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algorithm</title> <meta name="description" content="Search results for: memetic algorithm"> <meta name="keywords" content="memetic 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="memetic 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> 3597</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: memetic algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3477</span> Stealth Laser Dicing Process Improvement via Shuffled Frog Leaping Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pongchanun%20Luangpaiboon">Pongchanun Luangpaiboon</a>, <a href="https://publications.waset.org/abstracts/search?q=Wanwisa%20Sarasang"> Wanwisa Sarasang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a performance of shuffled frog leaping algorithm was investigated on the stealth laser dicing process. Effect of problem on the performance of the algorithm was based on the tolerance of meandering data. From the customer specification it could be less than five microns with the target of zero microns. Currently, the meandering levels are unsatisfactory when compared to the customer specification. Firstly, the two-level factorial design was applied to preliminary study the statistically significant effects of five process variables. In this study one influential process variable is integer. From the experimental results, the new operating condition from the algorithm was superior when compared to the current manufacturing condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stealth%20laser%20dicing%20process" title="stealth laser dicing process">stealth laser dicing process</a>, <a href="https://publications.waset.org/abstracts/search?q=meandering" title=" meandering"> meandering</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristics" title=" meta-heuristics"> meta-heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=shuffled%20frog%20leaping%20algorithm" title=" shuffled frog leaping algorithm"> shuffled frog leaping algorithm</a> </p> <a href="https://publications.waset.org/abstracts/5805/stealth-laser-dicing-process-improvement-via-shuffled-frog-leaping-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5805.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">341</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">3476</span> Analysis of Tandem Detonator Algorithm Optimized by Quantum Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomasz%20Robert%20Kuczerski">Tomasz Robert Kuczerski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The high complexity of the algorithm of the autonomous tandem detonator system creates an optimization problem due to the parallel operation of several machine states of the system. Many years of experience and classic analyses have led to a partially optimized model. Limitations on the energy resources of this class of autonomous systems make it necessary to search for more effective methods of optimisation. The use of the Quantum Approximate Optimization Algorithm (QAOA) in these studies shows the most promising results. With the help of multiple evaluations of several qubit quantum circuits, proper results of variable parameter optimization were obtained. In addition, it was observed that the increase in the number of assessments does not result in further efficient growth due to the increasing complexity of optimising variables. The tests confirmed the effectiveness of the QAOA optimization method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm%20analysis" title="algorithm analysis">algorithm analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20system" title=" autonomous system"> autonomous system</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20optimization" title=" quantum optimization"> quantum optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=tandem%20detonator" title=" tandem detonator"> tandem detonator</a> </p> <a href="https://publications.waset.org/abstracts/161188/analysis-of-tandem-detonator-algorithm-optimized-by-quantum-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161188.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">92</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">3475</span> Evolutionary Methods in Cryptography </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wafa%20Slaibi%20Alsharafat">Wafa Slaibi Alsharafat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithms (GA) are random algorithms as random numbers that are generated during the operation of the algorithm determine what happens. This means that if GA is applied twice to optimize exactly the same problem it might produces two different answers. In this project, we propose an evolutionary algorithm and Genetic Algorithm (GA) to be implemented in symmetric encryption and decryption. Here, user's message and user secret information (key) which represent plain text to be transferred into cipher text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GA" title="GA">GA</a>, <a href="https://publications.waset.org/abstracts/search?q=encryption" title=" encryption"> encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=decryption" title=" decryption"> decryption</a>, <a href="https://publications.waset.org/abstracts/search?q=crossover" title=" crossover"> crossover</a> </p> <a href="https://publications.waset.org/abstracts/21507/evolutionary-methods-in-cryptography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21507.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">445</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">3474</span> A Ratio-Weighted Decision Tree Algorithm for Imbalance Dataset Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doyin%20Afolabi">Doyin Afolabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Adewole"> Phillip Adewole</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladipupo%20Sennaike"> Oladipupo Sennaike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20dataset" title=" imbalance dataset"> imbalance dataset</a> </p> <a href="https://publications.waset.org/abstracts/157609/a-ratio-weighted-decision-tree-algorithm-for-imbalance-dataset-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157609.