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algorithms</title> <meta name="description" content="Search results for: estimation algorithms"> <meta name="keywords" content="estimation algorithms"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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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="estimation algorithms"> <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> 3836</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: estimation algorithms</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3836</span> The Modelling of Real Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Valeria%20Bondarenko">Valeria Bondarenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20model" title="mathematical model">mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20process" title=" random process"> random process</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiener%20process" title=" Wiener process"> Wiener process</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20Brownian%20motion" title=" fractional Brownian motion"> fractional Brownian motion</a> </p> <a href="https://publications.waset.org/abstracts/49210/the-modelling-of-real-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49210.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">3835</span> State Estimation of a Biotechnological Process Using Extended Kalman Filter and Particle Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Simutis">R. Simutis</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Galvanauskas"> V. Galvanauskas</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Levisauskas"> D. Levisauskas</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Repsyte"> J. Repsyte</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Grincas"> V. Grincas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn鈥檛 require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomass%20concentration" title="biomass concentration">biomass concentration</a>, <a href="https://publications.waset.org/abstracts/search?q=extended%20Kalman%20filter" title=" extended Kalman filter"> extended Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=specific%20growth%20rate" title=" specific growth rate"> specific growth rate</a> </p> <a href="https://publications.waset.org/abstracts/12940/state-estimation-of-a-biotechnological-process-using-extended-kalman-filter-and-particle-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12940.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">430</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">3834</span> Combined Localization, Beamforming, and Interference Threshold Estimation in Underlay Cognitive System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Nasr">Omar Nasr</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasser%20Naguib"> Yasser Naguib</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Hafez"> Mohamed Hafez </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims at providing an innovative solution for blind interference threshold estimation in an underlay cognitive network to be used in adaptive beamforming by secondary user Transmitter and Receiver. For the task of threshold estimation, blind detection of modulation and SNR are used. For the sake of beamforming several localization algorithms are compared to settle on best one for cognitive environment. Beamforming algorithms as LCMV (Linear Constraint Minimum Variance) and MVDR (Minimum Variance Distortion less) are also proposed and compared. The idea of just nulling the primary user after knowledge of its location is discussed against the idea of working under interference threshold. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=underlay" title=" underlay"> underlay</a>, <a href="https://publications.waset.org/abstracts/search?q=beamforming" title=" beamforming"> beamforming</a>, <a href="https://publications.waset.org/abstracts/search?q=MUSIC" title=" MUSIC"> MUSIC</a>, <a href="https://publications.waset.org/abstracts/search?q=MVDR" title=" MVDR"> MVDR</a>, <a href="https://publications.waset.org/abstracts/search?q=LCMV" title=" LCMV"> LCMV</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20estimation" title=" threshold estimation"> threshold estimation</a> </p> <a href="https://publications.waset.org/abstracts/17541/combined-localization-beamforming-and-interference-threshold-estimation-in-underlay-cognitive-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17541.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">582</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">3833</span> Parameters Estimation of Multidimensional Possibility Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Sorokin">Sergey Sorokin</a>, <a href="https://publications.waset.org/abstracts/search?q=Irina%20Sorokina"> Irina Sorokina</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Yazenin"> Alexander Yazenin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=possibility%20distribution" title="possibility distribution">possibility distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=parameters%20estimation" title=" parameters estimation"> parameters estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxmin%20u%5CE%20estimator" title=" Maxmin u\E estimator"> Maxmin u\E estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20model%20identification" title=" fuzzy model identification"> fuzzy model identification</a> </p> <a href="https://publications.waset.org/abstracts/16751/parameters-estimation-of-multidimensional-possibility-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16751.