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    Research on Optimization Method of Coal Blending for Carbon Emission Reduction Based on Bi-level Programming by CHEN Siqin, ZHU Yinan, LI Xiaochen, WANG Xuehai

    Published 2023-04-01
    “…As the “dual carbon” goal has been upgraded to a national strategy, coal-fired power plants are the top priority for carbon reduction in the power generation industry, and coal-fired power plants are facing a huge challenge in limiting carbon emissions. …”
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    Article
  3. 983

    Multi-objective Optimal Scheduling of Water-Carbon in Cascade Reservoirs during Impoundment for Carbon Emission Reduction by ZHOU Yan-lai, NING Zhi-hao, HE Jun-tao

    Published 2025-06-01
    “…[Methods] Given that current studies on cascade reservoir impoundment scheduling have not yet incorporated carbon reduction objectives, this study proposed a multi-objective water-carbon scheduling model for cascade reservoirs during impoundment period based on the carbon emission factor method.An early storage strategy for cascade reservoirs was developed,and three objectives—minimizing flood control risk,maximizing power generation,and minimizing greenhouse gas emissions—were established.The Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) was employed to derive optimal scheduling schemes for the impoundment period.…”
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    Model order reduction of boiler system using nature-inspired metaheuristic optimization of PID controller by Anurag Singh, Shekhar Yadav, Nitesh Tiwari, Dinesh Kumar Nishad, Saifullah Khalid

    Published 2025-04-01
    “…This study proposes a dual-stage optimization framework that integrates balanced truncation-based model order reduction with nature-inspired metaheuristic algorithms for PID controller tuning to address these issues. …”
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    Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm by Jianbin WANG, Shuchun WANG, Shangjin LIAO, Shuyuan SHI

    Published 2023-04-01
    “…With the rapid construction of the 5G wireless communication network, the energy consumption pressure of operators, and even the overall communication industry, is simultaneously highlighted.Achieving sustainable development of the industry through energy conservation and consumption reduction has become a new research direction for the current 5G network development.Taking the PRB rate as the load evaluation index, LSTM model was improved by using DCNN to extract the depth feature of the cell’s indicators.A set of DCNN-LSTM deep learning model that could predict the future value of PRB rate was proposed.On the basis of the improved algorithm, the network topology of the current 5G access network was optimized.An additional network element and its working system were designed.An intelligent energy-saving system, which ensured the network experience, of 5G base stations was realized.…”
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  12. 992

    Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci by Jie Lu, Xinhao Lu, Yixiao Wang, Hengdong Zhang, Lei Han, Baoli Zhu, Boshen Wang

    Published 2025-05-01
    “…LR and multiple ML algorithms were employed to establish the NIHL prediction model with accuracy, recall, precision, F-score, R2 and AUC as performance indicators. …”
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    Health State Prediction of Lithium-Ion Battery Based on Improved Sparrow Search Algorithm and Support Vector Regression by Deyang Yin, Xiao Zhu, Wanjie Zhang, Jianfeng Zheng

    Published 2024-11-01
    “…To enhance prediction performance, this paper introduces an SOH prediction model based on an improved sparrow algorithm and support vector regression (ISSA-SVR). …”
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    Article
  15. 995

    Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach by Qoriani Widayati, Kusworo Adi, R Rizal Isnanto, Eka Puji Agustini, Dewa Rizki Rahmat Julianto, Fawwaz Bimo Prakasa

    Published 2025-03-01
    “…Student loyalty is a crucial factor supporting the sustainability of higher education institutions. The aim of this study is to predict student loyalty using a machine learning approach, specifically the random forest algorithm. …”
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    Article
  16. 996

    A hybrid model combining environmental analysis and machine learning for predicting AI education quality by Xinyu Ren

    Published 2025-04-01
    “…Also, in order to evaluate the quality of AI training programs in higher education, a new approach based on the multilayer perceptron (MLP) algorithm was presented in which the capuchin search algorithm (CapSA) was used to adjust the weight vector of the neural network. …”
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  17. 997

    Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Yaqoob Wani

    Published 2025-06-01
    “…Abstract Machine learning has become an essential component across various domains, including the education sector. Accurately predicting students’ academic performance plays a critical role for teachers and school administrators—not only in enhancing the quality of education but also in influencing educational outcomes. …”
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  18. 998

    Integrating Genetic Algorithm and Geographically Weighted Approaches into Machine Learning Improves Soil pH Prediction in China by Wantao Zhang, Jingyi Ji, Binbin Li, Xiao Deng, Mingxiang Xu

    Published 2025-03-01
    “…This study integrates Geographic Weighted Regression (GWR) with three ML models (Random Forest, Cubist, and XGBoost) and designs and develops three geographically weighted machine learning models optimized by Genetic Algorithms to improve the prediction of soil pH values. …”
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  19. 999

    Machine learning-based academic performance prediction with explainability for enhanced decision-making in educational institutions by Wesam Ahmed, Mudasir Ahmad Wani, Pawel Plawiak, Souham Meshoul, Amena Mahmoud, Mohamed Hammad

    Published 2025-07-01
    “…Ten regression models including K-Nearest Neighbors Regressor, Linear Regression, CatBoost, XGBoost, AdaBoost, and ensemble voting regression (VR) algorithm based on the top five heterogeneous regressors as base models are employed to predict academic outcomes. …”
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  20. 1000

    Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

    Published 2025-07-01
    “…Experiments were conducted on data for noise reduction and correlation analysis, followed by analysis of the model’s prediction performance and generalization ability. …”
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