Showing 1 - 6 results of 6 for search '(( linear single model optimization algorithm ) OR ( binary data wolf optimization algorithm ))', query time: 0.10s Refine Results
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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. We model the problem as an integer linear programming problem that uses modified versions of the fractional repetition code by allowing different block sizes, and minimizes the recovery cost of all single node failure scenarios. …”
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    EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach by Muhammad Adeel Asghar (6724982)

    Published 2019
    “…To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. …”
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    Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods by Sivakavi Naga Venkata Bramareswara Rao (15944992)

    Published 2022
    “…From the results, it is found that the Levenberg–Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.…”
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    A hybrid model to predict the pressure gradient for the liquid-liquid flow in both horizontal and inclined pipes for unknown flow patterns by Md Ferdous Wahid (13485799)

    Published 2023
    “…The important feature subset is identified using the modified Binary Grey Wolf Optimization Particle Swarm Optimization (BGWOPSO) algorithm. …”
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    Uplink Noma in UAV-Assisted IoT Networks by Mrad, Ali

    Published 2022
    “…The obtained problem is non-convex mixed-integer non-linear program which is difficult to solve in a straightforward manner, hence alternating optimization technique is used where the original problem is divided into two subproblems. …”
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