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</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">3473</span> Flowing Online Vehicle GPS Data Clustering Using a New Parallel K-Means Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Orhun%20Vural">Orhun Vural</a>, <a href="https://publications.waset.org/abstracts/search?q=Oguz%20%20Bayat"> Oguz Bayat</a>, <a href="https://publications.waset.org/abstracts/search?q=Rustu%20Akay"> Rustu Akay</a>, <a href="https://publications.waset.org/abstracts/search?q=Osman%20N.%20Ucan"> Osman N. Ucan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a new parallel approach clustering of GPS data. Evaluation has been made by comparing execution time of various clustering algorithms on GPS data. This paper aims to propose a parallel based on neighborhood K-means algorithm to make it faster. The proposed parallelization approach assumes that each GPS data represents a vehicle and to communicate between vehicles close to each other after vehicles are clustered. This parallelization approach has been examined on different sized continuously changing GPS data and compared with serial K-means algorithm and other serial clustering algorithms. The results demonstrated that proposed parallel K-means algorithm has been shown to work much faster than other clustering algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parallel%20k-means%20algorithm" title="parallel k-means algorithm">parallel k-means algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20clustering" title=" parallel clustering"> parallel clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20on%20flowing%20data" title=" clustering on flowing data"> clustering on flowing data</a> </p> <a href="https://publications.waset.org/abstracts/86622/flowing-online-vehicle-gps-data-clustering-using-a-new-parallel-k-means-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86622.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">221</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">3472</span> Designing State Feedback Multi-Target Controllers by the Use of Particle Swarm Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyedmahdi%20Mousavihashemi">Seyedmahdi Mousavihashemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important subjects of interest in researches is 'improving' which result in various algorithms. In so many geometrical problems we are faced with target functions which should be optimized. In group practices, all the functions’ cooperation lead to convergence. In the study, the optimization algorithm of dense particles is used. Usage of the algorithm improves the given performance norms. The results reveal that usage of swarm algorithm for reinforced particles in designing state feedback improves the given performance norm and in optimized designing of multi-target state feedback controlling, the network will maintain its bearing structure. The results also show that PSO is usable for optimization of state feedback controllers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-objective" title="multi-objective">multi-objective</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced" title=" enhanced"> enhanced</a>, <a href="https://publications.waset.org/abstracts/search?q=feedback" title=" feedback"> feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle" title=" particle"> particle</a>, <a href="https://publications.waset.org/abstracts/search?q=design" title=" design"> design</a> </p> <a href="https://publications.waset.org/abstracts/60194/designing-state-feedback-multi-target-controllers-by-the-use-of-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60194.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">499</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3471</span> Sensor Network Routing Optimization by Simulating Eurygaster Life in Wheat Farms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fariborz%20Ahmadi">Fariborz Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Salehi"> Hamid Salehi</a>, <a href="https://publications.waset.org/abstracts/search?q=Khosrow%20Karimi"> Khosrow Karimi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A sensor network is set of sensor nodes that cooperate together to perform a predefined tasks. The important problem in this network is power consumption. So, in this paper one algorithm based on the eurygaster life is introduced to minimize power consumption by the nodes of these networks. In this method the search space of problem is divided into several partitions and each partition is investigated separately. The evaluation results show that our approach is more efficient in comparison to other evolutionary algorithm like genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title="evolutionary computation">evolutionary computation</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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20network%20optimization" title=" sensor network optimization"> sensor network optimization</a> </p> <a href="https://publications.waset.org/abstracts/41373/sensor-network-routing-optimization-by-simulating-eurygaster-life-in-wheat-farms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41373.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">428</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">3470</span> In-door Localization Algorithm and Appropriate Implementation Using Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adeniran%20K.