Zobrazeno 1 - 10
of 195
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
Autor:
Anser, Muhammad Khalid, Ali, Sajid, Mansoor, Abdul, Rahman, Shafiq ur, Lodhi, Muhammad Saeed, Naseem, Imran, Zaman, Khalid
Publikováno v:
In Sustainable Futures June 2024 7
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
Publikováno v:
In Borsa Istanbul Review March 2024 24(2):341-351
Autor:
Xu, Yuanyuan, Nassani, Abdelmohsen A., Qazi Abro, Muhammad Moinuddin, Naseem, Imran, Zaman, Khalid
Publikováno v:
In Heliyon 15 February 2024 10(3)
Publikováno v:
In Resources Policy February 2024 89
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