Search Results - (( basic generic functional algorithm ) OR ( java implementation phase algorithm ))
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1
Automatic generic process migration system in linux
Published 2012“…A fexible interface to the underlying checkpoint/ restart subsystem is designed which permits users to specify the migration mechanism according to process constraints. A migration algorithm is designed which attempts to exploit the unique features of the basic migration algorithms to form a generic algorithm. …”
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Thesis -
2
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…All the algorithm for the engine has been developed by using Java script language. …”
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3
Synthesis of Heat Exchangers Network (HEN) by Revisiting the Method Based on 2nd Law of Thermodynamics
Published 2009“…Formulation has been performed to obtain generic function equations for Cp, Hf, Sf, Hfg, Sfg. …”
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Final Year Project -
4
Optimal route checking using genetic algorithm for UiTM's bus services / Tengku Salman Fathi Tengku Jaafar
Published 2006“…Although from human logical thinking, the route can be generated easily but the calculation of checking the route whether it is optimal route or not is difficult and will take long time to be implemented. This research study with the development of the Optimal Route Checking Using Genetic Algorithm system should solve this scenario. …”
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5
Speech emotion verification system (SEVS) based on MFCC for real time applications
Published 2008“…Since features extracted using the MFCC simulates the function of the human cochlea, neural network (NN) and fuzzy neural network algorithm namely; Multi Layer Perceptron (MLP), Adaptive Network-based Fuzzy Inference System (ANFIS) and Generic Selforganizing Fuzzy Neural Network (GenSoFNN) were used to verify the different emotions. …”
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Proceeding Paper -
6
Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach
Published 2022“…The other one is the network training’s environment optimization that is done through hyperparameter optimization by selecting and fine-tuning high impact parameters which include Optimizer, Learning Rate and Dropout to reduce error rate (loss function). The strategies applied showed that the final accuracy obtained through the training after implementing a modification in the algorithm is at 81% accuracy rate compared to the basic model that recorded its final accuracy at 79% accuracy rate. …”
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