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  1. 1
    Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning

    Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

    Published 2024
    “…In this paper, the stacking ensemble method is used to increase the accuracy of the machine learning model for LSM where the base (first-level) learners use five ML algorithms namely decision tree (DT), k-nearest neighbor (KNN), AdaBoost, extreme gradient boosting (XGB) and random forest (RF). …”
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  2. 2
    Raspberry Pi-Based Finger Vein Recognition System Using PCANet

    Raspberry Pi-Based Finger Vein Recognition System Using PCANet by Quek, Ee Wen

    Published 2018
    “…A simple deep learning network, namely Principal Component Analysis Network (PCANet) is thus proposed. …”
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    Monograph
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  3. 3
    Image Splicing Detection With Constrained Convolutional Neural Network

    Image Splicing Detection With Constrained Convolutional Neural Network by Lee, Yang Yang

    Published 2019
    “…It is shown that CNN with constrained convolution algorithm can be used as a general image splicing detection task.…”
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    Thesis
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  4. 4
    Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach

    Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach by Wan Solehah, Wan Ahmad

    Published 2022
    “…The strategies applied showed that the final accuracy obtained through the training after implementing a modification in the algorithm is at 81% accuracy rate compared to the basic model that recorded its final accuracy at 79% accuracy rate. …”
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    Thesis
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  5. 5
    Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications

    Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications by Teo, Chee Huat

    Published 2016
    “…DSF algorithm is a hybrid stereo matching algorithm which divided into two phases. …”
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    Thesis
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  6. 6
    Routing performance enhancement in hierarchical torus network by link-selection algorithm

    Routing performance enhancement in hierarchical torus network by link-selection algorithm by Rahman, M.M. Hafizur, Horiguchi, Susumu

    Published 2005
    “…A hierarchical torus network (HTN) is a 2D-torus network of multiple basic modules, in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. …”
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  7. 7
    Dynamic communication performance enhancement in hierarchical torus network by selection algorithm

    Dynamic communication performance enhancement in hierarchical torus network by selection algorithm by Rahman, M.M. Hafizur, Sato, Yukinori, Inoguchi, Yasushi

    Published 2012
    “…A Hierarchical Torus Network (HTN) is a 2D-torus network of multiple basic modules, in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. …”
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  8. 8
    Adaptive routing algorithms and implementation for TESH network

    Adaptive routing algorithms and implementation for TESH network by Miura, Yasuyuki, Kaneko, Masahiro, Rahman, M.M. Hafizur, Watanabe, Shigeyoshi

    Published 2013
    “…It is found that the communication performance of a TESH network using these adaptive algorithms is better than when the dimension-order routing algorithm is used.…”
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  9. 9
    Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm

    Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm by Fatma Susilawati, Mohamad, Mumtazimah, Mohamad, Sarhan, AlDuais

    Published 2020
    “…We created the dynamic learning rate and dynamic momentum factor for increasing the efficiency of the algorithm. …”
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  10. 10
    Butterfly traffic pattern in selection algorithm on hierarchical torus network

    Butterfly traffic pattern in selection algorithm on hierarchical torus network by Rahman, M.M. Hafizur, Akhand, M. A. H, Shill, P.C., Inoguchi, Yasushi

    Published 2015
    “…A Hierarchical Torus Network (HTN) is a 2D- torus network of multiple basic modules, in which the basic modules are 3D-torus networks that are hierarchically inter- connected for higher-level networks. …”
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    Proceeding Paper
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  11. 11
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  12. 12
    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network

    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

    Published 2018
    “…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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  13. 13
    Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar

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

    Published 2021
    “…These algorithms were developed by using prewar shophouses dataset from 2004 until 2018 based on factors of heritage properties. …”
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  14. 14
    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding

    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding by Mahmoud, Omer, Anwar, Farhat, Salami, Momoh Jimoh Emiyoka

    Published 2007
    “…One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. …”
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  15. 15
    Analysis randomness properties of basic components of SNOW 3G cipher in mobile systems

    Analysis randomness properties of basic components of SNOW 3G cipher in mobile systems by Jassim, Khalid Fadhil, Alshaikhli, Imad Fakhri Taha

    Published 2016
    “…SNOW 3G is a stream cipher algorithm used as encryption algorithm in third generation mobile phone technology (3G-UMTS). …”
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  16. 16
    Optimization of chest X-ray exposure factors using machine learning algorithm

    Optimization of chest X-ray exposure factors using machine learning algorithm by Hamd, Zuhal Y., Alrebdi, H.I., Osman, Eyas G., Awwad, Areej, Alnawwaf, Layan, Nashri, Nawal, Alfnekh, Rema, Khandaker, Mayeen Uddin *

    Published 2023
    “…In this study, the chest X-ray exposure factors for 178 patients with different body mass index (BMI) values have been analyzed using the Python Machine Learning algorithm. …”
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  17. 17
    A bayesian network approach to identify factors affecting learning of Additional Mathematics

    A bayesian network approach to identify factors affecting learning of Additional Mathematics by Ong, Hong Choon, Kumarenthiran A/L Chandrasekaran

    Published 2015
    “…Constraint-based algorithms and score-based algorithms are used to generate the networks into several categories to compare and identify the strong relationships among the factors that affect the students’ learning of the subject. …”
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  18. 18
    High performance hierarchical torus network under matrix transpose traffic patterns

    High performance hierarchical torus network under matrix transpose traffic patterns by Rahman, M.M. Hafizur, Susumu, Horiguchi

    Published 2004
    “…In this paper, we present a deadlock-free routing algorithm for the HTN using virtual channels and evaluate the network’s dynamic communication performance under matrix transpose traffic, using the proposed routing algorithm. …”
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    Proceeding Paper
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  19. 19
    Three-term backpropagation algorithm for classification problem

    Three-term backpropagation algorithm for classification problem by Saman, Fadhlina Izzah

    Published 2006
    “…This algorithm utilizes two term parameters which are Learning Rate, α and Momentum Factor,β. …”
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  20. 20
    Reverse migration prediction model based on machine learning / Azreen Anuar

    Reverse migration prediction model based on machine learning / Azreen Anuar by Anuar, Azreen

    Published 2024
    “…A significant way to minimize the errors is by using a machine learning approach that can predict reverse migration intelligently depending on the tested dataset. …”
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