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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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

“…Even though a lot of research and work was done on this topic, it is still considered in its infantry because of the challenges that is still popping up such as bandwidth limitation, transferring data to single site and many others. In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
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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. …”
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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. …”

A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network by Xiaofang Pan (1895950)

CNN and HEVC Video Coding Features for Static Video Summarization by Issa, Obada

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Novel algorithms for accurate DNA base-calling by Mohammed, Omniyah Gul

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Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory by Nadia Rasheed (22997257)

Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

One-Machine Scheduling To Minimize Mean Tardiness With Minimum Number Tardy by Duffuaa, S. O.

“…In this paper the one-machine scheduling problem with the objective of minimizing the mean tardiness subject to maintaining a prescribed number of tardy jobs is analysed An algorithm for solving this problem is presented. …”
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One-Machine Scheduling To Minimize Mean Tardiness With Minimum Number Tardy by Duffuaa, S. O.

“…In this paper the one-machine scheduling problem with the objective of minimizing the mean tardiness subject to maintaining a prescribed number of tardy jobs is analysed An algorithm for solving this problem is presented. …”
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Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid (22047578)

Determination Of The Optimal Process Means And Production Cycles For Multistage Production Systems Subject To Process Deterioration by Al-Sultan, K.S.

“…The model rinds optimal initial settings of the process means and optimal cycle lengths. A Hook and Jeeves search algorithm is used to optimize the model, and a numerical example is provided. …”
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Competitive learning/reflected residual vector quantization for coding angiogram images by Mourn, W.A.H.

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Automatic Video Summarization Using HEVC and CNN Features by Issa, Obada

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Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”

Squirrel Search Algorithm for Portfolio Optimization by Dhaini, Mahdi

“…The inclusion of real-life constraints to the problem has led to the introduction of the extended Mean-Variance model. However, the successes of nature-inspired algorithms in hard computational optimization problems have encouraged researchers to design and apply these algorithms for a variety of optimization problems. …”
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A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM by Selim, S.Z.

“…In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions which are usually what the K-means algorithm obtains. …”
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