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  1. 1
    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control

    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control by Srazhidinov, Radik

    Published 2016
    “…Using this method, the need for the knowledge of the degree of nonlinearity in advance can be avoided. …”
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  2. 2
    An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition

    An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition by Mohd. Hasbullah, Omar

    Published 2011
    “…In order to use the algorithm effectively, users need to balance between detection accuracy and execution time. …”
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  3. 3
    Hybrid subjective evaluation method using weighted subsethood - based (WSBA) rule generation algorithm

    Hybrid subjective evaluation method using weighted subsethood - based (WSBA) rule generation algorithm by Othman, Mahmod, Khalid, Shaiful Annuar, Abdullah, Fader, Amir Hamzah, Shezrin Hawani, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…The use of fuzzy rules, which were extracted directly from input data through Weighted Subsethood-based (WSBA) Rule Generation Algorithm.WSBA rule generation use the subsethood values to generate the weights which finally produced the fuzzy general rules.The rules generated through the data provided knowledge in developed fuzzy rule The fuzzy rules embedded in the framework of subjective evaluation method showed advantages in generalizing the evaluation of the performance achievement, where the evaluation process can be conducted consistently in producing good evaluation results with the use of the membership set score.The results from the numerical examples are comparable to other fuzzy evaluation methods, even with the use of small rule size.…”
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  4. 4
    The compact genetic algorithm for likelihood estimator of first order moving average model

    The compact genetic algorithm for likelihood estimator of first order moving average model by Al-Dabbagh, R.D., Baba, M.S., Mekhilef, Saad, Kinsheel, A.

    Published 2012
    “…Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. …”
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  5. 5
    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
    “…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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  6. 6
    The development of semantic meta-database: an ontology based semantic integration of biological databases

    The development of semantic meta-database: an ontology based semantic integration of biological databases by Samsudin, Ruhaidah, Deris, Safaai, Othman, Muhammad Razib, Md. Illias, Rosli

    Published 2007
    “…The tool comprises two intelligent algorithms. The first algorithm combines parallel genetic algorithm with the split-and-merge algorithm. …”
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  7. 7
    Hybrid subjective evaluation of rule Exraction Algorithm using Weighted Subsethood-Based (WSBA)

    Hybrid subjective evaluation of rule Exraction Algorithm using Weighted Subsethood-Based (WSBA) by Othman, Mahmod, Ku-Mahamud, Ku Ruhana, Hawani, Shezrin, Hamzah, Amir, Khalid, Shaiful Annuar, Abdullah, Fader

    Published 2013
    “…Fuzzy rules are important elements that being highlighted in any fuzzy expert system.This research proposes the framework of subjective performance evaluation using fuzzy technique for ranking the performance of the financial performance of a company under a multi criteria environment.There are a lot of techniques used such as fuzzy similarity function, fuzzy synthetic decision and satisfaction function have been adopted.The framework is based on fuzzy multi-criteria decision-making that consists of fuzzy rules.The use of fuzzy rules, which were extracted directly from input data through Weighted Subsethood-based (WSBA) Rule Generation Algorithm.WSBA rule generation use the subsethood values to generate the weights which finally produced the fuzzy general rules.The rules generated through the data provided knowledge in developed fuzzy rule The fuzzy rules embedded in the framework of subjective evaluation method showed advantages in generalizing the evaluation of the performance achievement, where the evaluation process can be conducted consistently in producing good evaluation results with the use of the membership set score.The results from the numerical examples are comparable to other fuzzy evaluation methods, even with the use of small rule size.…”
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  8. 8
    Ontology-based metabolic pathway prediction using saccharomyces cerevisiae data from genbank, ecocyc and kegg

    Ontology-based metabolic pathway prediction using saccharomyces cerevisiae data from genbank, ecocyc and kegg by Zakaria, Yuslina