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">470</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">3832</span> An Enhanced Floor Estimation Algorithm for Indoor Wireless Localization Systems Using Confidence Interval Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kriangkrai%20Maneerat">Kriangkrai Maneerat</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutima%20Prommak"> Chutima Prommak </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Indoor wireless localization systems have played an important role to enhance context-aware services. Determining the position of mobile objects in complex indoor environments, such as those in multi-floor buildings, is very challenging problems. This paper presents an effective floor estimation algorithm, which can accurately determine the floor where mobile objects located. The proposed algorithm is based on the confidence interval of the summation of online Received Signal Strength (RSS) obtained from the IEEE 802.15.4 Wireless Sensor Networks (WSN). We compare the performance of the proposed algorithm with those of other floor estimation algorithms in literature by conducting a real implementation of WSN in our facility. The experimental results and analysis showed that the proposed floor estimation algorithm outperformed the other algorithms and provided highest percentage of floor accuracy up to 100% with 95-percent confidence interval. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=floor%20estimation%20algorithm" title="floor estimation algorithm">floor estimation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=floor%20determination" title=" floor determination"> floor determination</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-floor%20building" title=" multi-floor building"> multi-floor building</a>, <a href="https://publications.waset.org/abstracts/search?q=indoor%20wireless%20systems" title=" indoor wireless systems"> indoor wireless systems</a> </p> <a href="https://publications.waset.org/abstracts/9832/an-enhanced-floor-estimation-algorithm-for-indoor-wireless-localization-systems-using-confidence-interval-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9832.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">3831</span> Evaluation of Dual Polarization Rainfall Estimation Algorithm Applicability in Korea: A Case Study on Biseulsan Radar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chulsang%20Yoo">Chulsang Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Gildo%20Kim"> Gildo Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, unsuitable data. The results are as follows: (1) The radar rain rate equation including KDP, was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied. Acknowledgments: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2011012). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CSU-HIDRO%20algorithm" title="CSU-HIDRO algorithm">CSU-HIDRO algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20polarization%20radar" title=" dual polarization radar"> dual polarization radar</a>, <a href="https://publications.waset.org/abstracts/search?q=JPOLE%20algorithm" title=" JPOLE algorithm"> JPOLE algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20rainfall%20estimation%20algorithm" title=" radar rainfall estimation algorithm"> radar rainfall estimation algorithm</a> </p> <a href="https://publications.waset.org/abstracts/46874/evaluation-of-dual-polarization-rainfall-estimation-algorithm-applicability-in-korea-a-case-study-on-biseulsan-radar" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46874.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">214</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">3830</span> Blind Super-Resolution Reconstruction Based on PSF Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Osama%20A.%20Omer">Osama A. Omer</a>, <a href="https://publications.waset.org/abstracts/search?q=Amal%20Hamed"> Amal Hamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Successful blind image Super-Resolution algorithms require the exact estimation of the Point Spread Function (PSF). In the absence of any prior information about the imagery system and the true image; this estimation is normally done by trial and error experimentation until an acceptable restored image quality is obtained. Multi-frame blind Super-Resolution algorithms often have disadvantages of slow convergence and sensitiveness to complex noises. This paper presents a Super-Resolution image reconstruction algorithm based on estimation of the PSF that yields the optimum restored image quality. The estimation of PSF is performed by the knife-edge method and it is implemented by measuring spreading of the edges in the reproduced HR image itself during the reconstruction process. The proposed image reconstruction approach is using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. A series of experiment results show that the proposed method can outperform other previous work robustly and efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind" title="blind">blind</a>, <a href="https://publications.waset.org/abstracts/search?q=PSF" title=" PSF"> PSF</a>, <a href="https://publications.waset.org/abstracts/search?q=super-resolution" title=" super-resolution"> super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=knife-edge" title=" knife-edge"> knife-edge</a>, <a href="https://publications.waset.org/abstracts/search?q=blurring" title=" blurring"> blurring</a>, <a href="https://publications.waset.org/abstracts/search?q=bilateral" title=" bilateral"> bilateral</a>, <a href="https://publications.waset.org/abstracts/search?q=L1%20norm" title=" L1 norm"> L1 norm</a> </p> <a href="https://publications.waset.org/abstracts/1385/blind-super-resolution-reconstruction-based-on-psf-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1385.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3829</span> A Bayesian Multivariate Microeconometric Model for Estimation of Price Elasticity of Demand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jefferson%20Hernandez">Jefferson Hernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20Padilla"> Juan Padilla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=price%20elasticity" title="price elasticity">price elasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=volume" title=" volume"> volume</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20structures" title=" correlation structures"> correlation structures</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20models" title=" Bayesian models"> Bayesian models</a> </p> <a href="https://publications.waset.org/abstracts/122666/a-bayesian-multivariate-microeconometric-model-for-estimation-of-price-elasticity-of-demand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3828</span> Considering the Reliability of Measurements Issue in Distributed Adaptive Estimation Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wael%20M.