Ali Hasan Ali
@uobasrah.edu.iq
Department of Mathematics
University of Basrah
RESEARCH, TEACHING, or OTHER INTERESTS
Mathematics, Applied Mathematics, Analysis, Computational Mathematics
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Firas Ghanim, Fareeha Sami Khan, Ali Hasan Ali, and Abdon Atangana
Elsevier BV
Firas Ghanim, Ali Hasan Ali, Ghassan Ezzulddin Arif, and Ali Raza
Elsevier BV
S. Bilal, Muhammad Yasir, and Ali Hasan Ali
Elsevier BV
Fareeha Sami Khan, M. Khalid, Ali Hasan Ali, and F. Ghanim
Springer Science and Business Media LLC
Abstract Optimal control theory is an extension of the calculus of variations. It is a mathematical optimization method for deriving control strategies for a dynamic system. In this paper, the system of differential equations for which we aim to utilize control theory is TikTok, which is one of the most attractive internet platforms. TikTok has garnered immense popularity, surpassing other social media platforms. However, its addictive nature has raised concerns about mental health, including depression, eating disorders, anxiety, self-obsession, and narcissistic personality disorder among its users. This paper introduces a mathematical model for TikTok, considering the usage of this app as an epidemic. The model is rigorously validated through stability analysis of both local and global equilibrium. Moreover, disease-free and non-trivial equilibrium scenarios are discussed by calculating their reproduction numbers. This study aims to raise awareness of TikTok’s potential misuse and explore control theory solutions to mitigate addiction. Additionally, statistical data is used to visualize the numerical results and analyze the impact of control parameters on the TikTok model.
Ali Hasan Ali, Muhammad Amir, Jamshaid Ul Rahman, Ali Raza, and Ghassan Ezzulddin Arif
MDPI AG
The motivation behind this study is to simplify the complex mathematical formulations and reduce the time-consuming processes involved in traditional numerical methods for solving differential equations. This study develops a computational intelligence approach with a Morlet wavelet neural network (MWNN) to solve the nonlinear Van der Pol–Mathieu–Duffing oscillator (Vd-PM-DO), including parameter excitation and dusty plasma studies. The proposed technique utilizes artificial neural networks to model equations and optimize error functions using global search with a genetic algorithm (GA) and fast local convergence with an interior-point algorithm (IPA). We develop an MWNN-based fitness function to predict the dynamic behavior of nonlinear Vd-PM-DO differential equations. Then, we apply a novel hybrid approach combining WCA and ABC to optimize this fitness function, and determine the optimal weight and biases for MWNN. Three different variants of the Vd-PM-DO model were numerically evaluated and compared with the reference solution to demonstrate the correctness of the designed technique. Moreover, statistical analyses using twenty trials were conducted to determine the reliability and accuracy of the suggested MWNN-GA-IPA by utilizing mean absolute deviation (MAD), Theil’s inequality coefficient (TIC), and mean square error (MSE).
Marouan Kouki, Saira Shukat, Ikram Ullah, Mohammad Mahtab Alam, and Ali Hasan Ali
Elsevier BV
Saleh S Redhwan, Mohammed A Almalahi, Ali Hasan Ali, Maryam Ahmed Alyami, Mona Alsulami, and Najla Alghamdi
IOP Publishing
Abstract The objective of this work is to study the intricate dynamics of nonlinear periodic coupled systems, introducing a novel approach based on the proportional fractional generalized derivative. We establish and rigorously derive sufficient conditions for the existence, uniqueness, and stability of solutions for these systems. This ensures the mathematical validity of the systems, making them reliable for simulations, predictions, and control design. This represents a significant advancement in the field of fractional-order systems. Our analysis utilizes the Banach contraction mapping principle and the Leray-Schauder alternative to ensure the well-posedness of the system. We present a detailed mathematical analysis to discuss the stability outcomes, making the results accessible and readily applicable to a wide range of problems. Furthermore, to showcase the versatility and practical implications of our approach, we present a concrete example. This demonstration highlights the novelty and impact of our research, underscoring the power of the Caputo generalized proportional fractional derivative-based periodic coupled system.
Sarem H. Hadi, Khalid A. Challab, Ali Hasan Ali, and Abdullah A. Alatawi
Elsevier BV
Aqeel Ahmad, Muhammad Ali, Ali Hasan Ali, Magda Abd El-Rahman, Evren Hincal, and Husam A. Neamah
Springer Science and Business Media LLC
Refat Ullah Jan, Ikram Ullah, Mohammad Mahtab Alam, and Ali Hasan Ali
Elsevier BV
Muayyad Mahmood Khalil, Siddiq Ur Rehman, Ali Hasan Ali, Rashid Nawaz, and Belal Batiha
Elsevier BV
Jinxing Liu, Muhammad Nadeem, Ali Hasan Ali, Fawziah M. Alotaibi, and Loredana Florentina Iambor
Elsevier BV
Mustafa Inc, Shabbir Hussain, Ali Hasan Ali, Muhammad Sajid Iqbal, Romana Ashraf, Muhammad Akhtar Tarar, and Muhammad Adnan
Springer Science and Business Media LLC
AbstractSolitary wave solutions are of great interest to bio-mathematicians and other scientists because they provide a basic description of nonlinear phenomena with many practical applications. They provide a strong foundation for the development of novel biological and medical models and therapies because of their remarkable behavior and persistence. They have the potential to improve our comprehension of intricate biological systems and help us create novel therapeutic approaches, which is something that researchers are actively investigating. In this study, solitary wave solutions of the nonlinear Murray equation will be discovered using a modified extended direct algebraic method. These solutions represent a uniform variation in blood vessel shape and diameter that can be used to stimulate blood flow in patients with cardiovascular disease. These solutions are newly in the literature, and give researchers an important tool for grasping complex biological systems. To see how the solitary wave solutions behave, graphs are displayed using Matlab.
