Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Naseem, Imran"'
Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeut
Externí odkaz:
http://arxiv.org/abs/2212.10567
Publikováno v:
Computer Networks 201, 108564, 2021
Network traffic matrix estimation is an ill-posed linear inverse problem: it requires to estimate the unobservable origin destination traffic flows, X, given the observable link traffic flows, Y, and a binary routing matrix, A, which are such that Y
Externí odkaz:
http://arxiv.org/abs/2110.07926
Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether the proclaimed superiority over conventional algorithms is well-grounded or is a myth as their performance has never been
Externí odkaz:
http://arxiv.org/abs/2110.05201
Autor:
Sadiq, Alishba, Naseem, Imran, Khan, Shujaat, Moinuddin, Muhammad, Togneri, Roberto, Bennamoun, Mohammed
In this research, a novel adaptive filtering algorithm is proposed for complex domain signal processing. The proposed algorithm is based on Wirtinger calculus and is called as q-Complex Least Mean Square (q-CLMS) algorithm. The proposed algorithm cou
Externí odkaz:
http://arxiv.org/abs/2110.04453
Autor:
Hussain, Syed Saiq, Usman, Muhammad, Siddique, Taha Hasan Masood, Naseem, Imran, Togneri, Roberto, Bennamoun, Mohammed
In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed. The proposed method is based on the q-gradient which is also known as Jackson derivative. In contrast to t
Externí odkaz:
http://arxiv.org/abs/2106.01370
A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple ke
Externí odkaz:
http://arxiv.org/abs/2007.02592
Autor:
Ibrahim, Shakeel Muhammad, Ibrahim, Muhammad Sohail, Usman, Muhammad, Naseem, Imran, Moinuddin, Muhammad
Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the re
Externí odkaz:
http://arxiv.org/abs/1911.09332
Publikováno v:
2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)
Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system identification problem. The proposed algorithm employs the concept of time-space orthogonality and separately models the dynamics and nonlinear complexities of the system. T
Externí odkaz:
http://arxiv.org/abs/1908.01321
Publikováno v:
Circuits, Systems, and Signal Processing, vol. 36, no. 4, pp. 1639-1653, 2017
In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed fr
Externí odkaz:
http://arxiv.org/abs/1905.03546
Autor:
Sadiq, Alishba, Usman, Muhammad, Khan, Shujaat, Naseem, Imran, Moinuddin, Muhammad, Al-Saggaf, Ubaid M.
Channel estimation is an essential part of modern communication systems as it enhances the overall performance of the system. In recent past a variety of adaptive learning methods have been designed to enhance the robustness and convergence speed of
Externí odkaz:
http://arxiv.org/abs/1812.02588