%20Bazzi">Wael M. Bazzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Rastegarnia"> Amir Rastegarnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Azam%20Khalili"> Azam Khalili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filter" title="adaptive filter">adaptive filter</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20estimation" title=" distributed estimation"> distributed estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%0D%0Anetwork" title=" sensor network"> sensor network</a>, <a href="https://publications.waset.org/abstracts/search?q=IDLMS%20algorithm" title=" IDLMS algorithm"> IDLMS algorithm</a> </p> <a href="https://publications.waset.org/abstracts/27648/considering-the-reliability-of-measurements-issue-in-distributed-adaptive-estimation-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27648.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">634</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">3827</span> Agile Software Effort Estimation Using Regression Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mikiyas%20Adugna">Mikiyas Adugna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effort estimation is among the activities carried out in software development processes. An accurate model of estimation leads to project success. The method of agile effort estimation is a complex task because of the dynamic nature of software development. Researchers are still conducting studies on agile effort estimation to enhance prediction accuracy. Due to these reasons, we investigated and proposed a model on LASSO and Elastic Net regression to enhance estimation accuracy. The proposed model has major components: preprocessing, train-test split, training with default parameters, and cross-validation. During the preprocessing phase, the entire dataset is normalized. After normalization, a train-test split is performed on the dataset, setting training at 80% and testing set to 20%. We chose two different phases for training the two algorithms (Elastic Net and LASSO) regression following the train-test-split. In the first phase, the two algorithms are trained using their default parameters and evaluated on the testing data. In the second phase, the grid search technique (the grid is used to search for tuning and select optimum parameters) and 5-fold cross-validation to get the final trained model. Finally, the final trained model is evaluated using the testing set. The experimental work is applied to the agile story point dataset of 21 software projects collected from six firms. The results show that both Elastic Net and LASSO regression outperformed the compared ones. Compared to the proposed algorithms, LASSO regression achieved better predictive performance and has acquired PRED (8%) and PRED (25%) results of 100.0, MMRE of 0.0491, MMER of 0.0551, MdMRE of 0.0593, MdMER of 0.063, and MSE of 0.0007. The result implies LASSO regression algorithm trained model is the most acceptable, and higher estimation performance exists in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agile%20software%20development" title="agile software development">agile software development</a>, <a href="https://publications.waset.org/abstracts/search?q=effort%20estimation" title=" effort estimation"> effort estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=elastic%20net%20regression" title=" elastic net regression"> elastic net regression</a>, <a href="https://publications.waset.org/abstracts/search?q=LASSO" title=" LASSO"> LASSO</a> </p> <a href="https://publications.waset.org/abstracts/182726/agile-software-effort-estimation-using-regression-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182726.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">71</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">3826</span> Factors Influencing Soil Organic Carbon Storage Estimation in Agricultural Soils: A Machine Learning Approach Using Remote Sensing Data Integration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Sunantha">O. Sunantha</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Zhenfeng"> S. Zhenfeng</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Phattraporn"> S. Phattraporn</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Zeeshan"> A. Zeeshan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The decline of soil organic carbon (SOC) in global agriculture is a critical issue requiring rapid and accurate estimation for informed policymaking. While it is recognized that SOC predictors vary significantly when derived from remote sensing data and environmental variables, identifying the specific parameters most suitable for accurately estimating SOC in diverse agricultural areas remains a challenge. This study utilizes remote sensing data to precisely estimate SOC and identify influential factors in diverse agricultural areas, such as paddy, corn, sugarcane, cassava, and perennial crops. Extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR) models are employed to analyze these factors' impact on SOC estimation. The results show key factors influencing SOC estimation include slope, vegetation indices (EVI), spectral reflectance indices (red index, red edge2), temperature, land use, and surface soil moisture, as indicated by their averaged importance scores across XGBoost, RF, and SVR models. Therefore, using different machine learning algorithms for SOC estimation reveals varying influential factors from remote sensing data and environmental variables. This approach emphasizes feature selection, as different machine learning algorithms identify various key factors from remote sensing data and environmental variables for accurate SOC estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=factors%20influencing%20SOC%20estimation" title="factors influencing SOC estimation">factors influencing SOC estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing%20data" title=" remote sensing data"> remote sensing data</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20variables" title=" environmental variables"> environmental variables</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/188382/factors-influencing-soil-organic-carbon-storage-estimation-in-agricultural-soils-a-machine-learning-approach-using-remote-sensing-data-integration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188382.