%20Ademuwagun">Adeniran K. Ademuwagun</a>, <a href="https://publications.waset.org/abstracts/search?q=Alastair%20Allen"> Alastair Allen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The relationship dependence between RSS and distance in an enclosed environment is an important consideration because it is a factor that can influence the reliability of any localization algorithm founded on RSS. Several algorithms effectively reduce the variance of RSS to improve localization or accuracy performance. Our proposed algorithm essentially avoids this pitfall and consequently, its high adaptability in the face of erratic radio signal. Using 3 anchors in close proximity of each other, we are able to establish that RSS can be used as reliable indicator for localization with an acceptable degree of accuracy. Inherent in this concept, is the ability for each prospective anchor to validate (guarantee) the position or the proximity of the other 2 anchors involved in the localization and vice versa. This procedure ensures that the uncertainties of radio signals due to multipath effects in enclosed environments are minimized. A major driver of this idea is the implicit topological relationship among sensors due to raw radio signal strength. The algorithm is an area based algorithm; however, it does not trade accuracy for precision (i.e the size of the returned area). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anchor%20nodes" title="anchor nodes">anchor nodes</a>, <a href="https://publications.waset.org/abstracts/search?q=centroid%20algorithm" title=" centroid algorithm"> centroid algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=communication%20graph" title=" communication graph"> communication graph</a>, <a href="https://publications.waset.org/abstracts/search?q=radio%20signal%20strength" title=" radio signal strength"> radio signal strength</a> </p> <a href="https://publications.waset.org/abstracts/34186/in-door-localization-algorithm-and-appropriate-implementation-using-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34186.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">508</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">3469</span> Multi-Cluster Overlapping K-Means Extension Algorithm (MCOKE)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Baadel">Said Baadel</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadi%20Thabtah"> Fadi Thabtah</a>, <a href="https://publications.waset.org/abstracts/search?q=Joan%20Lu"> Joan Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard-partitioning techniques where each object is assigned to one cluster. In this paper, we propose an overlapping algorithm MCOKE which allows objects to belong to one or more clusters. The algorithm is different from fuzzy clustering techniques because objects that overlap are assigned a membership value of 1 (one) as opposed to a fuzzy membership degree. The algorithm is also different from other overlapping algorithms that require a similarity threshold to be defined as a priority which can be difficult to determine by novice users. <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=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=MCOKE" title=" MCOKE"> MCOKE</a>, <a href="https://publications.waset.org/abstracts/search?q=overlapping" title=" overlapping"> overlapping</a> </p> <a href="https://publications.waset.org/abstracts/18638/multi-cluster-overlapping-k-means-extension-algorithm-mcoke" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18638.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">575</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">3468</span> Genetic Algorithm to Construct and Enumerate 4×4 Pan-Magic Squares</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younis%20R.%20Elhaddad">Younis R. Elhaddad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20Alshaari"> Mohamed A. Alshaari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since 2700 B.C the problem of constructing magic squares attracts many researchers. Magic squares one of most difficult challenges for mathematicians. In this work, we describe how to construct and enumerate Pan- magic squares using genetic algorithm, using new chromosome encoding technique. The results were promising within reasonable time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=magic%20square" title=" magic square"> magic square</a>, <a href="https://publications.waset.org/abstracts/search?q=pan-magic%20square" title=" pan-magic square"> pan-magic square</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20intelligence" title=" computational intelligence"> computational intelligence</a> </p> <a href="https://publications.waset.org/abstracts/2917/genetic-algorithm-to-construct-and-enumerate-44-pan-magic-squares" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">576</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3467</span> An ALM Matrix Completion Algorithm for Recovering Weather Monitoring Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuqing%20Chen">Yuqing Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Xu"> Ying Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Renfa%20Li"> Renfa Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title="wireless sensor network">wireless sensor network</a>, <a href="https://publications.waset.org/abstracts/search?q=matrix%20completion" title=" matrix completion"> matrix completion</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20thresholding" title=" singular value thresholding"> singular value thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=augmented%20Lagrange%20multiplier" title=" augmented Lagrange multiplier"> augmented Lagrange multiplier</a> </p> <a href="https://publications.waset.org/abstracts/45997/an-alm-matrix-completion-algorithm-for-recovering-weather-monitoring-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45997.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">384</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">3466</span> Pruning Algorithm for the Minimum Rule Reduct Generation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sahin%20Emrah%20Amrahov">Sahin Emrah Amrahov</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatih%20Aybar"> Fatih Aybar</a>, <a href="https://publications.waset.org/abstracts/search?q=Serhat%20Dogan"> Serhat Dogan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we consider the rule reduct generation problem. