Dr. Mohammed K. A. Kaabar
Washington State University
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Academic Career Length
9Years (2018-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications23 October 2021 in Mathematical Problems in Engineering
Journal Article
MAM. Abu-shady
M. Abu-shadyMohammed K. A. KaabarA generalized fractional derivative (GFD) definition is proposed in this work. For a differentiable function expanded by a Taylor series, we show that . GFD is applied for some functions to investigate that the GFD coincides with the results from Caputo and Riemann–Liouville fractional derivatives. The solutions of the Riccati fractional differential equation are obtained via the GFD. A comparison with the Bernstein polynomial method , enhanced homotopy perturbation method , and conformable derivative is also discussed. Our results show that the proposed definition gives a much better accuracy than the well-known definition of the conformable derivative. Therefore, GFD has advantages in comparison with other related definitions. This work provides a new path for a simple tool for obtaining analytical solutions of many problems in the context of fractional calculus.
22 January 2021 in Advances in Difference Equations
Journal Article
MMM. M. Matar
M. M. Matar
Mohamed Abbas
Jehad AlzabutMohammed K. A. Kaabar
Sina EtemadSRShahram Rezapour
Shahram RezapourA newly proposed p-Laplacian nonperiodic boundary value problem is studied in this research paper in the form of generalized Caputo fractional derivatives. The existence and uniqueness of solutions are fully investigated for this problem using some fixed point theorems such as Banach and Schauder. This work is supported with an example to apply all obtained new results and validate their applicability.
31 October 2021 in Computational and Mathematical Methods in Medicine
Journal Article
ATAhsan Bin Tufail
Ahsan Bin Tufail
YMYong-Kui Ma
Yong-Kui MaMohammed K. A. KaabarFMFrancisco Martínez
Francisco Martínez
AJA. R. Junejo
A. R. Junejo
RKRahim Khan
Rahim Khan +1
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.
Journal Article
SRShahram Rezapour
Shahram RezapourAIAtika Imran
Atika ImranAHAzhar Hussain
Azhar HussainFGFrancisco Martínez González
Francisco Martínez GonzálezSina EtemadMohammed K. A. KaabarA nonlinear quantum boundary value problem (q-FBVP) formulated in the sense of quantum Caputo derivative, with fractional q-integro-difference conditions along with its fractional quantum-difference inclusion q-BVP are investigated in this research. To prove the solutions’ existence for these quantum systems, we rely on the notions such as the condensing functions and approximate endpoint criterion (AEPC). Two numerical examples are provided to apply and validate our main results in this research work.
1 January 2021 in Complexity
Journal Article
NDNadjette Debbouche
Nadjette Debbouche
AOAdel Ouannas
Adel Ouannas
IBIqbal Batiha
Iqbal BatihaGGGiuseppe Grassi
Giuseppe Grassi
Mohammed K. A. Kaabar
Awad Aljuaid +2
This study intends to examine different dynamics of the chaotic incommensurate fractional‐order Hopfield neural network model. The stability of the proposed incommensurate‐order model is analyzed numerically by continuously varying the values of the fractional‐order derivative and the values of the system parameters. It turned out that the formulated system using the Caputo differential operator exhibits many rich complex dynamics, including symmetry, bistability, and coexisting chaotic attractors. On the other hand, it has been detected that by adapting the corresponding controlled constants, such systems possess the so‐called offset boosting of three variables. Besides, the resultant periodic and chaotic attractors can be scattered in several forms, including 1D line, 2D lattice, and 3D grid, and even in an arbitrary location of the phase space. Several numerical simulations are implemented, and the obtained findings are illustrated through constructing bifurcation diagrams, computing Lyapunov exponents, calculating Lyapunov dimensions, and sketching the phase portraits in 2D and 3D projections.
11 December 2021 in Mathematical Methods in the Applied Sciences
Journal Article
SASindhu J. Achar
Sindhu J. Achar
CBChandrali Baishya
Chandrali Baishya
Mohammed K. A. KaabarAbout Me
Mohammed K. A. Kaabar received all his undergraduate and graduate degrees in Mathematics from Washington State University (WSU), Pullman, WA, USA. Kaabar has a global diverse experience in teaching and has worked as adjunct math professor, math lab instructor, and lecturer at various US institutions such as Moreno Valley College, Washington State University, and Colorado Early Colleges. He authored two math textbooks, and he is an invited referee for more than 200 Science, Technology, Engineering, and Mathematics (STEM) international conferences all over the world. He served as an editor for the American Mathematical Society (AMS) Graduate Student Blog, and he is also a full editor for an educational program (Mathematics Section) at California State University, Long Beach, CA, USA. He is an approved member of the Science and Democracy Network (SDN), which is a part of the Science, Technology, and Society program at the Harvard Kennedy School. Kaabar is also currently serving as an editor for 16 international scientific journals in applied mathematics and engineering. He is also a member of the expert editorial advisory board for the Big Data and Advanced Wireless Technologies Springer Book Series. His research interests are fractional calculus, applied mathematics, mathematical physics, and math education.