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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> 7249</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: statistical optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7249</span> A Comparative Analysis of a Custom Optimization Experiment with Confidence Intervals in Anylogic and Optquest</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Felipe%20Haro">Felipe Haro</a>, <a href="https://publications.waset.org/abstracts/search?q=Soheila%20Antar"> Soheila Antar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a custom optimization experiment developed in AnyLogic, based on genetic algorithms, designed to ensure reliable optimization results by incorporating Montecarlo simulations and achieving a specified confidence level. To validate the custom experiment, we compared its performance with AnyLogic's built-in OptQuest optimization method across three distinct problems. Statistical analyses, including Welch's t-test, were conducted to assess the differences in performance. The results demonstrate that while the custom experiment shows advantages in certain scenarios, both methods perform comparably in others, confirming the custom approach as a reliable and effective tool for optimization under uncertainty. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=confidence%20intervals" title=" confidence intervals"> confidence intervals</a>, <a href="https://publications.waset.org/abstracts/search?q=Montecarlo%20simulation" title=" Montecarlo simulation"> Montecarlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=optQuest" title=" optQuest"> optQuest</a>, <a href="https://publications.waset.org/abstracts/search?q=AnyLogic" title=" AnyLogic"> AnyLogic</a> </p> <a href="https://publications.waset.org/abstracts/193080/a-comparative-analysis-of-a-custom-optimization-experiment-with-confidence-intervals-in-anylogic-and-optquest" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193080.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">29</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">7248</span> Desing of PSS and SVC to Improve Power System Stability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Samkan">Mahmoud Samkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the design and assessment of new coordination between Power System Stabilizers (PSSs) and Static Var Compensator (SVC) in a multimachine power system via statistical method are proposed. The coordinated design problem of PSSs and SVC over a wide range of loading conditions is handled as an optimization problem. The Bacterial Swarming Optimization (BSO), which synergistically couples the Bacterial Foraging (BF) with the Particle Swarm Optimization (PSO), is employed to seek for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is enhanced. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the BSO based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BSO (BSOPSS) and optimized SVC controller based on BSO (BSOSVC). Moreover, a statistical T test is executed to validate the robustness of coordinated controller versus uncoordinated one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SVC" title="SVC">SVC</a>, <a href="https://publications.waset.org/abstracts/search?q=PSSs" title=" PSSs"> PSSs</a>, <a href="https://publications.waset.org/abstracts/search?q=multimachine%20power%20system" title=" multimachine power system"> multimachine power system</a>, <a href="https://publications.waset.org/abstracts/search?q=coordinated%20design" title=" coordinated design"> coordinated design</a>, <a href="https://publications.waset.org/abstracts/search?q=bacteria%20swarm%20optimization" title=" bacteria swarm optimization"> bacteria swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20assessment" title=" statistical assessment "> statistical assessment </a> </p> <a href="https://publications.waset.org/abstracts/36661/desing-of-pss-and-svc-to-improve-power-system-stability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36661.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">7247</span> Series-Parallel Systems Reliability Optimization Using Genetic Algorithm and Statistical Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Essa%20Abrahim%20Abdulgader%20Saleem">Essa Abrahim Abdulgader Saleem</a>, <a href="https://publications.waset.org/abstracts/search?q=Thien-My%20Dao"> Thien-My Dao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <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=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=redundancy" title=" redundancy"> redundancy</a> </p> <a href="https://publications.waset.org/abstracts/55689/series-parallel-systems-reliability-optimization-using-genetic-algorithm-and-statistical-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55689.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">344</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">7246</span> Statistical Optimization and Production of Rhamnolipid by P. aeruginosa PAO1 Using Prickly Pear Peel as a Carbon Source</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20M.%20Abo%20Elsoud">Mostafa M. Abo Elsoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Heba%20I.%20Elkhouly"> Heba I. Elkhouly</a>, <a href="https://publications.waset.org/abstracts/search?q=Nagwa%20M.