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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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  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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  7. 7
    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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  8. 8
    New Generalized Algorithm for Developing k-Step Higher Derivative Block Methods for Solving Higher Order Ordinary Differential Equations

    New Generalized Algorithm for Developing k-Step Higher Derivative Block Methods for Solving Higher Order Ordinary Differential Equations by Adeyeye, Oluwaseun, Omar, Zurni

    Published 2018
    “…This new algorithm utilizes the concept from the conventional Taylor series approach of developing linear multistep methods.Certain examples are given to show the simplicity involved in the usage of this new generalized algorithm.…”
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  9. 9
    Imputation Analysis of Time-Series Data Using a Random Forest Algorithm

    Imputation Analysis of Time-Series Data Using a Random Forest Algorithm by Nur Najmiyah, Jaafar, Muhammad Nur Ajmal, Rosdi, Khairur Rijal, Jamaludin, Faizir, Ramlie, Habibah, Abdul Talib

    Published 2024
    “…Overall, this study highlights the effectiveness of MissForest as the preferred imputation method for AVL time-series data.…”
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  10. 10
    Financial time series predicting using machine learning algorithms

    Financial time series predicting using machine learning algorithms by Tiong, Leslie Ching Ow *

    Published 2013
    “…Thus, this research motivates and aims to investigate the repeat behaviour and pattern of trends from the historical financial time series data, and utilise the strength of machine learning techniques to develop a promising financial time series predictor engine. …”
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  11. 11
    Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction

    Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction by Shoorangiz, Mohammadreza

    Published 2013
    “…The development sequence of such method can be divided into two parts: Developing network structure to employ complex fuzzy logic and proposing learning algorithm to train the system. …”
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  12. 12
    SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

    SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS by SETYO WIBOWO, TRI CHANDRA

    Published 2009
    “…Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented. …”
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  13. 13
    M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589)

    M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589) by Mansor, Rosnalini, Mat Kasim, Maznah, Othman, Mahmod, Zaini, Bahtiar Jamili

    “…The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of four main phases; data pre-processing, model development, model implementation and model evaluation. …”
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  14. 14
    Monitoring the impacts of drought on land use/cover: a developed object-based algorithm for NOAA AVHRR time series data

    Monitoring the impacts of drought on land use/cover: a developed object-based algorithm for NOAA AVHRR time series data by Mokhtari, Ahmad, Mansor, Shattri, Mahmud, Ahmad Rodzi, Mohd Shafri, Helmi Zulhaidi

    Published 2011
    “…The model works based on the seasonal values of Normalized-difference Vegetation Index (NDVT) in the study area. The algorithm was statistically compared with maximum likelihood supervised classification method. …”
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  15. 15
    Automated time series forecasting

    Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

    Published 2011
    “…Moving Average, Decomposition, Exponential Smoothing, Time Series Regressions and ARIMA) were used.The algorithm was developed in JAVA using up to date forecasting process such as data partition, several error measures and rolling process.Successfully, the results of the algorithm tally with the results of SPSS and Excel.This automatic forecasting will not just benefit forecaster but also end users who do not have in depth knowledge about forecasting techniques.…”
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  16. 16
    Parameter-driven count time series models / Nawwal Ahmad Bukhari

    Parameter-driven count time series models / Nawwal Ahmad Bukhari by Nawwal , Ahmad Bukhari

    Published 2018
    “…Simulation shows that MCEM algorithm and particle method are useful for the parameter estimation of the Poisson model. …”
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  17. 17
    A Mobile Application For Stock Price Prediction

    A Mobile Application For Stock Price Prediction by Choy, Yi Tou

    Published 2021
    “…A mobile application for stock price prediction using time series algorithms is developed to tackle the problem mentioned. …”
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  18. 18
    Development of a new method of crack modelling and prediction algorithm

    Development of a new method of crack modelling and prediction algorithm by Ainibu, Abiodun Musa, Shafie, Amir Akramin, Salami, Momoh Jimoh Emiyoka, Salami, A. F., Bamgbopa, Ibrahim A., Lawal, Wahab Adetunji

    Published 2011
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  19. 19
    Stroke-to-stroke matching in on-line signature verification

    Stroke-to-stroke matching in on-line signature verification by Ahmad Jaini, Azhar

    Published 2010
    “…Current methods make use of the DTW algorithm and its variant to segment them before comparing each of its data dimension. …”
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  20. 20
    Applications of wavelet method in stock exchange problem

    Applications of wavelet method in stock exchange problem by Samsul Ariffin Abdul Karim, Bakri Abdul Karim, Mohd Tahir Ismail, Mohammad Khatim Hasan, Jumat Sulaiman

    Published 2011
    “…One of wavelet advantages as compared to Fourier is, it has fast algorithm to evaluate the series expansion. In this present article, we will discuss the applications of fast wavelet algorithm namely Discrete Wavelet Transform (DWT) in finance such as denoising the time series by using wavelet thresholding. …”
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