Search Results - Automatic machine learning algorithm based on genetic algorithms

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  1. 1

    Construction of rapid early warning and comprehensive analysis models for urban waterlogging based on AutoML and comparison of the other three machine learning algorithms by Guo, Yuchen, Quan, Lihong, Song, Lili, Liang, Hao

    ISSN: 0022-1694, 1879-2707
    Published: Elsevier B.V 01.02.2022
    Published in Journal of hydrology (Amsterdam) (01.02.2022)
    “…•The nowcasting and inversion models for urban waterlogging constructed by the AML based on genetic algorithm was recommended based on the comparison of AML, CatBoost, XGBoost and BPDNN…”
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    Journal Article
  2. 2

    Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models by Ansari, Sam, Alnajjar, Khawla A., Saad, Mohamed, Abdallah, Saeed, El-Moursy, Ali A.

    ISSN: 2169-3536, 2169-3536
    Published: Piscataway IEEE 2022
    Published in IEEE access (2022)
    “… Automatic modulation recognition (AMR) plays a central role in many applications, especially in the military and security sectors…”
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    Journal Article
  3. 3

    A Hybrid Automatic System for the Diagnosis of Lung Cancer Based on Genetic Algorithm and Fuzzy Extreme Learning Machines by Daliri, Mohammad Reza

    ISSN: 0148-5598, 1573-689X
    Published: Boston Springer US 01.04.2012
    Published in Journal of medical systems (01.04.2012)
    “… The proposed method is based on combination of genetic algorithm (GA) for the feature selection and newly proposed approach, namely the extreme learning machines (ELM…”
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    Journal Article
  4. 4

    A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge by Keren, Liron Simon, Liberzon, Alex, Lazebnik, Teddy

    ISSN: 2045-2322, 2045-2322
    Published: London Nature Publishing Group UK 23.01.2023
    Published in Scientific reports (23.01.2023)
    “… SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods…”
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    Journal Article
  5. 5

    Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction by Chadalawada, Jayashree, Herath, H. M. V. V., Babovic, Vladan

    ISSN: 0043-1397, 1944-7973
    Published: Washington John Wiley & Sons, Inc 01.04.2020
    Published in Water resources research (01.04.2020)
    “…Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded…”
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    Journal Article
  6. 6

    An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm by Zeng, Yan, Huang, ChengChuang, Ni, YiJie, Zhou, ChunBao, Zhang, JiLin, Wang, Jue, Zhou, MingYao, Xue, MeiTing, Zhang, YunQuan

    ISSN: 2690-5965
    Published: IEEE 17.12.2023
    “… The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability…”
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    Conference Proceeding
  7. 7

    Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection by Albadr, Musatafa Abbas Abbood, Tiun, Sabrina, Ayob, Masri, AL-Dhief, Fahad Taha, Omar, Khairuddin, Hamzah, Faizal Amri

    ISSN: 1932-6203, 1932-6203
    Published: United States Public Library of Science 15.12.2020
    Published in PloS one (15.12.2020)
    “… Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria…”
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    Journal Article
  8. 8

    A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants by Zhao, Wei, Zhang, Haoran, Zheng, Jianqin, Dai, Yuanhao, Huang, Liqiao, Shang, Wenlong, Liang, Yongtu

    ISSN: 0360-5442, 1873-6785
    Published: Oxford Elsevier Ltd 15.05.2021
    Published in Energy (15.05.2021)
    “… First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA…”
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    Journal Article
  9. 9

    A Spatiotemporal Fuzzy Modeling Approach Combining Automatic Clustering and Hierarchical Extreme Learning Machines for Distributed Parameter Systems by Zhou, Gang, Zhang, Xianxia, Wang, Tangchen, Wang, Bing

    ISSN: 2227-7390, 2227-7390
    Published: Basel MDPI AG 01.02.2025
    Published in Mathematics (Basel) (01.02.2025)
    “… In this study, a three-dimensional fuzzy modeling method combining genetic algorithm (GA)-based automatic clustering and hierarchical extreme learning machine (HELM…”
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    Journal Article
  10. 10

    A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM by Xia, Zetao, Wang, Yining, Ma, Longhua, Zhu, Yang, Li, Yongjie, Tao, Jili, Tian, Guanzhong

    ISSN: 1424-8220, 1424-8220
    Published: Switzerland MDPI AG 24.12.2022
    Published in Sensors (Basel, Switzerland) (24.12.2022)
    “… This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework…”
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    Journal Article
  11. 11

    Automatic structural elucidation of vacancies in materials by active learning by Lourenço, Maicon Pierre, Herrera, Lizandra Barrios, Hostaš, Jiří, Calaminici, Patrizia, Köster, Andreas M, Tchagang, Alain, Salahub, Dennis R

