يعرض 21 - 40 نتائج من 84 نتيجة بحث عن '(((( develop forest algorithm ) OR ( element data algorithm ))) OR ( data lacking algorithm ))', وقت الاستعلام: 0.13s تنقيح النتائج
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    An ant colony optimization algorithm to improve software quality prediction models حسب Azar, D.

    منشور في 2011
    "…However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. …"
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    article
  3. 23

    XBeGene: Scalable XML Documents Generator by Example Based on Real Data حسب Harazaki, Manami

    منشور في 2012
    "…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …"
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    conferenceObject
  4. 24

    Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk حسب Mohamed Chaouch (17983846)

    منشور في 2025
    "…Additionally, an online classifier is developed for streaming data, combining online PCA with a kernel-based recursive classifier using a stochastic approximation algorithm. …"
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    A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai حسب ALGHANEM, HANI SUBHI MOHD

    منشور في 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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  7. 27

    Predict Student Success and Performance factors by analyzing educational data using data mining techniques حسب ATIF, MUHAMMAD

    منشور في 2022
    "…The research study is performed as experimental analysis and develop models from nine machine learning algorithms including KNN, Naïve Bayes, SVM, Logistic regression, Decision Tree, Random forest, Adaboost, Bagging Classifier, and voting Classifier. …"
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  8. 28
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    Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier حسب Syed Ali Jafar Zaidi (19563178)

    منشور في 2021
    "…Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. …"
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    Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks حسب Najam Us Sahar Riyaz (22927843)

    منشور في 2025
    "…This work identifies the most reliable machine learning (ML) strategies for forecasting corrosion inhibitor efficiency before synthesis, thereby shortening development cycles and reducing experimental cost. Drawing on more than fifteen harmonized datasets that span pyrimidines, ionic liquids, graphene oxides, and additional compound families, we benchmark traditional algorithms, such as artificial neural networks, support vector machines, k-nearest neighbors, random forests, against advanced graph-based and deep architectures including three-level directed message-passing neural networks, 2D3DMol-CIC, and graph convolutional networks. …"
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    Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms حسب Md Ferdous Wahid (13485799)

    منشور في 2022
    "…<p dir="ltr">Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) regimes. …"
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    Nonlinear analysis of shell structures using image processing and machine learning حسب M.S. Nashed (16392961)

    منشور في 2023
    "…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …"
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    Sentiment analysis for Arabizi in social media. (c2015) حسب Tobaili, Taha

    منشور في 2015
    "…Yalla 7abibi, it is very useful to have a data mining tool that can analyze the sentiment of Twitter users in the Arab world. …"
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    masterThesis
  15. 35

    The use of multi-task learning in cybersecurity applications: a systematic literature review حسب Shimaa Ibrahim (22155739)

    منشور في 2024
    "…Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. …"
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    Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation حسب Hariprasath Manoharan (14157966)

    منشور في 2023
    "…Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. …"
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    Correlation Clustering with Overlaps حسب Fakhereldine, Amin

    منشور في 2020
    "…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …"
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    masterThesis
  19. 39

    Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems حسب Ahmad K. Sleiti (14778229)

    منشور في 2022
    "…Data-driven algorithms with capabilities to predict the system’s dynamic behavior still need to be developed. …"
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    A Robust Deep Learning Approach for Distribution System State Estimation with Distributed Generation حسب Kfouri, Ronald

    منشور في 2023
    "…Also, to evaluate the robustness of the algorithms, we test the neural network, without retraining it, on multiple scenarios with noisier data and bad data. …"
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    masterThesis