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Joint energy-distortion aware algorithms for cooperative video streaming over LTE networks
Published 2013“…In the proposed approach, mobiles are grouped into collaborative clusters using a low-complexity clustering algorithm. In each cluster, collaboration is implemented by having a cluster head send the content to other cluster members using a short-range wireless communications technology. …”
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Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog- Cloud Computing Environment
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doctoralThesis -
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. …”
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A flexible genetic algorithm-fuzzy regression approach for forecasting: The case of bitumen consumption
Published 2019“…To show the applicability of the proposed approach, Iran’s bitumen consumption data in the period of 1991-2006 are used as a case study. …”
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Performance evaluation of load balancing algorithms for parallel single-phase iterative PDE solvers
Published 1994“…We review and evaluate the performances of six data mapping algorithms used for parallel single-phase iterative PDE solvers with irregular 2-dimensional meshes on multicomputers. …”
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Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands
Published 2020“…A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. …”
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A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai
Published 2024“…My research aims to develop a data-driven decision support framework for fleet management, focusing on leveraging advanced algorithms, including decision trees and random forests, to generate domain-specific AI models. …”
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K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation
Published 2022“…This paper introduces K Nearest Neighbor OveRsampling (KNNOR) Algorithm — a novel data augmentation technique that considers the distribution of data and takes into account the k nearest neighbors while generating artificial data points. …”
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KNNOR: An oversampling technique for imbalanced datasets
Published 2021“…<p>Predictive performance of Machine Learning (ML) models rely on the quality of data used for training the models. However, if the training data is not balanced among different classes, the performance of ML models deteriorate heavily. …”
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A Low-Cost Closed-Loop Solar Tracking System Based on the Sun Position Algorithm
Published 2019“…In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. …”
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Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment
Published 2023“…First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. …”
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Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks
Published 2024“…The proposed algorithm is evaluated on the 68-bus system and the Northeastern United States 25k-bus synthetic test system with credible contingencies using the PowerWorld simulator. …”
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Energy-Efficient VoI-Aware UAV-Assisted Data Collection in Wireless Sensor Networks
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Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms
Published 2022“…A total of eleven hundred experimental data points for nine FPs (two stratified and seven dispersed patterns) in horizontal wellbores are used to develop the MLs. …”
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Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems
Published 2022“…The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. …”