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algorithm</title> <meta name="description" content="Search results for: K-SVD algorithm"> <meta name="keywords" content="K-SVD 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" /> </a> <button class="d-block d-lg-none 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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="K-SVD 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> 3596</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: K-SVD algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3596</span> KSVD-SVM Approach for Spontaneous Facial Expression Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dawood%20Al%20Chanti">Dawood Al Chanti</a>, <a href="https://publications.waset.org/abstracts/search?q=Alice%20Caplier"> Alice Caplier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title="dictionary learning">dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20projection" title=" random projection"> random projection</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20and%20spontaneous%20facial%20expression" title=" pose and spontaneous facial expression"> pose and spontaneous facial expression</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/51683/ksvd-svm-approach-for-spontaneous-facial-expression-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51683.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">305</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">3595</span> Co-Evolutionary Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Rizk-Allah">R. M. Rizk-Allah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20fly%20optimization%20algorithm" title=" fruit fly optimization algorithm"> fruit fly optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization%20problems" title=" unconstrained optimization problems"> unconstrained optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/15923/co-evolutionary-fruit-fly-optimization-algorithm-and-firefly-algorithm-for-solving-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15923.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">536</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">3594</span> A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaohuizi%20Guo">Gaohuizi Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ning%20Zhang"> Ning Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20algorithm" title=" hybrid algorithm"> hybrid algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sine%20cosine%20algorithm" title=" sine cosine algorithm"> sine cosine algorithm</a> </p> <a href="https://publications.waset.org/abstracts/129731/a-hybrid-multi-objective-firefly-sine-cosine-algorithm-for-multi-objective-optimization-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129731.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">169</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">3593</span> Approximating Fixed Points by a Two-Step Iterative Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safeer%20Hussain%20Khan">Safeer Hussain Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contractive-like%20operator" title="contractive-like operator">contractive-like operator</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20algorithm" title=" iterative algorithm"> iterative algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20point" title=" fixed point"> fixed point</a>, <a href="https://publications.waset.org/abstracts/search?q=strong%20convergence" title=" strong convergence"> strong convergence</a> </p> <a href="https://publications.waset.org/abstracts/10341/approximating-fixed-points-by-a-two-step-iterative-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10341.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">550</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">3592</span> An Algorithm to Compute the State Estimation of a Bilinear Dynamical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Eqal%20Al%20Mazrooei">Abdullah Eqal Al Mazrooei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a mathematical algorithm which is used for estimating the states in the bilinear systems. This algorithm uses a special linearization of the second-order term by using the best available information about the state of the system. This technique makes our algorithm generalizes the well-known Kalman estimators. The system which is used here is of the bilinear class, the evolution of this model is linear-bilinear in the state of the system. Our algorithm can be used with linear and bilinear systems. We also here introduced a real application for the new algorithm to prove the feasibility and the efficiency for it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation%20algorithm" title="estimation algorithm">estimation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20systems" title=" bilinear systems"> bilinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=Kakman%20filter" title=" Kakman filter"> Kakman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20order%20linearization" title=" second order linearization"> second order linearization</a> </p> <a href="https://publications.waset.org/abstracts/51466/an-algorithm-to-compute-the-state-estimation-of-a-bilinear-dynamical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51466.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">486</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">3591</span> Handshake Algorithm for Minimum Spanning Tree Construction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nassiri%20Khalid">Nassiri Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Hibaoui%20Abdelaaziz%20et%20Hajar%20Moha"> El Hibaoui Abdelaaziz et Hajar Moha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce and analyse a probabilistic distributed algorithm for a construction of a minimum spanning tree on network. This algorithm is based on the handshake concept. Firstly, each network node is considered as a sub-spanning tree. And at each round of the execution of our algorithm, a sub-spanning trees are merged. The execution continues until all sub-spanning trees are merged into one. We analyze this algorithm by a stochastic process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Spanning%20tree" title="Spanning tree">Spanning tree</a>, <a href="https://publications.waset.org/abstracts/search?q=Distributed%20Algorithm" title=" Distributed Algorithm"> Distributed Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Handshake%20Algorithm" title=" Handshake Algorithm"> Handshake Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Matching" title=" Matching"> Matching</a>, <a href="https://publications.waset.org/abstracts/search?q=Probabilistic%20Analysis" title=" Probabilistic Analysis"> Probabilistic Analysis</a> </p> <a href="https://publications.waset.org/abstracts/17743/handshake-algorithm-for-minimum-spanning-tree-construction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17743.