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">35</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">3825</span> Channel Estimation for LTE Downlink</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashi%20Jain">Rashi Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The LTE systems employ Orthogonal Frequency Division Multiplexing (OFDM) as the multiple access technology for the Downlink channels. For enhanced performance, accurate channel estimation is required. Various algorithms such as Least Squares (LS), Minimum Mean Square Error (MMSE) and Recursive Least Squares (RLS) can be employed for the purpose. The paper proposes channel estimation algorithm based on Kalman Filter for LTE-Downlink system. Using the frequency domain pilots, the initial channel response is obtained using the LS criterion. Then Kalman Filter is employed to track the channel variations in time-domain. To suppress the noise within a symbol, threshold processing is employed. The paper draws comparison between the LS, MMSE, RLS and Kalman filter for channel estimation. The parameters for evaluation are Bit Error Rate (BER), Mean Square Error (MSE) and run-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LTE" title="LTE">LTE</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title=" channel estimation"> channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=RLS" title=" RLS"> RLS</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold" title=" threshold"> threshold</a> </p> <a href="https://publications.waset.org/abstracts/9169/channel-estimation-for-lte-downlink" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9169.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">356</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">3824</span> An Improved Data Aided Channel Estimation Technique Using Genetic Algorithm for Massive Multi-Input Multiple-Output</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kislu%20Noman">M. Kislu Noman</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Mohammed%20Shamsul%20Islam"> Syed Mohammed Shamsul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahriar%20Hassan"> Shahriar Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Raihana%20Pervin"> Raihana Pervin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing rate of wireless devices and high bandwidth operations, wireless networking and communications are becoming over crowded. To cope with such crowdy and messy situation, massive MIMO is designed to work with hundreds of low costs serving antennas at a time as well as improve the spectral efficiency at the same time. TDD has been used for gaining beamforming which is a major part of massive MIMO, to gain its best improvement to transmit and receive pilot sequences. All the benefits are only possible if the channel state information or channel estimation is gained properly. The common methods to estimate channel matrix used so far is LS, MMSE and a linear version of MMSE also proposed in many research works. We have optimized these methods using genetic algorithm to minimize the mean squared error and finding the best channel matrix from existing algorithms with less computational complexity. Our simulation result has shown that the use of GA worked beautifully on existing algorithms in a Rayleigh slow fading channel and existence of Additive White Gaussian Noise. We found that the GA optimized LS is better than existing algorithms as GA provides optimal result in some few iterations in terms of MSE with respect to SNR and computational complexity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title="channel estimation">channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=LMMSE" title=" LMMSE"> LMMSE</a>, <a href="https://publications.waset.org/abstracts/search?q=LS" title=" LS"> LS</a>, <a href="https://publications.waset.org/abstracts/search?q=MIMO" title=" MIMO"> MIMO</a>, <a href="https://publications.waset.org/abstracts/search?q=MMSE" title=" MMSE"> MMSE</a> </p> <a href="https://publications.waset.org/abstracts/103496/an-improved-data-aided-channel-estimation-technique-using-genetic-algorithm-for-massive-multi-input-multiple-output" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103496.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3823</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">3822</span> The Influence of Covariance Hankel Matrix Dimension on Algorithms for VARMA Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Celina%20Pestano-Gabino">Celina Pestano-Gabino</a>, <a href="https://publications.waset.org/abstracts/search?q=Concepcion%20Gonzalez-Concepcion"> Concepcion Gonzalez-Concepcion</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Candelaria%20Gil-Fari%C3%B1a"> M. Candelaria Gil-Fari帽a</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Some estimation methods for VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. It is known that if the data sample is populous enough and the dimension of the Hankel matrix is unnecessarily large, this may result in an unnecessary number of computations as well as in numerical problems. In this sense, the aim of this paper is two-fold. First, we provide some theoretical results for these matrices which translate into a lower dimension for the matrices normally used in the algorithms. This contribution thus serves to improve those methods from a numerical and, presumably, statistical point of view. Second, we have chosen an estimation algorithm to illustrate in practice our improvements. The results we obtained in a simulation of VARMA models show that an increase in the size of the Hankel matrix beyond the theoretical bound proposed as valid does not necessarily lead to improved practical results. Therefore, for future research, we propose conducting similar studies using any of the linear system estimation methods that depend on Hankel matrices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=covariances%20Hankel%20matrices" title="covariances Hankel matrices">covariances Hankel matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=Kronecker%20indices" title=" Kronecker indices"> Kronecker indices</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title=" system identification"> system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=VARMA%20models" title=" VARMA models"> VARMA models</a> </p> <a href="https://publications.waset.org/abstracts/55669/the-influence-of-covariance-hankel-matrix-dimension-on-algorithms-for-varma-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55669.