Search Results - (( learning models generating algorithm ) OR ( java simulation optimization algorithm ))
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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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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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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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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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Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
Published 2024Subjects:Article -
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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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Automated bilateral negotiation with incomplete information in the e-marketplace.
Published 2011“…The reason is that, SRT algorithm is sensitive to the accuracy of the learned preferences while MGT algorithm can generate Pareto-optimal offers even with an approximation of the learned preferences.…”
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New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…So, the application of the theory as part of the learning models was proposed in this thesis. Two different models for learning in data sets were proposed based on two different reduction algorithms. …”
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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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Next generation insect taxonomic classification by comparing different deep learning algorithms
Published 2022“…The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. …”
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Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation
Published 2020“…Subsequently, the Small Johnson Data Transformation is employed to transformed raw samples to a Normal Distribution. Next, samples are generated from Normal Distribution. To test the effectiveness of the proposed algorithm, the real and generated samples is added to training phase to build a prediction model using M5 Model Tree. …”
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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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Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying
Published 2019“…In the on-line learning model, the recursive kernel is also successfully used to generate a fixed reservoir with optimized information by dissipating and overwriting the information from the coming data in the learning part of the prediction model, which is helpful for improvement of forecasting performance. …”
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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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Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia
Published 2025“…Therefore, one of the aims of this research was to investigate the use of machine learning algorithms and its benefits. The machine learning algorithms investigated are specifically Gaussian process regression (GPR), ensemble of trees and neural networks. …”
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Adaptive genetic algorithm to improve negotiation process by agents e-commerce
Published 2011“…The proposed adaptive negotiation model is named Aspirated Genetic Algorithm (AGA) negotiation model which is a hybrid negotiation system composed of different negotiation strategies, Aspiration concept,genetic algorithm and Bayesian learning. …”
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Evaluation of machine learning algorithms in predicting CO 2 internal corrosion in oil and gas pipelines
Published 2019“…As there is limited data available for corrosion studies, a synthetic data was generated. The synthetic dataset was generated via random Gaussian function and incorporated de Waard-Milliams model, an empirical determination model for CO 2 internal corrosion. …”
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Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines
Published 2019“…As there is limited data available for corrosion studies, a synthetic data was generated. The synthetic dataset was generated via random Gaussian function and incorporated de Waard-Milliams model, an empirical determination model for CO2 internal corrosion. …”
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