Search Results - (( parameters detection method 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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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
Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…The simulation results indicate that the proposed method is suitable to detect a single outlier. As for the multiple outliers, a clustering algorithm is considered and a dendogram to visualise the clustering algorithm is used. …”
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5
Application of weighted average on modal parameters for damage detection algorithms: Case study on steel beam
Published 2011“…This study verifies the use of the proposed weighting method by Fayyadh and Abdul Razak (2011a) for damage detection algorithms applied on cracked steel beam. …”
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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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Weighting method for modal parameter based damage detection algorithms
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…This creates the jamming detection and classification parameters. The second stage is detecting jammers by integrating both lower layers by developing Integrated Combined Layer Algorithm (ICLA). …”
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10
Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm
Published 2014“…This study focuses on using GSA method, a new computational intelligence algorithm. …”
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11
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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12
Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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13
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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14
Improved intrusion detection algorithm based on TLBO and GA algorithms
Published 2021“…The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. …”
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Outlier detection in circular regression model using minimum spanning tree method
Published 2019“…The existence of outliers in a circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. …”
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16
Comparison of UAV flying height parameter for crack detection applications / Wan Nurdayini Batrisyia Wan Ghazali
Published 2024“…The key concern of this thesis is to determine the influence of flying height parameters on crack detection using UAVs. Looking at the current scenario of development and the age of the structures in urban areas, there is a high significance of efficient and accurate building inspection methods. …”
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Fuzzy modelling using butterfly optimization algorithm for phishing detection
Published 2020“…To generate the fuzzy parameter automatically, an optimization method is required and Butterfly Optimization Algorithm (BOA) is one of the good methods to be applied. …”
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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 -
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023“…It provides an increased convergence and globally optimized solutions. The algorithm has been tested using actual customer consumption data from SESB. 10 fold cross validation method is used to confirm the consistency of the detection accuracy. …”
Conference Paper -
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Novel chewing cycle approach for peak detection algorithm of chew count estimation
Published 2025“…The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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