%20Sidkey"> Nagwa M. Sidkey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Production of rhamnolipids by Pseudomonas aeruginosa has attracted a growing interest during the last few decades due to its high productivity compared with other microorganisms. In the current work, rhamnolipids production by P. aeruginosa PAO1 was statistically modeled using Taguchi orthogonal array, numerically optimized and validated. Prickly Pear Peel (Opuntia ficus-indica) has been used as a carbon source for production of rhamnolipid. Finally, the optimum conditions for rhamnolipid production were applied in 5L working volume bioreactors at different aerations, agitation and controlled pH for maximum rhamnolipid production. In addition, kinetic studies of rhamnolipids production have been reported. At the end of the batch bioreactor optimization process, rhamnolipids production by P. aeruginosa PAO1 has reached the worldwide levels and can be applied for its industrial production. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rhamnolipids" title="rhamnolipids">rhamnolipids</a>, <a href="https://publications.waset.org/abstracts/search?q=pseudomonas%20aeruginosa" title=" pseudomonas aeruginosa"> pseudomonas aeruginosa</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20optimization" title=" statistical optimization"> statistical optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=tagushi" title=" tagushi"> tagushi</a>, <a href="https://publications.waset.org/abstracts/search?q=opuntia%20ficus-indica" title=" opuntia ficus-indica"> opuntia ficus-indica</a> </p> <a href="https://publications.waset.org/abstracts/88618/statistical-optimization-and-production-of-rhamnolipid-by-p-aeruginosa-pao1-using-prickly-pear-peel-as-a-carbon-source" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88618.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">184</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">7245</span> On Mathematical Modelling and Optimization of Emerging Trends Processes in Advanced Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Agarana%20Michael%20C.">Agarana Michael C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Akinlabi%20Esther%20T."> Akinlabi Esther T.</a>, <a href="https://publications.waset.org/abstracts/search?q=Pule%20Kholopane"> Pule Kholopane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Innovation in manufacturing process technologies and associated product design affects the prospects for manufacturing today and in near future. In this study some theoretical methods, useful as tools in advanced manufacturing, are considered. In particular, some basic Mathematical, Operational Research, Heuristic, and Statistical techniques are discussed. These techniques/methods are very handy in many areas of advanced manufacturing processes, including process planning optimization, modelling and analysis. Generally the production rate requires the application of Mathematical methods. The Emerging Trends Processes in Advanced Manufacturing can be enhanced by using Mathematical Modelling and Optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modelling" title="mathematical modelling">mathematical modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20trends" title=" emerging trends"> emerging trends</a>, <a href="https://publications.waset.org/abstracts/search?q=advanced%20manufacturing" title=" advanced manufacturing"> advanced manufacturing</a> </p> <a href="https://publications.waset.org/abstracts/158822/on-mathematical-modelling-and-optimization-of-emerging-trends-processes-in-advanced-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158822.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">307</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">7244</span> Optimization of Media for Enhanced Fermentative Production of Mycophenolic Acid by Penicillium brevicompactum</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shraddha%20Digole">Shraddha Digole</a>, <a href="https://publications.waset.org/abstracts/search?q=Swarali%20Hingse"> Swarali Hingse</a>, <a href="https://publications.waset.org/abstracts/search?q=Uday%20Annapure"> Uday Annapure</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mycophenolic acid (MPA) is an immunosuppressant; produced by Penicillium Sp. Box-Behnken statistical experimental design was employed to optimize the condition of Penicillium brevicompactum NRRL 2011 for mycophenolic acid (MPA) production. Initially optimization of various physicochemical parameters and media components was carried out using one factor at a time approach and significant factors were screened by Taguchi L-16 orthogonal array design. Taguchi design indicated that glucose, KH2PO4 and MgSO4 had significant effect on MPA production. These variables were selected for further optimization studies using Box-Behnken design. Optimised fermentation condition, glucose (60 g/L), glycine (28 g/L), L-leucine (1.5g/L), KH2PO4 (3g/L), MgSO4.7H2O (1.5g/L), increased the production of MPA from 170 mg/L to 1032.54 mg/L. Analysis of variance (ANOVA) showed a high value of coefficient of determination R2 (0.9965), indicating a good agreement between experimental and predicted values and proves validity of the statistical model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Box-Behnken%20design" title="Box-Behnken design">Box-Behnken design</a>, <a href="https://publications.waset.org/abstracts/search?q=fermentation" title=" fermentation"> fermentation</a>, <a href="https://publications.waset.org/abstracts/search?q=mycophenolic%20acid" title=" mycophenolic acid"> mycophenolic acid</a>, <a href="https://publications.waset.org/abstracts/search?q=Penicillium%20brevicompactum" title=" Penicillium brevicompactum"> Penicillium brevicompactum</a> </p> <a href="https://publications.waset.org/abstracts/20009/optimization-of-media-for-enhanced-fermentative-production-of-mycophenolic-acid-by-penicillium-brevicompactum" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20009.