Search Results - intelligence a ((three algorithm) OR (tree algorithm))

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    E2IDS: an enhanced intelligent intrusion detection system based on decision tree algorithm by Bouke, Mohamed Aly, Abdullah, Azizol, ALshatebi, Sameer Hamoud, Abdullah, Mohd Taufik

    Published 2022
    “…Fortunately, Artificial Intelligence (AI) has recently attracted a lot of attention, and it is now a principal component of these systems. …”
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    Article
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    Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

    Published 2020
    “…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
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    Conference or Workshop Item
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    Optimizing tree planting areas through integer programming and improved genetic algorithm by Md Badarudin, Ismadi

    Published 2012
    “…The decision based on the highest number of trees is promoted among the three techniques. The process of block division and determining the optimal number of trees require a series of analysis. …”
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    Thesis
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    A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation by Rong, Li, Shari, Zalina, Ab Kadir, Mohd Zainal Abidin

    Published 2025
    “…A thematic analysis of 40 peer-reviewed articles was conducted using ATLAS.ti, revealing three dominant research themes: intelligent algorithms, building performance simulation techniques, and adaptive design for climate change. …”
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    Article
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    Novel genetic algorithm towards implementing a lining-layout optimization strategy. by Md Badarudin, Ismadi, Md Sultan, Abu Bakar, Sulaiman, Md. Nasir, Mamat, Ali, Tengku Muda Mohamed, Mahmud

    Published 2010
    “…Because there are many possible solutions with ambiguous results, a novelty of Genetic Algorithm (GA) for lining-layout with focusing on the two approaches which are 1) assigning the determined random values to the genes of chromosome, 2)avoiding the same solution of optimal blocks occurs, was applied to suggest the optimal solution intelligently. …”
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    Reverse migration prediction model based on machine learning / Azreen Anuar by Anuar, Azreen

    Published 2024
    “…And the third objective is to evaluate reverse migration prediction model based on machine learning analysis. For this purpose, three (3) algorithms have been assessed, namely, the Random Forest, Decision Tree, and Gradient Boosted Tree. …”
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    Thesis
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    An efficient and effective case classification method based on slicing by Shiba, Omar A. A., Sulaiman, Md. Nasir, Mamat, Ali, Ahmad, Fatimah

    Published 2006
    “…The algorithms are: Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5). …”
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    Article
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    Intelligent Computerised System Towards Implementing a Lining-Layout Optimisation Strategy by A.B. Md Sultan; , I. Md. Badrudin;, M.N. Sulaiman;, A. Mamat;, M. Tengku Muda Mohamed

    Published 2024
    “…Because there are many possible solutions with ambiguous results, a novelty of Genetic Algorithm (GA) for lining-layout with focusing on the two approaches which are assigning the determined random values to the genes of chromosome and avoiding the same solution of optimal blocks occurs, was applied to suggest the optimal solution intelligently. …”
    conference output::conference proceedings::conference paper
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    A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation by Mohd Ali, Nursabillilah, Besar, Rosli, Ab Aziz, Nor Azlina

    Published 2023
    “…These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM). …”
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    Article