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  1. 1241

    Apply Ridge Regression Model to Predict the Lateral Velocity Difference of Tight Reservoirs by HAN Longfei, ZHANG Yongfei, WANG Miaomiao, LI Yu

    Published 2024-12-01
    “…Finally, a ridge regression algorithm is used to establish a prediction model of the lateral wave time lag based on the logging data of five wells in WQ block. …”
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    Article
  2. 1242

    Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms. by Md Merajul Islam, Nobab Md Shoukot Jahan Kibria, Sujit Kumar, Dulal Chandra Roy, Md Rezaul Karim

    Published 2024-01-01
    “…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”
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    Article
  3. 1243

    Frequency Limited & Weighted Model Reduction Algorithm With Error Bound: Application to Discrete-Time Doubly Fed Induction Generator Based Wind Turbines for Power System by Sajid Bashir, Sammana Batool, Muhammad Imran, Mian Ilyas Ahmad, Fahad Mumtaz Malik, Muhammad Salman, Abdul Wakeel, Usman Ali

    Published 2021-01-01
    “…The proposed work produces steady and precise outcomes in contrast to conventional reduction methods, which shows the efficacy of the proposed algorithm.…”
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  4. 1244

    Aplicación de un algoritmo de reducción de grafos al Método de los Grafos Dicromáticos//Applying a graph reduction algorithm to Dichromatic Graphs Method by Rafael Rodríguez-Puente, Sergio Marrero-Osorio, Manuel Lazo-Cortés

    Published 2012-05-01
    “…For this, we define the equivalence relations and partitions necessary for the application of a graph reduction algorithm to a graph obtained from the application of Dichromatic Graph Method, this method has used in terms of rational design and computational problem solving in mechanical engineering. …”
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    Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea by Shuo Wang, Shuo Wang, Ke Yang, Ke Yang, Hui Peng, Hui Peng

    Published 2025-05-01
    “…Time decomposition algorithms, combined with machine learning, are effective tools to enhance the capabilities of inflow prediction models. …”
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    A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study by Guilherme Cassales, Serajis Salekin, Nick Lim, Dean Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

    Published 2025-05-01
    “…High-resolution tree stem radius measurements and predictive simulation through machine learning algorithms offer powerful opportunities for understanding these dynamics. …”
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    Article
  11. 1251

    Optimizing Solar Radiation Prediction Based on The Internet of Things Platform in Photovoltaic Power Plant by Neda Ashrafi Khozani, Maryam Mahmoudi, Shabnam Nasr Esfahani

    Published 2024-07-01
    “…Managers and designers encounter economic and managerial challenges due to the uncertainty and difficulty in predicting solar radiation levels. This research introduces a highly accurate prediction method utilizing tree-based methods, enhanced by meta-heuristic algorithms to boost performance. …”
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    Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network by Lilian Liu, Guobin Wang, Yubin Lan, Xinyu Xue, Suming Ding, Huizheng Wang, Cancan Song

    Published 2024-12-01
    “…The particle deposition distribution data under different operating parameters were obtained by EDEM simulation and data superposition methods, and a generalized regression neural network (GRNN) based on a genetic algorithm (GA) was used to establish the prediction model of particle deposition, which was validated by bench test. …”
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    Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

    Published 2025-01-01
    “…To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. …”
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