يعرض 201 - 220 نتائج من 902 نتيجة بحث عن '(((( developing based algorithm ) OR ( elements search algorithm ))) OR ( data using algorithm ))', وقت الاستعلام: 0.12s تنقيح النتائج
  1. 201
  2. 202

    The Effects of Data Mining on Small Businesses in Dubai حسب AlMutawa, Rasha

    منشور في 2011
    "…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …"
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  3. 203
  4. 204

    Spider monkey optimizations: application review and results حسب Abualigah, Laith

    منشور في 2024
    "…Optimization algorithms are applied to find efficient solutions in different problems in several fields such as the routing in wireless networks, cloud computing, big data, image processing and scheduling, and so forth. …"
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  5. 205

    A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation حسب Khaled Dhibi (16891524)

    منشور في 2021
    "…The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. …"
  6. 206

    Fuzzy simulated evolution algorithm for topology design of campusnetworks حسب Youssef, H.

    منشور في 2000
    "…To diversify the search, we have also incorporated tabu search-based characteristics in the allocation phase of the SE algorithm. …"
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    article
  7. 207

    An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting حسب Mohamed Massaoudi (16888710)

    منشور في 2021
    "…First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. …"
  8. 208

    Intelligent Bilateral Client Selection in Federated Learning Using Game Theory حسب Wehbi, Osama

    منشور في 2022
    "…To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. …"
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    masterThesis
  9. 209
  10. 210

    Convergence behavior of the normalized least mean fourth algorithm حسب Zerguine, A.

    منشور في 2000
    "…Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. …"
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    article
  11. 211

    Optimizing Document Classification: Unleashing the Power of Genetic Algorithms حسب Ghulam Mustafa (458105)

    منشور في 2023
    "…Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. …"
  12. 212

    Evolutionary algorithm for predicting all-atom protein structure حسب Mansour, Nashat

    منشور في 2011
    "…We present an improved version of a scatter search (SS) algorithm for predicting all-atoms protein structures using a recent energy model. …"
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    conferenceObject
  13. 213

    GenDE: A CRF-Based Data Extractor حسب Kayed, Mohammed

    منشور في 2020
    "…If the wrapper failed to work with the new page, a new wrapper/schema would be re-generated by calling an unsupervised wrapper induction system. In this paper, a new data extractor called GenDE is proposed. It verifies the site schema and extracts data from the Web pages using Conditional Random Fields (CRFs). …"
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  14. 214

    Three-phase simulated annealing algorithms for exam scheduling حسب Mansour, Nashat

    منشور في 2003
    "…We empirically compare 3PSA with a 4-phase clustering-based heuristic algorithm using realistic data. Our experimental results show that 3PSA produces good exam schedules, which are better than those of the clustering heuristic procedure.…"
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    conferenceObject
  15. 215

    Recent Advances of Chimp Optimization Algorithm: Variants and Applications حسب Daoud, Mohammad Sh.

    منشور في 2023
    "…Chimp Optimization Algorithm (ChOA) is one of the recent metaheuristics swarm intelligence methods. …"
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  16. 216
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  18. 218

    Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem حسب Abido, M. A.

    منشور في 2006
    "…The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. …"
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    article
  19. 219

    Multiobjective evolutionary algorithms for electric power dispatch problem حسب Abido, M.A.

    منشور في 2006
    "…The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. …"
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    article
  20. 220

    Global smart cities classification using a machine learning approach to evaluating livability, technology, and sustainability performance across key urban indices حسب Aya Hasan Alkhereibi (17151070)

    منشور في 2025
    "…Drawing on data from the Smart Cities Index (SCI) and other economic and sustainability competitiveness metrics, the study uses various <u>ML algorithms</u> to categorize cities into <u>performance classes</u>, ranging from high-achieving Class 1 to emerging Class 3 cities. …"