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  1. 841
  2. 842

    An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

    Published 2025-06-01
    “…Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. …”
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  3. 843
  4. 844

    Prediction of Anthocyanin Content in Purple-Leaf Lettuce Based on Spectral Features and Optimized Extreme Learning Machine Algorithm by Chunhui Liu, Haiye Yu, Yucheng Liu, Lei Zhang, Dawei Li, Junhe Zhang, Xiaokai Li, Yuanyuan Sui

    Published 2024-12-01
    “…The results indicated the following: (1) For the feature band selection methods, the UVE-CARS-SNV-DBO-ELM model achieved an R<sub>m</sub><sup>2</sup> of 0.8623, an RMSE<sub>m</sub> of 0.0098, an R<sub>v</sub><sup>2</sup> of 0.8617, and an RMSE<sub>v</sub> of 0.0095, resulting in an RPD of 2.7192, further demonstrating that UVE-CARS enhances feature band extraction based on UVE and indicating a strong model performance. (2) For the vegetation index, VI3 showed a better predictive accuracy than VI2. The VI3-WOA-ELM model achieved an R<sub>m</sub><sup>2</sup> of 0.8348, an RMSE<sub>m</sub> of 0.0109 mg/g, an R<sub>v</sub><sup>2</sup> of 0.812, an RMSE<sub>v</sub> of 0.011 mg/g, and an RPD of 2.3323, demonstrating good performance. (3) For the optimization algorithms, the DBO, SABO, and WOA all performed well in optimizing the ELM model. …”
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  5. 845

    Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

    Published 2025-05-01
    “…Therefore, this paper takes the star-rated hotels in the six districts of Tianjin as the research subject and proposes a few-shot hotel location prediction method based on meta-learning algorithms and transportation accessibility. …”
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  6. 846

    RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network by Jiaping Shen, Haiting Zhou, Muda Jin, Zhongping Jin, Qiang Wang, Yanchun Mu, Zhiming Hong

    Published 2025-02-01
    “…To address this challenge, this paper proposes a data-driven artificial neural network method, namely the CNN-LSTM bearing remaining life prediction model based on the fruit fly optimization algorithm (FOA). …”
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  7. 847
  8. 848

    A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

    Published 2025-08-01
    “…Yanhong Xu,1,&amp;ast; Yizheng Zu,2,&amp;ast; Ying Zhang,1,&amp;ast; Zewei Liang,1 Xia Xu,1,3– 5 Jianying Yan1,3– 5 1College of Clinical Medicine for Obstetrics &amp; Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 2The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 3Fujian Clinical Research Center for Maternal-Fetal Medicine, Fuzhou, Fujian, People’s Republic of China; 4Laboratory of Maternal-Fetal Medicine, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 5National Key Obstetric Clinical Specialty Construction Institution of China, Fuzhou, Fujian, People’s Republic of China&amp;ast;These authors contributed equally to this workCorrespondence: Xia Xu, College of Clinical Medicine for Obstetrics &amp; Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email xuxia0623@fjmu.edu.cn Jianying Yan, College of Clinical Medicine for Obstetrics &amp; Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email yanjy2019@fjmu.edu.cnPurpose: To analyze the risk factors for preterm birth in patients with early-onset preeclampsia (EOPE) based on multi-algorithm machine learning and to construct a predictive model to explore the predictive value of the model.Methods: A retrospective analysis was conducted on 442 EOPE patients from a single tertiary center, divided into preterm birth (&lt; 37 weeks, n=358) and term-born (≥ 37 weeks, n=84) groups. …”
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  9. 849

    Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition by Shaikh Yassir Yousouf Jewan, Deepak Gautam, Debbie Sparkes, Ajit Singh, Lawal Billa, Alessia Cogato, Erik Murchie, Vinay Pagay

    Published 2024-12-01
    “…The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. Our study aimed to assess the potential of multimodal data (hyperspectral vegetation indices (VIs), thermal indices, and canopy state variables) and ML algorithms to predict grapevine yield components and berry composition parameters. …”
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  10. 850
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    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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  12. 852
  13. 853

    Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms by Ouafa Belkacem, Ahmed Rezrazi, Kamel Aizi, Lokmane Abdelouahed, Maamar Laidi, Abdelhafid Touil, Leila Cherifi, Salah Hanini

    Published 2025-06-01
    “…This study introduces a novel and highly effective approach for predicting barite scale formation and inhibition by leveraging advanced artificial intelligence (AI) techniques. …”
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  14. 854

    Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction by Denisa Gándara, Hadis Anahideh, Matthew P. Ison, Lorenzo Picchiarini

    Published 2024-06-01
    “…Because predictive algorithms rely on historical data, they capture societal injustices, including racism. …”
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    Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

    Published 2024-09-01
    “…Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization. …”
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  17. 857

    Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine by Minjian Wang

    Published 2022-01-01
    “…In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. …”
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  18. 858

    A Hybrid Internet of Behavior Algorithm for Predicting IoT Data of Plant Growing using LSTM and NB Models by Khansaa Yaseen Ahmad, Omar Muayad Abdullah

    Published 2025-09-01
    “…The researches that compare the accuracy between classical statistical prediction procedures and deep learning algorithms represent an important and modern field. …”
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  19. 859

    A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm by G. Ramkumar, Satyajeet Sahoo, T. M. Amirthalakshmi, S. Ramesh, R. Thandaiah Prabu, Kasipandian Kasirajan, Antony V. Samrot, A. Ranjith

    Published 2021-01-01
    “…The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. …”
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  20. 860