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Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches
Published 2021Get full text
doctoralThesis -
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Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques
Published 2024“…An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. …”
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Salp swarm algorithm: survey, analysis, and new applications
Published 2024“…The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. …”
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Optimal multiobjective design of robust power system stabilizers using genetic algorithms
Published 2003“…Optimal multiobjective design of robust multimachine power system stabilizers (PSSs) using genetic algorithms is presented in this paper. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. …”
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Ensemble Deep Random Vector Functional Link Neural Network for Regression
Published 2022“…<p dir="ltr">Inspired by the ensemble strategy of machine learning, deep random vector functional link (dRVFL), and ensemble dRVFL (edRVFL) has shown state-of-the-art results on different datasets. …”
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. The overall cost function has been developed using fuzzy logic. …”
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Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. The overall cost function has been developed using fuzzy logic. …”
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Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids
Published 2017Get full text
doctoralThesis -
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Distributed optimal coverage control in multi-agent systems: Known and unknown environments
Published 2024“…The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function for optimizing the agents’ coverage and the cost function eventually aligns with the conventional Voronoi-based cost function. …”
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Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures
Published 2017“…Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. …”
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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation
Published 2023“…This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. …”
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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
Published 2022“…This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. …”
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Minimizing using BBO and DFO methods
Published 2022“…In order to apply this algorithm to real life problems, two numerical methods need to be developed: A numerical method to solve nonlinear differential equations, and a numerical method to nd the minimum of an objective function (given only numerically) in fi nite dimensional spaces. …”
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masterThesis