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  1. 1

    Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin by Mohd Nordin, Ahmad Nasreen Aqmal

    Published 2024
    “…The system design phase encompasses the development of system architecture, flowcharts, pseudocode, and interface design. The implementation phase focuses on the deployment of the Decision Tree algorithm and system evaluation through functionality testing. …”
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    Thesis
  2. 2

    Network instrusion prevention system ( NIPS) based on network intrusion detection system (NIDS) and ID3 algorithm decision tree classifier by Syurahbil, A

    Published 2011
    “…Network security has gained significant attention in research and industrial communities.Due to the increasing threat of the network intrusion,firewalls have become important elements of the security policy.Firewall performance highly depends toward number of rules,because the large more rules the consequence makes downhill performance progressively.Firewall can be allow or deny access network packets incoming and outgoing into Local Area Network(LAN),but firewall can not detect intrusion.To distinguishing an intrusion network packet or normal is very difficult and takes a lot of time.An analyst must review all the network traffics previously.In this study,a new way to make the rules that can determine network packet is intrusion or normal automatically.These rules implemented into firewall as prevention,which if there is a network packet that match these rules then network packet will be dropped.This is called Network Intrusion Prevention System(NIPS).These rules are generated based on Network Intrusion Detection System(NIDS)and Iterative Dichotomiser 3 (ID3)Algorithm Decision Tree Classifier,which as data training is intrusion network packet and normal network packets from previous network traffics.The experiment is successful,which can generate the rules then implemented into a firewall and drop the intrusion network packet automatically.Moreover,this way can minimize number of rules in firewall.…”
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    Thesis
  3. 3

    Photogrammetric unmanned aerial vehicle for digital terrain model estimation under oil palm tree canopy area / Suzanah Abdullah by Abdullah, Suzanah

    Published 2021
    “…At this stage, this study has formulated a new methodology for estimating DTM production under tree canopy conditions based on tree crown estimation produced from the digital surface model (DSM) and subsequently, the tree height estimation was determined as the final process in the DTM implementation. …”
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  4. 4
  5. 5

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

    Published 2012
    “…The simulation of planting lining design relative to possible solutions of dividing an area to blocks (block division) and selecting planting lining direction towards optimizing tree planting areas is a complex problem. …”
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  6. 6
  7. 7

    Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants by Masrom, S., Tarmizi, M.A., Halid, S., Rahman, R.A., Abd Rahman, A.S., Ibrahim, R.

    Published 2023
    “…The research elaborates on the design and implementation of machine learning models based on three algorithms: Decision Tree, Gradient Boosted Tree, and Support Vector Machine. …”
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    Article
  8. 8

    Robotic path planning using rapidly-exploring random trees by Sherwani, Fahad

    Published 2013
    “…This study concerns the implementation of Rapidly-Exploring Random Trees (RRTs) algorithm for an autonomous robot path planning. …”
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  9. 9

    Students’ attitude towards video-based learning: machine learning analysis with rapid software / Abdullah Sani Abd Rahman ... [et al.] by Abd Rahman, Abdullah Sani, Meutia, Rita, Hamid, Yusnaliza, Abdul Rahman, Rahayu

    Published 2022
    “…Three machine learning algorithms (Support Vector Machine, Random Forest, and Decision Tree) have been tested on the dataset in a rapid software platform. …”
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  10. 10

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…Four conventional classification algorithms: naïve bayes (NB), support vector machines (SVM), nearest neighbor (k-NN), and decision trees (J48) classifiers are implemented in identifying and categorizing tweet data of three political figures in Malaysia: Dato Seri Anwar, Dato Hadi Awang, and Lim Guang Eng, as either positive, negative, or neutral perceptions. …”
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  11. 11

    An object-based approach to detect tree stumps in a selective logging area using Unmanned Aerial Vehicle imagery by Aisyah Marliza Muhmad Kamarulzaman, Wan Shafrina Wan Mohd Jaafar, Siti Nor Maizah Saad, Hamdan Omar, Mohd. Rizaludin Mahmud

    Published 2021
    “…This research implemented a technique for detecting, segmenting, classifying, and measuring tree stumps by using the Multiresolution Segmentation Algorithm method. …”
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    Article
  12. 12

    An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis by Ong, Phaik Ling

    Published 2016
    “…In conclusion, the proposed algorithm is able to overcome the rare item issue by implementing covariance based support value normalization and high computational costs issue by implementing indexing enumeration tree structure.Future work of this study should focus on rule interpretation to generate more human understandable rule by novice in data mining. …”
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  13. 13
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    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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  15. 15

    Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

    Published 2005
    “…The algorithm involved the construction of the nodes of a lexicographic tree that represent frequent patterns. …”
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  16. 16

    Predicting Suicidal Ideation Via Social Media by Boon, Kar Lih

    Published 2023
    “…Three Machine Learning algorithms are implemented to predict the result and outcome which are Support Vector Machine (SVM), Decision Tree and Naive Bayes (NB). …”
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    Undergraduates Project Papers
  17. 17

    Comparison on machine learning algorithm to fast detection of malicious web pages by Wan Nurul Safawati, Wan Manan, Mohd Nizam, Mohmad Kahar, Noorlin, Mohd Ali

    Published 2021
    “…Compared to several decision tree method, Random Forest has shown promising and higher sensitivity result towards malicious data which is 98.3% compared to other classification algorithm…”
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    Conference or Workshop Item
  18. 18

    Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar by Ja'afar, Nur Shahirah

    Published 2021
    “…To overcome these limitations, this research has proposed five machine learning algorithms namely Linear Regression, Lasso, Ridge, Random Forest and Decision Tree. …”
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  19. 19

    Design of Predictive Model for TCM Tongue Diagnosis In Malaysia Using Machine Learning by Koe, Jia Chi

    Published 2020
    “…Six supervised machine learning algorithms (Linear Regression, Logistic Regression, K Nearest Neighbors (KNN), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest) are used to perform disease prediction. …”
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    Final Year Project / Dissertation / Thesis
  20. 20

    Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques by Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Kamarulzaman, Kamarudin, Mohd Aminudin, Jamlos

    Published 2022
    “…Four different ML algorithms are tested for prediction of G. boninense infection: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested which depicted DT algorithm achieves a satisfactory overall performance with high accuracy up to 93.1% and F1-score of 92.6% compared to other algorithms. …”
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    Article