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  1. 1341

    Evaluating the Predictive Power of Software Metrics for Fault Localization by Issar Arab, Kenneth Magel, Mohammed Akour

    Published 2025-06-01
    “…This study investigates the predictive power of various software metrics for fault localization by framing the task as a multi-class classification problem and evaluating it using the Defects4J dataset. …”
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    Article
  2. 1342

    Predictive analysis of doubly Type-Ⅱ censored models by Young Eun Jeon, Yongku Kim, Jung-In Seo

    Published 2024-10-01
    “…Second, it addresses prediction problems in a closed-form manner, ensuring computational efficiency. …”
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    Article
  3. 1343

    Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey by Beminate Lemma Seifu, Angwach Abrham Asnake, Alemayehu Kasu Gebrehana

    Published 2025-03-01
    “…Maternal unemployment had the most significant positive impact (+0.060) on predicting zero-dose children. Lack of maternal education was the second most significant positive factor (+0.048), indicating that mothers without formal education are more likely to have zero-dose children. …”
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    Article
  4. 1344

    Data-driven predictive models for sustainable smart buildings by Prabhu Rajaram, Gnana Swathika O․V․

    Published 2025-09-01
    “…In intelligent buildings, AI enables the analysis of vast amount of data collected from sensors and building systems, facilitating enhanced energy management, predictive maintenance, tailored comfort controls, and operational efficiencies derived from informed decisions based on operational conditions and occupant behavior trends, which aid in maximizing both functionality and sustainability of a building. …”
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    Article
  5. 1345

    Development of the predictive model for I stage breast cancer by A. Kh. Ismagilov, A. S. Vanesyan, D. R. Khuzina

    Published 2021-09-01
    “…Using a forward stepwise selection (binary regression), the most important prognostic factors were selected, on the basis of which the predictive model “Risk Assessment Algorithm for Recurrence of Breast Carcinoma” was constructed.Results. …”
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    Article
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    A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

    Published 2025-01-01
    “…This research proposes an ensemble classification method leveraging machine learning (ML) to predict cardiac problems, providing physicians with actionable insights for personalized diagnoses and treatments. …”
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    Article
  9. 1349

    Prediction of the Effect of Polymer Membrane Composition in a Dry Air Humidification Process via Neural Network Modeling by M. Fakhroleslam, A. Samimi, S.A. Mousavi, R. Rezaei

    Published 2016-01-01
    “…The results showed that the feed- forward neural network models, especially FFGA, were suitable for prediction of the effect of membrane composition and operating conditions on the performance of this type of membrane humidifiers…”
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    Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study by Jalal Hatem Hussein Bayati, Abid Mahboob, Laiba Amin, Muhammad Waheed Rasheed, Abdu Alameri

    Published 2025-08-01
    “…This study utilizes MATLAB program-based algorithms to calculate topological indices and machine learning algorithms to explore their ability to predict the physio-chemical properties of asthma drugs. …”
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    Article
  13. 1353
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    Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology by Hassan Bagheri, Reza Mohebian, Ali Moradzadeh, Behnia Azizzadeh Mehmandost Olya

    Published 2024-12-01
    “…Subsequently, the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM), belonging to the category of DL algorithms and harnessing the computational power of GPUs, is employed for the prediction of CBW, BVI, and FFV logs. …”
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    Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm Performance with Random Undersampling Technique to Predict Gestational Diabetes Mellitus Risk by Annisa Damayanti, Anna Baita

    Published 2025-03-01
    “…One of the machine learning methods that can be used to predict GDM is the Support Vector Machine (SVM) algorithm and the Random Forest (RF) algorithm. …”
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    A Novel Back Propagation Neural Network Based on the Harris Hawks Optimization Algorithm for the Remaining Useful Life Prediction of Lithium-Ion Batteries by Yuyang Zhou, Zijian Shao, Huanhuan Li, Jing Chen, Haohan Sun, Yaping Wang, Nan Wang, Lei Pei, Zhen Wang, Houzhong Zhang, Chaochun Yuan

    Published 2025-07-01
    “…In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. …”
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    Article