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  1. 17041

    Clothes consumption and disposal practices: a look at the profile of Brazilian apparel consumers by Rimena Canuto Oliveira, Irenilza de Alencar Nääs, Solimar Garcia

    Published 2024-12-01
    “…Only four attributes were chosen by the algorithm to build the trees (age, annual income, yearly spending on clothes and how long the clothes are worn). …”
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    Article
  2. 17042
  3. 17043

    Cultural Moduses of Students and Their Influence on the Formation of Life Plans by M. A. Golovchin

    Published 2024-07-01
    “…The practical significance of the research results lies in the possibility of their use within the framework of strategic design of youth and educational policies.…”
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  4. 17044
  5. 17045
  6. 17046
  7. 17047

    Design and Experiment of a Single-Disk Silage Corn Harvester by Wenxuan Wang, Wei Sun, Hui Li, Xiaokang Li, Yongwei Yuan

    Published 2025-03-01
    “…The ternary quadratic regression prediction model was constructed by using Isight 5.6 software, and the accuracy of the model was verified by variance analysis and field experiments. …”
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    Article
  8. 17048

    Transfer Kernel Extreme Learning Machine Based on Bidirectional Cross Domain Approximation by Yuanxiao Zeng, Huimin Li, Yanbing Song

    Published 2025-01-01
    “…Finally, by combining the predictions from both transfer KELMs, our model significantly boosts its robustness. …”
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    Article
  9. 17049

    Consumer Behaviour: Analysing Marketing Campaigns through Recommender Systems and Statistical Techniques

    Published 2024-07-01
    “…This approach addresses the formidable challenges of accurately predicting consumer behaviour. We provide a detailed introduction to recommendation systems, emphasizing their vital role in the modern marketing landscape. …”
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    Article
  10. 17050

    Hybrid CNN-based Recommendation System by Muhammad Alrashidi, Roliana Ibrahim, Ali Selamat

    Published 2024-02-01
    “…In order to enhance the accuracy of predictions and address the challenges posed by sparsity, the proposed model incorporates both the extracted attributes and explicit interactions between items and users. …”
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    Article
  11. 17051

    Noise correlations and neuronal diversity may limit the utility of winner-take-all readout in a pop out visual search task. by Ori Hendler, Ronen Segev, Maoz Shamir

    Published 2025-05-01
    “…The analysis identifies specific response statistics that require further empirical characterization to accurately predict WTA performance in biologically plausible models of visual pop out detection.…”
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  12. 17052

    A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting by Maria Krivova, Michael J. Olsen, Ben A. Leshchinsky

    Published 2025-02-01
    “…This outcome is contingent on the condition that the time between the epochs used for prediction and the forecasted epochs does not increase.…”
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    Article
  13. 17053

    Deterring Street Crimes Using Aerial Police: Data-Driven Joint Station Deployment and Patrol Path Planning for Policing UAVs by Zuyu Chen, Yan Liu, Shengze Hu, Xin Zhang, Yan Pan

    Published 2025-06-01
    “…In the first module, the street-level deterring effect of the UAVs is estimated using a prediction-enhanced method, which guides the UAVs to patrol the high-risk streets more efficiently. …”
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    Article
  14. 17054

    Modeling Lane Changes at Freeway On-Ramps With a Novel Car-Following Model Based on Desired Time Headways by Moritz Berghaus, Markus Oeser

    Published 2025-01-01
    “…The lane change model includes a leader selection algorithm, which enables merging vehicles to pass or be passed by vehicles in the main lane. …”
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    Article
  15. 17055

    A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities by Vaidhya Govindharajalu Kaliyaperumal, Paavai Anand Gopalan

    Published 2025-06-01
    “…In order to improve both model generalization and image quality, preprocessing is applied to images prior to prediction, and the proposed dataset is organized to handle multiple dynamic words. …”
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    Article
  16. 17056

    3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning by Anastasia Skonta, Myrto G. Bellou, Haralambos Stamatis

    Published 2025-07-01
    “…The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. …”
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  17. 17057

    Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning by Jos P. Kanning, Junfeng Wang, Shahab Abtahi, Mirjam I. Geerlings, Ynte M. Ruigrok

    Published 2025-03-01
    “…Using the UK Biobank, we identified aSAH cases via hospital-based ICD codes and analysed 618 baseline variables covering demographics, lifestyle, medical history, and physical measurements. The CatBoost ML algorithm and Shapley Additive Explanations (SHAP) identified the top 25 variables most influential in predicting aSAH. …”
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  18. 17058

    Learning Transferable Convolutional Proxy by SMI-Based Matching Technique by Wei Jin, Nan Jia

    Published 2020-01-01
    “…We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.…”
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  19. 17059

    Use of electrochemical energy storage systems in autonomous power supply systems to reduce fuel consumption of power installations by A. I. Fedotov, E. A. Fedotov, A. F. Abdullazyanov

    Published 2021-05-01
    “…The technique is valid for convex consumption characteristics of power plants. In the context of predicted changes in power profiles (work / weekend days), optimal control scenarios are calculated individually. …”
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  20. 17060

    Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam. by Raja Das, Pham Van Tien, Karl W Wegmann, Madhumita Chakraborty

    Published 2024-01-01
    “…The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. …”
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