Ali Hasan Ali, Ali Raza, Belal Batiha, Ahmed M. Abed, and Zaid Ameen Abduljabbar
Elsevier BV
Muhammad Nadeem, Omar Abu Arqub, Ali Hasan Ali, and Husam A. Neamah
Elsevier BV
Rana Jassim Mohammed, Mudhafar Jalil Jassim Ghrabat, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Iman Qays Abduljaleel, Ali Hasan Ali, Dhafer G. Honi, and Husam A. Neamah
Engineering, Technology & Applied Science Research
Successful medical treatment for patients with COVID-19 requires rapid and accurate diagnosis. Fighting the COVID-19 pandemic requires an automated system to diagnose the virus on Chest X-Ray (CXR) images. CXR images are frequently used in healthcare as they offer the potential for rapid and accurate disease diagnosis. SARS-CoV-2 targets the respiratory system, resulting in pneumonia with additional symptoms, such as dry cough, fatigue, and fever, which could be misdiagnosed as pneumonia, TB, or lung cancer. There is difficulty in differentiating the features of COVID-19 from other diseases that have similarities in CXR images. Automated Computer-Aided Diagnosis (CAD) systems incorporate machine or deep learning methods to improve efficiency and accuracy. CNNs are among the most widely used methods, as they have shown encouraging accuracy in identifying COVID-19 in CXR images. This study presents a hybrid deep learning model to provide faster diagnosis of COVID-19 infection using CXR images. The Densenet201 model was used for feature extraction and a Multi-Layer Perceptron (MLP) was used for classification. The proposed method achieved 98.82% accuracy and similar sensitivity, specificity, precision, recall, and F1 score. These results are promising when compared to other DL models trained in similar datasets.
F. Ghanim, Fareeha Sami Khan, Hiba F. Al-Janaby, and Ali Hasan Ali
Elsevier BV
Saleh S. Redhwan, Tariq A. Aljaaidi, Ali Hasan Ali, Maryam Ahmed Alyami, Mona Alsulami, and Najla Alghamdi
Elsevier BV
Warif B. Bassim, Abdulghafoor J. Salem, and Ali Hasan Ali
Elsevier BV
Zahraa Aamer, Shireen Jawad, Belal Batiha, Ali Hasan Ali, Firas Ghanim, and Alina Alb Lupaş
MDPI AG
Contracting cancer typically induces a state of terror among the individuals who are affected. Exploring how glucose excess, estrogen excess, and anxiety work together to affect the speed at which breast cancer cells multiply and the immune system’s response model is necessary to conceive of ways to stop the spread of cancer. This paper proposes a mathematical model to investigate the impact of psychological panic, glucose excess, and estrogen excess on the interaction of cancer and immunity. The proposed model is precisely described. The focus of the model’s dynamic analysis is to identify the potential equilibrium locations. According to the analysis, it is possible to establish four equilibrium positions. The stability analysis reveals that all equilibrium points consistently exhibit stability under the defined conditions. The transcritical bifurcation occurs when the glucose excess is taken as a bifurcation point. Numerical simulations are employed to validate the theoretical study, which shows that psychological panic, glucose excess, and estrogen excess could be significant contributors to the spread of tumors and weakness of immune function.
Faisal Javed, Aqeel Ahmad, Ali Hasan Ali, Evren Hincal, and Ayesha Amjad
IOP Publishing
Abstract In order to investigate the dynamics of the system, a mathematical model must be created to comprehend the dynamics of various prevalent diseases worldwide. The purpose of this investigation is to explore the early identification and treatment of conjunctivitis adenovirus by introducing vaccination methods for asymptomatic individuals. A mathematical model is constructed with the aim of strengthening the immune system. The ABC operator is then utilized to convert the model into a fractionally ordered one. The developed system is analyzed with analytical solutions by employing Sumudu transforms, including convergence analysis. The boundedness and uniqueness of the model are investigated using Banach space, which are key properties of such epidemic models. The uniqueness of the system is confirmed to ensure it has a unique solution. The stability of the newly constructed SEVIR system is investigated both qualitatively and statistically, and the system’s flip bifurcation has been verified. The developed system is examined through a Lyapunov function-based local and global stability study. The solution to the system is found using the Atangana-Toufik technique, a sophisticated method for reliable bounded solutions, employing various fractional values. Error analysis has also been conducted for the scheme. Simulations have been carried out to observe the real behavior and effects of the conjunctivitis virus, confirming that individuals with a strong immune response can recover without medication during the acute stage of infection. This helps to understand the real situation regarding the control of conjunctivitis adenovirus after early detection and treatment by introducing vaccination measures due to the strong immune response of the patients. Such investigations are useful for understanding the spread of the disease and for developing control strategies based on the justified outcomes.
Ahmed M. Abed, Hamna Shabbir, Niat Nigar, Ali Hasan Ali, and Ali Raza
Elsevier BV
Muhammad Owais Kulachi, Aqeel Ahmad, Evren Hincal, Ali Hasan Ali, Muhammad Farman, and Muhammad Taimoor
Elsevier BV
Ahmed Ali, Shireen Jawad, Ali Hasan Ali, and Matthias Winter
Elsevier BV
Mimoon Ismael, Saba Hat, Osama Alabdali, Showkat Ahmad Lone, and Ali Hasan Ali
Elsevier BV