Search Results - (( development programs normalization algorithm ) OR ( java swarm optimization algorithm ))
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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Parallel computation of maass cusp forms using mathematica
Published 2013“…We find that the parallel programming is about 5.75 times faster than the normal programming while its efficiency is capped at 0.443.…”
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Solving non-convex economic dispatch problem using computational intelligence technique
Published 2023Conference Paper -
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Logic Programming In Radial Basis Function Neural Networks
Published 2013“…Two techniques were developed. The first technique is to encode the logic programming in radial basis function neural networks. …”
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Semi-automatic cortical boundary detection / Noor Elaiza Abdul Khalid.
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Smart energy meter with adaptive communication data transfer algorithm for electrical energy monitoring
Published 2021“…The prototype is constructed through software and hardware development and has been programmed using Arduino IDE software. …”
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Data mining in network traffic using fuzzy clustering
Published 2003“…The network traffic model is very important to know whether that particular network traffic is normal or abnormal. In this project, I have developed a program to capture and filter the packets based on the application. …”
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Data mining in network traffic using fuzzy clustering
Published 2003“…The network traffic model is very important to know whether that particular network traffic is normal or abnormal. In this project, I have developed a program to capture and filter the packets based on the application. …”
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Multi -Objective Economic Dispatch Using Evolutionary Programming
Published 2023“…The designated algorithm of MOEP used MATLAB to run the simulations. …”
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Agent-based extraction algorithm for computational problem solving
Published 2015“…Modeling Computational Problem Solving (CPS) is the main issue in teaching programming where the given text is needed to be transformed into a model then later into a programming language. …”
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Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023Conference paper -
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Enhanced dynamic security assessment for power system under normal and fake tripping contingencies.
Published 2019“…The hybrid logistic model tree (hybrid LMT) approach proposed in this study combines the symmetrical uncertainties (SU) algorithm and the logistic model tree (LMT) algorithm. …”
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Neural network based model predictive control for a steel pickling process
Published 2009“…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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