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Veluchamy S, Sudharson S, Annamalai R, Bassfar Z, Aljaedi A, Jamal SS. Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model. J Imaging Inform Med 2024:10.1007/s10278-024-01077-y. [PMID: 38499705 DOI: 10.1007/s10278-024-01077-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/19/2023] [Accepted: 01/14/2024] [Indexed: 03/20/2024]
Abstract
The incidence of COVID-19, a virus that is responsible for infections in the upper respiratory tract and lungs, witnessed a daily rise in fatalities throughout the pandemic. The timely identification of COVID-19 can contribute to the formulation of strategies to control the disease and the selection of an appropriate treatment pathway. Given the necessity for broader COVID-19 diagnosis, researchers have developed more advanced, rapid, and efficient detection methods. By conducting an initial comparative analysis of various widely used convolutional neural network (CNN) models, we determine an appropriate CNN model. Subsequently, we enhance the chosen CNN model using the feature fusion strategy from multi-modal imaging datasets. Moreover, the Jaya optimization technique is employed to determine the optimal weighting for merging these dual features into a single feature vector. An SVM classifier is employed to categorize samples as either COVID-19 positive or negative. For the purpose of experimentation, a standard dataset consisting of 10,000 samples is used, divided equally between COVID-19 positive and negative classes. The experimental outcomes demonstrate that the proposed fine-tuned system, coupled with optimization techniques for multi-modal data, exhibits superior performance, achieving accuracy rates of 98.7% as compared to the existing state-of-the-art network models.
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Affiliation(s)
- S Veluchamy
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, 601103, India
| | - S Sudharson
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India.
| | - R Annamalai
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, 601103, India
| | - Zaid Bassfar
- Department of Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
| | - Amer Aljaedi
- College of Computing and Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha, 61413, Saudi Arabia
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2
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Wajeed MA, Tiwari S, Gupta R, Ahmad AJ, Agarwal S, Jamal SS, Hinga SK. A Breast Cancer Image Classification Algorithm with 2c Multiclass Support Vector Machine. J Healthc Eng 2023; 2023:3875525. [PMID: 37457494 PMCID: PMC10349674 DOI: 10.1155/2023/3875525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/14/2022] [Accepted: 03/17/2022] [Indexed: 07/18/2023]
Abstract
Breast cancer is the most frequent type of cancer in women; however, early identification has reduced the mortality rate associated with the condition. Studies have demonstrated that the earlier this sickness is detected by mammography, the lower the death rate. Breast mammography is a critical technique in the early identification of breast cancer since it can detect abnormalities in the breast months or years before a patient is aware of the presence of such abnormalities. Mammography is a type of breast scanning used in medical imaging that involves using x-rays to image the breasts. It is a method that produces high-resolution digital pictures of the breasts known as mammography. Immediately following the capture of digital images and transmission of those images to a piece of high-tech digital mammography equipment, our radiologists evaluate the photos to establish the specific position and degree of the sickness in the breast. When compared to the many classifiers typically used in the literature, the suggested Multiclass Support Vector Machine (MSVM) approach produces promising results, according to the authors. This method may pave the way for developing more advanced statistical characteristics based on most cancer prognostic models shortly. It is demonstrated in this paper that the suggested 2C algorithm with MSVM outperforms a decision tree model in terms of accuracy, which follows prior findings. According to our findings, new screening mammography technologies can increase the accuracy and accessibility of screening mammography around the world.
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Affiliation(s)
- Mohammed Abdul Wajeed
- Department of Computer Science and Engineering, Swami Vivekananda Institute of Technology, Secunderabad, Telangana, India
| | - Shivam Tiwari
- Department of Computer Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India
| | - Rajat Gupta
- Engineering and Technology, Career Point University, Kota, Rajasthan, India
| | - Aamir Junaid Ahmad
- Department of Computer Science and Engineering, Maulana Azad College of Engineering and Technology, Patna, India
| | - Seema Agarwal
- SRM institute of Science and Technology, Delhi-NCR, Campus, Ghaziabad, India
| | - Sajjad Shaukat Jamal
- Department of Mathematics College of Science, King Khalid University, Abha, Saudi Arabia
| | - Simon Karanja Hinga
- Department of Electrical and Electronic Engineering, Technical University of Mombasa, Mombasa, Kenya
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3
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Shukla PK, Aljaedi A, Pareek PK, Alharbi AR, Jamal SS. AES Based White Box Cryptography in Digital Signature Verification. Sensors (Basel) 2022; 22:9444. [PMID: 36502144 PMCID: PMC9740536 DOI: 10.3390/s22239444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/23/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
According to the standard paradigm, white box cryptographic primitives are used to block black box attacks and protect sensitive information. This is performed to safeguard the protected information and keys against black box assaults. An adversary in such a setting is aware of the method and can analyze many system inputs and outputs, but is blind to the specifics of how a critical instantiation primitive is implemented. This is the focus of white-box solutions, which are designed to withstand attacks that come from the execution environment. This is significant because an attacker may obtain unrestricted access to the program's execution in this environment. The purpose of this article is to assess the efficiency of white-box implementations in terms of security. Our contribution is twofold: first, we explore the practical implementations of white-box approaches, and second, we analyze the theoretical foundations upon which these implementations are built. First, a research proposal is crafted that details white-box applications of DES and AES encryption algorithms. To begin, this preparation is necessary. The research effort planned for this project also includes cryptanalysis of these techniques. Once the general cryptanalysis results have been examined, the white-box design approaches will be covered. We have decided to launch an investigation into creating a theoretical model for white box, since no prior formal definitions have been offered, and suggested implementations have not been accompanied by any assurance of security. This is due to the fact that no formal definition of "white box" has ever been provided. In this way lies the explanation for why this is the situation. We define WBC to encompass the security requirements of WBC specified over a white box cryptography technology and a security concept by studying formal models of obfuscation and shown security. This definition is the product of extensive investigation. This state-of-the-art theoretical model provides a setting in which to investigate the security of white-box implementations, leading to a wide range of positive and negative conclusions. As a result, this paper includes the results of a Digital Signature Algorithm (DSA) study which may be put to use in the real world with signature verification. Possible future applications of White Box Cryptography (WBC) research findings are discussed in light of these purposes and areas of investigation.
