Showing 1 - 20 results of 74 for search '(( elements mean algorithm ) OR ((( data code algorithm ) OR ( based colony algorithm ))))', query time: 0.14s Refine Results
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    Bee Colony Algorithm for Proctors Assignment. by Mansour, Nashat

    Published 2015
    “…The Bee Colony algorithm is a recent population-based search algorithm that mimics the natural behavior of swarms of honey bees during the process of collecting food. …”
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    article
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    A modified ant colony algorithm for evolutionary design of digital circuits by Sait, Sadiq M.

    Published 2003
    “…In this paper, a multiobjective optimization of logic circuits based on a modified ant colony (ACO) algorithm is presented. …”
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    article
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    Fuzzified ant colony optimization algorithm for efficient combinational circuits synthesis by Sarif, B.A.B.

    Published 2004
    “…Multiobjective optimization of logic circuits based on a fuzzified ant colony (ACO) algorithm is presented. …”
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    article
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    A combined ant colony optimization and simulated annealing algorithm to assess Stability and fault-proneness of classes based on internal software quality attributes by Azar, Danielle

    Published 2016
    “…., & Daoud, C. (2016). A Combined Ant Colony Optimization and Simulated Annealing Algorithm to Assess Stability and Fault-Proneness of Classes Based on Internal Software Quality Attributes. …”
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    article
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    Development of an Optimization Algorithm for Internet Data Traffic by Misbahuddin, Syed

    Published 2020
    “…The algorithm monitors data repetitions in IP datagram and prepares a compression code in response of this repetition. …”
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    article
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    Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect by Habeeb, Abdallah

    Published 2022
    “…The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). …”
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    Practical single node failure recovery using fractional repetition codes in data centers by Itani, May

    Published 2016
    “…FR codes consist of a concatenation of an outer maximum distance separable (MDS) code and an inner fractional repetition code that splits the data into several blocks and stores multiple replicas of each on different nodes in the system. …”
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    conferenceObject
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    UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data by Behrouz Ahadzadeh (19757022)

    Published 2024
    “…UniBFS exploits the inherent characteristic of binary algorithms-binary coding-to search the entire problem space for identifying relevant features while avoiding irrelevant ones. …”
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    Using artificial bee colony to optimize software quality estimation models. (c2015) by Abou Assi, Tatiana Antoine

    Published 2016
    “…In this thesis, we propose a novel heuristic based on Artificial Bee Colony (ABC) to optimize rule-based software quality prediction models. …”
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    masterThesis
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    Optimization of Piezoelectric Sensor-Actuator for Plate Vibration Control Using Evolutionary Computation: Modeling, Simulation and Experimentation by Asan G. A. Muthalif (16888818)

    Published 2021
    “…The analytical model is derived based on the Euler-Bernoulli model. The Optimal location of the collocated sensor-actuator, as well as PID controller gains, are determined using Ant Colony Optimization (ACO) technique, then compared with the Genetic Algorithm (GA) and enumerative method (EM). …”
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    Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars by Abathar Al-Hamrani (16494884)

    Published 2023
    “…Finally, a design equation that can predict the shear capacity of one-way BFRC-BFRP slabs was proposed based on genetic algorithm. The proposed model showed the best prediction accuracy compared to the available design codes and guidelines with a mean of predicted to experimental shear capacities (V<sub>pred</sub>/V<sub>exp</sub>) ratio of 0.97 and a coefficient of variation of 17.91%.…”