators</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20Hardy%20spaces" title=" weighted Hardy spaces"> weighted Hardy spaces</a>, <a href="https://publications.waset.org/abstracts/search?q=analytic%20functions" title=" analytic functions"> analytic functions</a> </p> <a href="https://publications.waset.org/abstracts/156372/notes-on-frames-in-weighted-hardy-spaces-and-generalized-weighted-composition-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156372.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">121</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">4346</span> Forecasting Unusual Infection of Patient Used by Irregular Weighted Point Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seema%20Vaidya">Seema Vaidya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=association%20rule" title="association rule">association rule</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=IWI%20mining" title=" IWI mining"> IWI mining</a>, <a href="https://publications.waset.org/abstracts/search?q=infrequent%20item%20set" title=" infrequent item set"> infrequent item set</a>, <a href="https://publications.waset.org/abstracts/search?q=frequent%20pattern%20growth" title=" frequent pattern growth"> frequent pattern growth</a> </p> <a href="https://publications.waset.org/abstracts/32862/forecasting-unusual-infection-of-patient-used-by-irregular-weighted-point-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32862.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4345</span> Some Results for F-Minimal Hypersurfaces in Manifolds with Density</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdelmalek">M. Abdelmalek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we study the hypersurfaces of constant weighted mean curvature embedded in weighted manifolds. We give a condition about these hypersurfaces to be minimal. This condition is given by the ellipticity of the weighted Newton transformations. We especially prove that two compact hypersurfaces of constant weighted mean curvature embedded in space forms and with the intersection in at least a point of the boundary must be transverse. The method is based on the calculus of the matrix of the second fundamental form in a boundary point and then the matrix associated with the Newton transformations. By equality, we find the weighted elementary symmetric function on the boundary of the hypersurface. We give in the end some examples and applications. Especially in Euclidean space, we use the above result to prove the Alexandrov spherical caps conjecture for the weighted case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weighted%20mean%20curvature" title="weighted mean curvature">weighted mean curvature</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20manifolds" title=" weighted manifolds"> weighted manifolds</a>, <a href="https://publications.waset.org/abstracts/search?q=ellipticity" title=" ellipticity"> ellipticity</a>, <a href="https://publications.waset.org/abstracts/search?q=Newton%20transformations" title=" Newton transformations"> Newton transformations</a> </p> <a href="https://publications.waset.org/abstracts/160174/some-results-for-f-minimal-hypersurfaces-in-manifolds-with-density" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160174.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4344</span> Adaptive Online Object Tracking via Positive and Negative Models Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaomei%20Li">Shaomei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yawen%20Wang"> Yawen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Gao"> Chao Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title="object tracking">object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking%20drift" title=" tracking drift"> tracking drift</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20least%20squares%20analysis" title=" partial least squares analysis"> partial least squares analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20and%20negative%20models%20matching" title=" positive and negative models matching"> positive and negative models matching</a> </p> <a href="https://publications.waset.org/abstracts/19382/adaptive-online-object-tracking-via-positive-and-negative-models-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19382.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">529</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">4343</span> A Nonlocal Means Algorithm for Poisson Denoising Based on Information Geometry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dongxu%20Chen">Dongxu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yipeng%20Li"> Yipeng Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an information geometry NonlocalMeans(NLM) algorithm for Poisson denoising. NLM estimates a noise-free pixel as a weighted average of image pixels, where each pixel is weighted according to the similarity between image patches in Euclidean space. In this work, every pixel is a Poisson distribution locally estimated by Maximum Likelihood (ML), all distributions consist of a statistical manifold. A NLM denoising algorithm is conducted on the statistical manifold where Fisher information matrix can be used for computing distribution geodesics referenced as the similarity between patches. This approach was demonstrated to be competitive with related state-of-the-art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title="image denoising">image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20noise" title=" Poisson noise"> Poisson noise</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal-means" title=" nonlocal-means"> nonlocal-means</a> </p> <a href="https://publications.waset.org/abstracts/51221/a-nonlocal-means-algorithm-for-poisson-denoising-based-on-information-geometry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51221.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">285</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">4342</span> Least Squares Method Identification of Corona Current-Voltage Characteristics and Electromagnetic Field in Electrostatic Precipitator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Nouri">H. Nouri</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20E.