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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  2. 2
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  3. 3
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  5. 5
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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    Final Year Project / Dissertation / Thesis
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  6. 6
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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    Citation Index Journal
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  7. 7
    SYSTEMATIC DESIGN OF SIMPLY STRUCTURED COMPENSATOR

    SYSTEMATIC DESIGN OF SIMPLY STRUCTURED COMPENSATOR by FUNG , CHUN TING

    Published 2005
    “…Effort has been taken by researchers to develop simple and systematic way to design these controllers. This project aims to develop the algorithm for the tuning method that based on Nyquist Stability Criterion and at later stage build a Neural Network Model to predict the tuning parameters for the PID controller. …”
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  8. 8
    Y-type Random 2-satisfiability In Discrete Hopfield Neural Network

    Y-type Random 2-satisfiability In Discrete Hopfield Neural Network by Guo, Yueling

    Published 2024
    “…The rule namely Y-Type Random 2-Satisfiability is proposed by combining the systematic and non-systematic logical rule. Next, the newly proposed logical rule as the symbolic instruction was implemented into the Discrete Hopfield Neural Network to govern the neurons of the network. …”
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  9. 9
    Synergizing intelligence and knowledge discovery: Hybrid black hole algorithm for optimizing discrete Hopfield neural network with negative based systematic satisfiability

    Synergizing intelligence and knowledge discovery: Hybrid black hole algorithm for optimizing discrete Hopfield neural network with negative based systematic satisfiability by Rusdi, Nur ‘Afifah, Zamri, Nur Ezlin, Kasihmuddin, Mohd Shareduwan Mohd, Romli, Nurul Atiqah, Manoharam, Gaeithry, Abdeen, Suad, Mansor, Mohd. Asyraf

    Published 2024
    “…Based on the findings, the development of the new systematic SAT and the implementation of the Hybrid Black Hole algorithm to optimize the retrieval capabilities of DHNN to achieve multi-objective functions result in updated final neuron states with high diversity, high attainment of global minima solutions, and produces states with a low similarity index. …”
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  10. 10
    Integrated applications of building information modeling in project cost management: a systematic review

    Integrated applications of building information modeling in project cost management: a systematic review by M. Farouk, Abdelrahman, Rahimi, A. Rahman

    Published 2023
    “…Purpose – Implementing building information modeling (BIM) in construction projects offers many benefits. …”
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  11. 11
    Data mining techniques for disease risk prediction model: A systematic literature review

    Data mining techniques for disease risk prediction model: A systematic literature review by Ahmad W.M.T.W., Ghani N.L.A., Drus S.M.

    Published 2023
    Conference Paper
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  12. 12
    Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network

    Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network by Muhammad Sidik, Siti Syatirah

    Published 2023
    “…One of the alternatives to improve the modeling of the Discrete Hopfield Neural Network is by implementing different variants of logical rules. …”
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  13. 13
    Rainfall prediction system using Artificial Neural Network (ANN) / Izzat Izzuddin Zulkarnain

    Rainfall prediction system using Artificial Neural Network (ANN) / Izzat Izzuddin Zulkarnain by Zulkarnain, Izzat Izzuddin

    Published 2024
    “…The methodology employed a systematic approach, including data collection, pre-processing, prototype architecture design, algorithm implementation, and evaluation. …”
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  14. 14
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  15. 15
    Modelling Autonomous Evacuation Navigation System (AENS) for optimal route using Dijkstra's algorithm

    Modelling Autonomous Evacuation Navigation System (AENS) for optimal route using Dijkstra's algorithm by Abu Samah, Khyrina Airin Fariza

    Published 2016
    “…Then, the modified DA has been implemented into the proposed conceptual model. The evaluation and validation have been executed through a case study using Pathfinder simulation software and experimental model as to support the hypothesis. …”
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  16. 16
    Data mining techniques for transformer failure prediction model: A systematic literature review

    Data mining techniques for transformer failure prediction model: A systematic literature review by Ravi N.N., Mohd Drus S., Krishnan P.S.

    Published 2023
    Conference Paper
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  17. 17
    Fusion of linear and nonlinear functional model for cadastral network adjustment / Nazurah Syahzanani Amiruddin

    Fusion of linear and nonlinear functional model for cadastral network adjustment / Nazurah Syahzanani Amiruddin by Amiruddin, Nazurah Syahzanani

    Published 2024
    “…Due to the significant discrepancy in functional models, this study begins with the identification of an algorithm to harmonize the linear (GNSS vectors) and non-linear (traverse) models. …”
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  18. 18
    Simplified approach to validate constitutive model formulation of orthotropic materials undergoing finite strain deformation

    Simplified approach to validate constitutive model formulation of orthotropic materials undergoing finite strain deformation by Mohd Nor, Mohd Khir, Ma`at, Norzarina

    Published 2016
    “…The chosen constitutive model was implemented into the DYNA3D source code. …”
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  19. 19
    Solving Sudoku puzzles in Binary Integer Linear Programming using Branch and Bound algorithm / Ahida Waliyyah Ahmad Fuad

    Solving Sudoku puzzles in Binary Integer Linear Programming using Branch and Bound algorithm / Ahida Waliyyah Ahmad Fuad by Ahmad Fuad, Ahida Waliyyah

    Published 2024
    “…Using the PuLP library in Python, the B&B algorithm is implemented to systematically explore feasible solutions while pruning infeasible branches, ensuring all Sudoku constraints are met. …”
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  20. 20
    Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score

    Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

    Published 2021
    “…The index of these weights is referred to as NLHRS, which was further used as a covariate in the simple LRA model to estimate the likelihood of CVDs. This conversion from a complex black-box nature of ML algorithms into simple statistical models yielded such models, which do not require automated systems for their implementation. …”
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