DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data حسب Assaf, Ali

"…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …"
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Design of acoustic absorbing metasurfaces using a data-driven approach حسب Hamza Baali (14603380)

الموضوعات:

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT حسب Dhananjay Bisen (19482454)

الموضوعات:

Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique حسب J. Jayapradha (21323774)

Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm حسب Nasser, Youssef

"…To achieve this goal, supervised machine learning was employed to train models on graphs with labeled data points, where each graph contains a set of points and a label indicating the centroid determined by K-means. …"
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Bird’s Eye View feature selection for high-dimensional data حسب Samir Brahim Belhaouari (16855434)

"…However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. …"

An enhanced k-means clustering algorithm for pattern discovery in healthcare data حسب Haraty, Ramzi A.

"…This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. …"
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Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models حسب Asan G. A. Muthalif (16888818)

Using Machine Learning Algorithms to Forecast Solar Energy Power Output حسب Ali Jassim Lari (22597940)

"…The data forecasting horizon used was a 24-h window in steps of 30 min. …"

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting حسب Mohamed Massaoudi (16888710)

الموضوعات:

Convergence behavior of the normalized least mean fourth algorithm حسب Zerguine, A.

"…The normalized least mean fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. …"
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A FAMILY OF NORMALIZED LEAST MEAN FOURTH ALGORITHMS حسب Zerguine, Azzedine

"…In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algorithm, the convergence behavior of these algorithms is independent of the input data correlation statistics. …"
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks حسب Najam Us Sahar Riyaz (22927843)

"…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R² = 0.05 to 0.99 in a representative thiophene set. …"

Convergence and steady-state analysis of the normalized least mean fourth algorithm حسب Zerguine, Azzedine

"…The normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. …"

Convergence and steady-state analysis of the normalized least mean fourth algorithm حسب Zerguine, Azzedine

"…The normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. …"
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Efficient Approximate Conformance Checking Using Trie Data Structures حسب Awad, Ahmed

الموضوعات: "…Estimation error,Runtime,Computational modeling,Data structures,Approximation algorithms,Encoding,Computational efficiency…"
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Heuristic Algorithm for Obtaining Approximate Optimum Stratification With Mixture of Ratio and Product Estimators حسب S. E. H. Rizvi (22052105)

A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model حسب Md. Nazmul Islam Shuzan (16888827)

Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study حسب Mutasim Baba, Fuad

"…Another new metric is introduced, 1 ◦C Percentage Error criterion that calculates the percentage of the number of hours with an error over 1 ◦C during the cali bration period, to select the best solutions from the Pareto Front solutions. 0.5 ◦C Percentage Error criterion is also used for the level of accuracy the model can achieve. It was found that the calibrated model achieved these metrics with RMSE of 0.3 ◦C, and MAD of 0.8 ◦C, and 85% of data points with an error less than 0.5 ◦C for a school building case.…"
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Simulated tempering and mean field annealing for mapping to multicomputers. (c1996) حسب Aghazarian, Gabriel I.

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