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  1. 1441
  2. 1442

    Link Prediction Based on the Derivation of Mapping Entropy by Hefei Hu, Yanan Wang, Zheng Li, Yang Tian, Yuemei Ren

    Published 2021-01-01
    “…The algorithms based on topological similarity play an important role in link prediction. …”
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    Article
  3. 1443
  4. 1444

    Churn prediction for SaaS company with machine learning by Hugo Eduardo Sanches, Ayslan Trevizan Possebom, Linnyer Beatrys Ruiz Aylon

    Published 2025-06-01
    “…Originality/value – By applying machine learning to churn prediction, this study offers valuable insights into the performance and comparative analysis of different algorithms in a real-world SaaS environment. …”
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    Article
  5. 1445

    Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures by Valerio Zippo, Elisa Robotti, Daniele Maestri, Pietro Fossati, David Valenza, Stefano Maggi, Gennaro Papallo, Masho Hilawie Belay, Simone Cerruti, Giorgio Porcu, Emilio Marengo

    Published 2025-02-01
    “…The proposed approach has a comprehensive connotation, starting from data pre-treatment and cleaning, to model building and prediction. Different machine learning algorithms are compared (Polynomial Regression, LASSO, Random Forests and Gradient Boosting, ANN, SVM, and k-NN), to provide the best predictive ability, also exploiting human reinforcement. …”
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    Article
  6. 1446
  7. 1447

    A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases by Norma Latif Fitriyani, Muhammad Syafrudin, Nur Chamidah, Marisa Rifada, Hendri Susilo, Dursun Aydin, Syifa Latif Qolbiyani, Seung Won Lee

    Published 2025-07-01
    “…This research presents a novel prediction model for CVDs utilizing a bagging algorithm that incorporates histogram gradient boosting as the estimator. …”
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    Article
  8. 1448

    A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools by Saad Javed Cheema, Masoud Karbasi, Gurjit S. Randhawa, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal, Farhat Abbas, Qamar Uz Zaman, Aitazaz A. Farooque

    Published 2025-08-01
    “…A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict Kc. …”
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    Article
  9. 1449

    The Influence of Network Structural Preference on Link Prediction by Yongcheng Wang, Yu Wang, Xinye Lin, Wei Wang

    Published 2020-01-01
    “…However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. …”
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    Article
  10. 1450

    Performance of Machine Learning Classifiers for Diabetes Prediction by Mijala Manandhar, Shaikat Baidya, Babalpreet Kaur, Katia Atoji

    Published 2024-08-01
    “…Future research should focus on integrating multiple datasets and exploring more complex ML algorithms to enhance prediction accuracy and generalization. …”
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    Article
  11. 1451

    Machine Learning and the Conundrum of Stroke Risk Prediction by Yaacoub Chahine, Matthew J Magoon, Bahetihazi Maidu, Juan C del Álamo, Patrick M Boyle, Nazem Akoum

    Published 2023-04-01
    “…The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. …”
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    Article
  12. 1452

    Location Prediction on Trajectory Data: A Review by Ruizhi Wu, Guangchun Luo, Junming Shao, Ling Tian, Chengzong Peng

    Published 2018-06-01
    “…This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. …”
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    Article
  13. 1453
  14. 1454

    A Unmanned Aerial Vehicle-Based Image Information Acquisition Technique for the Middle and Lower Sections of Rice Plants and a Predictive Algorithm Model for Pest and Disease Detection by Xiaoyan Guo, Yuanzhen Ou, Konghong Deng, Xiaolong Fan, Ruitao Gao, Zhiyan Zhou

    Published 2025-04-01
    “…Aiming at the technical bottleneck of monitoring rice stalk, pest, and grass damage in the middle and lower parts of rice, this paper proposes a UAV-based image information acquisition method and disease prediction algorithm model, which provides an efficient and low-cost solution for the accurate early monitoring of rice diseases, and helps improve the scientific and intelligent level of agricultural disease prevention and control. …”
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    Article
  15. 1455

    Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm by Harriet Laryea, Andrea Schiffauerova

    Published 2025-06-01
    “…The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. …”
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    Article
  16. 1456
  17. 1457

    Big data, dementia prediction and knowledge organization by D. Grant Campbell

    Published 2025-06-01
    “…This paper uses principles of knowledge organization to explore the application of big data algorithms to the task of predicting dementia diagnoses. …”
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    Article
  18. 1458
  19. 1459

    Malware prediction technique based on program gene by Da XIAO, Bohan LIU, Baojiang CUI, Xiaochen WANG, Suoxing ZHANG

    Published 2018-08-01
    “…With the development of Internet technology,malicious programs have risen explosively.In the face of executable files without source,the current mainstream malware detection uses feature detection based on similarity,with lack of analysis of malicious sources.To resolve this status,the definition of program gene was raised,a generic method of extracting program gene was designed,and a malicious program prediction method was proposed based on program gene.Utilizing machine learning and deep-learning algorithms,the forecasting system has good prediction ability,with the accuracy rate of 99.3% in the deep-learning model,which validates the role of program gene theory in the field of malicious program analysis.…”
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  20. 1460