Search Results - (( java implementation rsa algorithm ) OR ( parameters activation function algorithm ))

RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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AUTOMATED MODEL GENERATION OF FSM AND NUSMV MODEL FROM RSA JAVA SOURCE CODE FOR MODEL CHECKING by SALLEH AL-HUMAIKANI, MOHAMMED ABDULQAWI

“…RSA is one of these encryption algorithms that have been implemented in security systems. …”
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An improved RSA cryptosystem based on thread and CRT / Saheed Yakub Kayode and Gbolagade Kazeem Alagbe by Yakub Kayode, Saheed, Kazeem Alagbe, Gbolagade

“…Java programming language is used to implement the algorithm. …”
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Secure Image Steganography Using Encryption Algorithm by Siti Dhalila, Mohd Satar, Roslinda, Muda, Fatimah, Ghazali, Mustafa, Mamat, Nazirah, Abd Hamid, An, P.K

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

“…Digital Signature was encrypted by RSA Algorithm, a very powerful asymmetrical encryption. …”
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

“…To overcome these limitations, there have been attempts to use genetic algorithm (GA) to optimize some of these parameters. …”
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Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

“…The RBFNN model with softmax as the hidden layer activation function and identity as the outer layer activation function has the least predictive performance, as indicated by an R2 of 0.403 and a RMSE of 301.55. …”
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Identification of vocal and voice disorders by Murugesa Pandiyan, Paulraj, Prof. Madya Dr., Sazali, Yaacob, Prof. Dr., Mohd Rizon, Mohammed Juhari, Prof. Dr., Sivanandam, S. N., Muthusamy, Hariharan, Dr.

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The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo... by Moloody, Abbas, As’arry, Azizan, Hong, Tang Sai, Raja Kamil, ., Zolfagharian, Ali

“…Here, in this research by comprising three of the most effective variational techniques now, a Modified Differential Evolutionary Optimization Algorithm (MDEOA) method is suggested to handle the challenge of adjusting the PID controller parameters for the Intelligent Active Vibration Control (IAVC) of a Combined Single Link Robotics Flexible Manipulator (CSLRFM) in order to reduce the undesired effects of vibration. …”
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The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

“…The activation functions are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. …”
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A harmony search-based learning algorithm for epileptic seizure prediction by Kee, Huong Lai, Zainuddin, Zarita, Ong, Pauline

“…The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. …”
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Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant by Rahmat, Mohd Fua'ad, Rahmat, Mohd Fuaad, Abdul Wahab, Norhaliza, Che Razali, Mashitah, Gaya, Muhammad Sani, Samsudin, Sharatul Izah

“…The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. …”
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Loudspeaker nonlinearity compensation with inverse tangent hyperbolic function-based predistorter for active noise control by Sahib, Mouayad Abdulredha, Raja Ahmad, Raja Mohd Kamil

“…In active noise control (ANC), the performance of the filtered-x least mean squares (FXLMS) algorithm is degraded by the saturation of the loudspeaker in the secondary path. …”
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The tuning of error signal for back-propagation algorithms by Rengasamy, Renugah

“…This new algorithm is proven to be a better algorithm. The main purpose of this study is to evaluate the efficiency of improved two-term error function by applying three different values of ß parameter in the activation function. …”
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Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

“…There have two most uses activation function namely tansig and logsig. The essence of this study is that it compares the effect of activation functions (tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. …”
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Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms by Ayodele B.V., Mustapa S.I., Kanthasamy R., Zwawi M., Cheng C.K.

Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system by Krishnan P.S., Kiong T.S., Koh J., Yap D.

“…In this paper, an embedded parallel and distributed genetic algorithm (EPDGA) with dynamic parameter setting on a multiprocessor system is proposed. …”

Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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Real time nonlinear filtered-x lms algorithm for active noise control by Sahib, Mouayad Abdulredha

“…The NLFXLMS algorithm is a stochastic gradient algorithm that incorporates the derivative of a nonlinear plant model which is represented by the scaled error function (SEF) in the controller design. …”
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Comparison of different neural network training algorithms for wind velocity forecasting by KhalajiAssadi , Morteza, Safaei , Shervin

“…In addition, choosing the type of activation function is dependent on the amount of input data, which should be acceptably large.…”
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