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  1. 81
  2. 82

    Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review by Ruba, Sulaiman

    Published 2025
    “…Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. …”
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    article
  3. 83

    Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review by Ruba Sulaiman (17734065)

    Published 2025
    “…Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. …”
  4. 84

    A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai by ALGHANEM, HANI SUBHI MOHD

    Published 2024
    “…My research aims to develop a data-driven decision support framework for fleet management, focusing on leveraging advanced algorithms, including decision trees and random forests, to generate domain-specific AI models. …”
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  5. 85
  6. 86

    Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT) by Kouatli, Issam

    Published 2015
    “…Decision making and/or Decision Support Systems (DSS) using intelligent techniques like Genetic Algorithm and fuzzy logic is becoming popular in many new applications. …”
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  7. 87

    Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique by Al-Garni, Ahmed Z.

    Published 2006
    “…An artificial neural-network model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered feedforward back-propagation algorithm as a learning rule is developed. …”
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    article
  8. 88

    Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care by Regina Padmanabhan (14231606)

    Published 2022
    “…Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. …”
  9. 89

    Prediction the performance of multistage moving bed biological process using artificial neural network (ANN) by Fares Almomani (12585685)

    Published 2020
    “…The developed ANN-based model provides an efficient and robust tool for predicting and forecasting the performance of the ENR-BP.…”
  10. 90

    Enhancing Personalized Learning Experiences through AI-driven Analysis of xAPI Data by ODEH, HANEEN

    Published 2024
    “…The classification algorithms were able to predict the pass/fail statues with up to 93.5% accuracy. …”
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  11. 91

    KNNOR: An oversampling technique for imbalanced datasets by Ashhadul Islam (16869981)

    Published 2021
    “…<p>Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. …”
  12. 92

    Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma by Rawan AlSaad (14159019)

    Published 2019
    “…<h3>Background</h3><p dir="ltr">Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. …”
  13. 93

    The automation of the development of classification models and improvement of model quality using feature engineering techniques by Sjoerd Boeschoten (17347045)

    Published 2023
    “…This research used High-Resolution Orbitrap-based Mass Spectrometers (HRMS) data to create automated prediction models for the first time in literature. …”
  14. 94

    Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks by Mohamed Massaoudi (16888710)

    Published 2024
    “…In a postfault scenario, a copula of processing blocks is implemented to ensure the reliability of the proposed method where high-importance features are incorporated into the TCN-GWO model. The proposed algorithm unlocks scalability and system adaptability to operational variability by adopting numeric imputation and missing-data-tolerant techniques. …”
  15. 95

    A novel few shot learning derived architecture for long-term HbA1c prediction by Marwa Qaraqe (10135172)

    Published 2024
    “…For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. …”
  16. 96

    Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test by Hasan T. Abbas (8115014)

    Published 2019
    “…Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). …”
  17. 97

    Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network by Fares Almomani (12585685)

    Published 2020
    “…An ANN model consists of three layers, 15 neutrons and 260 <i>epochs</i> accurately predict the CMP with 99.1% of data within ±10% deviation of the mean experimental value. …”
  18. 98

    Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context by SALIM, MAHA JAWDAT

    Published 0024
    “…These platforms enhance academic performance by fostering collaborative learning environments and generating extensive data from every user interaction. Machine learning algorithms can process large and complex datasets to identify patterns and trends that may not be immediately apparent. …”
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  19. 99

    A Comprehensive Review of Digital Twin Technology in Building Energy Consumption Forecasting by Maissa Boukaf (22282279)

    Published 2024
    “…The digitalization of building energy forecasting systems, enhanced by Energy Digital Twin technology alongside IoT devices and advanced data-driven algorithms, offers substantial improvements in energy management and optimization, servicing, maintenance, and energy-efficient design. …”
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