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">658</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">3590</span> Digestion Optimization Algorithm: A Novel Bio-Inspired Intelligence for Global Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akintayo%20E.%20Akinsunmade">Akintayo E. Akinsunmade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The digestion optimization algorithm is a novel biological-inspired metaheuristic method for solving complex optimization problems. The algorithm development was inspired by studying the human digestive system. The algorithm mimics the process of food ingestion, breakdown, absorption, and elimination to eﬀectively and eﬃciently search for optimal solutions. This algorithm was tested for optimal solutions on seven diﬀerent types of optimization benchmark functions. The algorithm produced optimal solutions with standard errors, which were compared with the exact solution of the test functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20algorithm" title="bio-inspired algorithm">bio-inspired algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmark%20optimization%20functions" title=" benchmark optimization functions"> benchmark optimization functions</a>, <a href="https://publications.waset.org/abstracts/search?q=digestive%20system%20in%20human" title=" digestive system in human"> digestive system in human</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20development" title=" algorithm development"> algorithm development</a> </p> <a href="https://publications.waset.org/abstracts/194133/digestion-optimization-algorithm-a-novel-bio-inspired-intelligence-for-global-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194133.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">10</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">3589</span> Improving the Performance of Back-Propagation Training Algorithm by Using ANN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Pratap%20Singh%20Kirar">Vishnu Pratap Singh Kirar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20minima" title=" local minima"> local minima</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20convergence%20rate" title=" fast convergence rate"> fast convergence rate</a> </p> <a href="https://publications.waset.org/abstracts/22746/improving-the-performance-of-back-propagation-training-algorithm-by-using-ann" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22746.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">498</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">3588</span> Tabu Random Algorithm for Guiding Mobile Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Worrall">Kevin Worrall</a>, <a href="https://publications.waset.org/abstracts/search?q=Euan%20McGookin"> Euan McGookin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of optimization algorithms is common across a large number of diverse fields. This work presents the use of a hybrid optimization algorithm applied to a mobile robot tasked with carrying out a search of an unknown environment. The algorithm is then applied to the multiple robots case, which results in a reduction in the time taken to carry out the search. The hybrid algorithm is a Random Search Algorithm fused with a Tabu mechanism. The work shows that the algorithm locates the desired points in a quicker time than a brute force search. The Tabu Random algorithm is shown to work within a simulated environment using a validated mathematical model. The simulation was run using three different environments with varying numbers of targets. As an algorithm, the Tabu Random is small, clear and can be implemented with minimal resources. The power of the algorithm is the speed at which it locates points of interest and the robustness to the number of robots involved. The number of robots can vary with no changes to the algorithm resulting in a flexible algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=control" title=" control"> control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent" title=" multi-agent"> multi-agent</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20and%20rescue" title=" search and rescue"> search and rescue</a> </p> <a href="https://publications.waset.org/abstracts/92647/tabu-random-algorithm-for-guiding-mobile-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92647.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">239</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">3587</span> Hybrid Bee Ant Colony Algorithm for Effective Load Balancing and Job Scheduling in Cloud Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Yeboah">Thomas Yeboah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud Computing is newly paradigm in computing that promises a delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). As Cloud Computing is a newly style of computing on the internet. It has many merits along with some crucial issues that need to be resolved in order to improve reliability of cloud environment. These issues are related with the load balancing, fault tolerance and different security issues in cloud environment.In this paper the main concern is to develop an effective load balancing algorithm that gives satisfactory performance to both, cloud users and providers. This proposed algorithm (hybrid Bee Ant Colony algorithm) is a combination of two dynamic algorithms: Ant Colony Optimization and Bees Life algorithm. Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues whiles the Bees Life algorithm is used for optimization of job scheduling in cloud environment. The results of the proposed algorithm shows that the hybrid Bee Ant Colony algorithm outperforms the performances of both Ant Colony algorithm and Bees Life algorithm when evaluated the proposed algorithm performances in terms of Waiting time and Response time on a simulator called CloudSim. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bees%20life%20algorithm" title=" bees life algorithm"> bees life algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20algorithm" title=" scheduling algorithm"> scheduling algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20balancing" title=" load balancing"> load balancing</a> </p> <a href="https://publications.waset.org/abstracts/27139/hybrid-bee-ant-colony-algorithm-for-effective-load-balancing-and-job-scheduling-in-cloud-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27139.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">628</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">3586</span> Evolution of Multimodulus Algorithm Blind Equalization Based on Recursive Least Square Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sardar%20Ameer%20Akram%20Khan">Sardar Ameer Akram Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Amin%20Sheikh"> Shahzad Amin Sheikh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Blind equalization is an important technique amongst equalization family. Multimodulus algorithms based on blind equalization removes the undesirable effects of ISI and cater ups the phase issues, saving the cost of rotator at the receiver end. In this paper a new algorithm combination of recursive least square and Multimodulus algorithm named as RLSMMA is proposed by providing few assumption, fast convergence and minimum Mean Square Error (MSE) is achieved. The excellence of this technique is shown in the simulations presenting MSE plots and the resulting filter results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20equalizations" title="blind equalizations">blind equalizations</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20modulus%20algorithm" title=" constant modulus algorithm"> constant modulus algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-modulus%20algorithm" title=" multi-modulus algorithm"> multi-modulus algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=recursive%20%20least%20square%20algorithm" title=" recursive least square algorithm"> recursive least square algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=quadrature%20amplitude%20modulation%20%28QAM%29" title=" quadrature amplitude modulation (QAM)"> quadrature amplitude modulation (QAM)</a> </p> <a href="https://publications.waset.org/abstracts/24704/evolution-of-multimodulus-algorithm-blind-equalization-based-on-recursive-least-square-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24704.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">644</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">3585</span> A Comparative Study of GTC and PSP Algorithms for Mining Sequential Patterns Embedded in Database with Time Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safa%20Adi">Safa Adi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm (level wise algorithms). We will compare two previous approaches GTC and PSP, that resumes the general principles of GSP. Furthermore this paper will discuss PG-hybrid algorithm, that using PSP and GTC. The results show that PSP and GTC are more efficient than GSP. On the other hand, the GTC algorithm performs better than PSP. The PG-hybrid algorithm use PSP algorithm for the two first passes on the database, and GTC approach for the following scans. Experiments show that the hybrid approach is very efficient for short, frequent sequences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=database" title="database">database</a>, <a href="https://publications.waset.org/abstracts/search?q=GTC%20algorithm" title=" GTC algorithm"> GTC algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=PSP%20algorithm" title=" PSP algorithm"> PSP algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20patterns" title=" sequential patterns"> sequential patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20constraints" title=" time constraints"> time constraints</a> </p> <a href="https://publications.waset.org/abstracts/97812/a-comparative-study-of-gtc-and-psp-algorithms-for-mining-sequential-patterns-embedded-in-database-with-time-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97812.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">390</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">3584</span> A Genetic Based Algorithm to Generate Random Simple Polygons Using a New Polygon Merge Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Nourollah">Ali Nourollah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Movahedinejad"> Mohsen Movahedinejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a new algorithm to generate random simple polygons from a given set of points in a two dimensional plane is designed. The proposed algorithm uses a genetic algorithm to generate polygons with few vertices. A new merge algorithm is presented which converts any two polygons into a simple polygon. This algorithm at first changes two polygons into a polygonal chain and then the polygonal chain is converted into a simple polygon. The process of converting a polygonal chain into a simple polygon is based on the removal of intersecting edges. The merge algorithm has the time complexity of O ((r+s) *l) where r and s are the size of merging polygons and l shows the number of intersecting edges removed from the polygonal chain. It will be shown that 1 < l < r+s. The experiments results show that the proposed algorithm has the ability to generate a great number of different simple polygons and has better performance in comparison to celebrated algorithms such as space partitioning and steady growth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Divide%20and%20conquer" title="Divide and conquer">Divide and conquer</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=merge%20polygons" title=" merge polygons"> merge polygons</a>, <a href="https://publications.waset.org/abstracts/search?q=Random%20simple%20polygon%20generation." title=" Random simple polygon generation. "> Random simple polygon generation. </a> </p> <a href="https://publications.waset.org/abstracts/21488/a-genetic-based-algorithm-to-generate-random-simple-polygons-using-a-new-polygon-merge-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21488.