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">457</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">7243</span> Crashworthiness Optimization of an Automotive Front Bumper in Composite Material</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Boria">S. Boria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last years, the crashworthiness of an automotive body structure can be improved, since the beginning of the design stage, thanks to the development of specific optimization tools. It is well known how the finite element codes can help the designer to investigate the crashing performance of structures under dynamic impact. Therefore, by coupling nonlinear mathematical programming procedure and statistical techniques with FE simulations, it is possible to optimize the design with reduced number of analytical evaluations. In engineering applications, many optimization methods which are based on statistical techniques and utilize estimated models, called meta-models, are quickly spreading. A meta-model is an approximation of a detailed simulation model based on a dataset of input, identified by the design of experiments (DOE); the number of simulations needed to build it depends on the number of variables. Among the various types of meta-modeling techniques, Kriging method seems to be excellent in accuracy, robustness and efficiency compared to other ones when applied to crashworthiness optimization. Therefore the application of such meta-model was used in this work, in order to improve the structural optimization of a bumper for a racing car in composite material subjected to frontal impact. The specific energy absorption represents the objective function to maximize and the geometrical parameters subjected to some design constraints are the design variables. LS-DYNA codes were interfaced with LS-OPT tool in order to find the optimized solution, through the use of a domain reduction strategy. With the use of the Kriging meta-model the crashworthiness characteristic of the composite bumper was improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite%20material" title="composite material">composite material</a>, <a href="https://publications.waset.org/abstracts/search?q=crashworthiness" title=" crashworthiness"> crashworthiness</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/72849/crashworthiness-optimization-of-an-automotive-front-bumper-in-composite-material" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72849.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">260</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">7242</span> Curve Fitting by Cubic Bezier Curves Using Migrating Birds Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy. <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=Bezier%20curves" title=" Bezier curves"> Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20optimization" title=" heuristic optimization"> heuristic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migrating%20birds%20optimization" title=" migrating birds optimization"> migrating birds optimization</a> </p> <a href="https://publications.waset.org/abstracts/78026/curve-fitting-by-cubic-bezier-curves-using-migrating-birds-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78026.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">346</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">7241</span> Statistical Optimization of Vanillin Production by Pycnoporus Cinnabarinus 1181</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swarali%20Hingse">Swarali Hingse</a>, <a href="https://publications.waset.org/abstracts/search?q=Shraddha%20Digole"> Shraddha Digole</a>, <a href="https://publications.waset.org/abstracts/search?q=Uday%20Annapure"> Uday Annapure</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study investigates the biotransformation of ferulic acid to vanillin by Pycnoporus cinnabarinus and its optimization using one-factor-at-a-time method as well as statistical approach. Effect of various physicochemical parameters and medium components was studied using one-factor-at-a-time method. Screening of the significant factors was carried out using L25 Taguchi orthogonal array and then these selected significant factors were further optimized using response surface methodology (RSM). Significant media components obtained using Taguchi L25 orthogonal array were glucose, KH2PO4 and yeast extract. Further, a Box Behnken design was used to investigate the interactive effects of the three most significant media components. The final medium obtained after optimization using RSM containing glucose (34.89 g/L), diammonium tartrate (1 g/L), yeast extract (1.47 g/L), MgSO4•7H2O (0.5 g/L), KH2PO4 (0.15 g/L), and CaCl2•2H2O (20 mg/L) resulted in amplification of vanillin production from 30.88 mg/L to 187.63 mg/L. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ferulic%20acid" title="ferulic acid">ferulic acid</a>, <a href="https://publications.waset.org/abstracts/search?q=pycnoporus%20cinnabarinus" title=" pycnoporus cinnabarinus"> pycnoporus cinnabarinus</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology" title=" response surface methodology"> response surface methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=vanillin" title=" vanillin"> vanillin</a> </p> <a href="https://publications.waset.org/abstracts/20013/statistical-optimization-of-vanillin-production-by-pycnoporus-cinnabarinus-1181" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20013.