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101
Single channel speech denoising by DDPG reinforcement learning agent
Published 2025“…<p dir="ltr">Speech denoising (SD) covers the algorithms that suppress the background noise from the contaminated speech and improve its clarity. …”
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Iterative Methods for the Solution of a Steady State Biofilter Model
Published 2017Get full text
doctoralThesis -
105
A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT
Published 2023“…This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. …”
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106
Minimizing using BBO and DFO methods
Published 2022“…In [1], Nour and Zeidan proposed a numerical algorithm to solve optimal control problems involving sweeping processes. …”
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masterThesis -
107
Boosting the visibility of services in microservice architecture
Published 2023“…Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. …”
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Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence
Published 2020“…Each cluster in the swarm has its own cluster best position which is the best position within a cluster and the global best position is located by clusters communication. …”
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Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV
Published 2013Get full text
doctoralThesis -
111
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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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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Entire hypergeometric approximants for the ground state energy perturbation series of the quartic, sextic and octic anharmonic oscillators
Published 2023“…Besides, in literature, we barely can find a resummation algorithm for which the convergence is faster for stronglydivergent ( Gevrey − k , k > 1) series than a divergent one ( Gevrey − 1 ). …”
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Optimal shunt compensators at nonsinusoidal busbars
Published 1995Get full text
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Power system output feedback stabilizer design via geneticalgorithms
Published 1997“…Various objective functions are presented allowing the selection of the output feedback gains to place the closed loop eigenvalues in the left hand side of a vertical line in the complex s plane, within an open sector in the complex s plane, or within a vertical strip in the complex s plane. …”
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Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network
Published 2024“…At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10<sup>−7</sup> and 6.40 × 10<sup>−7</sup>, respectively, for X1 and X2 loading conditions at 45° angle). …”
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The Use of Enumerative Techniques in Topological Optimization of Computer Networks Subject to Fault Tolerance and Reliability
Published 2003“…Experimental results obtained from a set of randomly generated networks using the proposed algorithms are presented and compared to those obtained using the existing techniques [1], [2]. …”
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A lightweight adaptive compression scheme for energy-efficient mobile-to-mobile file sharing applications
Published 2011“…Moreover, if the transmission rate over the wireless medium is high then the need for compression might be reduced or even eliminated as data is transferred efficiently within a limited amount of time. We evaluate and optimize the performance of the proposed adaptive compression scheme using experimental measurements in different scenarios and as a function of various parameters. …”
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