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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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  4. 964

    Algorithm for teaching students of the special educational department of physical education by Egorychev, Aleksey O., Egorycheva, Elina V.

    Published 2025-06-01
    “…Among students with health restrictions, a high proportion has a passive level of needs for physical education and sports activities. It is topical to create pedagogical conditions for a gradual transition from a passive state to a situational or active-activity state in relation to physical education and sports activities. …”
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  5. 965

    Predictable and non-stationary processes of interval PREDICTION BASED ON stochastic differential equations by A. V. Ausiannikau

    Published 2019-06-01
    “…Predictability of such processes is defined. Algorithms of interval prediction in the discrete and continuous time are received.…”
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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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  9. 969

    GAN data reconstruction based prediction method of telecom subscriber loss by Kehong A, Xiaodong HU

    Published 2023-03-01
    “…Users are the core of operators’ interests.With the introduction of the policy of transferring network with a number, the competition between operators becomes more and more fierce.In order to accurately predict subscriber loss tendency in advance, a prediction method of subscriber loss based on generative adversarial network data reconstruction was proposed.Firstly, the dirty data in the telecom subscriber loss data was used by effective data preprocessing method.Secondly, the GAN was used to reconstruct the telecom subscriber loss data to solve the problem of the imbalance of the telecom subscriber loss data.Finally, extreme gradient boosting algorithm was used to train the telecom subscriber loss prediction model based on GAN reconstruction and the SMOTE sampling model based on synthetic minority oversampling technique sampling method respectively, and compare the prediction accuracy of the two models.The experimental results show that the prediction accuracy of the GAN reconstructed telecom subscriber loss prediction model is increased by 6.75%, the accuracy rate is increased by 25.91%, the recall rate is increased by 30.91%, and the F1-score is increased by 28.73% compared with the unreconstructed prediction model.This method can effectively improve the accuracy of telecom subscriber loss prediction.…”
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  10. 970

    An Ensemble Model for Predicting Cardiovascular Disease utilizing Nature Inspired Optimization by Annwesha Banerjee Majumder, Somsubhra Gupta, Sourav Majumder, Dharmpal Singh

    Published 2024-12-01
    “… This paper represents an efficient model for heart disease prediction model utilizing an ensemble mechanism optimized through BAT algorithm. …”
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  11. 971

    Dissolved Oxygen Prediction Based on SOA-SVM and SOA-BP Models by ZHANG Xuekun

    Published 2021-01-01
    Subjects: “…dissolved oxygen prediction…”
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  12. 972

    Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning by Asraa Muayed Abdalah, Noor Redha Alkazaz

    Published 2024-12-01
    Subjects: “…Artificial Intelligence, Machine Learning Algorithms, Face Recognition, Age Prediction, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN)…”
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    Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction by Babu Kumar, Radhakrishnan Soundararajan, Kanimozhi Natesan, Roobini Maridhas Santhi

    Published 2023-12-01
    “…Clinical data analysis must predict cardiovascular disease. Machine learning (ML) may aid decision making and prediction using the healthcare field’s massive data set. …”
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    Enhanced Dung Beetle Optimizer-Optimized KELM for Pile Bearing Capacity Prediction by Bohang Chen, Mingwei Hai, Gaojian Di, Bin Zhou, Qi Zhang, Miao Wang, Yanxiu Guo

    Published 2025-07-01
    “…The IDBO algorithm was then utilized to optimize the hyperparameters of the KELM model for predicting pile bearing capacity. …”
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    Study of SDN intrusion intent identification algorithm based on Bayesian attack graph by Zhiyong LUO, Yu ZHANG, Qing WANG, Weiwei SONG

    Published 2023-04-01
    Subjects: “…SDN security prediction;intrusion intention;attack graph;PageRank algorithm…”
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    Mechanism-learning prediction model for pitting depth of buried pipeline based on HMOGWO-RF by Fulin SONG, Hong ZHAO, Xingyuan MIAO

    Published 2024-11-01
    “…By leveraging the combination of the three-objective HMOGWO algorithm and the RF model, it significantly outperformed the three-objective MOGWO algorithm, the two-objective HMOGWO algorithm, the two-objective MOGWO algorithm, as well as both the single-objective GWO algorithm and the single-objective PSO algorithm in terms of prediction performance and stability. …”
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    Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale by Harsh Vazirani, Xiaofeng Wu, Anurag Srivastava, Debajyoti Dhar, Divyansh Pathak

    Published 2024-11-01
    “…This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. …”
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