Search Results - (( learning model getting 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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Thesis -
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
Comparison of malware detection model using supervised machine learning algorithms / Syamir Mohd Shahirudin
Published 2022“…The Windows malware dataset has been trained and tested by these three machine learning algorithms to get the percentage detection accuracy. …”
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Student Project -
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
Classification model for water quality using machine learning techniques
Published 2015“…This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. …”
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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 -
10
Hybrid bat algorithm-artificial neural network for modeling operating photovoltaic module temperature: article / Noor Rasyidah Hussin
Published 2014“…Bat Algorithm (BA) was hybrid based Multi-Layer Feedforward Neural Network (MLFNN) for modeling the temperature operating of photovoltaic module. …”
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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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12
Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor / Muhammad Nasrul Hakim Adenan
Published 2013“…The ANN model performance can be optimized by altering certain parameters in the learning algorithm. …”
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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“…The ANN model performance can be optimized by altering certain parameters in the learning algorithm. …”
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EEG-based emotion recognition using machine learning algorithms
Published 2024“…Training will be conducted so the model can learn and capture patterns of data. Moreover, fine-tuning of model will be applied to get the optimal performance in machine learning model. …”
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Final Year Project / Dissertation / Thesis -
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Hybrid bat algorithm hybrid-artificial neural network for modeling operating photovoltaic module temperature / Noor Rasyidah Hussin
Published 2014“…Bat Algorithm (BA) was hybrid based Multi-Layer Feedforward Neural Network (MLFNN) for modeling the temperature operating of photovoltaic module. …”
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16
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…In other words, in order to get a good result, the BPNN learning algorithm needs to be executed several times with different topology structures and parameter values in order to determine the best set of parameter values used in the BPNN. …”
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17
Waste management using machine learning and deep learning algorithms
Published 2020“…For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. …”
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Training functional link neural network with ant lion optimizer
Published 2020“…Since FLNN uses Backpropagation algorithm as the standard learning algorithm, the method however prone to get trapped in local minima which affect its performance. …”
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Conference or Workshop Item -
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Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
Published 2019“…One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. …”
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20
Applications of machine learning to friction stir welding process optimization
Published 2020“…A computational method used in machine learning to learn or get directly information from data without relying on a prearranged model equation. …”
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