An enhanced k-means clustering algorithm for pattern discovery in healthcare data by 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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DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data by 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 by Hamza Baali (14603380)

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Convergence behavior of the normalized least mean fourth algorithm by 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 by 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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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

“…This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …”
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Convergence and steady-state analysis of the normalized least mean fourth algorithm by 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 by 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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A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT by Dhananjay Bisen (19482454)

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Simulated tempering and mean field annealing for mapping to multicomputers. (c1996) by Aghazarian, Gabriel I.

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

On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

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Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data by Rajesh Kumar Dhanaraj (19646269)

“…<p dir="ltr">In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. …”

A Survey of Data Clustering Techniques by Sobeh, Salma

“…This survey examines seven widely recognized clustering techniques, namely k-means, G-means, DBSCAN, Agglomerative hierarchical clustering, Two-stage density (DBSCAN and k-means) algorithm, Two-levels (DBSCAN and hierarchical) clustering algorithm, and Two-stage MeanShift and K-means clustering algorithm and compares them over a real dataset - The Blockchain dataset, including prominent cryptocurrencies like Binance, Bitcoin, Doge, and Ethereum, under several metrics such as silhouette coefficient, Calinski-Harabasz, Davies-Bouldin Index, time complexity, and entropy.…”
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How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation by Joni Salminen (7434770)

A combined algorithm based on finite elements and a modified methodof characteristics for the analysis of the corona in wire-ductelectrostatic precipitators by Al-Hamouz, Z.M.

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A reduced model for phase-change problems with radiation using simplified PN approximations by Belhamadia, Youssef

“…The integro-differential equation for the full radiative transfer is replaced by a set of differential equations which are independent of the angle variable and easy to solve using conventional computational methods. To solve the coupled equations, we implement a second-order implicit scheme for the time integration and a mixed finite element method for the space discretization. …”
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Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices by Khawam, Yahya Bader

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Brain Source Localization in the Presence of Leadfield Perturbations by Momin, Rabiya Nakhat

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

“…It recorded the best values in all evaluation metrics: an average mean absolute error of 0.13, mean absolute percentage error of 0.6, root-mean-square error of 0.28 and R-squared value of 0.89.…”