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">7240</span> From Theory to Practice: Harnessing Mathematical and Statistical Sciences in Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ullah">Zahid Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Atlas%20Khan"> Atlas Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title="data analytics">data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20sciences" title=" mathematical sciences"> mathematical sciences</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</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=interdisciplinary%20collaboration" title=" interdisciplinary collaboration"> interdisciplinary collaboration</a>, <a href="https://publications.waset.org/abstracts/search?q=practical%20applications" title=" practical applications"> practical applications</a> </p> <a href="https://publications.waset.org/abstracts/167377/from-theory-to-practice-harnessing-mathematical-and-statistical-sciences-in-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167377.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">101</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">7239</span> Lambda-Levelwise Statistical Convergence of a Sequence of Fuzzy Numbers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Berna%20Benli">F. Berna Benli</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%96zg%C3%BCr%20Keskin"> Özgür Keskin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lately, many mathematicians have been studied the statistical convergence of a sequence of fuzzy numbers. We know that Lambda-statistically convergence is a kind of convergence between ordinary convergence and statistical convergence. In this paper, we will introduce the new kind of convergence such as λ-levelwise statistical convergence. Then, we will define the concept of the λ-levelwise statistical cluster and limit points of a sequence of fuzzy numbers. Also, we will discuss the relations between the sets of λ-levelwise statistical cluster points and λ-levelwise statistical limit points of sequences of fuzzy numbers. This work has been extended in this paper, where some relations have been considered such that when lambda-statistical limit inferior and lambda-statistical limit superior for lambda-statistically convergent sequences of fuzzy numbers are equal. Furthermore, lambda-statistical boundedness condition for different sequences of fuzzy numbers has been studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20number" title="fuzzy number">fuzzy number</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-levelwise%20statistical%20cluster%20points" title=" λ-levelwise statistical cluster points"> λ-levelwise statistical cluster points</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-levelwise%20statistical%20convergence" title=" λ-levelwise statistical convergence"> λ-levelwise statistical convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-levelwise%20statistical%20limit%20points" title=" λ-levelwise statistical limit points"> λ-levelwise statistical limit points</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-statistical%20cluster%20points" title=" λ-statistical cluster points"> λ-statistical cluster points</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-statistical%20convergence" title=" λ-statistical convergence"> λ-statistical convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%BB-statistical%20limit%20%20points" title=" λ-statistical limit points"> λ-statistical limit points</a> </p> <a href="https://publications.waset.org/abstracts/20755/lambda-levelwise-statistical-convergence-of-a-sequence-of-fuzzy-numbers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20755.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">482</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">7238</span> A Mean–Variance–Skewness Portfolio Optimization Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=skewness" title=" skewness"> skewness</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a> </p> <a href="https://publications.waset.org/abstracts/102472/a-mean-variance-skewness-portfolio-optimization-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102472.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">205</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">7237</span> Improved Whale Algorithm Based on Information Entropy and Its Application in Truss Structure Optimization Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serges%20Mendomo%20%20Meye">Serges Mendomo Meye</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Guowei"> Li Guowei</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Zhenzhong"> Shen Zhenzhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Gan%20Lei"> Gan Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Liqun"> Xu Liqun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20entropy" title="information entropy">information entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20optimization" title=" structural optimization"> structural optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=truss%20structure" title=" truss structure"> truss structure</a>, <a href="https://publications.waset.org/abstracts/search?q=whale%20algorithm" title=" whale algorithm"> whale algorithm</a> </p> <a href="https://publications.waset.org/abstracts/139986/improved-whale-algorithm-based-on-information-entropy-and-its-application-in-truss-structure-optimization-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139986.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">256</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">7236</span> Improved Particle Swarm Optimization with Cellular Automata and Fuzzy Cellular Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Javadzadeh">Ramin Javadzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=cellular%20learning%20automata" title=" cellular learning automata"> cellular learning automata</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=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/24739/improved-particle-swarm-optimization-with-cellular-automata-and-fuzzy-cellular-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24739.