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms, that are used to solve this problem, are well-known. Alternative to these algorithms, we develop Pruning Rule Generation (PRG) algorithm. We compare the PRG algorithm with RG and MRG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title="rough sets">rough sets</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20rules" title=" decision rules"> decision rules</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20induction" title=" rule induction"> rule induction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/17254/pruning-algorithm-for-the-minimum-rule-reduct-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17254.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">528</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">3465</span> Error Estimation for the Reconstruction Algorithm with Fan Beam Geometry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nirmal%20Yadav">Nirmal Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanuja%20Srivastava"> Tanuja Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shannon theory is an exact method to recover a band limited signals from its sampled values in discrete implementation, using sinc interpolators. But sinc based results are not much satisfactory for band-limited calculations so that convolution with window function, having compact support, has been introduced. Convolution Backprojection algorithm with window function is an approximation algorithm. In this paper, the error has been calculated, arises due to this approximation nature of reconstruction algorithm. This result will be defined for fan beam projection data which is more faster than parallel beam projection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title="computed tomography">computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution%20backprojection" title=" convolution backprojection"> convolution backprojection</a>, <a href="https://publications.waset.org/abstracts/search?q=radon%20transform" title=" radon transform"> radon transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fan%20beam" title=" fan beam"> fan beam</a> </p> <a href="https://publications.waset.org/abstracts/25009/error-estimation-for-the-reconstruction-algorithm-with-fan-beam-geometry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25009.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">490</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">3464</span> On the Use of Analytical Performance Models to Design a High-Performance Active Queue Management Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahram%20Jamali">Shahram Jamali</a>, <a href="https://publications.waset.org/abstracts/search?q=Samira%20Hamed"> Samira Hamed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20queue%20management" title="active queue management">active queue management</a>, <a href="https://publications.waset.org/abstracts/search?q=RED" title=" RED"> RED</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20model" title=" Markov model"> Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20early%20detection%20algorithm" title=" random early detection algorithm "> random early detection algorithm </a> </p> <a href="https://publications.waset.org/abstracts/33934/on-the-use-of-analytical-performance-models-to-design-a-high-performance-active-queue-management-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33934.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">539</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">3463</span> A Game-Theory-Based Price-Optimization Algorithm for the Simulation of Markets Using Agent-Based Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juan%20Manuel%20Sanchez-Cartas">Juan Manuel Sanchez-Cartas</a>, <a href="https://publications.waset.org/abstracts/search?q=Gonzalo%20Leon"> Gonzalo Leon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A price competition algorithm for ABMs based on game theory principles is proposed to deal with the simulation of theoretical market models. The algorithm is applied to the classical Hotelling’s model and to a two-sided market model to show it leads to the optimal behavior predicted by theoretical models. However, when theoretical models fail to predict the equilibrium, the algorithm is capable of reaching a feasible outcome. Results highlight that the algorithm can be implemented in other simulation models to guarantee rational users and endogenous optimal behaviors. Also, it can be applied as a tool of verification given that is theoretically based. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agent-based%20models" title="agent-based models">agent-based models</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithmic%20game%20theory" title=" algorithmic game theory"> algorithmic game theory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-sided%20markets" title=" multi-sided markets"> multi-sided markets</a>, <a href="https://publications.waset.org/abstracts/search?q=price%20optimization" title=" price optimization"> price optimization</a> </p> <a href="https://publications.waset.org/abstracts/59770/a-game-theory-based-price-optimization-algorithm-for-the-simulation-of-markets-using-agent-based-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59770.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">455</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">3462</span> Discrete State Prediction Algorithm Design with Self Performance Enhancement Capacity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smail%20Tigani">Smail Tigani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ouzzif"> Mohamed Ouzzif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20state" title="discrete state">discrete state</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Chains" title=" Markov Chains"> Markov Chains</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=auto-adaptive%20systems" title=" auto-adaptive systems"> auto-adaptive systems</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20Simulation" title=" Monte Carlo Simulation"> Monte Carlo Simulation</a> </p> <a href="https://publications.waset.org/abstracts/20238/discrete-state-prediction-algorithm-design-with-self-performance-enhancement-capacity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20238.