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  1. 1
    A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island

    A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island by Mohd Shafri, Helmi Zulhaidi, Ramle, F. S. H.

    Published 2009
    “…This study investigates a new approach in image classification. Two classifiers were used to classify SPOT 5 satellite image; Decision Tree (DT) and Support Vector Machine (SVM). …”
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  2. 2
    Diagnosis and treatment recommender system for myocardial infarction using decision tree and Support Vector Machines (SVM) / Wan Marzuqiamrin Wan Mansor

    Diagnosis and treatment recommender system for myocardial infarction using decision tree and Support Vector Machines (SVM) / Wan Marzuqiamrin Wan Mansor by Wan Mansor, Wan Marzuqiamrin

    Published 2025
    “…The decision tree algorithm functions after this point. The prototype processes collected clinical data using these algorithms to confirm diagnoses while determining the level of patient severity. …”
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  3. 3
    Image classification using two dimensional wavelet coefficients with parallel computing

    Image classification using two dimensional wavelet coefficients with parallel computing by Ong, Yew Fai

    Published 2020
    “…This research algorithm demonstrated a very promising result with Support Vector Machines, this algorithm produces a 90% of accuracies whereas the decision tree algorithm gets 100% accuracies. …”
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  4. 4
    Improvement on rooftop classification of worldview-3 imagery using object-based image analysis

    Improvement on rooftop classification of worldview-3 imagery using object-based image analysis by Norman, Masayu

    Published 2019
    “…To overcome such challenges, this study attempts to comprehend and improve the remote sensing technology for rooftop classification using the Worldview-3 (WV-3) image and object-based image analysis (OBIA) method. …”
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  5. 5
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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  6. 6
    Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification

    Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

    Published 2020
    “…Nevertheless, AC is not required for LCM if the original multi-spectral image is used. The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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  7. 7
    Secure Access To Authorized Resources Based On Fingerprint Authentication

    Secure Access To Authorized Resources Based On Fingerprint Authentication by Elmadani, Ahmed Baba

    Published 2003
    “…The A VL tree is used to increase the compression ratio as its compression algorithm works efficiently for all types of data. …”
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  8. 8
    Power line corridor vegetation encroachment detection from satellite images using retinanet and support vector machine

    Power line corridor vegetation encroachment detection from satellite images using retinanet and support vector machine by Fathi Mahdi Elsiddig Haroun, Mr.

    Published 2023
    “…Also, the achieved classification recall was 98.272% using the SVM Radial Basis Function (RBF) kernel. …”
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  9. 9
    Phylogenetic tree classification system using machine learning algorithm

    Phylogenetic tree classification system using machine learning algorithm by Tan, Jia Kae

    Published 2015
    “…A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. …”
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    Final Year Project Report / IMRAD
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  10. 10
    Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image

    Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi

    Published 2014
    “…This algorithm provides a decision tree output to represent the knowledge model, enabled a faster classification of intra-urban classes, and disabled the subjectivities which are related to the interaction of the analyst. …”
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  11. 11
    Drone-based surveillance of palm tress ecosystems

    Drone-based surveillance of palm tress ecosystems by Mansor, Ya’akob, Baki, Sharudin Omar, Sahwee, Zulhilmy, Mengyue, Cheng, Wu, Yuanyuan

    Published 2024
    “…The study aims to improve the accuracy and efficiency of palm health detection by integrating MATLAB's initial object recognition with advanced deep learning algorithms. The initial phase of the research focuses on elucidating the challenges associated with detecting palm tree health issues using conventional image processing methods in MATLAB. …”
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  12. 12
    VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS

    VEHICLE CLASSIFICATION FROM CCTV IMAGES AND VIDEOS by KAMARUDDIN, ERNA SHAFFIQA

    Published 2018
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  13. 13
    Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

    Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi

    Published 2016
    “…Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. …”
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  14. 14
    Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images

    Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi, Mansor, Shattri, Ahmad, Noordin

    Published 2014
    “…A large number of attributes were discovered with the C4.5 DM algorithm, which also generated the classification model as a decision tree. …”
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  15. 15
    Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image

    Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image by Shojanoori, Razieh

    Published 2016
    “…The method of maximum likelihood classification and support vector machines leads to the lowest classification accuracy since these algorithms extract only the spectral information of each pixel and consequently fail to utilize spatial, color and textural information.…”
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  16. 16
    Decision tree as knowledge management tool in image classification

    Decision tree as knowledge management tool in image classification by Kusrini, , Harjoko, Agus

    Published 2008
    “…Expert System has been grown so fast as a science that study how to make computer capable of solving problems that typically can only be solved by expert.It has been realized that the biggest challenge of developing expert system is the process include expert’s knowledge into the system.This research tries to model expert’s knowledge management using case based reasoning method.The knowledge itself is not inputted directly by the expert, but rather the system will learn the knowledge from what the expert did to the previous cases.This research takes image classification as the problem to be solved.As the knowledge development technique, we build decision tree by using C4.5 algorithm.Variables used for building the decision tree are the image visual features.…”
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  17. 17
    Formulation of invariants for discrete orthogonal moments and image classification / Pee Chih Yang

    Formulation of invariants for discrete orthogonal moments and image classification / Pee Chih Yang by Pee, Chih Yang

    Published 2013
    “…Discrete Tchebichef moments are selected as the implementation platform of the proposed algorithms.To evaluate the performance of invariant algorithms, empirical studies have been carried out on large set of binary images which consist of numbers, English letters, symbols, Chinese characters and objects like animals, trees and company logos under noiseless and noisy conditions. …”
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  18. 18
    Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram

    Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram by Faris Francis Singaram, Fareena

    Published 2021
    “…The parameters involved to estimate the mangrove age are differences feature selection and different supervised machine learning algorithm. The support vector machine (SVM) which is one of the machines learning algorithms for object-based image analysis (OBIA) method is used in this study for the classification of the mangrove from other LULC. …”
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  19. 19
    Processing and classification of landsat and sentinel images for oil palm plantation detection

    Processing and classification of landsat and sentinel images for oil palm plantation detection by Mohd Ibrahim, Azhar, Asming, Muhammad Anwar Azizan, Abir, Intiaz Mohammad

    Published 2022
    “…Therefore, this paper attempts to determine the best methods for image classification, especially for land cover classification of oil palm plantations. …”
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  20. 20
    Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

    Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining by Mohd Hanafi Ahmad Hijazi, Chuntao Jiang, Frans Coenen, Yalin Zheng

    Published 2011
    “…The resulting decomposition is then stored in a tree structure to which a weighted frequent sub-tree mining algorithm is applied. …”
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