Search Results - (( developing mixed _ algorithm ) OR ( java implication based algorithm ))
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The application of neural network data mining algorithm into mixed pixel classification in geographic information system environment
Published 2007“…Region growing segmentation and radial basis function algorithms are considered a powerful tool to minimize the mixed pixel classification error.…”
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Improved expectation maximization algorithm for Gaussian mixed model using the kernel method
Published 2013“…We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. …”
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Real time De-mixing system based on LMS adaptive algorithm for blind two source signals separation
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Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA
Published 2014“…The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.…”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Mixed Unscented Kalman Filter and differential evolution for parameter identification
Published 2013“…DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. …”
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Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor / Muhammad Nasrul Hakim Adenan
Published 2013“…Backpropagation is used as the learning method of ANN model. The algorithm will be developed in MATLAB. The objective of this project is to develop ANN model for ISFET sensor that able to estimate the main ion in mixed solution by learning the pattern of the input and output of the sensor. …”
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Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor: article / Muhammad Nasrul Hakim Adenan and Maizatul Zolkapli
Published 2013“…Backpropagation is used as the learning method of ANN model. The algorithm will be developed in MATLAB. The objective of this project is to develop ANN model for ISFET sensor that able to estimate the main ion in mixed solution by learning the pattern of the input and output of the sensor. …”
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Investigating feed mix problem approaches: An overview and potential solution
Published 2010“…Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem.…”
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Enhancement of text-based advanced encryption standard algorithm (AES) in the android platform / Mohamad Zakirin Mohammad Zahari
Published 2014“…This will help to speed up the algorithm because mix column step was using high calculation and needs a lot of time to process it. …”
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A bacteria foraging algorithm for solving integrated multi-period cell formation and subcontracting production planning in a dynamic cellular manufacturing system
Published 2011“…In addition, a newly-developed BFA-based optimisation algorithm has been compared with the branch and bound algorithm. …”
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A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites
Published 2019“…Many test data generation strategies based on meta-heuristic algorithms such as Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search (HS), Cuckoo Search (CS), Bat Algorithm (BA) and Bees Algorithm have been developed in recent years. …”
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Recent research in cooperative path planning algorithms for multi-agent using mixed- integer linear programming
Published 2016“…This paper will review and compare the performances of those existing methods that can find solution without graph search algorithm such as Mixed-Integer Linear Programming (MILP) techniques which exactly solves the problem and then propose four alternative MILP formulations which are computationally less intensive and suited for real-time purposes, but yield a theoretically guaranteed suboptimal solution.…”
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A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…Among the latest Evaluation Algorithm (EA) have been developed is Differential Evolution (DE). …”
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Diagnosis of Poor Control Loop Performance: Development of a Model Based Compensation Algorithm for Control Valve Stiction Nonlinearity
Published 2016“…The objective of this paper is to develop a model based compensation algorithm using optimization approach of MIQP based MPC backlash compensator for control valve stiction nonlinearity. …”
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Mathematical models and optimization algorithms for low-carbon Location-Inventory-Routing Problem with uncertainty
Published 2024“…Given the NP-Hard nature of the three models proposed in this thesis, two metaheuristic algorithms have been developed. A hybrid Particle Swarm Optimization-Bacterial Foraging Algorithm is developed for solving the single objective LIRP model. …”
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Capacity planning for mixed-load tester under demand and testing time uncertainty
Published 2018“…Currently,the company’s issue is low tester utilization of about 71%,well below the target of 96%.The objective of this research is to improve tester utilization while achieving the production target under uncertain demand and testing time and also to determine the break-even point on the testers required.A novel approach of integrating a mathematical model,robust optimization model,genetic algorithm,simulation model and cost–volume –profit analysis was developed.Firstly,a mathematical model of mixed-load tester was formulated.Next,a set of discrete scenarios was proposed to address uncertain demand and testing time.A robust optimization and genetic algorithm model was developed to optimize the number of testers under the described uncertainties.Next,these scenarios were simulated using the Pro Model simulation software to validate the proposed models and to evaluate throughput and tester utilization.Finally,the cost–volume–profit analysis was performed for scenarios that require additional testers at various levels of uncertainties.The results showed that the proposed solution improved tester utilization by 25% compared to the current system.This research has contribution by developing novel hybrid methodology and able to provide useful insights to assist company’s managers to plan and allocate resources according to variations in customers’ demands and testing time.…”
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