%20Achouri"> I. E. Achouri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Grimes"> A. Grimes</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ait%20Said"> H. Ait Said</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Aissou"> M. Aissou</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Zebboudj"> Y. Zebboudj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to analysis the behaviour of DC corona discharge in wire-to-plate electrostatic precipitators (ESP). Current-voltage curves are particularly analysed. Experimental results show that discharge current is strongly affected by the applied voltage. The proposed method of current identification is to use the method of least squares. Least squares problems that of into two categories: linear or ordinary least squares and non-linear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. A closed-form solution (or closed form expression) is any formula that can be evaluated in a finite number of standard operations. The non-linear problem has no closed-form solution and is usually solved by iterative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrostatic%20precipitator" title="electrostatic precipitator">electrostatic precipitator</a>, <a href="https://publications.waset.org/abstracts/search?q=current-voltage%20characteristics" title=" current-voltage characteristics"> current-voltage characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20method" title=" least squares method"> least squares method</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20field" title=" electric field"> electric field</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20field" title=" magnetic field"> magnetic field</a> </p> <a href="https://publications.waset.org/abstracts/39751/least-squares-method-identification-of-corona-current-voltage-characteristics-and-electromagnetic-field-in-electrostatic-precipitator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39751.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">431</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">4341</span> Integrating Process Planning, WMS Dispatching, and WPPW Weighted Due Date Assignment Using a Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Tar%C4%B1k%20Cakar"> Tar谋k Cakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Cil"> Ibrahim Cil</a>, <a href="https://publications.waset.org/abstracts/search?q=Muharrem%20Dugenci"> Muharrem Dugenci</a>, <a href="https://publications.waset.org/abstracts/search?q=Caner%20Erden"> Caner Erden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conventionally, process planning, scheduling, and due-date assignment functions are performed separately and sequentially. The interdependence of these functions requires integration. Although integrated process planning and scheduling, and scheduling with due date assignment problems are popular research topics, only a few works address the integration of these three functions. This work focuses on the integration of process planning, WMS scheduling, and WPPW due date assignment. Another novelty of this work is the use of a weighted due date assignment. In the literature, due dates are generally assigned without considering the importance of customers. However, in this study, more important customers get closer due dates. Typically, only tardiness is punished, but the JIT philosophy punishes both earliness and tardiness. In this study, all weighted earliness, tardiness, and due date related costs are penalized. As no customer desires distant due dates, such distant due dates should be penalized. In this study, various levels of integration of these three functions are tested and genetic search and random search are compared both with each other and with ordinary solutions. Higher integration levels are superior, while search is always useful. Genetic searches outperformed random searches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</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=random%20search" title=" random search"> random search</a> </p> <a href="https://publications.waset.org/abstracts/51544/integrating-process-planning-wms-dispatching-and-wppw-weighted-due-date-assignment-using-a-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51544.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">394</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">4340</span> Sparse Principal Component Analysis: A Least Squares Approximation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Merola">Giovanni Merola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse Principal Components Analysis aims to find principal components with few non-zero loadings. We derive such sparse solutions by adding a genuine sparsity requirement to the original Principal Components Analysis (PCA) objective function. This approach differs from others because it preserves PCA's original optimality: uncorrelatedness of the components and least squares approximation of the data. To identify the best subset of non-zero loadings we propose a branch-and-bound search and an iterative elimination algorithm. This last algorithm finds sparse solutions with large loadings and can be run without specifying the cardinality of the loadings and the number of components to compute in advance. We give thorough comparisons with the existing sparse PCA methods and several examples on real datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SPCA" title="SPCA">SPCA</a>, <a href="https://publications.waset.org/abstracts/search?q=uncorrelated%20components" title=" uncorrelated components"> uncorrelated components</a>, <a href="https://publications.waset.org/abstracts/search?q=branch-and-bound" title=" branch-and-bound"> branch-and-bound</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20elimination" title=" backward elimination"> backward elimination</a> </p> <a href="https://publications.waset.org/abstracts/14630/sparse-principal-component-analysis-a-least-squares-approximation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14630.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4339</span> CompPSA: A Component-Based Pairwise RNA Secondary Structure Alignment Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Badr">Ghada Badr</a>, <a href="https://publications.waset.org/abstracts/search?q=Arwa%20Alturki"> Arwa Alturki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The biological function of an RNA molecule depends on its structure. The objective of the alignment is finding the homology between two or more RNA secondary structures. Knowing the common functionalities between two RNA structures allows a better understanding and a discovery of other relationships between them. Besides, identifying non-coding RNAs -that is not translated into a protein- is a popular application in which RNA structural alignment is the first step A few methods for RNA structure-to-structure alignment have been developed. Most of these methods are partial structure-to-structure, sequence-to-structure, or structure-to-sequence alignment. Less attention is given in the literature to the use of efficient RNA structure representation and the structure-to-structure alignment methods are lacking. In this paper, we introduce an O(N2) Component-based Pairwise RNA Structure Alignment (CompPSA) algorithm, where structures are given as a component-based representation and where N is the maximum number of components in the two structures. The proposed algorithm compares the two RNA secondary structures based on their weighted component features rather than on their base-pair details. Extensive experiments are conducted illustrating the efficiency of the CompPSA algorithm when compared to other approaches and on different real and simulated datasets. The CompPSA algorithm shows an accurate similarity measure between components. The algorithm gives the flexibility for the user to align the two RNA structures based on their weighted features (position, full length, and/or stem length). Moreover, the algorithm proves scalability and efficiency in time and memory performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alignment" title="alignment">alignment</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA%20secondary%20structure" title=" RNA secondary structure"> RNA secondary structure</a>, <a href="https://publications.waset.org/abstracts/search?q=pairwise" title=" pairwise"> pairwise</a>, <a href="https://publications.waset.org/abstracts/search?q=component-based" title=" component-based"> component-based</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/70264/comppsa-a-component-based-pairwise-rna-secondary-structure-alignment-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70264.