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">243</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">3821</span> Genetic Algorithms for Parameter Identification of DC Motor ARMAX Model and Optimal Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Mansouri">A. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Krim"> F. Krim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents two techniques for DC motor parameters identification. We propose a numerical method using the adaptive extensive recursive least squares (AERLS) algorithm for real time parameters estimation. This algorithm, based on minimization of quadratic criterion, is realized in simulation for parameters identification of DC motor autoregressive moving average with extra inputs (ARMAX). As advanced technique, we use genetic algorithms (GA) identification with biased estimation for high dynamic performance speed regulation. DC motors are extensively used in variable speed drives, for robot and solar panel trajectory control. GA effectiveness is derived through comparison of the two approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARMAX%20model" title="ARMAX model">ARMAX model</a>, <a href="https://publications.waset.org/abstracts/search?q=DC%20motor" title=" DC motor"> DC motor</a>, <a href="https://publications.waset.org/abstracts/search?q=AERLS" title=" AERLS"> AERLS</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20identification" title=" parameter identification"> parameter identification</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20speed%20regulation" title=" PID speed regulation"> PID speed regulation</a> </p> <a href="https://publications.waset.org/abstracts/9107/genetic-algorithms-for-parameter-identification-of-dc-motor-armax-model-and-optimal-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9107.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">380</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">3820</span> Maximum Likelihood Estimation Methods on a Two-Parameter Rayleigh Distribution under Progressive Type-Ii Censoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Fundi%20Murithi">Daniel Fundi Murithi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data from economic, social, clinical, and industrial studies are in some way incomplete or incorrect due to censoring. Such data may have adverse effects if used in the estimation problem. We propose the use of Maximum Likelihood Estimation (MLE) under a progressive type-II censoring scheme to remedy this problem. In particular, maximum likelihood estimates (MLEs) for the location (碌) and scale (位) parameters of two Parameter Rayleigh distribution are realized under a progressive type-II censoring scheme using the Expectation-Maximization (EM) and the Newton-Raphson (NR) algorithms. These algorithms are used comparatively because they iteratively produce satisfactory results in the estimation problem. The progressively type-II censoring scheme is used because it allows the removal of test units before the termination of the experiment. Approximate asymptotic variances and confidence intervals for the location and scale parameters are derived/constructed. The efficiency of EM and the NR algorithms is compared given root mean squared error (RMSE), bias, and the coverage rate. The simulation study showed that in most sets of simulation cases, the estimates obtained using the Expectation-maximization algorithm had small biases, small variances, narrower/small confidence intervals width, and small root of mean squared error compared to those generated via the Newton-Raphson (NR) algorithm. Further, the analysis of a real-life data set (data from simple experimental trials) showed that the Expectation-Maximization (EM) algorithm performs better compared to Newton-Raphson (NR) algorithm in all simulation cases under the progressive type-II censoring scheme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectation-maximization%20algorithm" title="expectation-maximization algorithm">expectation-maximization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Newton-Raphson%20method" title=" Newton-Raphson method"> Newton-Raphson method</a>, <a href="https://publications.waset.org/abstracts/search?q=two-parameter%20Rayleigh%20distribution" title=" two-parameter Rayleigh distribution"> two-parameter Rayleigh distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20type-II%20censoring" title=" progressive type-II censoring"> progressive type-II censoring</a> </p> <a href="https://publications.waset.org/abstracts/122112/maximum-likelihood-estimation-methods-on-a-two-parameter-rayleigh-distribution-under-progressive-type-ii-censoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122112.