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  1. 1
    Optimisation of fed-batch fermentation process using deep reinforcement learning

    Optimisation of fed-batch fermentation process using deep reinforcement learning by Chai, Wan Ying

    Published 2023
    “…The performance of the proposed algorithm was compared with a pre-determined exponential feeding profile and a genetic algorithm. …”
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  2. 2
    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage

    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…Relative mass release of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms. …”
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  3. 3
    An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance

    An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance by Raheem, Ajiboye Adeleke

    Published 2016
    “…Feed-forward Neural Networks, is a multilayer perceptron and a network structure capable of modelling the class prediction as a nonlinear combination of the inputs. …”
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  4. 4
    GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE

    GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE by MOHAMMED SHARIFF, NUR ATIQAH

    Published 2020
    “…This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. …”
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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
    “…In this thesis, the approach has been analyzed and algorithms that simulate the new approach have been mapped out.…”
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  6. 6
    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm

    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm by Nukman, Y., Hassan, M.A., Harizam, M.Z.

    Published 2013
    “…The simulation results showed that the developed GA-Taguchi ANN model could reduce the maximum prediction error below 10. …”
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  7. 7
    Ensemble dual recursive learning algorithms for identifying flow with leakage

    Ensemble dual recursive learning algorithms for identifying flow with leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…Relative mass release of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms. …”
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  8. 8
    Modelling and optimization of catalytic-dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network - genetic algorithm technique

    Modelling and optimization of catalytic-dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network - genetic algor... by Saidina Amin, Nor Aishah, Istadi, Istadi

    Published 2007
    “…Effects of CH4/CO2 feed ratio, total feed flow rate, discharge voltage and reactor wall temperature on the performance of the reactor was investigated by the ANN-based model simulation. …”
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  9. 9
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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  10. 10
    Energy cost optimization in high speed hard turning using simulated annealing algorithm

    Energy cost optimization in high speed hard turning using simulated annealing algorithm by Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas, Hassan, Muhammad Hasibul, Shaffiar, Norhashimah

    Published 2015
    “…Then, the Simulated Annealing Algorithm (SAA) has been used to optimize the cutting parameters. …”
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  11. 11
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  12. 12
    Multi objective optimisation for high speed end milling using simulated annealing algorithm

    Multi objective optimisation for high speed end milling using simulated annealing algorithm by Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas, Shaffiar, Norhashimah, Seder, Amin M. F., Riza, Muhammad

    Published 2015
    “…This paper presents the optimization of machining parameters in end milling processes by using the simulated annealing algorithm (SAA) as one of the unconventional methods in optimization. …”
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  13. 13
    Provider independent cryptographic tools

    Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

    Published 2003
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    Monograph
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  14. 14
    Predictive modelling of machining parameters of S45C mild steel

    Predictive modelling of machining parameters of S45C mild steel by Abbas, Adnan Jameel

    Published 2016
    “…The artificial neural network type Network Fitting Tool (NFTOOL) is used as a modeling technique for manipulating the ideal algorithm parameters. …”
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  15. 15
    Mobile machine vision for railway surveillance system using deep learning algorithm

    Mobile machine vision for railway surveillance system using deep learning algorithm by Kit, Guan Lim, Daniel Siruno, Min, Keng Tan, Chung, Fan Liau, Sha, Huang, Tze, Kenneth Kin Teo

    Published 2021
    “…This model can be implemented with Raspberry Pi to simulate the object detection algorithm virtually. …”
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  16. 16
    Process fault detection and diagnosis using Boolean representation on fatty acid fractionation column

    Process fault detection and diagnosis using Boolean representation on fatty acid fractionation column by Othman, M. R., Ali, Mohamad Wijayanuddin, Kamsah, Mohd. Zaki

    Published 2003
    “…The algorithm utilizes process simulator to develop plant model in order to conduct sensitivity analysis and provide dynamic data on selected fault. …”
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  17. 17
    Mathematical modelling and numerical simulation of CO2/CH4 separation in a polymeric membrane

    Mathematical modelling and numerical simulation of CO2/CH4 separation in a polymeric membrane by Gilassi, S., Rahmanian, N.

    Published 2015
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  18. 18
    Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network

    Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network by Md Akib, Afif, Saad, Nordin, Asirvadam, Vijanth

    Published 2011
    “…Relative mass released of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms for updating the linear weight. …”
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  19. 19
    Using an Enhanced Feed-Forward BP Network for Predictive Model Building From Students’ Data

    Using an Enhanced Feed-Forward BP Network for Predictive Model Building From Students’ Data by Ajiboye, Adeleke Raheem, Ruzaini, Abdullah Arshah, Qin, Hongwu

    Published 2015
    “…Both models are trained and simulated with sets of untrained data. …”
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  20. 20
    Recursive linear network modeling for detecting gas leak

    Recursive linear network modeling for detecting gas leak by Md Akib, Afifi, Saad, Nordin, Asirvadam, Vijanth

    Published 2010
    “…Relative mass loss of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithm. …”
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