    ISSN: 1463-9084, 1463-9084
    Published: 27.10.2022
    Published in Physical chemistry chemical physics : PCCP (27.10.2022)
    “… In this work, we have developed an artificial intelligence method based on active learning (AL) or Bayesian optimization for the automatic structural elucidation of vacancies in solids and nanoparticle…”
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    Journal Article
  12. 12

    Automatic and intelligent content visualization system based on deep learning and genetic algorithm by İnce, Murat

    ISSN: 0941-0643, 1433-3058
    Published: London Springer London 01.02.2022
    Published in Neural computing & applications (01.02.2022)
    “…Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment…”
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    Journal Article
  13. 13

    Multiple objects automatic detection of GPR data based on the AC-EWV and Genetic Algorithm by Cui, Guangyan, Xu, Jie, Wang, Yanhui, Zhao, Shengsheng

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 01.01.2023
    “…The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects…”
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    Journal Article
  14. 14

    Automatically Designing Network-Based Deep Transfer Learning Architectures Based on Genetic Algorithm for In-Situ Tool Condition Monitoring by Liu, Yuekai, Yu, Yaoxiang, Guo, Liang, Gao, Hongli, Tan, Yongwen

    ISSN: 0278-0046, 1557-9948
    Published: New York IEEE 01.09.2022
    “… The limitation of in-situ TCM based on traditional deep learning lies in several aspects: the requirement of sufficient labeled data of health conditions, the empirically manual designed architecture, and the labor-intensive tuning of hyperparameters…”
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    Journal Article
  15. 15

    Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm by Gaber, Ahmed, Youness, Hassan A., Hamdy, Alaa, Abdelaal, Hammam M., Hassan, Ammar M.

    ISSN: 2076-3417, 2076-3417
    Published: Basel MDPI AG 01.01.2022
    Published in Applied sciences (01.01.2022)
    “… To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues…”
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    Journal Article
  16. 16

    Flexible Automatic Design of GaN PA Based on Gaussian Process-Assisted Non-Dominated Sorting Genetic Algorithm by Wang, Weiwei, Sun, Bingjie, Chen, Shichang, Xu, Kuiwen, Cai, Jialin, Raffo, Antonio, Donato, Nicola, Crupi, Giovanni, Wang, Gaofeng

    ISSN: 0278-0070, 1937-4151
    Published: IEEE 2025
    “… It employs a Gaussian process-assisted fast non-dominated sorting genetic algorithm (GP-NSGA-II) for both circuit synthesis and layout optimization…”
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    Journal Article
  17. 17

    Auto-Machine Learning-Based W-Band High-Efficiency Oscillator Design by Xie, Bingchuan, Zhang, Rui, Tian, Lu, Li, Haixuan, Wang, Yong, Liu, Kegang

    ISSN: 0018-9383, 1557-9646
    Published: New York IEEE 01.10.2024
    Published in IEEE transactions on electron devices (01.10.2024)
    “…In this article, automatic machine learning (ML) technology and global optimization algorithms have been combined to achieve a fast and high-performance extended interaction oscillator (EIO) design…”
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    Journal Article
  18. 18

    Medical Image Segmentation Using Automatic Optimized U-Net Architecture Based on Genetic Algorithm by Khouy, Mohammed, Jabrane, Younes, Ameur, Mustapha, Hajjam El Hassani, Amir

    ISSN: 2075-4426, 2075-4426
    Published: Basel MDPI AG 25.08.2023
    Published in Journal of personalized medicine (25.08.2023)
    “… With the advent of deep learning, many manual design-based methods have been proposed and have shown promising results in achieving state-of-the-art performance in biomedical image segmentation…”
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    Journal Article
  19. 19

    Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning by Wang, Liang, Zhang, PeiPei, Feng, Yanhui, Lv, Wenzhi, Min, Xiangde, Liu, Zhiyong, Li, Jin, Feng, Zhaoyan

    ISSN: 1471-2407, 1471-2407
    Published: London BioMed Central 28.03.2025
    Published in BMC cancer (28.03.2025)
    “… This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization…”
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    Journal Article
  20. 20

    Automatic microarray image segmentation with clustering-based algorithms by Shao, Guifang, Li, Dongyao, Zhang, Junfa, Yang, Jianbo, Shangguan, Yali

    ISSN: 1932-6203, 1932-6203
    Published: United States Public Library of Science 22.01.2019
    Published in PloS one (22.01.2019)
    “… However, state-of-art clustering-based segmentation algorithms are sensitive to noises. To solve this problem and improve the segmentation accuracy, in this article, several improvements are introduced into the fast and simple clustering methods…”
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    Journal Article