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Showing 1 - 20 results of 207 for search '(( element method algorithm ) OR ((( elements 5 algorithms ) OR ( neural modeling algorithm ))))', query time: 0.16s Refine Results
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    A Parallel Neural Networks Algorithm for the Clique Partitioning Problem by Harmanani, Haidar M.

    Published 2002
    “…In this paper we present a parallel algorithm to solve the above problem for arbitrary graphs using a Hopfield Neural Network model of computation. …”
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    Design of adaptive arrays based on element position perturbations by Dawoud, M.M.

    Published 1993
    “…The main advantage of using this technique over the other commonly used methods is that the amplitudes and phases of the array elements can be used mainly to steer the main beam towards the desired signal. …”
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    Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala (22330876)

    Published 2025
    “…These challenges highlighted the necessity for more powerful and flexible models. In this study, we introduce an innovative method for the multi-classification of breast cancer histopathological images utilizing Bidirectional Recurrent Neural Networks (BRNN). …”
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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
    “…Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. …”
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    A neural networks algorithm for data path synthesis by Harmanani, Haidar M.

    Published 2003
    “…The algorithm is driven by a motion equation that determines the neurons firing conditions based on the modified Hopfield neural network model of computation. …”
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    A kernelization algorithm for d-Hitting Set by Abu-Khzam, Faisal N.

    Published 2010
    “…For 3-Hitting Set, an arbitrary instance is reduced into an equivalent one that contains at most 5k2+k elements. This kernelization is an improvement over previously known methods that guarantee cubic-order kernels. …”
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    A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs† by Harmanani, Haidar

    Published 2010
    “…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and k colours. …”
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    Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy by Haitao Xu (435549)

    Published 2023
    “…Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. …”
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    A systematic review of text classification research based on deep learning models in Arabic language by Wahdan, Ahlam

    Published 2020
    “…The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. …”
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    NEURAL NETWORK MODEL FOR PLANNED REPLACEMENT OF BOEING 737 BRAKES by Al-Garni, Ahmed Z.

    Published 2020
    “…., Boeing 737, is analyzed using the Artificial Neural Network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. …”
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    Modelling Exchange Rates during Currency Crisis using Neural Networks by Nasr, G. E.

    Published 2006
    “…The models are built using the feedforward ANN structure trained by the backpropagation algorithm. …”
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    Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks by Najam Us Sahar Riyaz (22927843)

    Published 2025
    “…Drawing on more than fifteen harmonized datasets that span pyrimidines, ionic liquids, graphene oxides, and additional compound families, we benchmark traditional algorithms, such as artificial neural networks, support vector machines, k-nearest neighbors, random forests, against advanced graph-based and deep architectures including three-level directed message-passing neural networks, 2D3DMol-CIC, and graph convolutional networks. …”
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