Showing 1 - 20 results of 69 for search '(( spatial modeling algorithm ) OR ((( element network algorithm ) OR ( element data algorithm ))))', query time: 0.12s Refine Results
  1. 1
  2. 2

    Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams by Eduardo Feo-Flushing (23276023)

    Published 2021
    “…Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. …”
  3. 3

    Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala (22330876)

    Published 2025
    “…<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. …”
  4. 4

    CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments by Ishaq Ansari (22047902)

    Published 2024
    “…<p dir="ltr">In our research, we address the problem of coordination and planning in heterogeneous multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this problem has been framed as a task allocation problem that maps tasks to robots. …”
  5. 5

    A Parallel Neural Networks Algorithm for the Clique Partitioning Problem by Harmanani, Haidar M.

    Published 2002
    “…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and c cliques. …”
    Get full text
    Get full text
    article
  6. 6

    A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS by Youssef, H.

    Published 2020
    “…In this paper, we present a Simulated Evolution algorithm for the design of campus network topology. …”
    Get full text
    article
  7. 7

    Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

    Published 2023
    “…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
  8. 8

    Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

    Published 2023
    “…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
  9. 9

    Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

    Published 2020
    “…The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
    Get full text
    article
  10. 10

    Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm by Youssef, H.

    Published 2020
    “…The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. …”
    Get full text
    article
  11. 11

    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. …”
  12. 12

    STEM: spatial speech separation using twin-delayed DDPG reinforcement learning and expectation maximization by Muhammad Salman Khan (7202543)

    Published 2025
    “…In this paper, a novel speech separation algorithm is proposed that integrates the twin-delayed deep deterministic (TD3) policy gradient reinforcement learning (RL) agent with the expectation maximization (EM) algorithm for clustering the spatial cues of individual sources separated on azimuth. …”
  13. 13
  14. 14
  15. 15

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

    Published 2000
    “…We present an approach based on the simulated evolution algorithm for the design of campus network topology. …”
    Get full text
    Get full text
    article
  16. 16
  17. 17

    MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

    Published 2022
    “…Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. …”
  18. 18
  19. 19

    Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction by Syed Mohammad (21075689)

    Published 2025
    “…It is noted that spatial relationships within molecules are crucial in predicting hERG blockers. …”
  20. 20

    XBeGene: Scalable XML Documents Generator by Example Based on Real Data by Harazaki, Manami

    Published 2012
    “…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
    Get full text
    Get full text
    Get full text
    Get full text
    conferenceObject