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Showing 1 - 20 results of 149 for search '(( elements deer algorithm ) OR ((( spatial modeling algorithm ) OR ( data existing algorithm ))))', query time: 0.13s Refine Results
  1. 1

    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. …”
  2. 2

    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. …”
  3. 3

    Data Embedding in HEVC Video by Modifying the Partitioning of Coding Units by Shanableh, Tamer

    Published 2019
    “…The data embedding algorithm guarantees that a maximum of one partition is modified per message segment, therefore, in a given CU, either 0, 1 or 2 partitions are modified. …”
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  4. 4

    Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch (17983846)

    Published 2025
    “…<p dir="ltr">This paper introduces novel nonparametric supervised learning techniques for classifying massive datasets, addressing key limitations of existing methods in Big and Streaming Data framework. …”
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    Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm by Ayman Khallel Al-Ani (17541447)

    Published 2023
    “…Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate central servers. …”
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    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. …”
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    A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

    Published 2022
    “…The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. …”
  10. 10

    A Novel Genetic Algorithm Optimized Adversarial Attack in Federated Learning for Android-Based Mobile Systems by Faria Nawshin (21841598)

    Published 2025
    “…<p dir="ltr">Federated Learning (FL) is gaining traction in Android-based consumer electronics, enabling collaborative model training across decentralized devices while preserving data privacy. However, the increasing adoption of FL in these devices exposes them to adversarial attacks that can compromise user data and device security. …”
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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
    “…<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. …”
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    Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence by Atiq Ur Rehman (8843024)

    Published 2020
    “…Keeping in view the need of an optimization algorithm with fast convergence speed, suitable for high dimensional data space, this article proposes a novel concept of Multi-Cluster Jumping PSO. …”
  14. 14

    Data Redundancy Management in Connected Environments by Mansour, Elio

    Published 2020
    “…In this work, we focus on managing sensor data redundancies in connected environments. Existing works often suffer from (i) disregarding either network core or edge device redundancies; (ii) disregarding the limited capabilities of edge devices; and (iii) disregarding sensors mobility and the dynamicity of the network. …”
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  15. 15

    Unsupervised outlier detection in multidimensional data by Atiq ur Rehman (14153391)

    Published 2022
    “…<p>Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. …”
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    The Effects of Data Mining on Small Businesses in Dubai by AlMutawa, Rasha

    Published 2011
    “…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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  18. 18

    An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting by Mohamed Massaoudi (16888710)

    Published 2021
    “…First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. …”
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    Data redundancy management for leaf-edges in connected environments by Mansour, Elio

    Published 2022
    “…In this work, we focus on managing data redundancies in connected environments. Existing approaches suffer from (i) disregarding edge data redundancies either at the edge or at the core of the network; (ii) disregarding sensor mobility and the dynamicity of the network; (iii) disregarding the limited resources of edge devices; (iv) disregarding network/infrastructure resources; and (v) disregarding data consumer needs/requirements when cleaning the data redundancies. …”
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