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">458</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">4338</span> A Weighted K-Medoids Clustering Algorithm for Effective Stability in Vehicular Ad Hoc Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rejab%20Hajlaoui">Rejab Hajlaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Moulahi"> Tarek Moulahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Herv%C3%A9%20Guyennet"> Herv茅 Guyennet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a highway scenario, the vehicle speed can exceed 120 kmph. Therefore, any vehicle can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To ensure an effective stability in vehicular ad hoc networks with minimum broadcasting storm, we have developed a weighted algorithm based on the k-medoids clustering algorithm (WKCA). Indeed, the number of clusters and the initial cluster heads will not be selected randomly as usual, but considering the available transmission range and the environment size. Then, to ensure optimal assignment of nodes to clusters in both k-medoids phases, the combined weight of any node will be computed according to additional metrics including direction, relative speed and proximity. Empirical results prove that in addition to the convergence speed that characterizes the k-medoids algorithm, our proposed model performs well both AODV-Clustering and OLSR-Clustering protocols under different densities and velocities in term of end-to-end delay, packet delivery ratio, and throughput. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication" title="communication">communication</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithm" title=" clustering algorithm"> clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=k-medoids" title=" k-medoids"> k-medoids</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor" title=" sensor"> sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicular%20ad%20hoc%20network" title=" vehicular ad hoc network"> vehicular ad hoc network</a> </p> <a href="https://publications.waset.org/abstracts/69086/a-weighted-k-medoids-clustering-algorithm-for-effective-stability-in-vehicular-ad-hoc-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69086.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">238</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">4337</span> Adaptive Anchor Weighting for Improved Localization with Levenberg-Marquardt Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Basak%20Can">Basak Can</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces an iterative and weighted localization method that utilizes a unique cost function formulation to significantly enhance the performance of positioning systems. The system employs locators, such as Gateways (GWs), to estimate and track the position of an End Node (EN). Performance is evaluated relative to the number of locators, with known locations determined through calibration. Performance evaluation is presented utilizing low cost single-antenna Bluetooth Low Energy (BLE) devices. The proposed approach can be applied to alternative Internet of Things (IoT) modulation schemes, as well as Ultra WideBand (UWB) or millimeter-wave (mmWave) based devices. In non-line-of-sight (NLOS) scenarios, using four or eight locators yields a 95th percentile localization performance of 2.2 meters and 1.5 meters, respectively, in a 4,305 square feet indoor area with BLE 5.1 devices. This method outperforms conventional RSSI-based techniques, achieving a 51% improvement with four locators and a 52 % improvement with eight locators. Future work involves modeling interference impact and implementing data curation across multiple channels to mitigate such effects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lateration" title="lateration">lateration</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares" title=" least squares"> least squares</a>, <a href="https://publications.waset.org/abstracts/search?q=Levenberg-Marquardt%20algorithm" title=" Levenberg-Marquardt algorithm"> Levenberg-Marquardt algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=path-loss" title=" path-loss"> path-loss</a>, <a href="https://publications.waset.org/abstracts/search?q=RMS%20error" title=" RMS error"> RMS error</a>, <a href="https://publications.waset.org/abstracts/search?q=RSSI" title=" RSSI"> RSSI</a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=shadow%20fading" title=" shadow fading"> shadow fading</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20localization" title=" weighted localization"> weighted localization</a> </p> <a href="https://publications.waset.org/abstracts/190165/adaptive-anchor-weighting-for-improved-localization-with-levenberg-marquardt-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190165.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">25</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">4336</span> Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soha%20A.%20Bahanshal">Soha A. Bahanshal</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20G.%20Kim"> Byung G. Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor. <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=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20fuzzy%20weighted%20k-nearest%20neighbor" title=" hybrid fuzzy weighted k-nearest neighbor"> hybrid fuzzy weighted k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetic%20hospital%20readmission" title=" diabetic hospital readmission"> diabetic hospital readmission</a> </p> <a href="https://publications.waset.org/abstracts/129397/hybrid-fuzzy-weighted-k-nearest-neighbor-to-predict-hospital-readmission-for-diabetic-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129397.