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">163</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">3819</span> Study of Adaptive Filtering Algorithms and the Equalization of Radio Mobile Channel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Elkassimi">Said Elkassimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Said%20Safi"> Said Safi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Manaut"> B. Manaut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presented a study of three algorithms, the equalization algorithm to equalize the transmission channel with ZF and MMSE criteria, application of channel Bran A, and adaptive filtering algorithms LMS and RLS to estimate the parameters of the equalizer filter, i.e. move to the channel estimation and therefore reflect the temporal variations of the channel, and reduce the error in the transmitted signal. So far the performance of the algorithm equalizer with ZF and MMSE criteria both in the case without noise, a comparison of performance of the LMS and RLS algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filtering%20second%20equalizer" title="adaptive filtering second equalizer">adaptive filtering second equalizer</a>, <a href="https://publications.waset.org/abstracts/search?q=LMS" title=" LMS"> LMS</a>, <a href="https://publications.waset.org/abstracts/search?q=RLS%20%20Bran%20A" title=" RLS Bran A"> RLS Bran A</a>, <a href="https://publications.waset.org/abstracts/search?q=Proakis%20%28B%29%20MMSE" title=" Proakis (B) MMSE"> Proakis (B) MMSE</a>, <a href="https://publications.waset.org/abstracts/search?q=ZF" title=" ZF"> ZF</a> </p> <a href="https://publications.waset.org/abstracts/32853/study-of-adaptive-filtering-algorithms-and-the-equalization-of-radio-mobile-channel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32853.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">313</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">3818</span> DOA Estimation Using Golden Section Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niharika%20Verma">Niharika Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20Santosh"> Sandeep Santosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DOA technique is a localization technique used in the communication field. Various algorithms have been developed for direction of arrival estimation like MUSIC, ROOT MUSIC, etc. These algorithms depend on various parameters like antenna array elements, number of snapshots and various others. Basically the MUSIC spectrum is evaluated and peaks obtained are considered as the angle of arrivals. The angles evaluated using this process depends on the scanning interval chosen. The accuracy of the results obtained depends on the coarseness of the interval chosen. In this paper, golden section search is applied to the MUSIC algorithm and therefore, more accurate results are achieved. Initially the coarse DOA estimations is done using the MUSIC algorithm in the range -90 to 90 degree at the interval of 10 degree. After the peaks obtained then fine DOA estimation is done using golden section search. Also, the partitioning method is applied to estimate the number of signals incident on the antenna array. Dependency of the algorithm on the number of snapshots is also being explained. Hence, the accurate results are being determined using this algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Direction%20of%20Arrival%20%28DOA%29" title="Direction of Arrival (DOA)">Direction of Arrival (DOA)</a>, <a href="https://publications.waset.org/abstracts/search?q=golden%20section%20search" title=" golden section search"> golden section search</a>, <a href="https://publications.waset.org/abstracts/search?q=MUSIC" title=" MUSIC"> MUSIC</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20of%20snapshots" title=" number of snapshots"> number of snapshots</a> </p> <a href="https://publications.waset.org/abstracts/24181/doa-estimation-using-golden-section-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24181.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">446</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">3817</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">3816</span> Hierarchical Clustering Algorithms in Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Abdullah">Z. Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Hamdan"> A. R. Hamdan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical" title=" hierarchical"> hierarchical</a> </p> <a href="https://publications.waset.org/abstracts/31217/hierarchical-clustering-algorithms-in-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31217.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">885</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">3815</span> Influence of the Line Parameters in Transmission Line Fault Location</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marian%20Dragomir">Marian Dragomir</a>, <a href="https://publications.waset.org/abstracts/search?q=Alin%20Dragomir"> Alin Dragomir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the paper, two fault location algorithms are presented for transmission lines which use the line parameters to estimate the distance to the fault. The first algorithm uses only the measurements from one end of the line and the positive and zero sequence parameters of the line, while the second one uses the measurements from both ends of the line and only the positive sequence parameters of the line. The algorithms were tested using a transmission grid transposed in MATLAB. In a first stage it was established a fault location base line, where the algorithms mentioned above estimate the fault locations using the exact line parameters. After that, the positive and zero sequence resistance and reactance of the line were calculated again for different ground resistivity values and then the fault locations were estimated again in order to compare the results with the base line results. The results show that the algorithm which uses the zero sequence impedance of the line is the most sensitive to the line parameters modifications. The other algorithm is less sensitive to the line parameters modification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation%20algorithms" title="estimation algorithms">estimation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20location" title=" fault location"> fault location</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20parameters" title=" line parameters"> line parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20tool" title=" simulation tool"> simulation tool</a> </p> <a href="https://publications.waset.org/abstracts/61697/influence-of-the-line-parameters-in-transmission-line-fault-location" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61697.