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">533</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">3583</span> Orthogonal Basis Extreme Learning Algorithm and Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Li">Ying Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Li"> Yan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20basis%20extreme%20learning" title=" orthogonal basis extreme learning"> orthogonal basis extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a> </p> <a href="https://publications.waset.org/abstracts/15129/orthogonal-basis-extreme-learning-algorithm-and-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15129.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">534</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">3582</span> An IM-COH Algorithm Neural Network Optimization with Cuckoo Search Algorithm for Time Series Samples</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wullapa%20Wongsinlatam">Wullapa Wongsinlatam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation%20algorithm" title=" back propagation algorithm"> back propagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20minima%20problem" title=" local minima problem"> local minima problem</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20optimization" title=" metaheuristic optimization"> metaheuristic optimization</a> </p> <a href="https://publications.waset.org/abstracts/100995/an-im-coh-algorithm-neural-network-optimization-with-cuckoo-search-algorithm-for-time-series-samples" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100995.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">152</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">3581</span> An Optimized RDP Algorithm for Curve Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean-Pierre%20Lomaliza">Jean-Pierre Lomaliza</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang-Seok%20Moon"> Kwang-Seok Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanhoon%20Park"> Hanhoon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well-known that Ramer Douglas Peucker (RDP) algorithm greatly depends on the method of choosing starting points. Therefore, this paper focuses on finding such starting points that will optimize the results of RDP algorithm. Specifically, this paper proposes a curve approximation algorithm that finds flat points, called essential points, of an input curve, divides the curve into corner-like sub-curves using the essential points, and applies the RDP algorithm to the sub-curves. The number of essential points play a role on optimizing the approximation results by balancing the degree of shape information loss and the amount of data reduction. Through experiments with curves of various types and complexities of shape, we compared the performance of the proposed algorithm with three other methods, i.e., the RDP algorithm itself and its variants. As a result, the proposed algorithm outperformed the others in term of maintaining the original shapes of the input curve, which is important in various applications like pattern recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curve%20approximation" title="curve approximation">curve approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=essential%20point" title=" essential point"> essential point</a>, <a href="https://publications.waset.org/abstracts/search?q=RDP%20algorithm" title=" RDP algorithm"> RDP algorithm</a> </p> <a href="https://publications.waset.org/abstracts/29359/an-optimized-rdp-algorithm-for-curve-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29359.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">535</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">3580</span> A New Dual Forward Affine Projection Adaptive Algorithm for Speech Enhancement in Airplane Cockpits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Djendi%20Mohmaed">Djendi Mohmaed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a dual adaptive algorithm, which is based on the combination between the forward blind source separation (FBSS) structure and the affine projection algorithm (APA). This proposed algorithm combines the advantages of the source separation properties of the FBSS structure and the fast convergence characteristics of the APA algorithm. The proposed algorithm needs two noisy observations to provide an enhanced speech signal. This process is done in a blind manner without the need for ant priori information about the source signals. The proposed dual forward blind source separation affine projection algorithm is denoted (DFAPA) and used for the first time in an airplane cockpit context to enhance the communication from- and to- the airplane. Intensive experiments were carried out in this sense to evaluate the performance of the proposed DFAPA algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20algorithm" title="adaptive algorithm">adaptive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20enhancement" title=" speech enhancement"> speech enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20mismatch" title=" system mismatch"> system mismatch</a>, <a href="https://publications.waset.org/abstracts/search?q=SNR" title=" SNR"> SNR</a> </p> <a href="https://publications.waset.org/abstracts/165920/a-new-dual-forward-affine-projection-adaptive-algorithm-for-speech-enhancement-in-airplane-cockpits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165920.