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4335</span> A Fuzzy Nonlinear Regression Model for Interval Type-2 Fuzzy Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Poleshchuk">O. Poleshchuk</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Komarov"> E. Komarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20type-2%20fuzzy%20sets" title="interval type-2 fuzzy sets">interval type-2 fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression" title=" fuzzy regression"> fuzzy regression</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20interval" title=" weighted interval"> weighted interval</a> </p> <a href="https://publications.waset.org/abstracts/6138/a-fuzzy-nonlinear-regression-model-for-interval-type-2-fuzzy-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6138.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">373</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">4334</span> Detection Method of Federated Learning Backdoor Based on Weighted K-Medoids</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xun%20Li">Xun Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Haojie%20Wang"> Haojie Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=federated%20learning" title="federated learning">federated learning</a>, <a href="https://publications.waset.org/abstracts/search?q=backdoor%20attack" title=" backdoor attack"> backdoor attack</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=k-medoids" title=" k-medoids"> k-medoids</a>, <a href="https://publications.waset.org/abstracts/search?q=backdoor%20defense" title=" backdoor defense"> backdoor defense</a> </p> <a href="https://publications.waset.org/abstracts/168344/detection-method-of-federated-learning-backdoor-based-on-weighted-k-medoids" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168344.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">114</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">4333</span> Solving Process Planning, Weighted Apparent Tardiness Cost Dispatching, and Weighted Processing plus Weight Due-Date Assignment Simultaneously Using a Hybrid Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Caner%20Erden"> Caner Erden</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Hulusi%20Kokcam"> Abdullah Hulusi Kokcam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mumtaz%20Ipek"> Mumtaz Ipek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process planning, scheduling, and due date assignment are three important manufacturing functions which are studied independently in literature. There are hundreds of works on IPPS and SWDDA problems but a few works on IPPSDDA problem. Integrating these three functions is very crucial due to the high relationship between them. Since the scheduling problem is in the NP-Hard problem class without any integration, an integrated problem is even harder to solve. This study focuses on the integration of these functions. Sum of weighted tardiness, earliness, and due date related costs are used as a penalty function. Random search and hybrid metaheuristics are used to solve the integrated problem. Marginal improvement in random search is very high in the early iterations and reduces enormously in later iterations. At that point directed search contribute to marginal improvement more than random search. In this study, random and genetic search methods are combined to find better solutions. Results show that overall performance becomes better as the integration level increases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</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=hybrid%20search" title=" hybrid search"> hybrid search</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20search" title=" random search"> random search</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a> </p> <a href="https://publications.waset.org/abstracts/68613/solving-process-planning-weighted-apparent-tardiness-cost-dispatching-and-weighted-processing-plus-weight-due-date-assignment-simultaneously-using-a-hybrid-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4332</span> Mobile Platform鈥檚 Attitude Determination Based on Smoothed GPS Code Data and Carrier-Phase Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ramdani">Mohamed Ramdani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassen%20Abdellaoui"> Hassen Abdellaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdenour%20Boudrassen"> Abdenour Boudrassen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile platform鈥檚 attitude estimation approaches mainly based on combined positioning techniques and developed algorithms; which aim to reach a fast and accurate solution. In this work, we describe the design and the implementation of an attitude determination (AD) process, using only measurements from GPS sensors. The major issue is based on smoothed GPS code data using Hatch filter and raw carrier-phase measurements integrated into attitude algorithm based on vectors measurement using least squares (LSQ) estimation method. GPS dataset from a static experiment is used to investigate the effectiveness of the presented approach and consequently to check the accuracy of the attitude estimation algorithm. Attitude results from GPS multi-antenna over short baselines are introduced and analyzed. The 3D accuracy of estimated attitude parameters using smoothed measurements is over 0.27掳. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attitude%20determination" title="attitude determination">attitude determination</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%20code%20data%20smoothing" title=" GPS code data smoothing"> GPS code data smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=hatch%20filter" title=" hatch filter"> hatch filter</a>, <a href="https://publications.waset.org/abstracts/search?q=carrier-phase%20measurements" title=" carrier-phase measurements"> carrier-phase measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=least-squares%20attitude%20estimation" title=" least-squares attitude estimation"> least-squares attitude estimation</a> </p> <a href="https://publications.waset.org/abstracts/108141/mobile-platforms-attitude-determination-based-on-smoothed-gps-code-data-and-carrier-phase-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108141.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">155</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">4331</span> Using Scale Invariant Feature Transform Features to Recognize Characters in Natural Scene Images </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belaynesh%20Chekol">Belaynesh Chekol</a>, <a href="https://publications.waset.org/abstracts/search?q=Numan%20%C3%87elebi"> Numan 脟elebi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN" title=" KNN"> KNN</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20scene%20image" title=" natural scene image"> natural scene image</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a> </p> <a href="https://publications.waset.org/abstracts/58580/using-scale-invariant-feature-transform-features-to-recognize-characters-in-natural-scene-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58580.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 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