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  1. 1
    Schelkunoff array synthesis methods using adaptive-iterative algorithm

    Schelkunoff array synthesis methods using adaptive-iterative algorithm by Abdul Latiff, Nurul Mu'azzah

    Published 2003
    “…Basically, this algorithm is a combination of iterative algorithm with adaptive algorithm. …”
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  2. 2
    Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance

    Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance by Nor Azlan, Nor Asmaa Alyaa

    Published 2021
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  3. 3
    Differential evolution for neural networks learning enhancement

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. …”
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  4. 4
    Quarter-sweep arithmetic mean iterative algorithm to solve fourth-order parabolic equations

    Quarter-sweep arithmetic mean iterative algorithm to solve fourth-order parabolic equations by Sulaiman, Jumat, Hasan, Mohammad Khatim, Othman, Mohamed, Yaacob, Zaihar

    Published 2009
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  5. 5
    Solving Blasius equation using semi analytic iterative method / Nurul Atikah Halmi and Nur Azyyati Ayob

    Solving Blasius equation using semi analytic iterative method / Nurul Atikah Halmi and Nur Azyyati Ayob by Halmi, Nurul Atikah, Ayob, Nur Azyyati

    Published 2018
    “…Variational iterative method (VIM) and Iteration method existing results was chosen to be compare with SAIM expectation outcomes. …”
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  6. 6
    Comparative study of Newton’s method, Broyden’s method and Steepest Descent method / Nur Alia Syahirah Mazlan and Nurzahida Mohd Zaharuddin

    Comparative study of Newton’s method, Broyden’s method and Steepest Descent method / Nur Alia Syahirah Mazlan and Nurzahida Mohd Zaharuddin by Mazlan, Nur Alia Syahirah, Mohd Zaharuddin, Nurzahida

    Published 2019
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  7. 7
    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets

    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

    Published 2019
    “…The proposed algorithm is grounded on the two famous metaheuristic algorithms: cuckoo search (CS) and covariance matrix adaptation evolution strategy (CMA-es). …”
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  8. 8
    Email spam classification based on deep learning methods: A review

    Email spam classification based on deep learning methods: A review by Tusher, Ekramul Haque, Mohd Arfian, Ismail, Anis Farihan, Mat Raffei

    Published 2025
    “…Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. …”
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  9. 9
    Solving large systems of linear equations using iterative methods (Jacobi-Davidson, Gauss-Seidel and Successive Over-Relaxation) / Fatini Dalili Mohammed

    Solving large systems of linear equations using iterative methods (Jacobi-Davidson, Gauss-Seidel and Successive Over-Relaxation) / Fatini Dalili Mohammed by Mohammed, Fatini Dalili

    Published 2017
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  10. 10
    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification

    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification by Loo, C.K., Liew, W.S., Sayeed, M.S.

    Published 2012
    “…Individual emotional states are highly variable and are subject to evolution from personal experiences. For this reason, the above system is designed to be able to perform learning and classification in real-time to account for inter-individual and intra-individual emotional drift over time. …”
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  11. 11
    Artificial fish swarm optimization for multilayer network learning in classification problems

    Artificial fish swarm optimization for multilayer network learning in classification problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam

    Published 2012
    “…Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN.In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems.The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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  12. 12
    Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems

    Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

    Published 2012
    “…In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems. …”
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  13. 13
    Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches

    Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches by Mohd Pauzi, Nur Fazlinda

    Published 2015
    “…If iteration available is long, method 3+0+0 is more appropriate, otherwise method 2+1+0 is the best choice. …”
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  14. 14
    DESIGN AND IMPLEMENTATION OF RIPEMD-160 HASH FUNCTION USING VERILOG HDL

    DESIGN AND IMPLEMENTATION OF RIPEMD-160 HASH FUNCTION USING VERILOG HDL by Sharifah Azizah, Wan Alwi

    Published 2022
    “…This project proposes to design an iterative method of RIPEMD-160 in order to investigate the security algorithm and its implementation. …”
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    Final Year Project Report / IMRAD
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  15. 15
    Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm

    Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

    Published 2011
    “…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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  16. 16
    Master-leader-slave cuckoo search with parameter control for ANN optimization and its real-world application to water quality prediction

    Master-leader-slave cuckoo search with parameter control for ANN optimization and its real-world application to water quality prediction by Jaddi N.S., Abdullah S., Malek M.A.

    Published 2023
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  17. 17
    DESIGN AND IMPLEMENTATION OF RIPEMD-160 ALGORITHM ON RECONFIGURABLE HARDWARE

    DESIGN AND IMPLEMENTATION OF RIPEMD-160 ALGORITHM ON RECONFIGURABLE HARDWARE by FLORISCA, ALDINA GODULUS

    Published 2019
    “…The RIPEMD-160 can improve the security in storing information. This project used iterative method to investigate the security algorithm and implemented the algorithm. …”
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    Final Year Project Report / IMRAD
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  18. 18
    A New Quadratic Binary Harris Hawk Optimization For Feature Selection

    A New Quadratic Binary Harris Hawk Optimization For Feature Selection by Abdullah, Abdul Rahim, Too, Jing Wei, Mohd Saad, Norhashimah

    Published 2019
    “…A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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  19. 19
    Feature selection optimization using hybrid relief-f with self-adaptive differential evolution

    Feature selection optimization using hybrid relief-f with self-adaptive differential evolution by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran, Ahmad Nazri, Azree Shahrel, Mohamed, Raihani, Abd Manaf, Syaifulnizam

    Published 2017
    “…Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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  20. 20
    Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

    Improved whale optimization algorithm for feature selection in Arabic sentiment analysis by Tubishat, Mohammad, Abushariah, Mohammad A.M., Idris, Norisma, Aljarah, Ibrahim

    Published 2019
    “…To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. …”
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