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">3461</span> An Algorithm of Regulation of Glucose-Insulin Concentration in the Blood</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Selma">B. Selma</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Chouraqui"> S. Chouraqui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The pancreas is an elongated organ that extends across the abdomen, below the stomach. In addition, it secretes certain enzymes that aid in food digestion. The pancreas also manufactures hormones responsible for regulating blood glucose levels. In the present paper, we propose a mathematical model to study the homeostasis of glucose and insulin in healthy human, and a simulation of this model, which depicts the physiological events after a meal, will be represented in ordinary humans. The aim of this paper is to design an algorithm which regulates the level of glucose in the blood. The algorithm applied the concept of expert system for performing an algorithm control in the form of an "active" used to prescribe the rate of insulin infusion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. The results showed a performance of the control system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=modeling" title="modeling">modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=regulation" title=" regulation"> regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=glucose-insulin" title=" glucose-insulin"> glucose-insulin</a>, <a href="https://publications.waset.org/abstracts/search?q=blood" title=" blood"> blood</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20system" title=" control system"> control system</a> </p> <a href="https://publications.waset.org/abstracts/76765/an-algorithm-of-regulation-of-glucose-insulin-concentration-in-the-blood" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76765.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">3460</span> Power Allocation Algorithm for Orthogonal Frequency Division Multiplexing Based Cognitive Radio Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bircan%20Demiral">Bircan Demiral</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cognitive radio (CR) is the promising technology that addresses the spectrum scarcity problem for future wireless communications. Orthogonal Frequency Division Multiplexing (OFDM) technology provides more power band ratios for cognitive radio networks (CRNs). While CR is a solution to the spectrum scarcity, it also brings up the capacity problem. In this paper, a novel power allocation algorithm that aims at maximizing the sum capacity in the OFDM based cognitive radio networks is proposed. Proposed allocation algorithm is based on the previously developed water-filling algorithm. To reduce the computational complexity calculating in water filling algorithm, proposed algorithm allocates the total power according to each subcarrier. The power allocated to the subcarriers increases sum capacity. To see this increase, Matlab program was used, and the proposed power allocation was compared with average power allocation, water filling and general power allocation algorithms. The water filling algorithm performed worse than the proposed algorithm while it performed better than the other two algorithms. The proposed algorithm is better than other algorithms in terms of capacity increase. In addition the effect of the change in the number of subcarriers on capacity was discussed. Simulation results show that the increase in the number of subcarrier increases the capacity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio%20network" title="cognitive radio network">cognitive radio network</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20allocation" title=" power allocation"> power allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20filling" title=" water filling"> water filling</a> </p> <a href="https://publications.waset.org/abstracts/92207/power-allocation-algorithm-for-orthogonal-frequency-division-multiplexing-based-cognitive-radio-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92207.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3459</span> Multi-Objective Variable Neighborhood Search Algorithm to Solving Scheduling Problem with Transportation Times</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Khalili">Majid Khalili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective variable neighborhood algorithm (MOVNS). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOVNS provides sound performance comparing with other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=no-wait%20hybrid%20flowshop%20scheduling%3B%20multi-objective%20variable%20neighborhood%20algorithm%3B%20makespan%3B%20total%20weighted%20tardiness" title="no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness">no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness</a> </p> <a href="https://publications.waset.org/abstracts/15098/multi-objective-variable-neighborhood-search-algorithm-to-solving-scheduling-problem-with-transportation-times" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15098.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3458</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">3457</span> Part of Speech Tagging Using Statistical Approach for Nepali Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Archit%20Yajnik">Archit Yajnik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title="hidden markov model">hidden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=POS%20tagging" title=" POS tagging"> POS tagging</a>, <a href="https://publications.waset.org/abstracts/search?q=viterbi%20algorithm" title=" viterbi algorithm"> viterbi algorithm</a> </p> <a href="https://publications.waset.org/abstracts/61160/part-of-speech-tagging-using-statistical-approach-for-nepali-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61160.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">326</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">3456</span> Implementation of Iterative Algorithm for Earthquake Location</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussain%20K.