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">613</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">7235</span> Non-Stationary Stochastic Optimization of an Oscillating Water Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20L.%20Jal%C3%B3n">María L. Jalón</a>, <a href="https://publications.waset.org/abstracts/search?q=Feargal%20Brennan"> Feargal Brennan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-stationary%20stochastic%20optimization" title="non-stationary stochastic optimization">non-stationary stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillating%20water" title=" oscillating water"> oscillating water</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20variability" title=" temporal variability"> temporal variability</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20energy" title=" wave energy"> wave energy</a> </p> <a href="https://publications.waset.org/abstracts/75300/non-stationary-stochastic-optimization-of-an-oscillating-water-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75300.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7234</span> Two-Stage Approach for Solving the Multi-Objective Optimization Problem on Combinatorial Configurations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liudmyla%20Koliechkina">Liudmyla Koliechkina</a>, <a href="https://publications.waset.org/abstracts/search?q=Olena%20Dvirna"> Olena Dvirna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20set" title="discrete set">discrete set</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20combinatorial%20optimization" title=" linear combinatorial optimization"> linear combinatorial optimization</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%20solutions" title=" Pareto solutions"> Pareto solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20permutation%20set" title=" partial permutation set"> partial permutation set</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20graph" title=" structural graph"> structural graph</a> </p> <a href="https://publications.waset.org/abstracts/133824/two-stage-approach-for-solving-the-multi-objective-optimization-problem-on-combinatorial-configurations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133824.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">172</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">7233</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">542</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">7232</span> Model of Optimal Centroids Approach for Multivariate Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pham%20Van%20Nha">Pham Van Nha</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Cam%20Binh"> Le Cam Binh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20optimization" title="analysis of optimization">analysis of optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20based%20optimization" title=" artificial intelligence based optimization"> artificial intelligence based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20for%20learning%20and%20data%20analysis" title=" optimization for learning and data analysis"> optimization for learning and data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a> </p> <a href="https://publications.waset.org/abstracts/126058/model-of-optimal-centroids-approach-for-multivariate-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126058.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">214</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7231</span> Investigated Optimization of Davidson Path Loss Model for Digital Terrestrial Television (DTTV) Propagation in Urban Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pitak%20Keawbunsong">Pitak Keawbunsong</a>, <a href="https://publications.waset.org/abstracts/search?q=Sathaporn%20Promwong"> Sathaporn Promwong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an investigation on the efficiency of the optimized Davison path loss model in order to look for a suitable path loss model to design and planning DTTV propagation for small and medium urban areas in southern Thailand. Hadyai City in Songkla Province is chosen as the case study to collect the analytical data on the electric field strength. The optimization is conducted through the least square method while the efficiency index is through the statistical value of relative error (RE). The result of the least square method is the offset and slop of the frequency to be used in the optimized process. The statistical result shows that RE of the old Davidson model is at the least when being compared with the optimized Davison and the Hata models. Thus, the old Davison path loss model is the most accurate that further becomes the most optimized for the plan on the propagation network design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DTTV%20propagation" title="DTTV propagation">DTTV propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20loss%20model" title=" path loss model"> path loss model</a>, <a href="https://publications.waset.org/abstracts/search?q=Davidson%20model" title=" Davidson model"> Davidson model</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20method" title=" least square method"> least square method</a> </p> <a href="https://publications.waset.org/abstracts/43812/investigated-optimization-of-davidson-path-loss-model-for-digital-terrestrial-television-dttv-propagation-in-urban-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43812.