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Affiliation(s)
- Piyush Kumar Shukla
- Department of Computer Science & Engineering, University Institute of Technology (UIT), Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Technological University of Madhya Pradesh, Bhopal 462033, Madhya Pradesh, India
| | - Amer Aljaedi
- College of Computing and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Piyush Kumar Pareek
- Department of Computer Science and Engineering, Head of IPR Cell, Nitte Meenakshi Institute of Technology, Bengaluru 560064, Karnataka, India
| | - Adel R. Alharbi
- College of Computing and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia
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Zia Ur Rehman M, Ahmed F, Alsuhibany SA, Jamal SS, Zulfiqar Ali M, Ahmad J. Classification of Skin Cancer Lesions Using Explainable Deep Learning. Sensors (Basel) 2022; 22:s22186915. [PMID: 36146271 PMCID: PMC9505745 DOI: 10.3390/s22186915] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 05/14/2023]
Abstract
Skin cancer is among the most prevalent and life-threatening forms of cancer that occur worldwide. Traditional methods of skin cancer detection need an in-depth physical examination by a medical professional, which is time-consuming in some cases. Recently, computer-aided medical diagnostic systems have gained popularity due to their effectiveness and efficiency. These systems can assist dermatologists in the early detection of skin cancer, which can be lifesaving. In this paper, the pre-trained MobileNetV2 and DenseNet201 deep learning models are modified by adding additional convolution layers to effectively detect skin cancer. Specifically, for both models, the modification includes stacking three convolutional layers at the end of both the models. A thorough comparison proves that the modified models show their superiority over the original pre-trained MobileNetV2 and DenseNet201 models. The proposed method can detect both benign and malignant classes. The results indicate that the proposed Modified DenseNet201 model achieves 95.50% accuracy and state-of-the-art performance when compared with other techniques present in the literature. In addition, the sensitivity and specificity of the Modified DenseNet201 model are 93.96% and 97.03%, respectively.
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Affiliation(s)
| | - Fawad Ahmed
- Department of Cyber Security, Pakistan Navy Engineering College, National University of Sciences & Technology, Karachi 75350, Pakistan
| | - Suliman A. Alsuhibany
- Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
- Correspondence:
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia
| | | | - Jawad Ahmad
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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Shah D, Shah T, Naseer Y, Jamal SS, Hussain S. Cryptographically strong S-P boxes and their application in steganography. Journal of Information Security and Applications 2022. [DOI: 10.1016/j.jisa.2022.103174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Sathya M, Jeyaselvi M, Joshi S, Pandey E, Pareek PK, Jamal SS, Kumar V, Atiglah HK. Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes. J Healthc Eng 2022; 2022:5821938. [PMID: 35242297 PMCID: PMC8888099 DOI: 10.1155/2022/5821938] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/14/2021] [Indexed: 11/18/2022]
Abstract
In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper.
