Showing 41 - 60 results of 76 for search '(( element study algorithm ) OR ((( waste processing algorithm ) OR ( neural finding algorithm ))))', query time: 0.13s Refine Results
  1. 41

    Identification And Weather Sensitivity Of Physically Based Model Of Residential Air-Conditioners For Direct Load Control: A Case Study by El-Ferik, S

    Published 2020
    “…In this work, we address the identification problem of the parameters of an aggregated elemental physically based model representing a housing unit with an AC system. …”
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  2. 42

    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%). …”
  3. 43

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

    Published 2025
    “…The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. …”
  4. 44

    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. …”
  5. 45

    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. …”
  6. 46

    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. …”
  7. 47

    A Fully Optical Laser Based System for Damage Detection and Localization in Rail Tracks Using Ultrasonic Rayleigh Waves: A Numerical and Experimental Study by Masurkar, Faeez

    Published 2022
    “…The present study focuses on investigating the structural integrity of rail track sections of the high-speed railways using the Rayleigh waves generated and sensed using a fully non-contact optical Laser system. …”
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  8. 48
  9. 49

    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. …”
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  10. 50
  11. 51

    Inception voltage of corona in bipolar ionized fields-effect oncorona power loss by Al-Hamouz, Z.

    Published 1996
    “…In this paper, an iterative finite element based algorithm is presented as a numerical tool for the solution of the bipolar ionized field around high voltage direct current (HVDC) transmission lines. …”
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    article
  12. 52

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

    Published 2017
    “…As a byproduct, we obtain a kernelization algorithm that delivers linear-size kernels when the two edge-edit bounds are small constants.…”
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  13. 53
  14. 54

    Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information by M. Ghoniem, Rania

    Published 2019
    “…For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. …”
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  15. 55
  16. 56

    Application of Metastructures for Targeted Low-Frequency Vibration Suppression in Plates by Ratiba F. Ghachi (14152455)

    Published 2022
    “…<h2>Purpose</h2> <p>We present an approach that combines finite element analysis and genetic algorithms to find the optimal configuration of local resonators created in the host structure to suppress their vibration in a target low-frequency range. …”
  17. 57

    Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning by ALFALASI, FARIS Jr

    Published 2023
    “…To categorize electronic text in these two cases, deep learning models such as convolutional neural networks and recurrent neural networks and a combination of CNN-RNN were trained on this data. …”
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  18. 58

    Sentiment Analysis of Dialectal Speech: Unveiling Emotions through Deep Learning Models by EZZELDIN, KHALED MOHAMED KHALED

    Published 2024
    “…Dialect Speech Sentiment Analysis is an evolutional field where machine learning algorithms are utilized to detect emotions in spoken language. …”
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  19. 59

    Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3 by Chakar, Joseph

    Published 2020
    “…We also explore variational dropout: a technique that finds individual and unbounded dropout rates for each neural network weight. …”
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  20. 60

    Identification of physically based models of residential air-conditioners for direct load control management by El-Ferik, S.

    Published 2004
    “…In this work, we address the problem of identifying the parameters of an aggregated elemental model representing a housing unit with an A/C system. …”
    Get full text
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    article