Search Results - (( data training programmes algorithm ) OR ( java application optimisation algorithm ))

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  1. 1

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

    Published 2019
    “…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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    Article
  2. 2

    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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    Thesis
  3. 3

    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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    Article
  4. 4

    Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

    Published 2015
    “…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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    Thesis
  5. 5
  6. 6

    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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    Article
  7. 7

    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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    Article
  8. 8

    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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    Proceeding Paper
  9. 9

    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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    Conference or Workshop Item
  10. 10

    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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    Article
  11. 11

    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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    Conference or Workshop Item
  12. 12

    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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  13. 13

    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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    Conference or Workshop Item
  14. 14

    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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