Search Results - (( knowledge selection based algorithm ) OR ( java simulation optimization algorithm ))
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1
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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Article -
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
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. …”
Review -
5
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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6
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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7
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 -
8
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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9
SURE-Autometrics algorithm for model selection in multiple equations
Published 2016“…This automatic model selection algorithm is better than non-algorithm procedure which requires knowledge and extra time. …”
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10
Fuzzy genetic algorithms for combinatorial optimisation problems
Published 2012“…Fuzzy Logic Controllers (FLCs) are considered as knowledge-based systems, incorporating human knowledge. …”
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11
Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image
Published 2014“…In this study, Data Mining was performed using C4.5 algorithm to select the appropriate attributes for object-based classification. …”
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12
Static and self-scalable filter range selection algorithms for peer-to-peer networks
Published 2011“…Two multiple selection algorithm, which are known as “static filter range selection algorithm” and “self-scalable selection algorithm” are proposed. …”
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13
Kernel and multi-class classifiers for multi-floor wlan localisation
Published 2016“…For fingerprint database optimisation, novel access point (AP) selection algorithms which are based on variant AP selection are investigated to improve computational accuracy compared to existing AP selection algorithms such as Max-Mean and InfoGain. …”
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14
Lexicon-based and immune system based learning methods in Twitter sentiment analysis
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15
Statistical fixed range multiple selection algorithm for peer-to-peer system
Published 2010“…In this research, a new multiple selection algorithm, which is known as "statistical fixed range multiple selection algorithm" is proposed. …”
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16
Static range multiple selection algorithm for peer-to-peer system
Published 2011“…In this research, a new multiple selection algorithm, which is known as "static range statistical multiple selection algorithm" is proposed. …”
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17
AGENT MEETING SCHEDULER
Published 2011“…A deep study of agent algorithm is conducted based on current available agent meeting scheduler from combination of software agent and algorithm data structure knowledge. …”
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Final Year Project -
18
The implementation of z-numbers in fuzzy clustering algorithm for wellness of chronic kidney disease patients
Published 2019“…A case study of the CKD patients with the selected indicators is considered to demonstrate the capability of z-numbers to handle the knowledge of human being and uncertain information and also will present the idea in developing a robust and reliable fuzzy clustering algorithm particularly in dealing with knowledge of human being using z-numbers.…”
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19
Knowledge base tuning using genetic algorithm for fuzzy behavior-based autonomous mobile robot
Published 2005“…This paper presents a new schema to overcome behavior-based problems based on Fuzzy Logic Controller (FLC) where the fuzzy knowledge bases are tuned automatically by Genetic Algorithm (GAs), known as Genetic Fuzzy System (GFS). …”
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20
Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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