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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
    Immune-based technique for undergraduate programmes recommendation / Muhammad Azrill Mohd Zamri

    Immune-based technique for undergraduate programmes recommendation / Muhammad Azrill Mohd Zamri by Mohd Zamri, Muhammad Azrill

    Published 2017
    “…Future implementations of this technique should consider a higher amount of training data to produce a higher accuracy.…”
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  8. 8
    Making programmer effective for software development teams: An extended study

    Making programmer effective for software development teams: An extended study by Gilal, A.R., Jaafar, J., Abro, A., Umrani, W.A., Basri, S., Omar, M.

    Published 2017
    “…In order to find the possible combination of personality types between team-leader and programmer, this study applied Genetic Algorithm (GA) and Johnson's Algorithm (JA) on data. …”
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  9. 9
    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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  10. 10
    Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

    Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management by Aizat Hilmi Zamzam, Ayman Khallel Ibrahim Al-Ani, Ahmad Khairi Abdul Wahab, Khin Wee Lai, Suresh Chandra Satapathy, Azira Khalil, Khairunnisa Hasikin, Muhammad Mokhzaini Azizan

    Published 2024
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  11. 11
    A machine learning approach of predicting high potential archers by means of physical fitness indicators

    A machine learning approach of predicting high potential archers by means of physical fitness indicators by Muazu Musa, Rabiu, Abdul Majeed, Anwar P.P., Taha, Zahari, Chang, Siow Wee, Ab. Nasir, Ahmad Fakhri, Abdullah, Mohamad Razali

    Published 2019
    “…The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. …”
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  12. 12
    A machine learning approach of predicting high potential archers by means of physical fitness indicators

    A machine learning approach of predicting high potential archers by means of physical fitness indicators by Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Zahari, Taha

    Published 2019
    “…The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. …”
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  13. 13
    Artificial Intelligence (AI) to predict dental student academic performance based on pre university results

    Artificial Intelligence (AI) to predict dental student academic performance based on pre university results by Abdullah, Adilah Syahirah, Ahmad Amin, Afifah Munirah, Lestari, Widya, Sukotjo, Cortino, Utomo, Chandra Prasetyo, Ismail, Azlini

    Published 2021
    “…Dataset output variables include the number of repeat papers, repeat years, distinctions, and graduation on time. Exploratory Data Analysis will be performed with training and testing data. …”
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  14. 14
    An Intelligence Technique For Denial Of Service (Dos) Attack Detection

    An Intelligence Technique For Denial Of Service (Dos) Attack Detection by Wan Nurul Safawati, Wan Manan, Tuan Muhammad, Safiuddin

    Published 2017
    “…Therefore, this paper concern about Denial of Service (DoS) attack, detection using Neural Network. The data used in training and testing was KDD 99 data set based on the Defense Advanced Research Projects Agency (DARPA) intrusion detection programme, which is publicly accessible by Lincoln Labs. …”
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  15. 15
    An intelligence technique for denial of service (DoS) attack detection

    An intelligence technique for denial of service (DoS) attack detection by Wan Nurulsafawati, Wan Manan, Tuan Muhammad, Safiuddin, Zarina, Dzolkhifli, Mohd Hafiz, Mohd Hassin

    Published 2018
    “…Therefore, this paper concern about Denial of Service (DoS) attack, detection using Neural Network. The data used in training and testing was KDD 99 data set based on the Defense Advanced Research Projects Agency (DARPA) intrusion detection programme, which is publicly accessible by Lincoln Labs. …”
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  16. 16
    Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system

    Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system by Leow, R.S., Ibrahim, F., Moghavvemi, M.

    Published 2007
    “…The system includes a programmable visual stimulator, EEG amplifier with filter system, data acquisition card, and signal processing and classification algorithms. …”
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  17. 17
    Preliminary study on fault detection using artificial neural network for water-cooled reactors

    Preliminary study on fault detection using artificial neural network for water-cooled reactors by Abdul Karim, Julia, Lanyau, Tony, Maskin, Masleha, Anuar, M. A. S., Che Soh, Azura, Abdul Rahman, Ribhan Zafira

    Published 2020
    “…This work was carried out to discover the use of an artificial neural network (ANN) to model and develop a fault detection programme in the RTP cooling system. Using actual data from the reactor to train the multilayer network model with backpropagation algorithm. …”
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  18. 18
    FPGA-enabled binarised convolutional neural networks toward real-time embedded object recognition system

    FPGA-enabled binarised convolutional neural networks toward real-time embedded object recognition system by Shuto, Daisuke, Abbas, Z., Sulaiman, N., Tamukoh, H.

    Published 2017
    “…FPGAs consist of a matrix of reconfigurable logic gates allowing parallel computing which befits most image processing algorithms such as the CNN. We train the binarised CNN on one of our datasets that contain images of several kinds of food and beverages. …”
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  19. 19
    Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps

    Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps by Song-Quan Ong, Toke Thomas Høye

    Published 2024
    “…RESULTS: Our results show that using the MobileNetV2 architecture with transparent sticky traps as training data, the model predicted the pest species on transparent sticky traps with an accuracy of at least 0.95 and on other sticky trap colours with at least 0.85 of the F1 score. …”
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