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">346</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">7230</span> Cuckoo Search (CS) Optimization Algorithm for Solving Constrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sait%20Ali%20Uymaz">Sait Ali Uymaz</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%BClay%20Tezel"> Gülay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</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=constrained%20optimization%20problems" title=" constrained optimization problems"> constrained optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=penalty%20method" title=" penalty method"> penalty method</a> </p> <a href="https://publications.waset.org/abstracts/13991/cuckoo-search-cs-optimization-algorithm-for-solving-constrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13991.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">7229</span> Quantum Statistical Machine Learning and Quantum Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Alzeley">Omar Alzeley</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Utev"> Sergey Utev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20techniques" title=" simulation techniques"> simulation techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20probability" title=" quantum probability"> quantum probability</a>, <a href="https://publications.waset.org/abstracts/search?q=tensor%20product" title=" tensor product"> tensor product</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/52720/quantum-statistical-machine-learning-and-quantum-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7228</span> An Algorithm of Set-Based Particle Swarm Optimization with Status Memory for Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Hino">Takahiro Hino</a>, <a href="https://publications.waset.org/abstracts/search?q=Michiharu%20Maeda"> Michiharu Maeda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is an optimization approach that achieves the social model of bird flocking and fish schooling. PSO works in continuous space and can solve continuous optimization problem with high quality. Set-based particle swarm optimization (SPSO) functions in discrete space by using a set. SPSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In this paper, we present an algorithm of SPSO with status memory to decide the position based on the previous position for solving traveling salesman problem (TSP). In order to show the effectiveness of our approach. We examine SPSOSM for TSP compared to the existing algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization%20problems" title="combinatorial optimization problems">combinatorial optimization problems</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=set-based%20particle%20swarm%20optimization" title=" set-based particle swarm optimization"> set-based particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/47282/an-algorithm-of-set-based-particle-swarm-optimization-with-status-memory-for-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47282.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">558</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">7227</span> Developing Model for Fuel Consumption Optimization in Aviation Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somesh%20Kumar%20Sharma">Somesh Kumar Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunanad%20Gupta"> Sunanad Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The contribution of aviation to society and economy is undisputedly significant. The aviation industry drives economic and social progress by contributing prominently to tourism, commerce and improved quality of life. Identifying the amount of fuel consumed by an aircraft while moving in both airspace and ground networks is critical to air transport economics. Aviation fuel is a major operating cost parameter of the aviation industry and at the same time it is prone to various constraints. This article aims to develop a model for fuel consumption of aviation product. The paper tailors the information for the fuel consumption optimization in terms of information development, information evaluation and information refinement. The information is evaluated and refined using statistical package R and Factor Analysis which is further validated with neural networking. The study explores three primary dimensions which are finally summarized into 23 influencing variables in contrast to 96 variables available in literature. The 23 variables explored in this study should be considered as highly influencing variables for fuel consumption which will contribute significantly towards fuel optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuel%20consumption" title="fuel consumption">fuel consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=civil%20aviation%20industry" title=" civil aviation industry"> civil aviation industry</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networking" title=" neural networking"> neural networking</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/27055/developing-model-for-fuel-consumption-optimization-in-aviation-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27055.