Search Results - Applications and Simulation Support Systems General Terms Algorithms

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  1. 1

    Short-term wind speed prediction model based on long short-term memory network with feature extraction by Tian, Zhongda, Yu, Xiyan, Feng, Guokui

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2025
    Published in Earth science informatics (01.04.2025)
    “… Firstly, for reducing the uncertainty and complexity of short-term wind speed, data cluster is carried out by K-means clustering algorithm…”
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    Journal Article
  2. 2

    Machine learning model for predicting long-term energy consumption in buildings by Hussien, Aseel, Maksoud, Aref, Al-Dahhan, Aisha, Abdeen, Ahmed, Baker, Thar

    ISSN: 2730-7239, 2730-7239
    Published: Cham Springer International Publishing 01.12.2025
    Published in Discover Internet of things (01.12.2025)
    “… Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design…”
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    Journal Article
  3. 3

    Estimation of suspended sediment load utilizing a super-optimized deep learning approach informed by the red fox optimization algorithm by Malekpour, Mohammad Mahdi, Ahmadi, Mohammad Mehdi, Gugliotta, Marcello, Tabari, Mahmoud Mohammad Rezapour, Qaderi, Kourosh

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2025
    Published in Earth science informatics (01.03.2025)
    “… In this study, we employed the Long Short-Term Memory (LSTM) model to estimate the suspended sediment concentration in the Mississippi River, United States of America…”
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  4. 4

    Sparse Multiple Kernel Learning for Signal Processing Applications by Subrahmanya, N., Shin, Y.C.

    ISSN: 0162-8828, 1939-3539, 1939-3539
    Published: Los Alamitos, CA IEEE 01.05.2010
    “…In many signal processing applications, grouping of features during model development and the selection of a small number of relevant groups can be useful to improve the interpretability of the learned parameters…”
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  5. 5

    Ultra-short-term photovoltaic power prediction based on modal reconstruction and BiLSTM-CNN-Attention model by Liu, Wei, Liu, Qian, Li, Yulin

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024
    Published in Earth science informatics (01.06.2024)
    “…Accurate ultra-short-term photovoltaic (PV) power prediction is crucial for ensuring the power…”
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  6. 6

    Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients by Alexandre Teixeira, César, Direito, Bruno, Bandarabadi, Mojtaba, Le Van Quyen, Michel, Valderrama, Mario, Schelter, Bjoern, Schulze-Bonhage, Andreas, Navarro, Vincent, Sales, Francisco, Dourado, António

    ISSN: 0169-2607, 1872-7565, 1872-7565
    Published: Kidlington Elsevier Ireland Ltd 01.05.2014
    “…The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known…”
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  7. 7

    Improving hospital bed utilisation through simulation and optimisation: with application to a 40% increase in patient volume in a Norwegian General Hospital by Holm, Lene Berge, Lurås, Hilde, Dahl, Fredrik A

    ISSN: 1872-8243, 1386-5056, 1872-8243
    Published: Ireland 01.02.2013
    “… We present a generic discrete event simulation model of patient flow through the wards of a hospital…”
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    Journal Article
  8. 8

    Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan by Hussain, Dostdar, Khan, Aftab Ahmed

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020
    Published in Earth science informatics (01.09.2020)
    “…), support vector regression (SVR), and random forest (RF), are explored to forecast Hunza river flow in Pakistan using in-situ dataset for the period from 1962 to 2008…”
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  9. 9

    Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa by Igwebuike, Ndubuisi, Ajayi, Moyinoluwa, Okolie, Chukwuma, Kanyerere, Thokozani, Halihan, Todd

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
    Published in Earth science informatics (01.01.2025)
    “…Groundwater models are valuable tools to quantify the response of groundwater level to hydrological stresses induced by climate variability and groundwater…”
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  10. 10

    GARD: a genetic algorithm for recombination detection by Kosakovsky Pond, Sergei L., Posada, David, Gravenor, Michael B., Woelk, Christopher H., Frost, Simon D.W.