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">355</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">3814</span> Channel Estimation for Orthogonal Frequency Division Multiplexing Systems over Doubly Selective Channels Base on DCS-DCSOMP Algorithm</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=Furui%20Huo"> Furui Huo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianhong%20Xiang"> Jianhong Xiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Doppler shift generated by high-speed movement and multipath effects in the channel are the main reasons for the generation of a time-frequency doubly-selective (DS) channel. There is severe inter-carrier interference (ICI) in the DS channel. Channel estimation for an orthogonal frequency division multiplexing (OFDM) system over a DS channel is very difficult. The simultaneous orthogonal matching pursuit algorithm under distributed compressive sensing theory (DCS-SOMP) has been used in channel estimation for OFDM systems over DS channels. However, the reconstruction accuracy of the DCS-SOMP algorithm is not high enough in the low SNR stage. To solve this problem, in this paper, we propose an improved DCS-SOMP algorithm based on the inner product difference comparison operation (DCS-DCSOMP). The reconstruction accuracy is improved by increasing the number of candidate indexes and designing the comparison conditions of inner product difference. We combine the DCS-DCSOMP algorithm with the basis expansion model (BEM) to reduce the complexity of channel estimation. Simulation results show the effectiveness of the proposed algorithm and its advantages over other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OFDM" title="OFDM">OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=doubly%20selective" title=" doubly selective"> doubly selective</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title=" channel estimation"> channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a> </p> <a href="https://publications.waset.org/abstracts/162873/channel-estimation-for-orthogonal-frequency-division-multiplexing-systems-over-doubly-selective-channels-base-on-dcs-dcsomp-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162873.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">95</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">3813</span> Simulation of 3-D Direction-of-Arrival Estimation Using MUSIC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duckyong%20Kim">Duckyong Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Kang%20Park"> Jong Kang Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Tae%20Kim"> Jong Tae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DOA (Direction of Arrival) estimation is an important method in array signal processing and has a wide range of applications such as direction finding, beam forming, and so on. In this paper, we briefly introduce the MUSIC (Multiple Signal Classification) Algorithm, one of DOA estimation methods for analyzing several targets. Then we apply the MUSIC algorithm to the two-dimensional antenna array to analyze DOA estimation in 3D space through MATLAB simulation. We also analyze the design factors that can affect the accuracy of DOA estimation through simulation, and proceed with further consideration on how to apply the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DOA%20estimation" title="DOA estimation">DOA estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=MUSIC%20algorithm" title=" MUSIC algorithm"> MUSIC algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20spectrum" title=" spatial spectrum"> spatial spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=array%20signal%20processing" title=" array signal processing"> array signal processing</a> </p> <a href="https://publications.waset.org/abstracts/88658/simulation-of-3-d-direction-of-arrival-estimation-using-music-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88658.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">379</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">3812</span> Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ouahab%20Kadri">Ouahab Kadri</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Hayet%20Mouss"> Leila Hayet Mouss</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20algorithms" title="ant colony algorithms">ant colony algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20and%20dynamic%20systems" title=" complex and dynamic systems"> complex and dynamic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/42293/fault-diagnosis-of-manufacturing-systems-using-anttreestoch-with-parameter-optimization-by-aco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42293.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">298</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">3811</span> Hybrid Deep Learning and FAST-BRISK 3D Object Detection Technique for Bin-Picking Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thanakrit%20Taweesoontorn">Thanakrit Taweesoontorn</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarucha%20Yanyong"> Sarucha Yanyong</a>, <a href="https://publications.waset.org/abstracts/search?q=Poom%20Konghuayrob"> Poom Konghuayrob</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotic%20vision" title="robotic vision">robotic vision</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=applications%20of%20robotics" title=" applications of robotics"> applications of robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligent" title=" artificial intelligent"> artificial intelligent</a> </p> <a href="https://publications.waset.org/abstracts/176550/hybrid-deep-learning-and-fast-brisk-3d-object-detection-technique-for-bin-picking-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176550.