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Affiliation(s)
- M. Sathya
- Department of Information Science and Engineering, AMC Engineering College, Bengaluru, Karnataka 560083, India
| | - M. Jeyaselvi
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India
| | - Shubham Joshi
- Department of Computer Engineering, SVKM'S NMIMS MPSTME Shirpur, Maharashtra 425405, India
| | - Ekta Pandey
- Applied Science Department, Bundhelkhand Institute of Engineering and Technology, Jhansi, Uttar Pradesh, India
| | - Piyush Kumar Pareek
- Department of Computer Science & Engineering & Head of IPR Cell, Nitte Meenakshi Institute of Technology, Bengaluru, India
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Vinay Kumar
- Department of Computer Engineering and Application, GLA University, Mathura, India
| | - Henry Kwame Atiglah
- Department of Electrical and Electronics Engineering, Tamale Technical University, Tamale, Ghana
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Wang Z, Jamal SS, Yang B, Pham VT. Complex behavior of COVID-19's mathematical model. Eur Phys J Spec Top 2021; 231:885-891. [PMID: 34804378 PMCID: PMC8595961 DOI: 10.1140/epjs/s11734-021-00309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
It is almost more than a year that earth has faced a severe worldwide problem called COVID-19. In December 2019, the origin of the epidemic was found in China. After that, this contagious virus was reported almost all over the world with different variants. Besides all the healthcare system attempts, quarantine, and vaccination, it is needed to study the dynamical behavior of this disease specifically. One of the practical tools that may help scientists analyze the dynamical behavior of epidemic disease is mathematical models. Accordingly, here, a novel mathematical system is introduced. Also, the complex behavior of this model is investigated considering different dynamical analyses. The results represent that some range of parameters may lead the model to chaotic behavior. Moreover, comparing the two same bifurcation diagrams with different initial conditions reveals that the model has multi-stability.
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Affiliation(s)
- Zhen Wang
- Shaanxi International Joint Research Center for Applied Technology of Controllable Neutron Source, School of Science, Xijing University, Xi’an, 710123 People’s Republic of China
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia
| | - Baonan Yang
- Shaanxi International Joint Research Center for Applied Technology of Controllable Neutron Source, School of Science, Xijing University, Xi’an, 710123 People’s Republic of China
| | - Viet-Thanh Pham
- Nonlinear Systems and Applications, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Latif S, Driss M, Boulila W, Huma ZE, Jamal SS, Idrees Z, Ahmad J. Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions. Sensors (Basel) 2021; 21:7518. [PMID: 34833594 PMCID: PMC8625089 DOI: 10.3390/s21227518] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 10/29/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022]
Abstract
The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.
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Affiliation(s)
- Shahid Latif
- School of Information Science and Engineering, Fudan University, Shanghai 200433, China; (S.L.); (Z.I.)
| | - Maha Driss
- Security Engineering Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia;
- RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba 2010, Tunisia;
| | - Wadii Boulila
- RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba 2010, Tunisia;
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia
| | - Zil e Huma
- Department of Electrical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan;
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia;
| | - Zeba Idrees
- School of Information Science and Engineering, Fudan University, Shanghai 200433, China; (S.L.); (Z.I.)
| | - Jawad Ahmad
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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Abstract
Pythagorean fuzzy set (PFS) introduced by Yager (2013) is the extension of intuitionistic fuzzy set (IFS) introduced by Atanassov (1983). PFS is also known as IFS of type-2. Pythagorean fuzzy soft set (PFSS), introduced by Peng et al. (2015) and later studied by Guleria and Bajaj (2019) and Naeem et al. (2019), are very helpful in representing vague information that occurs in real world circumstances. In this article, we introduce the notion of Pythagorean fuzzy soft topology (PFS-topology) defined on Pythagorean fuzzy soft set (PFSS). We define PFS-basis, PFS-subspace, PFS-interior, PFS-closure and boundary of PFSS. We introduce Pythagorean fuzzy soft separation axioms, Pythagorean fuzzy soft regular and normal spaces. Furthermore, we present an application of PFSSs to multiple criteria group decision making (MCGDM) using choice value method in the real world problems which yields the optimum results for investment in the stock exchange. We also render an application of PFS-topology in medical diagnosis using TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). The applications are accompanied by Algorithms, flow charts and statistical diagrams.
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Affiliation(s)
- Muhammad Riaz
- Department of Mathematics, University of the Punjab, Lahore, Pakistan
| | - Khalid Naeem
- Department of Mathematics & Statistics, The University of Lahore, Pakistan
| | - Muhammad Aslam
- Department of Mathematics, King Khalid University, Abha, Saudi Arabia
| | - Deeba Afzal
- Department of Mathematics & Statistics, The University of Lahore, Pakistan
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Masood F, Ahmad J, Shah SA, Jamal SS, Hussain I. A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map. Entropy (Basel) 2020; 22:e22030274. [PMID: 33286048 PMCID: PMC7516729 DOI: 10.3390/e22030274] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 02/21/2020] [Accepted: 02/23/2020] [Indexed: 11/16/2022]
Abstract
Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme.
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Affiliation(s)
- Fawad Masood
- Department of Electrical Engineering, Institute of Space Technology, Islamabad Highway 1, Islamabad 44000, Pakistan;
| | - Jawad Ahmad
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK;
| | - Syed Aziz Shah
- School of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
- Correspondence:
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha 62529, Saudi Arabia;
| | - Iqtadar Hussain
- Department of Mathematics, Statistics, Physics, Qatar University, Doha 2713, Qatar;
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