Yazar "Salman, Mohammad Shukri" seçeneğine göre listele

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    Back-propagation algorithm with variable adaptive momentum
    (ELSEVIER SCIENCE BV, 2016) Hameed, Alaa Ali; Karlik, Bekir; Salman, Mohammad Shukri
    In this paper, we propose a novel machine learning classifier by deriving a new adaptive momentum back-propagation (BP) artificial neural networks algorithm. The proposed algorithm is a modified version of the BP algorithm to improve its convergence behavior in both sides, accelerate the convergence process for accessing the optimum steady-state and minimizing the error misadjustment to improve the recognized patterns superiorly. This algorithm is controlled by the learning rate parameter which is dependent on the eigenvalues of the autocorrelation matrix of the input. It provides low error performance for the weights update. To discuss the performance measures of this proposed algorithm and the other supervised learning algorithms such as k-nearest neighbours (k-NN), Naive Bayes (NB), linear discriminant analysis (LDA), support vector machines (SVM), BP, and BP with adaptive momentum (PBPAM) have been compared in term of speed of convergence, Sum of Squared Error (SSE), and accuracy by implementing benchmark problem - XOR and seven datasets from UCI repository. (C) 2016 Elsevier B.V. All rights reserved.
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    The logic transformations for reducing the complexity of the discernibility function-based attribute reduction problem
    (SPRINGER LONDON LTD, 2016) Hacibeyoglu, Mehmet; Salman, Mohammad Shukri; Selek, Murat; Kahramanli, Sirzat
    The basic solution for locating an optimal reduct is to generate all possible reducts and select the one that best meets the given criterion. Since this problem is NP-hard, most attribute reduction algorithms use heuristics to find a single reduct with the risk to overlook for the best ones. There is a discernibility function (DF)-based approach that generates all reducts but may fail due to memory overflows even for datasets with dimensionality much below the medium. In this study, we show that the main shortcoming of this approach is its excessively high space complexity. To overcome this, we first represent a DF of attributes by a bit-matrix (BM). Second, we partition the BM into no more than sub-BMs (SBMs). Third, we convert each SBM into a subset of reducts by preventing the generation of redundant products, and finally, we unite the subsets into a complete set of reducts. Among the SBMs of a BM, the most complex one is the first SBM with a space complexity not greater than the square root of that of the original BM. The proposed algorithm converts such a SBM with attributes into the subset of reducts with the worst case space complexity of .
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    A New 2-D Convex Combination of Recursive Inverse Algorithms
    (IEEE, 2014) Hameed, Alaa Ali; Salman, Mohammad Shukri; Karlik, Bekir
    De-noising magnetic resonance images (MRI) has recently become an interesting topic in medical diagnosis applications. Many algorithms have been proposed for this purpose. However, these algorithms usually suffer from poor performance or time consumption. In this paper, we propose a 2-D version of the recently proposed convex recursive inverse (RI) algorithm that provides fast convergence at the beginning to save time and then provides high performance in terms of noise removal. To test the algorithm, a de-noising experiment has been conducted on MR image that is assumed to be corrupted by an additive white Gaussian noise (AWGN). Simulations show that the proposed algorithm successfully recovers the image.
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    A New Sparse Convex Combination of ZA-LLMS and RZA-LLMS Algorithms
    (IEEE, 2015) Salman, Mohammad Shukri; Hameed, Alaa Ali; Turan, Cemil; Karlik, Bekir
    In the last decade, several algorithms have been proposed for performance improvement of adaptive filters in sparse system identification. In this paper, we propose a new convex combination of two different algorithms as zero-attracting leaky least-mean-square (ZA-LLMS) and reweighted zero-attracting leaky-least-mean square (RZA-LLMS) algorithms in sparse system identification setting. The performances of the aforementioned algorithms has been tested and compared to the result of the new combination. Simulations show that the proposed algorithm has a good ability to track the MSD curves of the other algorithms in additive white Gaussian noise (AWGN) and additive correlated Gaussian noise (ACGN) environments.