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">345</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">7226</span> Optimizing Performance of Tablet's Direct Compression Process Using Fuzzy Goal Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Al-Refaie">Abbas Al-Refaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims at improving the performance of the tableting process using statistical quality control and fuzzy goal programming. The tableting process was studied. Statistical control tools were used to characterize the existing process for three critical responses including the averages of a tablet’s weight, hardness, and thickness. At initial process factor settings, the estimated process capability index values for the tablet’s averages of weight, hardness, and thickness were 0.58, 3.36, and 0.88, respectively. The L<sub>9</sub> array was utilized to provide experimentation design. Fuzzy goal programming was then employed to find the combination of optimal factor settings. Optimization results showed that the process capability index values for a tablet’s averages of weight, hardness, and thickness were improved to 1.03, 4.42, and 1.42, respectively. Such improvements resulted in significant savings in quality and production costs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20goal%20programming" title="fuzzy goal programming">fuzzy goal programming</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20capability" title=" process capability"> process capability</a>, <a href="https://publications.waset.org/abstracts/search?q=tablet%20optimization" title=" tablet optimization"> tablet optimization</a> </p> <a href="https://publications.waset.org/abstracts/60977/optimizing-performance-of-tablets-direct-compression-process-using-fuzzy-goal-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60977.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">276</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">7225</span> Predictive Maintenance of Industrial Shredders: Efficient Operation through Real-Time Monitoring Using Statistical Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Federico%20Pittino">Federico Pittino</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Arnold"> Thomas Arnold</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title="predictive maintenance">predictive maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=circular%20economy" title=" circular economy"> circular economy</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20shredder" title=" industrial shredder"> industrial shredder</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20optimization" title=" cost optimization"> cost optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20machine%20learning" title=" statistical machine learning"> statistical machine learning</a> </p> <a href="https://publications.waset.org/abstracts/135437/predictive-maintenance-of-industrial-shredders-efficient-operation-through-real-time-monitoring-using-statistical-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135437.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">133</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">7224</span> Application of the Global Optimization Techniques to the Optical Thin Film Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Li">D. Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical thin films are used in a wide variety of optical components and there are many software tools programmed for advancing multilayer thin film design. The available software packages for designing the thin film structure may not provide optimum designs. Normally, almost all current software programs obtain their final designs either from optimizing a starting guess or by technique, which may or may not involve a pseudorandom process, that give different answers every time, depending upon the initial conditions. With the increasing power of personal computers, functional methods in optimization and synthesis of optical multilayer systems have been developed such as DGL Optimization, Simulated Annealing, Genetic Algorithms, Needle Optimization, Inductive Optimization and Flip-Flop Optimization. Among these, DGL Optimization has proved its efficiency in optical thin film designs. The application of the DGL optimization technique to the design of optical coating is presented. A DGL optimization technique is provided, and its main features are discussed. Guidelines on the application of the DGL optimization technique to various types of design problems are given. The innovative global optimization strategies used in a software tool, OnlyFilm, to optimize multilayer thin film designs through different filter designs are outlined. OnlyFilm is a powerful, versatile, and user-friendly thin film software on the market, which combines optimization and synthesis design capabilities with powerful analytical tools for optical thin film designers. It is also the only thin film design software that offers a true global optimization function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20coatings" title="optical coatings">optical coatings</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20software" title=" design software"> design software</a>, <a href="https://publications.waset.org/abstracts/search?q=thin%20film%20design" title=" thin film design"> thin film design</a> </p> <a href="https://publications.waset.org/abstracts/80917/application-of-the-global-optimization-techniques-to-the-optical-thin-film-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80917.