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  1. 1
    Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

    Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm by Mohd Zain, Mohamad Zihin, Kanesan, Jeevan, Kendall, Graham, Chuah, Joon Huang

    Published 2018
    “…DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. …”
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  2. 2
    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fed-batch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yusof

    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fed-batch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yuso... by Mamat, Kamaruddin, Mohd Yusof, Farida Zuraina

    Published 2008
    “…In this work, a genetic algorithm was used to estimate both yield and kinetic coefficients of an unstructured model representing a fed-batch high-cell density fermentation of Escherichia coli (E. coli). …”
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  3. 3
    Model predictive control on fed-batch penicillin fermentation process

    Model predictive control on fed-batch penicillin fermentation process by Chew, Li Mei

    Published 2009
    “…In this research study the development of optimization strategies for a fed-batch penicillin fermentation process using model predictive controller was simulated using MATLAB 7.1 software. …”
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  4. 4
    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain

    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

    Published 2018
    “…Two multi-objective fed-batch models are also used as case studies to verify the performance of the proposed algorithm. …”
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  5. 5
    Genetic algorithm for control and optimisation of exothermic batch process

    Genetic algorithm for control and optimisation of exothermic batch process by Tan, Min Keng

    Published 2013
    “…As a result, improved multivariable genetic algorithm (IMGA) with adaptable fitness function ability is introduced in this work. …”
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  6. 6
    Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems

    Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems by Zulkifli, Munierah, Abd Rahmin, Nor Aliza, Wah, June Leong

    Published 2023
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  7. 7
    Hybrid neural network - prior knowledge model in temperature control of a semi-batch polymerization process

    Hybrid neural network - prior knowledge model in temperature control of a semi-batch polymerization process by Ng, C.W., Hussain, Mohd Azlan

    Published 2004
    “…The simulation results show the advantages and robustness of utilizing the neural network in this hybrid strategy especially when an adaptive algorithm is implemented.…”
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  8. 8
    SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA

    SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA by AHMED IZZELDIN, HUZAIFA TAWFEIG

    Published 2012
    “…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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  9. 9
    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fedbatch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yusof

    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fedbatch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yusof by Mamat, Kamaruddin, Mohd Yusof, Farida Zuraina

    Published 2008
    “…In this work, a genetic algorithm was used to estimate both yield and kinetic coefficients of an unstructured model representing a fed-batch high-cell density fermentation of Escherichia coli (E.coli). …”
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  10. 10
    Impatient task mapping in elastic cloud using genetic algorithm

    Impatient task mapping in elastic cloud using genetic algorithm by Mehdi, Nawfal A., Mamat, Ali, Ibrahim, Hamidah, K. Subramaniam, Shamala

    Published 2011
    “…Conclusion: Batch mapping via genetic algorithms with throughput as a fitness function can be used to map jobs to cloud resources.…”
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  11. 11
    Increasing the speed of convergence of an artificial neural network based ARMA coefficients determination technique

    Increasing the speed of convergence of an artificial neural network based ARMA coefficients determination technique by Aibinu, Abiodun Musa, Salami, Momoh Jimoh Emiyoka, Shafie, Amir Akramin, Najeeb, Athaur Rahman

    Published 2008
    “…The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination.…”
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  12. 12
    Car dealership web application

    Car dealership web application by Yap, Jheng Khin

    Published 2022
    “…In the web service, adaptive random forest regressor and classifier, which were implemented by third-party River Python library, were used to train models that automatically detected and adapted to drift over time. …”
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  13. 13
    Multi objective bee colony optimization framework for grid job scheduling

    Multi objective bee colony optimization framework for grid job scheduling by Alyaseri, Sana, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.Job scheduling problem is one of the key issues because of high heterogeneous and dynamic nature of resources and applications in the grid computing environment.Bee colony approach has been used to solve this problem because it can be easily adapted to the grid scheduling environment.The bee algorithms have shown encouraging results in terms of time and co st.In this paper a framework for multi objective bee colony optimization is proposed to schedule batch jobs to available resources where the number of jobs is greater than the number of resources.Pareto analysis and k-means analysis are integrated in the bee colony optimization algorithm to facilitate the scheduling of jobs to resources.…”
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  14. 14
    Locust- inspired meta-heuristic algorithm for optimising cloud computing performance

    Locust- inspired meta-heuristic algorithm for optimising cloud computing performance by Fadhil, Mohammed Alaa

    Published 2023
    “…The proposed algorithm is evaluated using the WorkflowSim simulation with a real dataset. …”
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  15. 15
    Deep learning-based item classification for retail automation

    Deep learning-based item classification for retail automation by Ling, Ji Xiang

    Published 2025
    “…The CNN model was optimized for both accuracy and speed, incorporating regularization techniques such as dropout and batch normalization. Real-time processing was achieved through the integration of object detection algorithms like YOLO and image segmentation techniques. …”
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  16. 16
    An Education-Based System for Two-Way Translations of Sign Language

    An Education-Based System for Two-Way Translations of Sign Language by Muhammad Faiz Aiman, Che Umar, Hock Tze, Wong@ Farrah Wong, Sariah, Abang, Jamal, Ahmad Dargham, Seng Kheau, Chung, Mazlina, Mamat

    Published 2025
    “…The research begins with a comparison of various algorithms used in previous studies, aiming to identify the most suitable one. …”
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  17. 17
    Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation

    Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation by Chen, Qipeng, Xiong, Qiaoqiao, Huang, Haisong, Tang, Saihong, Liu, Zhenghong

    Published 2024
    “…The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. …”
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  18. 18
    Young and mature oil palm tree detection and counting using convolutional neural network deep learning method

    Young and mature oil palm tree detection and counting using convolutional neural network deep learning method by Abd Mubin, Nurulain, Nadarajoo, Eiswary, Mohd Shafri, Helmi Zulhaidi, Hamedianfar, Alireza

    Published 2019
    “…The training process reduces loss using adaptive gradient algorithm with a mini batch of size 20 for all the training sets used. …”
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  19. 19
    A Novel Method for Fashion Clothing Image Classification Based on Deep Learning

    A Novel Method for Fashion Clothing Image Classification Based on Deep Learning by Yoon Shin, Seong, Jo, Gwanghyun, Wang, Guangxing

    Published 2023
    “…Furthermore, the study adopted the approximate dynamic learning rate update algorithm in the model training to realize the learning rate’s self-adaptation, ensure the model’s rapid convergence, and shorten the training time. …”
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  20. 20
    Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour

    Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour by Neyamadpour, Ahmad

    Published 2010
    “…These results show that,for all the arrays (2D and 3D) except 3D pole - dipole data, resilient propagation is the most efficient algorithm for training the DC resistivity data. In the case of 3D study of pole - dipole data, the gradient descent with momentum and an adaptive learning rate algorithm is found to be the most efficient paradigm. …”
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