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">135</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">3579</span> A High-Level Co-Evolutionary Hybrid Algorithm for the Multi-Objective Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid distributed algorithm has been suggested for the multi-objective job shop scheduling problem. Many new approaches are used at design steps of the distributed algorithm. Co-evolutionary structure of the algorithm and competition between different communicated hybrid algorithms, which are executed simultaneously, causes to efficient search. Using several machines for distributing the algorithms, at the iteration and solution levels, increases computational speed. The proposed algorithm is able to find the Pareto solutions of the big problems in shorter time than other algorithm in the literature. Apache Spark and Hadoop platforms have been used for the distribution of the algorithm. The suggested algorithm and implementations have been compared with results of the successful algorithms in the literature. Results prove the efficiency and high speed of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20algorithms" title="distributed algorithms">distributed algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark" title=" Apache Spark"> Apache Spark</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a> </p> <a href="https://publications.waset.org/abstracts/72317/a-high-level-co-evolutionary-hybrid-algorithm-for-the-multi-objective-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72317.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">3578</span> A Transform Domain Function Controlled VSSLMS Algorithm for Sparse System Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cemil%20Turan">Cemil Turan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shukri%20Salman"> Mohammad Shukri Salman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filtering" title="adaptive filtering">adaptive filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20system%20identification" title=" sparse system identification"> sparse system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=TD-LMS%20algorithm" title=" TD-LMS algorithm"> TD-LMS algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=VSSLMS%20algorithm" title=" VSSLMS algorithm"> VSSLMS algorithm</a> </p> <a href="https://publications.waset.org/abstracts/72335/a-transform-domain-function-controlled-vsslms-algorithm-for-sparse-system-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72335.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">360</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">3577</span> A Hybrid ICA-GA Algorithm for Solving Multiobjective Optimization of Production Planning Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Ramzi%20Jasim">Omar Ramzi Jasim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalal%20Sultan%20Ashour"> Jalal Sultan Ashour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problems in operation and it can potentially lead to poor customer satisfaction. In this paper, a hybrid evolutionary algorithm (ICA-GA) is presented, which integrates the merits of both imperialist competitive algorithm (ICA) and genetic algorithm (GA) for solving multi-objective MPS problems. In the presented algorithm, the colonies in each empire has be represented a small population and communicate with each other using genetic operators. By testing on 5 production scenarios, the numerical results of ICA-GA algorithm show the efficiency and capabilities of the hybrid algorithm in finding the optimum solutions. The ICA-GA solutions yield the lower inventory level and keep customer satisfaction high and the required overtime is also lower, compared with results of GA and SA in all production scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=master%20production%20scheduling" title="master production scheduling">master production scheduling</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=imperialist%20competitive%20algorithm" title=" imperialist competitive algorithm"> imperialist competitive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20algorithm" title=" hybrid algorithm"> hybrid algorithm</a> </p> <a href="https://publications.waset.org/abstracts/46493/a-hybrid-ica-ga-algorithm-for-solving-multiobjective-optimization-of-production-planning-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46493.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">3576</span> An Algorithm for Herding Cows by a Swarm of Quadcopters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeryes%20Danial">Jeryes Danial</a>, <a href="https://publications.waset.org/abstracts/search?q=Yosi%20Ben%20Asher"> Yosi Ben Asher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Algorithms for controlling a swarm of robots is an active research field, out of which cattle herding is one of the most complex problems to solve. In this paper, we derive an independent herding algorithm that is specifically designed for a swarm of quadcopters. The algorithm works by devising flight trajectories that cause the cows to run-away in the desired direction and hence herd cows that are distributed in a given field towards a common gathering point. Unlike previously proposed swarm herding algorithms, this algorithm does not use a flocking model but rather stars each cow separately. The effectiveness of this algorithm is verified experimentally using a simulator. We use a special set of experiments attempting to demonstrate that the herding times of this algorithm correspond to field diameter small constant regardless of the number of cows in the field. This is an optimal result indicating that the algorithm groups the cows into intermediate groups and herd them as one forming ever closing bigger groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm" title="swarm">swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=independent" title=" independent"> independent</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed" title=" distributed"> distributed</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a> </p> <a href="https://publications.waset.org/abstracts/134795/an-algorithm-for-herding-cows-by-a-swarm-of-quadcopters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3575</span> A Review Paper on Data Mining and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sikander%20Singh%20Cheema">Sikander Singh Cheema</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasmeen%20Kaur"> Jasmeen Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields. <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=KDD" title=" KDD"> KDD</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=descriptive%20mining" title=" descriptive mining"> descriptive mining</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20mining" title=" predictive mining"> predictive mining</a> </p> <a href="https://publications.waset.org/abstracts/43637/a-review-paper-on-data-mining-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43637.