%20Chaiel">Hussain K. Chaiel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development in the field of the digital signal processing (DSP) and the microelectronics technology reduces the complexity of the iterative algorithms that need large number of arithmetic operations. Virtex-Field Programmable Gate Arrays (FPGAs) are programmable silicon foundations which offer an important solution for addressing the needs of high performance DSP designer. In this work, Virtex-7 FPGA technology is used to implement an iterative algorithm to estimate the earthquake location. Simulation results show that an implementation based on block RAMB36E1 and DSP48E1 slices of Virtex-7 type reduces the number of cycles of the clock frequency. This enables the algorithm to be used for earthquake prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DSP" title="DSP">DSP</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake" title=" earthquake"> earthquake</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20algorithm" title=" iterative algorithm "> iterative algorithm </a> </p> <a href="https://publications.waset.org/abstracts/28897/implementation-of-iterative-algorithm-for-earthquake-location" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28897.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">389</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">3455</span> Alternative Key Exchange Algorithm Based on Elliptic Curve Digital Signature Algorithm Certificate and Usage in Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Andreasyan">A. Andreasyan</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Connors"> C. Connors</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Elliptic Curve Digital Signature algorithm-based X509v3 certificates are becoming more popular due to their short public and private key sizes. Moreover, these certificates can be stored in Internet of Things (IoT) devices, with limited resources, using less memory and transmitted in network security protocols, such as Internet Key Exchange (IKE), Transport Layer Security (TLS) and Secure Shell (SSH) with less bandwidth. The proposed method gives another advantage, in that it increases the performance of the above-mentioned protocols in terms of key exchange by saving one scalar multiplication operation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cryptography" title="cryptography">cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=elliptic%20curve%20digital%20signature%20algorithm" title=" elliptic curve digital signature algorithm"> elliptic curve digital signature algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20exchange" title=" key exchange"> key exchange</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security%20protocol" title=" network security protocol"> network security protocol</a> </p> <a href="https://publications.waset.org/abstracts/120384/alternative-key-exchange-algorithm-based-on-elliptic-curve-digital-signature-algorithm-certificate-and-usage-in-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120384.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">146</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">3454</span> Hybrid Gravity Gradient Inversion-Ant Colony Optimization Algorithm for Motion Planning of Mobile Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meng%20Wu">Meng Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20planning" title="motion planning">motion planning</a>, <a href="https://publications.waset.org/abstracts/search?q=gravity%20gradient%20inversion%20algorithm" title=" gravity gradient inversion algorithm"> gravity gradient inversion algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title=" ant colony optimization"> ant colony optimization</a> </p> <a href="https://publications.waset.org/abstracts/110462/hybrid-gravity-gradient-inversion-ant-colony-optimization-algorithm-for-motion-planning-of-mobile-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3453</span> Penguins Search Optimization Algorithm for Chaotic Synchronization System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofiane%20Bououden">Sofiane Bououden</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilyes%20Boulkaibet"> Ilyes Boulkaibet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In terms of security of the information signal, the meta-heuristic Penguins Search Optimization Algorithm (PeSOA) is applied to synchronize chaotic encryption communications in the case of sensitive dependence on initial conditions in chaotic generator oscillator. The objective of this paper is the use of the PeSOA algorithm to exploring search space with random and iterative processes for synchronization of symmetric keys in both transmission and reception. Simulation results show the effectiveness of the PeSOA algorithm in generating symmetric keys of the encryption process and synchronizing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic" title="meta-heuristic">meta-heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=PeSOA" title=" PeSOA"> PeSOA</a>, <a href="https://publications.waset.org/abstracts/search?q=chaotic%20systems" title=" chaotic systems"> chaotic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=encryption" title=" encryption"> encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronization%20optimization" title=" synchronization optimization"> synchronization optimization</a> </p> <a href="https://publications.waset.org/abstracts/141318/penguins-search-optimization-algorithm-for-chaotic-synchronization-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141318.