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    The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

    Published 2012
    “…The results show that the proposed algorithm extensively improves the learning process of conventional Back Propagation algorithm.…”
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    Thesis
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    Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms by Ayodele B.V., Mustapa S.I., Kanthasamy R., Zwawi M., Cheng C.K.

    Published 2023
    “…Chemical activation; Gasification; Learning algorithms; Machine learning; Multilayer neural networks; Neurons; Plastics industry; Predictive analytics; Rubber; Rubber industry; Activation functions; MLP neural networks; Model architecture; Multi layer perceptron; Neural network algorithm; Optimized performance; Process operation; Radial Basis Function(RBF); Hydrogen production…”
    Article
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    Jogging activity recognition using k-NN algorithm by Afifah Ismail

    Published 2022
    “…The k-NN algorithm is a simple and easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. …”
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    Academic Exercise
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    Securing cloud data system (SCDS) for key exposure using AES algorithm by Thabet Albatol, Mohammed Samer Hasan

    Published 2021
    “…The AES algorithm has its own structure to encrypt and decrypt sensitive data that make the attackers difficult to get the real data when encrypting by AES algorithm. …”
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    Thesis
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    Underwater Image Recognition using Machine Learning by Divya, N.K., Manjula, Sanjay Koti, Priyadarshini, S

    Published 2024
    “…A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. …”
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    Article
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    Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

    Published 2004
    “…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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    Thesis
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    Interactive framework for dynamic modelling and active vibration control of flexible structures by Mat Darus, Intan Zaurah, Mohd. Hashim, Siti Zaiton, Tokhi, M. O.

    Published 2008
    “…This paper presents the implementation of an interactive learning environment for dynamic simulation and active vibration control of flexible structures. …”
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    Article
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    Internet of Things (IoT) based activity recognition strategies in smart homes: a review by Babangida, Lawal, Perumal, Thinagaran, Mustapha, Norwati, Yaakob, Razali

    Published 2022
    “…The obtained data can be subjected to extensive preprocessing and feature extraction tasks before being learned using appropriate machine learning or deep learning algorithms to generate a model capable of managing human activities more effectively. …”
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    Article
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    Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset by Afiefah Hannani, Abdul Halim

    Published 2023
    “…Therefore, cyberbullying detection using a Machine Learning approach is suggested. This study presents the comparison of three Machine Learning algorithms for the detection using cyberbullying activity on social media platforms specifically Twitter. …”
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    Undergraduates Project Papers
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    Particle swarm optimization for neural network learning enhancement by Abdull Hamed, Haza Nuzly

    Published 2006
    “…In this study, the latest optimization algorithm, Particle Swarm Optimization (PSO) is chosen and applied in feedforward neural network to enhance the learning process in terms of convergence rate and classification accuracy. …”
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    Thesis
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    A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

    Published 2005
    “…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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    Monograph
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