Search alternatives:
ii algorithm » rd algorithm (Expand Search), _ algorithms (Expand Search)
finding » findings (Expand Search)
Showing 21 - 40 results of 61 for search '(((( complement ii algorithm ) OR ( complement based algorithm ))) OR ( neural finding algorithm ))', query time: 0.12s Refine Results
  1. 21
  2. 22
  3. 23

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

    Published 2023
    “…One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). …”
  4. 24

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

    Published 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.…”
  5. 25

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

    Published 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%). …”
  6. 26

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

    Published 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. …”
  7. 27

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

    Published 2022
    “…In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. …”
  8. 28

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

    Published 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. …”
  9. 29

    Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben Romdhane, Haifa

    Published 2023
    “…The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. …”
    Get full text
  10. 30

    Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben-Romdhane, Haïfa

    Published 2023
    “…The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. …”
    Get full text
    article
  11. 31

    A novel technique for fast multiplication by Sait, Sadiq M.

    Published 2020
    “…These partial products are then added using a tree of carry-save-adders, and ® nally the sum and carry vectors are added using a carry-lookahead adder. In the case of 2 s complement multiplication the tree of carry-save-adders also receives a correction output produced in parallel with the partial products. …”
    Get full text
    article
  12. 32

    A Literature Review on System Dynamics Modeling for Sustainable Management of Water Supply and Demand by Khawar Naeem (17984062)

    Published 2023
    “…The solution approaches included the genetic algorithm (GA), particle swarm optimization (PSO), and the non-dominated sorting genetic algorithm (NSGA-II). …”
  13. 33

    A novel technique for fast multiplication by Sait, Sadiq M.

    Published 1995
    “…These partial products are then added using a tree of carry-save-adders, and finally the sum and carry vectors are added using a carry-look-ahead adder. In case of 2's complement multiplication the tree of carry-save-adders also receives a correction output produced in parallel with the partial products. …”
    Get full text
    Get full text
    article
  14. 34

    Multidimensional Gains for Stochastic Approximation by Saab, Samer S.

    Published 2019
    “…Necessary and sufficient conditions for M≥ N algorithms are presented pertaining to algorithm stability and convergence of the estimate error covariance matrix. …”
    Get full text
    Get full text
    Get full text
    Get full text
    article
  15. 35
  16. 36
  17. 37
  18. 38

    A new approach and faster exact methods for the maximum common subgraph problem by Abu-Khzam, Faisal N.

    Published 2017
    “…This structure contains a large number of naturally-ordered cliques that are present in the association graph’s complement. A detailed analysis shows that the proposed algorithm requires O((m+1)n) time, which is a superior worst-case bound to those known for previously-analyzed algorithms in the setting of the MCS problem. …”
    Get full text
    Get full text
    Get full text
    Get full text
    conferenceObject
  19. 39
  20. 40