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Showing 1 - 20 results of 1,304 for search 'Machine learning reduction models', query time: 0.21s Refine Results
  1. 1

    Machine learning models and dimensionality reduction for improving the Android malware detection by Pablo Morán, Antonio Robles-Gómez, Andres Duque, Llanos Tobarra, Rafael Pastor-Vargas

    Published 2024-12-01
    “…This work first proposes a new efficient dimensionality reduction of features, as well as the application of several supervised machine learning algorithms for prediction purposes. …”
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    Article
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    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Saad Ahmed Dheyab, Shaymaa Mohammed Abdulameer, Salama Mostafa

    Published 2022-12-01
    “…The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. …”
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    Article
  3. 3

    Physics-informed machine learning for automatic model reduction in chemical reaction networks by Joseph Pateras, Colin Zhang, Shriya Majumdar, Ayush Pal, Preetam Ghosh

    Published 2025-03-01
    “…Abstract Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. …”
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    Article
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    Unsupervised Machine Learning Approaches for Test Suite Reduction by Anila Sebastian, Hira Naseem, Cagatay Catal

    Published 2024-12-01
    “…Over the past decade, machine learning-based solutions have emerged, demonstrating remarkable effectiveness and efficiency. …”
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    Article
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    Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models by Zonglin Yi, Yi Zhou, Hao Liu, Li Li, Yan Zhao, Jiayuan Li, Yixuan Mao, Fangyuan Su, Cheng-Meng Chen

    Published 2025-05-01
    “…Machine-learning models are trained based on the organic and inorganic electrolyte solvents that possess experimentally identified reduction mechanisms. …”
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    Article
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    Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning by Matt von Hippel, Matthias Wilhelm

    Published 2025-05-01
    “…In this paper, we investigate the use of machine-learning techniques to find improved heuristics. …”
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    Article
  13. 13

    Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity by Nasir Khan, Mehdi Razavifar, Qazi Adnan Ahmad, Muhammad Siyar, Masoud Riazi, Waqar Khan, Jafar Qajar

    Published 2025-08-01
    “…In the second part of the study, a Machine Learning (ML) model was developed using the experimental data. …”
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    Article
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    Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

    Published 2022-03-01
    “…The study objective is to build a model based on machine learning that can predict the detection of SARS-CoV-2 from medical data. …”
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    Article
  17. 17

    Impact of dimensionality reduction techniques on student performance prediction using machine learning by Koushik Roy, Huu-Hoa Nguyen, Dewan Md. Farid

    Published 2023-10-01
    “…The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. …”
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    Article
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    Impact of dimensionality reduction techniques on student performance prediction using machine learning by Koushik Roy, Huu-Hoa Nguyen, Dewan Md. Farid

    Published 2023-10-01
    “…The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. …”
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    Article
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    Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction by Vuri Ayu Setyowati, Shiho Mukaida, Kaito Nagita, Takashi Harada, Shuji Nakanishi, Kazuyuki Iwase

    Published 2024-12-01
    “…In this study, we investigated the impact of the electrode fabrication and electrolysis conditions on the product selectivity of Ag electrocatalysts using a machine learning (ML) approach. Specifically, we explored the experimental conditions for obtaining the desired H2/CO mixture ratio with high CO efficiency. …”
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    Article