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Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid
Published 2006“…In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. …”
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2
Complex-valued nonlinear adaptive filters for noncircular signals
Published 2017“…The augmented CNGD has shown low Means Square Error (MSE) capabilities and have optimal performance than the conventional algorithm. …”
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Thesis -
3
Improving Network Consistency and Data Availability Using Fuzzy C Mean Clustering Algorithm in Wireless Sensor Networks
Published 2024thesis::doctoral thesis -
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A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction
Published 2025“…The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed
Published 2012“…The application of FES optimized by GA on regionalization creates opportunities for further researches which utilizes different types of optimization like Ant Colony Optimization (ACO), ANN’s, Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA).…”
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6
Integrating genetic algorithms and fuzzy c-means for anomaly detection
Published 2005“…Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. …”
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Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia
Published 2024“…Post-optimization ANN model was trained using eleven different leaning algorithms. …”
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Development of multi-objective optimization methods for integrated scheduling of handling equipment (AGVs, QCs, SP-AS/RS) in automated container terminals
Published 2012“…Therefore, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm, were developed to optimize the integrated scheduling of handling equipment. …”
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10
Multi objective bee colony optimization framework for grid job scheduling
Published 2013“…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.Job scheduling problem is one of the key issues because of high heterogeneous and dynamic nature of resources and applications in the grid computing environment.Bee colony approach has been used to solve this problem because it can be easily adapted to the grid scheduling environment.The bee algorithms have shown encouraging results in terms of time and co st.In this paper a framework for multi objective bee colony optimization is proposed to schedule batch jobs to available resources where the number of jobs is greater than the number of resources.Pareto analysis and k-means analysis are integrated in the bee colony optimization algorithm to facilitate the scheduling of jobs to resources.…”
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An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title
Published 2019“…The enhanced method combines three transformation methods for analyzing the iris image and extracting its local features. It uses a weighted K-means clustering algorithm based on the improved FA to optimize the initial clustering centers of K-means algorithm, known as Weighted K-means clustering-Improved Firefly Algorithm (WKIFA). …”
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12
Globalization of Barzilai and Borwein Method for Unconstrained Optimization
Published 2009“…Then we discuss the (BB) method that is important in the area of optimization. A review of the minimization methods currently available that can be used to solve unconstrained optimization is also given. …”
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13
CROWDSOURCING-BASED MOBILE APPLICATION FOR SUPERMARKET STOCK AVAILABILITY CHECKER
Published 2020“…This project is implementing spatial crowdsourcing technology in hope to optimize the frequent updates of the stock availability by means of contributions from users. …”
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Recent research in cooperative path planning algorithms for multi-agent using mixed- integer linear programming
Published 2016“…For a group of agents, cooperative path planning is crucial to ensure that a given mission is accomplished in the shortest time possible with optimal solution. Optimal means that the resulting path has minimal length hence the total consumed energy by the agents is the least. …”
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Enhancing Solutions Of Capacity Vehicle Routing Problem Based On An Improvement Ant Colony System Algorithm
Published 2019“…The paper aims to discover an optimal route for VRP by using Improvement Ant Colony System Algorithm (IACS). …”
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A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone
Published 2019“…However, there is a need for optimizing content quality of experience (QoE) in limited available energy. …”
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17
A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone
Published 2019“…However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. …”
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A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis meth...
Published 2020“…Given the rapid development of dehazing image algorithms, selecting the optimal algorithm based on multiple criteria is crucial in determining the efficiency of an algorithm. …”
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Taguchi's T-method with nearest integer-based binary bat algorithm for prediction
Published 2023“…In this paper, a swarm-based binary bat optimization algorithm with a nearest integer discretization approach is integrated with the Taguchi�s T-method. …”
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Multiobjective deep reinforcement learning for recommendation systems
Published 2022“…These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. …”
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