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">591</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">3574</span> Optimum Design of Grillage Systems Using Firefly Algorithm Optimization Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Erdal">F. Erdal</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Dogan"> E. Dogan</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20E.%20Uz"> F. E. Uz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, firefly optimization based optimum design algorithm is presented for the grillage systems. Naming of the algorithm is derived from the fireflies, whose sense of movement is taken as a model in the development of the algorithm. Fireflies’ being unisex and attraction between each other constitute the basis of the algorithm. The design algorithm considers the displacement and strength constraints which are implemented from LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Construction). It selects the appropriate W (Wide Flange)-sections for the transverse and longitudinal beams of the grillage system among 272 discrete W-section designations given in LRFD-AISC so that the design limitations described in LRFD are satisfied and the weight of the system is confined to be minimal. Number of design examples is considered to demonstrate the efficiency of the algorithm presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fire%EF%AC%82y%20algorithm" title="fireﬂy algorithm">fireﬂy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20grillage%20systems" title=" steel grillage systems"> steel grillage systems</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20design" title=" optimum design"> optimum design</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20search%20techniques" title=" stochastic search techniques"> stochastic search techniques</a> </p> <a href="https://publications.waset.org/abstracts/14634/optimum-design-of-grillage-systems-using-firefly-algorithm-optimization-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14634.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">435</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3573</span> Presenting a Job Scheduling Algorithm Based on Learning Automata in Computational Grid</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roshanak%20Khodabakhsh%20Jolfaei">Roshanak Khodabakhsh Jolfaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Javad%20Akbari%20Torkestani"> Javad Akbari Torkestani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a cooperative environment for problem-solving, it is necessary that grids develop efficient job scheduling patterns with regard to their goals, domains and structure. Since the Grid environments facilitate distributed calculations, job scheduling appears in the form of a critical problem for the management of Grid sources that influences severely on the efficiency for the whole Grid environment. Due to the existence of some specifications such as sources dynamicity and conditions of the network in Grid, some algorithm should be presented to be adjustable and scalable with increasing the network growth. For this purpose, in this paper a job scheduling algorithm has been presented on the basis of learning automata in computational Grid which the performance of its results were compared with FPSO algorithm (Fuzzy Particle Swarm Optimization algorithm) and GJS algorithm (Grid Job Scheduling algorithm). The obtained numerical results indicated the superiority of suggested algorithm in comparison with FPSO and GJS. In addition, the obtained results classified FPSO and GJS in the second and third position respectively after the mentioned algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20grid" title="computational grid">computational grid</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20scheduling" title=" job scheduling"> job scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20automata" title=" learning automata"> learning automata</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20scheduling" title=" dynamic scheduling"> dynamic scheduling</a> </p> <a href="https://publications.waset.org/abstracts/40508/presenting-a-job-scheduling-algorithm-based-on-learning-automata-in-computational-grid" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40508.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">343</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">3572</span> A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Noman%20Qasem">Sultan Noman Qasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title="radial basis function network">radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning" title=" hybrid learning"> hybrid learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15843/a-multi-objective-evolutionary-algorithm-of-neural-network-for-medical-diseases-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15843.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">563</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">3571</span> A Hybrid Tabu Search Algorithm for the Multi-Objective Job Shop Scheduling Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid Tabu Search (TS) algorithm is suggested for the multi-objective job shop scheduling problems (MO-JSSPs). The algorithm integrates several shifting bottleneck based neighborhood structures with the Giffler & Thompson algorithm, which improve efficiency of the search. Diversification and intensification are provided with local and global left shift algorithms application and also new semi-active, active, and non-delay schedules creation. The suggested algorithm is tested in the MO-JSSPs benchmarks from the literature based on the Pareto optimality concept. Different performances criteria are used for the multi-objective algorithm evaluation. The proposed algorithm is able to find the Pareto solutions of the test problems in shorter time than other algorithm of the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title="tabu search">tabu search</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20shop%20scheduling" title=" job shop scheduling"> job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimality" title=" Pareto optimality"> Pareto optimality</a> </p> <a href="https://publications.waset.org/abstracts/71920/a-hybrid-tabu-search-algorithm-for-the-multi-objective-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71920.