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">323</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">7223</span> Optimization of Interface Radio of Universal Mobile Telecommunication System Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Mohamed%20Amine">O. Mohamed Amine</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khireddine"> A. Khireddine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Telecoms operators are always looking to meet their share of the other customers, they try to gain optimum utilization of the deployed equipment and network optimization has become essential. This project consists of optimizing UMTS network, and the study area is an urban area situated in the center of Algiers. It was initially questions to become familiar with the different communication systems (3G) and the optimization technique, its main components, and its fundamental characteristics radios were introduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UMTS" title="UMTS">UMTS</a>, <a href="https://publications.waset.org/abstracts/search?q=UTRAN" title=" UTRAN"> UTRAN</a>, <a href="https://publications.waset.org/abstracts/search?q=WCDMA" title=" WCDMA"> WCDMA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/52556/optimization-of-interface-radio-of-universal-mobile-telecommunication-system-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52556.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">393</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">7222</span> Periodic Topology and Size Optimization Design of Tower Crane Boom</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wu%20Qinglong">Wu Qinglong</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Qicai"> Zhou Qicai</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiong%20Xiaolei"> Xiong Xiaolei</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Richeng"> Zhang Richeng </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to achieve the layout and size optimization of the web members of tower crane boom, a truss topology and cross section size optimization method based on continuum is proposed considering three typical working conditions. Firstly, the optimization model is established by replacing web members with web plates. And the web plates are divided into several sub-domains so that periodic soft kill option (SKO) method can be carried out for topology optimization of the slender boom. After getting the optimized topology of web plates, the optimized layout of web members is formed through extracting the principal stress distribution. Finally, using the web member radius as design variable, the boom compliance as objective and the material volume of the boom as constraint, the cross section size optimization mathematical model is established. The size optimization criterion is deduced from the mathematical model by Lagrange multiplier method and Kuhn-Tucker condition. By comparing the original boom with the optimal boom, it is identified that this optimization method can effectively lighten the boom and improve its performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tower%20crane%20boom" title="tower crane boom">tower crane boom</a>, <a href="https://publications.waset.org/abstracts/search?q=topology%20optimization" title=" topology optimization"> topology optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=size%20optimization" title=" size optimization"> size optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=periodic" title=" periodic"> periodic</a>, <a href="https://publications.waset.org/abstracts/search?q=SKO" title=" SKO"> SKO</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20criterion" title=" optimization criterion"> optimization criterion</a> </p> <a href="https://publications.waset.org/abstracts/74618/periodic-topology-and-size-optimization-design-of-tower-crane-boom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74618.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">562</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">7221</span> Topology Optimization of Composite Structures with Material Nonlinearity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mengxiao%20Li">Mengxiao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Johnson%20Zhang"> Johnson Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, topology optimization technique is widely used to define the layout design of structures that are presented as truss-like topologies. However, due to the difficulty in combining optimization technique with more realistic material models where their nonlinear properties should be considered, the achieved optimized topologies are commonly unable to apply straight towards the practical design problems. This study presented an optimization procedure of composite structures where different elastic stiffness, yield criteria, and hardening models are assumed for the candidate materials. From the results, it can be concluded that a more explicit modeling has the significant influence on the resulting topologies. Also, the isotropic or kinematic hardening is important for elastoplastic structural optimization design. The capability of the proposed optimization procedure is shown through several cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=topology%20optimization" title="topology optimization">topology optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=material%20composition" title=" material composition"> material composition</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20%20modeling" title=" nonlinear modeling"> nonlinear modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=hardening%20rules" title=" hardening rules"> hardening rules</a> </p> <a href="https://publications.waset.org/abstracts/63520/topology-optimization-of-composite-structures-with-material-nonlinearity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63520.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">7220</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">25</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=statistical%20optimization&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=statistical%20optimization&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=statistical%20optimization&page=4">4</a></li> <li class="page-item"><a class="page-link" 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