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">97</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">3810</span> Performance Analysis of Ad-Hoc Network Routing Protocols</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Baddari">I. Baddari</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Riahla"> A. Riahla</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mezghich"> M. Mezghich</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today in the literature, we discover a lot of routing algorithms which some have been the subject of normalization. Two great classes Routing algorithms are defined, the first is the class reactive algorithms and the second that of algorithms proactive. The aim of this work is to make a comparative study between some routing algorithms. Two comparisons are considered. The first will focus on the protocols of the same class and second class on algorithms of different classes (one reactive and the other proactive). Since they are not based on analytical models, the exact evaluation of some aspects of these protocols is challenging. Simulations have to be done in order to study their performances. Our simulation is performed in NS2 (Network Simulator 2). It identified a classification of the different routing algorithms studied in a metrics such as loss of message, the time transmission, mobility, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ad-hoc%20network%20routing%20protocol" title="ad-hoc network routing protocol">ad-hoc network routing protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=NS2" title=" NS2"> NS2</a>, <a href="https://publications.waset.org/abstracts/search?q=delay" title=" delay"> delay</a>, <a href="https://publications.waset.org/abstracts/search?q=packet%20loss" title=" packet loss"> packet loss</a>, <a href="https://publications.waset.org/abstracts/search?q=wideband" title=" wideband"> wideband</a>, <a href="https://publications.waset.org/abstracts/search?q=mobility" title=" mobility"> mobility</a> </p> <a href="https://publications.waset.org/abstracts/23093/performance-analysis-of-ad-hoc-network-routing-protocols" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23093.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">400</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">3809</span> Motion Estimator Architecture with Optimized Number of Processing Elements for High Efficiency Video Coding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seongsoo%20Lee">Seongsoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motion estimation occupies the heaviest computation in HEVC (high efficiency video coding). Many fast algorithms such as TZS (test zone search) have been proposed to reduce the computation. Still the huge computation of the motion estimation is a critical issue in the implementation of HEVC video codec. In this paper, motion estimator architecture with optimized number of PEs (processing element) is presented by exploiting early termination. It also reduces hardware size by exploiting parallel processing. The presented motion estimator architecture has 8 PEs, and it can efficiently perform TZS with very high utilization of PEs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20estimation" title="motion estimation">motion estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=test%20zone%20search" title=" test zone search"> test zone search</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20efficiency%20video%20coding" title=" high efficiency video coding"> high efficiency video coding</a>, <a href="https://publications.waset.org/abstracts/search?q=processing%20element" title=" processing element"> processing element</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/70881/motion-estimator-architecture-with-optimized-number-of-processing-elements-for-high-efficiency-video-coding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70881.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">3808</span> Lithium-Ion Battery State of Charge Estimation Using One State Hysteresis Model with Nonlinear Estimation Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Farag">Mohammed Farag</a>, <a href="https://publications.waset.org/abstracts/search?q=Mina%20Attari"> Mina Attari</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Andrew%20Gadsden"> S. Andrew Gadsden</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeid%20R.%20Habibi"> Saeid R. Habibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Battery state of charge (SOC) estimation is an important parameter as it measures the total amount of electrical energy stored at a current time. The SOC percentage acts as a fuel gauge if it is compared with a conventional vehicle. Estimating the SOC is, therefore, essential for monitoring the amount of useful life remaining in the battery system. This paper looks at the implementation of three nonlinear estimation strategies for Li-Ion battery SOC estimation. One of the most common behavioral battery models is the one state hysteresis (OSH) model. The extended Kalman filter (EKF), the smooth variable structure filter (SVSF), and the time-varying smoothing boundary layer SVSF are applied on this model, and the results are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=state%20of%20charge%20estimation" title="state of charge estimation">state of charge estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=battery%20modeling" title=" battery modeling"> battery modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=one-state%20hysteresis" title=" one-state hysteresis"> one-state hysteresis</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering%20and%20estimation" title=" filtering and estimation"> filtering and estimation</a> </p> <a href="https://publications.waset.org/abstracts/68017/lithium-ion-battery-state-of-charge-estimation-using-one-state-hysteresis-model-with-nonlinear-estimation-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">444</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3807</span> Comparative Analysis of Feature Extraction and Classification Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20L.%20Ujjwal">R. L. Ujjwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhishek%20Jain"> Abhishek Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=age%20group" title=" age group"> age group</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title=" face detection"> face detection</a> </p> <a href="https://publications.waset.org/abstracts/58670/comparative-analysis-of-feature-extraction-and-classification-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58670.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">368</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span 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