Search Results - (( module integration types algorithm ) OR ( java simulation 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“…The simulation is implemented with iFogSim and java programming language. …”
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2
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. 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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3
Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Monograph -
4
Integral-derivative (I-D) and modulation-index-curve prediction control techniques for three-phase AC-DC buck-type converter with simplified voltage-based SPWM /Azrita Alias
Published 2015“…The algorithm of the modulation-index-curve prediction technique is obtained through I-D control model by determining closed-loop dominant poles. …”
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5
OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT
Published 2023“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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7
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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8
Separable Recursive Training Algorithms with Switching Module
Published 2009“…A novel hybrid or separable recursive training strategies are de rived for the training of feedforward neural networks which incoporates a switching module. This new technique for updating weights combines non linear recursive training algorithms for the optimization of nonlinear weights with recursive least square type algorithms for the training of linear weights in one integrated routine. …”
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Book Section -
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Study of machine learning in computer vision using Raspberry Pi
Published 2024text::Final Year Project -
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Resource management in grid computing using ant colony optimization
Published 2011“…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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Monograph -
11
Railway shortest path planner application using ant colony optimization algorithm / Muhammad Hassan Firdaus Ruslan
Published 2017“…For the process module, Ant Colony Optimization (ACO) algorithm was used to find the shortest path. …”
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12
Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter
Published 2019“…Cloudsim is used as Discrete Event Simulation tool and Java as coding language to evaluate LACE algorithm. …”
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13
SENSORY EXPERT: DEVICE FOR SENSORY TEST
Published 2018“…In the study, the concept of creating heating element is by varying the current flows into the peltier module by using Force Sensitive resistive (FSR) sensor but it is find that the amount of current flows into the peltier module is very low which causes circuit switching method is implemented by using three poles four throw switch with different type of resistors in the circuit. …”
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14
Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments
Published 2011“…Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. …”
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15
Ensemble Separable Recurs ive Techniques for MLP Networks
Published 2010“…Novel hybrid or separable recursive training strategies are proposed for the training of feedforward neural networks which include switching modules and ensemble between them. This new technique for updating weights combines nonlinear recursive training algorithms for the optimization of nonlinear weights with recursive least square (RLS) type algorithms for the training of linear weights in one integrated routine. …”
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Modeling and Implementation of Omni-directional soccer robot with wide vision scope applied in Robocup-MSL
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Genetic algorithms for VLSI micro-Cell layout area optimization based on binary tree
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Working Paper -
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SG-PBFS : Shortest Gap-Priority Based Fair Scheduling technique for job scheduling in cloud environment
Published 2024“…To conduct this experiment, we employed the CloudSim simulator, which is implemented using the Java programming language.…”
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Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection
Published 2015“…The developed bearing fault diagnosis system has been evaluated and assessed for various working conditions such as rotating speeds, bearing types, fault types and fault sizes. Results show that the reinforced network classifier with GA feature selection algorithm has successfully increased the classification accuracy of training process and testing process by 13.87% and 14.21% respectively compared to the conventional neural network classifier. …”
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