Search Results - (( developing fraction learning algorithm ) OR ( java application rsa algorithm ))

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  1. 1
    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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    Final Year Project
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  2. 2
    AUTOMATED MODEL GENERATION OF FSM AND NUSMV MODEL FROM RSA JAVA SOURCE CODE FOR MODEL CHECKING

    AUTOMATED MODEL GENERATION OF FSM AND NUSMV MODEL FROM RSA JAVA SOURCE CODE FOR MODEL CHECKING by SALLEH AL-HUMAIKANI, MOHAMMED ABDULQAWI

    Published 2021
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  3. 3
    An improved RSA cryptosystem based on thread and CRT / Saheed Yakub Kayode and Gbolagade Kazeem Alagbe

    An improved RSA cryptosystem based on thread and CRT / Saheed Yakub Kayode and Gbolagade Kazeem Alagbe by Yakub Kayode, Saheed, Kazeem Alagbe, Gbolagade

    Published 2017
    “…Java programming language is used to implement the algorithm. …”
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  4. 4
    Secure Image Steganography Using Encryption Algorithm

    Secure Image Steganography Using Encryption Algorithm by Siti Dhalila, Mohd Satar, Roslinda, Muda, Fatimah, Ghazali, Mustafa, Mamat, Nazirah, Abd Hamid, An, P.K

    Published 2016
    “…A system based on the proposed algorithm will be implemented using Java and it will be more secured due to double-layer of security mechanisms which are RSA and Diffie-Hellman.…”
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    Conference or Workshop Item
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  5. 5
    Digitally signed electronic certificate for workshop / Azinuddin Baharum

    Digitally signed electronic certificate for workshop / Azinuddin Baharum by Baharum, Azinuddin

    Published 2017
    “…Digital Signature was encrypted by RSA Algorithm, a very powerful asymmetrical encryption. …”
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  6. 6
    Feedforward neural network for solving particular fractional differential equations

    Feedforward neural network for solving particular fractional differential equations by Admon, Mohd Rashid

    Published 2024
    “…Then, a single hidden layer of FNN based on Chelyshkov polynomials with an extreme learning machine algorithm (SHLFNNCP-ELM) is constructed for solving FDEsC. …”
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  7. 7
    A new modern scheme for solving fractal–fractional differential equations based on deep feedforward neural network with multiple hidden layer

    A new modern scheme for solving fractal–fractional differential equations based on deep feedforward neural network with multiple hidden layer by Admon, Mohd Rashid, Senu, Norazak, Ahmadian, Ali, Majid, Zanariah Abdul, Salahshour, Soheil

    Published 2024
    “…The recent development of knowledge in fractional calculus introduced an advanced superior operator known as fractal–fractional derivative (FFD). …”
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  8. 8
    A novel softsign fractional-order controller optimized by an intelligent nature-inspired algorithm for magnetic levitation control

    A novel softsign fractional-order controller optimized by an intelligent nature-inspired algorithm for magnetic levitation control by Ahmad, Mohd Ashraf, Izci, Davut, Ekinci, Serdar, Mohd Tumari, Mohd Zaidi

    Published 2025
    “…The performance of the proposed approach was extensively benchmarked against four modern metaheuristic algorithms (greater cane rat algorithm, catch fish optimization algorithm, RIME algorithm and artificial hummingbird algorithm) under identical conditions. …”
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  9. 9
    A hybrid technique of deep learning neural networks with finite difference method for higher order fractional Volterra-Fredholm integro-differential equations with φ-Caputo operator

    A hybrid technique of deep learning neural networks with finite difference method for higher order fractional Volterra-Fredholm integro-differential equations with φ-Caputo operato... by Alsa’Di, Kawthar, Nik Long, Nik Mohd Asri

    Published 2025
    “…Moreover, a new hybrid technique which is the combination of deep learning artificial neural network and finite difference method (FDL-ANN) is developed to approximate the solution of higher order VFIDEs. …”
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  10. 10
    Predictive modelling of nanofluids thermophysical properties using machine learning

    Predictive modelling of nanofluids thermophysical properties using machine learning by Olanrewaju, Alade Ibrahim

    Published 2021
    “…This thesis aimed to develop machine learning algorithms to estimate the thermophysical properties of commonly used nanofluids. …”
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  11. 11
    Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations

    Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations by Shloof, Aml Melad Asan

    Published 2024
    “…In the second part of the thesis, we developed the Hilfer fractal-fractional derivative definition. …”
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  12. 12
    An improved vulnerability exploitation prediction model with novel cost function and custom trained word vector embedding

    An improved vulnerability exploitation prediction model with novel cost function and custom trained word vector embedding by Hoque M.S., Jamil N., Amin N., Lam K.-Y.

    Published 2023
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  13. 13
    Development of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learning

    Development of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learning by Jwaid W.M., Al-Hussein Z.S.M., Sabry A.H.

    Published 2023
    “…The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. …”
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  14. 14
    Application of artificial neural network to predict brake specific fuel consumption of retrofitted cng engine

    Application of artificial neural network to predict brake specific fuel consumption of retrofitted cng engine by Jahirul, M.I., Saidur, Rahman, Masjuki, Haji Hassan

    Published 2009
    “…The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. …”
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  15. 15
    Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique

    Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique by Yahaya, Noor Zaitun, Ghazali, Nurul Adyani, Ahmad, Sabri, Mohammad Asri, Mohammad Akmal, Ibrahim, Zul Fahdli, Ramli, Nor Azman

    Published 2017
    “…Using the number of trees between 2,500-3,500, learning rate of 0.01, and interaction depth of 5 were found to be the best setting for developing the ozone boosting model. …”
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  16. 16
    Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models

    Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models by Najafi Khaboshan, Hasan, Jaliliantabar, Farzad, Abdul Adam, Abdullah, Panchal, Satyam, Azarinia, Amiratabak

    Published 2024
    “…Finally, an artificial neural network model is developed using the backpropagation learning technique coupled with the gradient descent optimization algorithm. …”
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  17. 17
    Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz

    Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz by Abd Aziz, Nur Hasyimah

    Published 2020
    “…Next, the system was successfully developed by implementing the best CNN model as a classifier. …”
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  18. 18
    Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization

    Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization by Nader Ibrahim Namazi, Sameer Alshehri, Rawan Bafail, Bader Huwaimel, Amal M. Alsubaiyel, Ali H. Alamri, Ahmed D. Alatawi, Hossam Kotb, Mohd Sani Sarjadi, Md. Lutfor Rahman, Mohammed A.S. Abourehab

    Published 2022
    “…Finally, the optimal values are (P = 400 bar, T = 3.38 K 102, Y = 1.064 10ˉ³ mol fraction) using this model.…”
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