Search Results - (( evolution classification modeling algorithm ) OR ( program implementation tree algorithm ))

Refine Results
  1. 1
  2. 2

    Optimizing tree planting areas through integer programming and improved genetic algorithm by Md Badarudin, Ismadi

    Published 2012
    “…Therefore, a hybrid algorithm through an incorporation of Integer Programming and Improved Genetic Algorithm was proposed for planting lining design. …”
    Get full text
    Get full text
    Thesis
  3. 3
  4. 4

    Comparison Of Phylogenetic Trees Using Difference Distance Function Method by Maziah, Medin

    Published 2005
    “…The pre-processing is implemented using the Microsoft Visual C++. The phylogenetic tree is build using the PHYLlP (the PHYlogeny Inference Package), a package of programs for inferring phylogenies (evolutionary trees). …”
    Get full text
    Get full text
    Final Year Project Report / IMRAD
  5. 5

    Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan by Mohd Hanis, Rani, Abdullah, Embong

    Published 2013
    “…The objective was to identify and implement the most accurate algorithm for the KPTM dataset and to come up with a good prediction model using decision tree technique. …”
    Get full text
    Get full text
    Conference or Workshop Item
  6. 6

    Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

    Published 2004
    “…The classification accuracy obtained from the CST method is compared to other selected classification methods such as Value Difference Metric (VDM), Pre-Category Feature Importance (PCF), Cross-Category Feature Importance (CCF), Instance-Based Algorithm (IB4), Decision Tree Algorithms such as Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5), Rough Set methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) and Neural Network methods such as the Multilayer method.…”
    Get full text
    Get full text
    Thesis
  7. 7

    Classification of Immunosignature Using Random Forests for Cancer Diagnosis by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

    Published 2015
    “…In this work, we will develop a robust classification model that can be utilized in cancer diagnosis using immunofingerprint data. …”
    Get full text
    Get full text
    Get full text
    Proceeding Paper
  8. 8
  9. 9
  10. 10
  11. 11

    Deep learning detector for pests and plant disease recognition by Ileladewa, Oluwatimilehin Adekunle

    Published 2020
    “…Meanwhile, evolution in deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. …”
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
  12. 12

    Y-type Random 2-satisfiability In Discrete Hopfield Neural Network by Guo, Yueling

    Published 2024
    “…Finally, a new logic mining model namely Y-Type Random 2-Satisfiability Reverse Analysis was proposed, which showed optimal performances in terms of several metrics as compared to the existing classification models. …”
    Get full text
    Get full text
    Thesis
  13. 13

    Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…However, the learning complexity of classification is increased due to the expansion number of learning model. …”
    Get full text
    Get full text
    Thesis
  14. 14

    Digital economy tax compliance model in Malaysia using machine learning approach by Raja Azhan Syah Raja Wahab, Azuraliza Abu Bakar

    Published 2021
    “…The experimental results show that the ensemble method can improve the single classification model’s accuracy with the highest classification accuracy of 87.94% compared to the best single classification model. …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15

    Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

    Published 2021
    “…Feature selection was used to sort out key features for further classification. News classification into factors affecting stock market turning point was done using Naïve Bayes, Deep Learning, Generalized Linear Model (GLM) and Support Vector Machine (SVM). …”
    Get full text
    Get full text
    Book Section
  16. 16
  17. 17

    Rapid software framework for the implementation of machine learning classification models by Rahman, A.S.A., Masrom, S., Rahman, R.A., Ibrahim, R.

    Published 2021
    “…Additionally, this paper explains comparisons of results between two platforms of rapid software; the proposed software and Python program. The machine learning model in the two platforms were tested on breast cancer and tax avoidance datasets with Decision Tree algorithm. …”
    Get full text
    Get full text
    Article
  18. 18
  19. 19

    Lightning Fault Classification for Transmission Line Using Support Vector Machine by Asman S.H., Aziz N.F.A., Kadir M.Z.A.A., Amirulddin U.A.U., Roslan N., Elsanabary A.

    Published 2024
    “…The proposed method was implemented in the MATLAB/SIMULINK programming platform. …”
    Conference Paper
  20. 20

    Classification with degree of importance of attributes for stock market data mining by Khokhar, Rashid Hafeez, Md. Sap, Mohd. Noor

    Published 2004
    “…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
    Get full text
    Get full text
    Article