    Published 2006
    “…This approach capable to predict metabolic pathway of S.cerevisiae with 87 percent accuracy compared to 80 percent accuracy using PathoLogic algorithm…”
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  9. 9
    Online food ordering application using Boyer-Moore algorithm / Muhammad Zulhilmie Dzulkifli

    Online food ordering application using Boyer-Moore algorithm / Muhammad Zulhilmie Dzulkifli by Dzulkifli, Muhammad Zulhilmie

    Published 2021
    “…The main goal of this study is to create an online food ordering app using the corresponding Boyer-Moore string matching algorithm. …”
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  10. 10
    Web based clustering tool using K-MEAN++ algorithm / Muhammad Nur Syazwanie Aznan

    Web based clustering tool using K-MEAN++ algorithm / Muhammad Nur Syazwanie Aznan by Aznan, Muhammad Nur Syazwanie Aznan

    Published 2019
    “…Which is why this project objective is to develop a web based clustering tool using K-MEAN++ algorithm. This project will use the rapid application development (RAD) methodology since this are the most suitable method for developing the system. …”
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  11. 11
    High performance visualization of human tumor growth software

    High performance visualization of human tumor growth software by Alias, Norma, Mohd. Said, Norfarizan, Khalid, Siti Nur Hidayah, Sin, Dolly Tien Ching, Phang, Tau Ing

    Published 2008
    “…The result of finite difference approximation (explicit, Crank-Nicolson and fully implicit methods) will be presented graphically. The implementation of parallel algorithm based on parallel computing system is used to visualize the growth of human tumour. …”
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  12. 12
    Development of a Negotiation Support Model for Value Management in Construction

    Development of a Negotiation Support Model for Value Management in Construction by UTOMO, CHRISTIONO

    Published 2009
    “…This methodology combines value analysis method using Function Analysis System Technique (FAST); Life Cycle Cost analysis, group decision analysis method based on Analytical Hierarchy Process (AHP), and Game theory-based agent system to develop a negotiation support. …”
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  13. 13
    New Learning Models for Generating Classification Rules Based on Rough Set Approach

    New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

    Published 2000
    “…Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm, rough set theory, and others. …”
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  14. 14
    Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization

    Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

    Published 2022
    “…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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  15. 15
    High performance simulation for brain tumours growth using parabolic equation on heterogeneous parallel computer system

    High performance simulation for brain tumours growth using parabolic equation on heterogeneous parallel computer system by Pheng H. S., Norma Alias, Norfarizan Mohd Said

    Published 2007
    “…The implementation of parallel algorithm based on parallel computing system is used to capture the growth of brain tumour. …”
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  16. 16
    Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function

    Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function by Kamaruddin, Siti Sakira, Hamdan, Abdul Razak, Abu Bakar, Azuraliza, Mat Nor, Fauzias

    Published 2012
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  17. 17
    Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process

    Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

    Published 2004
    “…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. ANN is a computer-based simulation of the living nervous system which works quite differently from conventional programming. …”
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  18. 18
    Implementing Station-to-Station protocol using Multi Prime RSA Cryptosystem / Muhammad Arif Musa Abdullah

    Implementing Station-to-Station protocol using Multi Prime RSA Cryptosystem / Muhammad Arif Musa Abdullah by Abdullah, Muhammad Arif Musa

    Published 2023
    “…It will include the coding of python programming coding and calculation of proposed method. The study recommends to use another type of digital signature algorithm to be used in the proposed method. …”
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  19. 19
    A framework of modified adaptive neuro-fuzzy inference engine

    A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

    Published 2012
    “…One of the vital significant issues for constructing a high quality neuro-fuzzy system is the creation of the knowledge base, which mainly consists of membership functions and fuzzy rules. …”
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  20. 20
    What, how and when to use knowledge in neural network application

    What, how and when to use knowledge in neural network application by Wan Ishak, Wan Hussain, Abdul Rahman, Shuzlina

    Published 2004
    “…; How to use this knowledge?; and When to use this knowledge? …”
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