    ISSN: 1367-4803, 1367-4811, 1460-2059, 1367-4811
    Published: Oxford Oxford University Press 15.12.2006
    Published in Bioinformatics (15.12.2006)
    “…: We developed a likelihood-based model selection procedure that uses a genetic algorithm to search multiple sequence alignments for evidence of recombination breakpoints and identify putative recombinant sequences…”
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  11. 11

    Detection of multivariate geochemical anomalies using machine learning (ML) algorithms in Dehaq Pb-Zn mineralization, Sanandaj-Sirjan zone, Isfahan, Iran by Amirajlo, Poorya, Hassani, Hossein, Beiranvand Pour, Amin, Habibkhah, Narges

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
    Published in Earth science informatics (01.01.2025)
    “… Regression-based machine learning algorithms such as Isolation Forest (IF) and One-Class Support Vector Machine (OCSVM…”
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  12. 12

    Multiple Functional ECG Signal is Processing for Wearable Applications of Long-Term Cardiac Monitoring by Liu, Xin, Zheng, Yuanjin, Phyu, Myint Wai, Zhao, Bin, Je, Minkyu, Yuan, Xiaojun

    ISSN: 0018-9294, 1558-2531, 1558-2531
    Published: New York, NY IEEE 01.02.2011
    “…, and is highly suitable for wearable applications of long-term cardiac monitoring…”
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  13. 13

    Predicting the long-term stability of compact multiplanet systems by Tamayo, Daniel, Cranmer, Miles, Hadden, Samuel, Rein, Hanno, Battaglia, Peter, Obertas, Alysa, Armitage, Philip J, Ho, Shirley, Spergel, David N, Gilbertson, Christian, Hussain, Naireen, Silburt, Ari, Jontof-Hutter, Daniel, Menou, Kristen

    ISSN: 1091-6490, 1091-6490
    Published: United States 04.08.2020
    “…] orbits, thus achieving speed-ups of up to [Formula: see text] over full simulations. This computationally opens up the stability-constrained characterization of multiplanet systems…”
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  14. 14

    Machine learning model combined with CEEMDAN algorithm for monthly precipitation prediction by Shen, Zi-yi, Ban, Wen-chao

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
    Published in Earth science informatics (01.06.2023)
    “…),a support vector machine (SVM) and the long short-term memory (LSTM) neural network. The CEEMDAN algorithm is first used to decompose the precipitation time series data…”
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  15. 15

    Sparrow search algorithm optimized LSTM model for CSI-based indoor localization by Wang, Yan, Zhou, Yuqing

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
    Published in Earth science informatics (01.01.2025)
    “… Additionally, it supports various advanced positioning algorithms, making it suitable for complex indoor environments and enabling real-time precise positioning…”
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  16. 16

    GPU accelerated biochemical network simulation by Zhou, Yanxiang, Liepe, Juliane, Sheng, Xia, Stumpf, Michael P. H., Barnes, Chris

    ISSN: 1367-4803, 1367-4811, 1367-4811, 1460-2059
    Published: Oxford Oxford University Press 15.03.2011
    Published in Bioinformatics (15.03.2011)
    “…Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive…”
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  17. 17

    Prediction of channel sinuosity in perennial rivers using Bayesian Mutual Information theory and support vector regression coupled with meta-heuristic algorithms by Haghbin, Masoud, Sharafati, Ahmad, Motta, Davide

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021
    Published in Earth science informatics (01.12.2021)
    “…Support Vector Regression (SVR) combined with Invasive Weeds Optimization (IWO), standalone SVR, and Radial Basis Function Neural Networks are applied to estimate channel sinuosity in perennial rivers…”
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  18. 18

    Comparative analysis of machine learning algorithms and statistical models for predicting crown width of Larix olgensis by Qiu, Siyu, Liang, Ruiting, Wang, Yifu, Luo, Mi, Sun, Yujun

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022
    Published in Earth science informatics (01.12.2022)
    “…-NearestNeighbors, the Random Forest, the Gradient Boosting Decision Tree, the Support Vector Regression, the Voting…”
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  19. 19

    Phasor S-FLIM: a new paradigm for fast and robust spectral fluorescence lifetime imaging by Scipioni, Lorenzo, Rossetta, Alessandro, Tedeschi, Giulia, Gratton, Enrico

    ISSN: 1548-7091, 1548-7105, 1548-7105
    Published: United States Nature Publishing Group 01.05.2021
    Published in Nature methods (01.05.2021)
    “… However, their combination is typically inefficient and slow in terms of acquisition and processing…”
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  20. 20

    A hybrid approach combining the multi-dimensional time series k-means algorithm and long short-term memory networks to predict the monthly water demand according to the uncertainty in the dataset by Niknam, Azar, Zare, Hasan Khademi, Hosseininasab, Hassan, Mostafaeipour, Ali

    ISSN: 1865-0473, 1865-0481
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
    Published in Earth science informatics (01.06.2023)
    “…An authentic water consumption forecast is an auxiliary tool to support the management of the water supply and demand in urban areas…”
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