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221
Convergence and steady-state analysis of the normalized least mean fourth algorithm
Published 2007article -
222
Convergence and steady-state analysis of the normalized least mean fourth algorithm
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223
The buffered work-pool approach for search-tree based optimization algorithms
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conferenceObject -
224
An ant colony optimization algorithm to improve software quality prediction models
Published 2011“…We use an ant colony optimization algorithm in the adaptation process. The approach is validated on stability of classes in object-oriented software systems and can easily be used for any other software quality characteristic. …”
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Metaheuristic algorithm for testing web 2.0 applications. (c2012)
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masterThesis -
227
A stochastic iterative learning control algorithm with application to an induction motor
Published 2004“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
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228
Approximation and heuristic algorithms for computing backbones in asymmetric ad-hoc networks
Published 2018“…We consider the problem of dominating set-based virtual backbone used for routing in asymmetric wireless ad-hoc networks. …”
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229
Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms
Published 2022“…Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R<sup>2</sup>), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37–0.79. …”
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230
Unsupervised Deep Learning for Classification Of Bats Calls Using Acoustic Data
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doctoralThesis -
231
On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach
Published 2022“…The design is carried out based on the selection of the optimal model parameters using a search optimization algorithm called GridSearchCV. …”
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Spectral energy balancing system with massive MIMO based hybrid beam forming for wireless 6G communication using dual deep learning model
Published 2024“…In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. …”
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234
Customs Trade Facilitation and Compliance for Ecommerce using Blockchain and Data Mining
Published 2021“…An integrated web application is developed to mock up the end-to-end process in ecommerce. Additionally, the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed for modelling the two proposed clustering algorithms to identify transactional risks. …”
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235
Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle
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doctoralThesis -
236
A Framework for Predictive Modeling in Sustainable Projects
Published 2012Get full text
doctoralThesis -
237
Exploring Sentiment Analysis using Different Machine Learning Algorithms on Dialectal Arabic
Published 2021“…The study explores sentiment analysis using different machine learning algorithms on dialectal Arabic text dataset. In this study, we used twitter as our data source. …”
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238
Application of Machine Learning Algorithms to Enhance Money Laundering and Financial Crime Detection
Published 2011“…In order to analyze the performance of machine learning algorithms, data was provided by a bank to be used for educational purposes and shall remain undisclosed. …”
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A new family of multi-step quasi-Newton algorithms for unconstrained optimization
Published 1999“…It concentrates on deriving a variable-metric family of minimum curvature algorithms for unconstrained optimization. The derivation is based on considering a rational model, with a certain tuning parameter, where the aim is to develop a general framework that encompasses all possible two-step minimum curvature algorithms generated by appropriate parameter choices. …”
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