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">443</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">3570</span> A Learning-Based EM Mixture Regression Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Cheng%20Tian">Yi-Cheng Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Miin-Shen%20Yang"> Miin-Shen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm. <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=EM%20algorithm" title=" EM algorithm"> EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20regression%20model" title=" mixture regression model"> mixture regression model</a> </p> <a href="https://publications.waset.org/abstracts/25163/a-learning-based-em-mixture-regression-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25163.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">510</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">3569</span> Quick Sequential Search Algorithm Used to Decode High-Frequency Matrices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20M.%20Siddeq">Mohammed M. Siddeq</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20H.%20Rasheed"> Mohammed H. Rasheed</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20M.%20Salih"> Omar M. Salih</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcos%20A.%20Rodrigues"> Marcos A. Rodrigues</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research proposes a data encoding and decoding method based on the Matrix Minimization algorithm. This algorithm is applied to high-frequency coefficients for compression/encoding. The algorithm starts by converting every three coefficients to a single value; this is accomplished based on three different keys. The decoding/decompression uses a search method called QSS (Quick Sequential Search) Decoding Algorithm presented in this research based on the sequential search to recover the exact coefficients. In the next step, the decoded data are saved in an auxiliary array. The basic idea behind the auxiliary array is to save all possible decoded coefficients; this is because another algorithm, such as conventional sequential search, could retrieve encoded/compressed data independently from the proposed algorithm. The experimental results showed that our proposed decoding algorithm retrieves original data faster than conventional sequential search algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=matrix%20minimization%20algorithm" title="matrix minimization algorithm">matrix minimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=decoding%20sequential%20search%20algorithm" title=" decoding sequential search algorithm"> decoding sequential search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title=" image compression"> image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT" title=" DCT"> DCT</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a> </p> <a href="https://publications.waset.org/abstracts/151394/quick-sequential-search-algorithm-used-to-decode-high-frequency-matrices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151394.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">150</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">3568</span> ACOPIN: An ACO Algorithm with TSP Approach for Clustering Proteins in Protein Interaction Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamaludin%20Sallim">Jamaludin Sallim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rozlina%20Mohamed"> Rozlina Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Roslina%20Abdul%20Hamid"> Roslina Abdul Hamid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=searching%20algorithm" title=" searching algorithm"> searching algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20functional%20module" title=" protein functional module"> protein functional module</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20interaction%20network" title=" protein interaction network "> protein interaction network </a> </p> <a href="https://publications.waset.org/abstracts/22367/acopin-an-aco-algorithm-with-tsp-approach-for-clustering-proteins-in-protein-interaction-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22367.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">612</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">3567</span> Text Based Shuffling Algorithm on Graphics Processing Unit for Digital Watermarking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zayar%20Phyo">Zayar Phyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ei%20Chaw%20Htoon"> Ei Chaw Htoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a New-LSB based Steganography method, the Fisher-Yates algorithm is used to permute an existing array randomly. However, that algorithm performance became slower and occurred memory overflow problem while processing the large dimension of images. Therefore, the Text-Based Shuffling algorithm aimed to select only necessary pixels as hiding characters at the specific position of an image according to the length of the input text. In this paper, the enhanced text-based shuffling algorithm is presented with the powered of GPU to improve more excellent performance. The proposed algorithm employs the OpenCL Aparapi framework, along with XORShift Kernel including the Pseudo-Random Number Generator (PRNG) Kernel. PRNG is applied to produce random numbers inside the kernel of OpenCL. The experiment of the proposed algorithm is carried out by practicing GPU that it can perform faster-processing speed and better efficiency without getting the disruption of unnecessary operating system tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LSB%20based%20steganography" title="LSB based steganography">LSB based steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=Fisher-Yates%20algorithm" title=" Fisher-Yates algorithm"> Fisher-Yates algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=text-based%20shuffling%20algorithm" title=" text-based shuffling algorithm"> text-based shuffling algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenCL" title=" OpenCL"> OpenCL</a>, <a href="https://publications.waset.org/abstracts/search?q=XORShiftKernel" title=" XORShiftKernel"> XORShiftKernel</a> </p> <a 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