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  1. 1
    A Model for Evaluation of Cryptography Algorithm on UUM Portal

    A Model for Evaluation of Cryptography Algorithm on UUM Portal by Norliana, Abdul Majid

    Published 2004
    “…The purpose of this project are to construct and provide guidelines to develop a simulation model to evaluate cryptography algorithm in terms of encryption speed and descryption speed on UUM portal. …”
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  2. 2
    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
    “…This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. …”
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  3. 3
    Factor Strategy Model: Proofs of Prototype Concept for Software Quality Evaluation

    Factor Strategy Model: Proofs of Prototype Concept for Software Quality Evaluation by Nathan, Darmawan, Ooi, Keng Boon, Chong, Alain Yee Loong, Tan, Boon In

    Published 2010
    “…The objective of this study is to provide software developers some measures to evaluate software quality of a Java program. Studies on software quality requirements, software quality models (as a quantifying measurement technique) and various good design rules are required for. …”
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  4. 4
    Fuzzy modeling using Bat Algorithm optimization for classification

    Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

    Published 2018
    “…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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    Undergraduates Project Papers
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  5. 5
    PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM

    PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

    Published 2011
    “…To overcome these limitations, there have been attempts to use genetic algorithm (GA) to optimize some of these parameters. …”
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  6. 6
    Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations

    Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

    Published 2022
    “…The RBFNN model with softmax as the hidden layer activation function and identity as the outer layer activation function has the least predictive performance, as indicated by an R2 of 0.403 and a RMSE of 301.55. …”
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  7. 7
    Identification of vocal and voice disorders

    Identification of vocal and voice disorders by Murugesa Pandiyan, Paulraj, Prof. Madya Dr., Sazali, Yaacob, Prof. Dr., Mohd Rizon, Mohammed Juhari, Prof. Dr., Sivanandam, S. N., Muthusamy, Hariharan, Dr.

    Published 2011
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  8. 8
    The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robotics flexible manipulator

    The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo... by Moloody, Abbas, As’arry, Azizan, Hong, Tang Sai, Raja Kamil, ., Zolfagharian, Ali

    Published 2025
    “…Here, in this research by comprising three of the most effective variational techniques now, a Modified Differential Evolutionary Optimization Algorithm (MDEOA) method is suggested to handle the challenge of adjusting the PID controller parameters for the Intelligent Active Vibration Control (IAVC) of a Combined Single Link Robotics Flexible Manipulator (CSLRFM) in order to reduce the undesired effects of vibration. …”
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  9. 9
    The effect of adaptive parameters on the performance of back propagation

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

    Published 2012
    “…The activation functions are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. …”
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  10. 10
    A harmony search-based learning algorithm for epileptic seizure prediction

    A harmony search-based learning algorithm for epileptic seizure prediction by Kee, Huong Lai, Zainuddin, Zarita, Ong, Pauline

    Published 2016
    “…The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. …”
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  11. 11
    Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant

    Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant by Rahmat, Mohd Fua'ad, Rahmat, Mohd Fuaad, Abdul Wahab, Norhaliza, Che Razali, Mashitah, Gaya, Muhammad Sani, Samsudin, Sharatul Izah

    Published 2014
    “…The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. …”
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  12. 12
    Loudspeaker nonlinearity compensation with inverse tangent hyperbolic function-based predistorter for active noise control

    Loudspeaker nonlinearity compensation with inverse tangent hyperbolic function-based predistorter for active noise control by Sahib, Mouayad Abdulredha, Raja Ahmad, Raja Mohd Kamil

    Published 2014
    “…In active noise control (ANC), the performance of the filtered-x least mean squares (FXLMS) algorithm is degraded by the saturation of the loudspeaker in the secondary path. …”
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  13. 13
    The tuning of error signal for back-propagation algorithms

    The tuning of error signal for back-propagation algorithms by Rengasamy, Renugah

    Published 2008
    “…This new algorithm is proven to be a better algorithm. The main purpose of this study is to evaluate the efficiency of improved two-term error function by applying three different values of ß parameter in the activation function. …”
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  14. 14
    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar

    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

    Published 2019
    “…There have two most uses activation function namely tansig and logsig. The essence of this study is that it compares the effect of activation functions (tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. …”
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  15. 15
    Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms

    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
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  16. 16
    Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system

    Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system by Krishnan P.S., Kiong T.S., Koh J., Yap D.

    Published 2023
    “…In this paper, an embedded parallel and distributed genetic algorithm (EPDGA) with dynamic parameter setting on a multiprocessor system is proposed. …”
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  17. 17
    Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms

    Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms by Bakar, Abubakar Diwani

    Published 2016
    “…The latest effort to solve this selection problem is the development of the metrics selection model that uses genetic algorithm (GA). However, the process failed to state clearly the encoding strategy in its initial stage. …”
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  18. 18
    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
    “…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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  19. 19
    Real time nonlinear filtered-x lms algorithm for active noise control

    Real time nonlinear filtered-x lms algorithm for active noise control by Sahib, Mouayad Abdulredha

    Published 2012
    “…The NLFXLMS algorithm is a stochastic gradient algorithm that incorporates the derivative of a nonlinear plant model which is represented by the scaled error function (SEF) in the controller design. …”
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  20. 20
    Comparison of different neural network training algorithms for wind velocity forecasting

    Comparison of different neural network training algorithms for wind velocity forecasting by KhalajiAssadi , Morteza, Safaei , Shervin

    Published 2016
    “…In addition, choosing the type of activation function is dependent on the amount of input data, which should be acceptably large.…”
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