يعرض 21 - 40 نتائج من 65 نتيجة بحث عن '(((( element data algorithm ) OR ( processing mold algorithm ))) OR ( neural finding algorithm ))', وقت الاستعلام: 0.12s تنقيح النتائج
  1. 21
  2. 22
  3. 23
  4. 24

    A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT حسب Dhananjay Bisen (19482454)

    منشور في 2023
    "…This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. …"
  5. 25

    PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits حسب Hind Almerekhi (7434776)

    منشور في 2022
    "…Therefore, in this study, we find the turning points (i.e., toxicity triggers) making conversations toxic. …"
  6. 26
  7. 27
  8. 28

    Correlation Clustering with Overlaps حسب Fakhereldine, Amin

    منشور في 2020
    "…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …"
    احصل على النص الكامل
    احصل على النص الكامل
    احصل على النص الكامل
    masterThesis
  9. 29

    The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs حسب Muhammad Kashif (3923483)

    منشور في 2023
    "…One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). …"
  10. 30
  11. 31

    A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass حسب Uzma Nawaz (21980708)

    منشور في 2025
    "…This study not only examines the well-known challenges such as limited availability of data but provides a novel, structured taxonomy of deep learning techniques tailored for the monitoring of seagrass, highlighting their unique advantages and limitations within diverse marine environments. By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…"
  12. 32

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

    منشور في 2025
    "…A second study on larger database of credit scoring confirms these findings, showing that the online classifier achieves an F1-score of 96.40% and an accuracy of 93.08%, closely matching the performance of neural networks (96.46%, 93.22%) and boosting (96.51%, 93.31%). …"
  13. 33

    Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach حسب Haitham Osman (11737057)

    منشور في 2025
    "…A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. Considering optimum conditions, the system attains an exergetic efficiency of 30.13 % and a power generation of 7.24 MW, with a cost rate of 232.06 $/h and a payback period of 4.09 years. …"
  14. 34

    Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods حسب Sivakavi Naga Venkata Bramareswara Rao (15944992)

    منشور في 2022
    "…In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. …"
  15. 35

    Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces حسب Uzair Sajjad (19646296)

    منشور في 2021
    "…In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. …"
  16. 36

    Multidimensional Gains for Stochastic Approximation حسب Saab, Samer S.

    منشور في 2019
    "…Necessary and sufficient conditions for M≥ N algorithms are presented pertaining to algorithm stability and convergence of the estimate error covariance matrix. …"
    احصل على النص الكامل
    احصل على النص الكامل
    احصل على النص الكامل
    احصل على النص الكامل
    article
  17. 37
  18. 38

    Recursive Parameter Identification Of A Class Of Nonlinear Systems From Noisy Measurements حسب Emara-Shabaik, Husam

    منشور في 2020
    "…The model structure is made up of two linear dynamic elements separated by a nonlinear static one. The nonlinear element is assumed to be of the polynomial type with known order; The identification is based on input/output data where the output is contaminated with measurement noise. …"
    احصل على النص الكامل
    article
  19. 39
  20. 40

    On the complexity of multi-parameterized cluster editing حسب Abu-Khzam, Faisal

    منشور في 2017
    "…In other words, Cluster Editing can be solved efficiently when the number of false positives/negatives per single data element is expected to be small compared to the minimum cluster size. …"
    احصل على النص الكامل
    احصل على النص الكامل
    احصل على النص الكامل
    احصل على النص الكامل
    article