Showing 1 - 20 results of 464 for search '(((( implementing ii algorithm ) OR ( experiments _ algorithm ))) OR ( level coding algorithm ))*', query time: 0.14s Refine Results
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    Topics in graph algorithms by Abu-Khzam, Faisal Nabih

    Published 2003
    “…This is achieved by implementing some algorithms for the vertex cover problem, and conducting experiments on real data sets. …”
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    masterThesis
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    Computational Experience On Four Algorithms For The Hard Clustering Problem by AlSultan, K.S.

    Published 2020
    “…We test these algorithms on several clustering problems from the literature as well as several random problems and we report on our computational experience.…”
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    article
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    Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network by Mohammad Reza Chalak Qazani (13893261)

    Published 2024
    “…The proposed model was developed and implemented using MATLAB software. A Pareto front was derived from the MOGA by employing the T2FNN within the process, identifying fourteen optimal solutions.…”
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    A Parallel Algorithm for Inverse Halftoning and its Hardware Implementation by Siddiqi, Umair F.

    Published 2020
    “…Keywords: (1) Inverse Halftoning (2) Hardware Implementation (3) Look-Up Table Inverse Halftoning (4) Complex Programmable Logic Devices (CPLD) (5) Image Processing…”
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    article
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    Fuzzy simulated evolution algorithm for VLSI cell placement by Sait, Sadiq M.

    Published 2003
    “…In this paper, we present a fuzzy simulated evolution (FSE) algorithm to tackle this problem. Identification of near optimal solutions is achieved through a novel goal-directed fuzzy search approach. …”
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    article
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    Allocating data to distributed-memory multiprocessors by genetic algorithms by Mansour, Nashat

    Published 2016
    “…These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. …”
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    article
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    Tensile Test Optimization Using the Design of Experiment and Soft Computing by Mehdi Moayyedian (14880358)

    Published 2023
    “…This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). …”
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    Optimized FPGA Implementation of PWAM-Based Control of Three—Phase Nine—Level Quasi Impedance Source Inverter by Syed Rahman (569240)

    Published 2019
    “…Since, PWAM control algorithm is more complex than PSCPWM, FPGA based implementation for PWAM control is discussed. …”
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    A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network by Xiaofang Pan (1895950)

    Published 2019
    “…Moreover, various standard drift related experiments are conducted, of which the results validate our proposed algorithm's superior robustness for the wide range of real-life applications.…”
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    Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant by Shahbaz Hussain (9765320)

    Published 2019
    “…Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. …”
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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%.…”
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