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">195</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">3452</span> A Genetic Algorithm Based Permutation and Non-Permutation Scheduling Heuristics for Finite Capacity Material Requirement Planning Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watchara%20Songserm">Watchara Songserm</a>, <a href="https://publications.waset.org/abstracts/search?q=Teeradej%20Wuttipornpun"> Teeradej Wuttipornpun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capacitated%20MRP" title="capacitated MRP">capacitated MRP</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=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=automotive%20industries" title=" automotive industries"> automotive industries</a>, <a href="https://publications.waset.org/abstracts/search?q=flow%20shop" title=" flow shop"> flow shop</a>, <a href="https://publications.waset.org/abstracts/search?q=application%20in%20industry" title=" application in industry"> application in industry</a> </p> <a href="https://publications.waset.org/abstracts/67589/a-genetic-algorithm-based-permutation-and-non-permutation-scheduling-heuristics-for-finite-capacity-material-requirement-planning-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67589.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">489</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">3451</span> Hyperspectral Image Classification Using Tree Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shreya%20Pare">Shreya Pare</a>, <a href="https://publications.waset.org/abstracts/search?q=Parvin%20Akhter"> Parvin Akhter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20images" title=" hyperspectral images"> hyperspectral images</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20distribution%20margin" title=" large distribution margin"> large distribution margin</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20fuzzy%20entropy%20function" title=" modified fuzzy entropy function"> modified fuzzy entropy function</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20thresholding" title=" multilevel thresholding"> multilevel thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20search%20algorithm" title=" tree search algorithm"> tree search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20image%20classification%20using%20tree%20search%20algorithm" title=" hyperspectral image classification using tree search algorithm"> hyperspectral image classification using tree search algorithm</a> </p> <a href="https://publications.waset.org/abstracts/143284/hyperspectral-image-classification-using-tree-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143284.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">3450</span> Consumer Load Profile Determination with Entropy-Based K-Means Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20P.%20Panapakidis">Ioannis P. Panapakidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Marios%20N.%20Moschakis"> Marios N. Moschakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means. <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=load%20profiling" title=" load profiling"> load profiling</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20modeling" title=" load modeling"> load modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency%20and%20quality" title=" energy efficiency and quality"> energy efficiency and quality</a> </p> <a href="https://publications.waset.org/abstracts/89525/consumer-load-profile-determination-with-entropy-based-k-means-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89525.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">164</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">3449</span> Forward Stable Computation of Roots of Real Polynomials with Only Real Distinct Roots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nevena%20Jakov%C4%8Devi%C4%87%20Stor">Nevena Jakovčević Stor</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivan%20Slapni%C4%8Dar"> Ivan Slapničar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Any polynomial can be expressed as a characteristic polynomial of a complex symmetric arrowhead matrix. This expression is not unique. If the polynomial is real with only real distinct roots, the matrix can be chosen as real. By using accurate forward stable algorithm for computing eigen values of real symmetric arrowhead matrices we derive a forward stable algorithm for computation of roots of such polynomials in O(n^2 ) operations. The algorithm computes each root to almost full accuracy. In some cases, the algorithm invokes extended precision routines, but only in the non-iterative part. Our examples include numerically difficult problems, like the well-known Wilkinson’s polynomials. Our algorithm compares favorably to other method for polynomial root-finding, like MPSolve or Newton’s method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=roots%20of%20polynomials" title="roots of polynomials">roots of polynomials</a>, <a href="https://publications.waset.org/abstracts/search?q=eigenvalue%20decomposition" title=" eigenvalue decomposition"> eigenvalue decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=arrowhead%20matrix" title=" arrowhead matrix"> arrowhead matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20relative%20accuracy" title=" high relative accuracy"> high relative accuracy</a> </p> <a href="https://publications.waset.org/abstracts/40100/forward-stable-computation-of-roots-of-real-polynomials-with-only-real-distinct-roots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40100.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">417</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">3448</span> A Hybrid Pareto-Based Swarm Optimization Algorithm for the Multi-Objective Flexible 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 new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=swarm-based%20optimization" title="swarm-based optimization">swarm-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20optimality" title=" Pareto optimality"> Pareto optimality</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling" title=" flexible job shop scheduling"> flexible 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/72144/a-hybrid-pareto-based-swarm-optimization-algorithm-for-the-multi-objective-flexible-job-shop-scheduling-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=memetic%20algorithm&page=4" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=memetic%20algorithm&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=memetic%20algorithm&page=2">2</a></li> <li class="page-item"><a class="page-link" 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