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

Refine Results
  1. 1
  2. 2

    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
    “…The model design is Decision Tree (DT) algorithm-based, with an approach to data balancing since the data set used is highly unbalanced and one more approach for feature selection. …”
    Get full text
    Get full text
    Article
  3. 3

    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. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  4. 4
  5. 5

    An algorithm for the selection of planting lining technique towards optimizing land area: an algorithm for planting lining technique selection by Md Badarudin, Ismadi, Md Sultan, Abu Bakar, Sulaiman, Md. Nasir, Mamat, Ali, Tengku Muda Mohamed, Mahmud

    Published 2012
    “…The huge possible solution and uncertain result make the problem complex and it requires an intelligent expect for the solution. The algorithm is designed based on two basic works in which to calculate number of trees and divide an area into blocks. …”
    Get full text
    Get full text
    Conference or Workshop Item
  6. 6

    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. …”
    Get full text
    Get full text
    Thesis
  7. 7

    Intelligent cooperative web caching policies for media objects based on J48 decision tree and naïve Bayes supervised machine learning algorithms in structured peer-to-peer systems by Ibrahim, Hamidah, Mohammed, Waheed Yasin, Udzir, Nur Izura, Abdul Hamid, Nor Asilah Wati

    Published 2016
    “…Moreover, traditional web caching policies such as Least Recently Used (LRU), Least Frequently Used (LFU), and Greedy Dual Size (GDS) suffer from caching pollution (i.e. media objects that are stored in the cache are not frequently visited which negatively affects on the performance of web proxy caching). In this work, intelligent cooperative web caching approaches based on J48 decision tree and Naïve Bayes (NB) supervised machine learning algorithms are presented. …”
    Get full text
    Get full text
    Get full text
    Article
  8. 8

    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. …”
    Get full text
    Get full text
    Get full text
    Article
  9. 9
  10. 10
  11. 11

    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
    “…This paper presents the strategies for optimizing planting areas.The three strategies considered for preparing field lining; 1) 600 line-direction 2) selecting the best line-direction for single block and 3) selecting the best line-directions for many separate blocks,might lead to different numbers of trees. …”
    Get full text
    Get full text
    Article
  12. 12

    Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms by Lei, X., Mohamad, U.H., Sarlan, A., Shutaywi, M., Daradkeh, Y.I., Mohammed, H.O.

    Published 2022
    “…For the objective of classifying FI in terms of fraud or not, the Intelligent Information System for Financial Institutions (IISFI) relying on Supervised ML (SML) Algorithms has been created in this work. …”
    Get full text
    Get full text
    Article
  13. 13
  14. 14
  15. 15

    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. …”
    Get full text
    Get full text
    Thesis
  16. 16
  17. 17

    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). …”
    Get full text
    Get full text
    Get full text
    Article
  18. 18

    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
    “…This project presents the strategies for optimizing planting areas. The three strategies considered for preparing field lining; 1) 60' line-direction 2) selecting the best line-direction for single block and 3) selecting the best line-directions for many separate blocks, might lead to different numbers of trees. …”
    conference output::conference proceedings::conference paper
  19. 19
  20. 20