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  1. 1
    Human activity recognition via accelerometer and gyro sensors

    Human activity recognition via accelerometer and gyro sensors by Tee, Jia Lin

    Published 2023
    “…To implement the data engineering system proposed, two mobile applications, SensorData and SensorDataLogger with user-friendly interfaces and intuitive functionalities are developed using Java programming language and Android Studio. …”
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  2. 2
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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  3. 3
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  4. 4
    Provider independent cryptographic tools

    Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

    Published 2003
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    Monograph
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  5. 5
    Toxic Gas Dispersion Model Based On Neural Pattern Recognition Networks

    Toxic Gas Dispersion Model Based On Neural Pattern Recognition Networks by Roslan, Nurfarah Arina

    Published 2022
    “…Following the best selection of neural network algorithm, BR algorithm is further trained using 50-70% training with 10-28 hidden neurons. …”
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    Monograph
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  6. 6
    Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network

    Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

    Published 2021
    “…Prior to the development of ANN-based WQI prediction model, the BR algorithm was chosen with two-, three-, four-, five- and six-neuron architectures for 60% and 70% training. …”
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  7. 7
    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)

    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

    Published 2019
    “…Due to that, many algorithms employ different training algorithms to guide the network for providing an accurate result with less training and testing error. …”
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  8. 8
    Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets

    Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets by Adedayo, Ojo O., Mohd Isa, Maryam, Che Soh, Azura, Abbas, Zulkifly

    Published 2014
    “…The ANN training data were obtained from Open-ended Coaxial Probe (OCP) microwave measurements and the quasi-static admittance model, the ANN was trained with four different training algorithms: Levenberg Marquardt (LM) algorithm, Gradient Descent with Momentum (GDM) algorithm, Resilient Backpropagation (RP) algorithm and Gradient Descent with Adaptive learning rate (GDA) algorithm. …”
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  9. 9
    Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

    Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction by Heng S.Y., Ridwan W.M., Kumar P., Ahmed A.N., Fai C.M., Birima A.H., El-Shafie A.

    Published 2023
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  10. 10
    Analysis of training function for NNARX in solar radiation prediction modeling

    Analysis of training function for NNARX in solar radiation prediction modeling by Mohd Rizman, Sultan Mohd, Juliana, Johari, Fazlina, Ahmat Ruslan, Noorfadzli, Abdul Razak, Salmiah, Ahmad, Ahmad Syahiman, Mohd Shah

    Published 2022
    “…Each Training Function algorithm will be used in modeling development and their prediction output will be compared with the actual output. …”
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  11. 11
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. …”
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  12. 12
    Neural network algorithm-based fall detection modelling

    Neural network algorithm-based fall detection modelling by Mohd Yusoff, Ainul Husna, Koh, Cheng Zhi, Ngadimon, Khairulnizam, Md Salleh, Salihatun

    Published 2020
    “…The simulated result shows that the training model of Type 2 is the best model with a training result of 6.1551mse, 40 epochs, time 0.06s, and 0.92742 accuracy. …”
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  13. 13
    Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization

    Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization by Wong, Yong Jie, Arumugasamy, Senthil Kumar, Jewaratnam, Jegalakshimi

    Published 2018
    “…A multilayer feedforward neural network (FFNN) model with 11 different training algorithms is developed for the multivariable nonlinear biopolymerization of polycaprolactone (PCL). …”
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  14. 14
    Real-Time Video Processing Using Native Programming on Android Platform

    Real-Time Video Processing Using Native Programming on Android Platform by Saipullah, Khairul Muzzammil

    Published 2012
    “…However for the Android platform that based on the JAVA language, most of the software algorithm is running on JAVA that consumes more time to be compiled. …”
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  15. 15
    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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  16. 16
    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
    “…Integration of global search algorithm into ANN model further improved the model performance. …”
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  17. 17
    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm

    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm by Nukman, Y., Hassan, M.A., Harizam, M.Z.

    Published 2013
    “…In some cases, the prediction errors of Taguchi ANN model was larger than 10 even with Levenberg Marquardt training algorithm. …”
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  18. 18
    Water Quality Parameter Prediction Using Machine Learning Method

    Water Quality Parameter Prediction Using Machine Learning Method by Muhammad Shaiful Bin Abdul Halim

    Published 2023
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  19. 19
    Sentiment classification for malay newspaper using clonal selection algorithm / Nur Fitri Nabila Mohamad Nasir

    Sentiment classification for malay newspaper using clonal selection algorithm / Nur Fitri Nabila Mohamad Nasir by Mohamad Nasir, Nur Fitri Nabila

    Published 2013
    “…The experimental results show that our method can achieve better performance in clonal selection algorithm sentiment classification and the data collected cannot be used at once in this model because training data is very time-consuming if using all the data. …”
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  20. 20
    Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms

    Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms by Veza, I., Irianto, Panchal, H., Paristiawan, P.A., Idris, M., Fattah, I.M.R., Putra, N.R., Silambarasan, R.

    Published 2022
    “…The specific objective of this study is to predict the HHV of 350 samples of biomass from the proximate analysis by developing an ANN model which was trained with 11 different algorithms. …”
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