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Alqahtani A, Alsubai S, Rahamathulla MP, Gumaei A, Sha M, Zhang YD, Khan MA. Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification. Diagnostics (Basel) 2023; 13:2831. [PMID: 37685369 PMCID: PMC10486793 DOI: 10.3390/diagnostics13172831] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/09/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance.
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Affiliation(s)
- Abdullah Alqahtani
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; (S.A.); (A.G.)
| | - Mohamudha Parveen Rahamathulla
- School of Podiatric Medicine, The University of Texas Rio Grande Valley, Harlingen, TX 78550, USA;
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Abdu Gumaei
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; (S.A.); (A.G.)
| | - Mohemmed Sha
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Muhammad Attique Khan
- Department of CS, HITEC University, Taxila 47080, Pakistan;
- Department of Computer Science and Mathematics, Lebanese American University, Beirut 1102-2801, Lebanon
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2
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Rafi S, Akbar MA, Yu W, Alsanad A, Gumaei A, Sarwar MU. Exploration of DevOps testing process capabilities: An ISM and fuzzy TOPSIS analysis. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108377] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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3
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Gumaei A, Al-Rakhami MS, Hassan MM, De Albuquerque VHC, Camacho D. An Effective Approach for Rumor Detection of Arabic Tweets Using eXtreme Gradient Boosting Method. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3461697] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Twitter is currently one of the most popular microblogging platforms allowing people to post short messages, news, thoughts, and so on. The Twitter user community is growing very fast. It has an average of 328 million active accounts today, making it one of the most common media for getting information during any influential or important event. Because it is freely used by the public, some credibility checking is required, especially when it comes to events of high importance. Automatic rumor detection in Arabic tweets is a challenging task due to the changes in the structural and morphological nature of the Arabic language, which makes the detection of rumors more difficult than in other languages. In this article, we proposed an effective approach for rumor detection of Arabic tweets using an eXtreme gradient boosting (XGBoost) classifier. We conducted a set of experiments on a public dataset that contained a large number of rumor and non-rumor tweets. The model uses a comprehensive set of features, including content-based, user-based, and topic-based features, allowing one to look at credibility from different angles. The experimental results demonstrated that the proposed XGBoost-based approach achieves 97.18% accuracy on 60% of the dataset as a training set, which is the highest accuracy rate compared with the other methods used in recent related work.
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Affiliation(s)
- Abdu Gumaei
- College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia
| | - Mabrook S. Al-Rakhami
- College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia
| | - Mohammad Mehedi Hassan
- College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia
| | - Victor Hugo C. De Albuquerque
- LAPISCO, Federal Institute of Education, Science and Technology of Ceará, Fortaleza, Fortaleza/CE, Brazil and ARMTEC Robotics Technology, Fortaleza/CE, Portugal, Brazil
| | - David Camacho
- Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain
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Mahmood T, Ali Z, Ullah K, Khan Q, AlSalman H, Gumaei A, Rahman SMM. Complex pythagorean fuzzy aggregation operators based on confidence levels and their applications. Math Biosci Eng 2022; 19:1078-1107. [PMID: 34903027 DOI: 10.3934/mbe.2022050] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The most important influence of this assessment is to analyze some new operational laws based on confidential levels (CLs) for complex Pythagorean fuzzy (CPF) settings. Moreover, to demonstrate the closeness between finite numbers of alternatives, the conception of confidence CPF weighted averaging (CCPFWA), confidence CPF ordered weighted averaging (CCPFOWA), confidence CPF weighted geometric (CCPFWG), and confidence CPF ordered weighted geometric (CCPFOWG) operators are invented. Several significant features of the invented works are also diagnosed. Moreover, to investigate the beneficial optimal from a large number of alternatives, a multi-attribute decision-making (MADM) analysis is analyzed based on CPF data. A lot of examples are demonstrated based on invented works to evaluate the supremacy and ability of the initiated works. For massive convenience, the sensitivity analysis and merits of the identified works are also explored with the help of comparative analysis and they're graphical shown.
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Affiliation(s)
- Tahir Mahmood
- Department of Mathematics & Statistics, International Islamic University Islamabad, Pakistan
| | - Zeeshan Ali
- Department of Mathematics & Statistics, International Islamic University Islamabad, Pakistan
| | - Kifayat Ullah
- Department of Mathematics, Riphah Institute of Computing and Applied Sciences, Riphah International University Lahore, Lahore 54000, Pakistan
| | - Qaisar Khan
- Department of Pure and Applied Mathematics, University of Haripur, Haripur, Khyber Pakhtunkhwa 22620, Pakistan
| | - Hussain AlSalman
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Abdu Gumaei
- Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen
| | - Sk Md Mizanur Rahman
- Information and Communication Engineering Technology, School of Engineering Technology and Applied Science, Centennial College, Toronto, Canada
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5
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Nasir A, Jan N, Khan SU, Gumaei A, Alothaim A. Analysis of Communication and Network Securities Using the Concepts of Complex Picture Fuzzy Relations. Comput Intell Neurosci 2021; 2021:9427492. [PMID: 34754304 PMCID: PMC8572629 DOI: 10.1155/2021/9427492] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 09/07/2021] [Revised: 10/03/2021] [Accepted: 10/07/2021] [Indexed: 11/23/2022]
Abstract
In our lives, we cannot avoid the uncertainty. Randomness, rough knowledge, and vagueness lead us to uncertainty. In mathematics, the fuzzy set (FS) theory and logics are used to model uncertain events. This article defines a new concept of complex picture fuzzy relation (CPFR) in the field of FS theory. In addition, the types of CPFRs are also discussed to make the paper more fruitful. Today's complex network architecture faces the ever-changing threats. The cyber-attackers are always trying to discover, catch, and exploit the weaknesses in the networks. So, the security measures are essential to avoid and dismantle such threats. The CPFR has a vast structure composed of levels of membership, abstinence, and nonmembership which models uncertainty better than any other structures in the theory. Moreover, a CPFR has the ability to cope with multivariable problems. Therefore, this article proposes modeling techniques based on the complex picture fuzzy information which are used to study the effectiveness and ineffectiveness of different network securities against several threats and cyber-attack practices. Moreover, the strength and preeminence of the proposed methods are verified by studying their comparison with the existing methods.
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Affiliation(s)
- Abdul Nasir
- Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan 29050, Khyber Pakhtunkhwa, Pakistan
| | - Naeem Jan
- Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan 29050, Khyber Pakhtunkhwa, Pakistan
| | - Sami Ullah Khan
- Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan 29050, Khyber Pakhtunkhwa, Pakistan
| | - Abdu Gumaei
- Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen
| | - Abdulrahman Alothaim
- STC's Artificial Intelligence Chair, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Hassan MM, Hassan MR, Huda S, Uddin MZ, Gumaei A, Alsanad A. A predictive intelligence approach to classify brain–computer interface based eye state for smart living. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107453] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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7
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Rehman SU, Talha Waheed M, Jan N, Gumaei A, Al-Rakhami M. Some Rational Coupled Fuzzy Cone Contraction Theorems in Fuzzy Cone Metric Spaces with an Application. Journal of Mathematics 2021; 2021:1-21. [DOI: 10.1155/2021/4764441] [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] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
In this paper, we establish the new concept of rational coupled fuzzy cone contraction mapping in fuzzy cone metric spaces and prove some unique rational-type coupled fixed-point theorems in the framework of fuzzy cone metric spaces by using “the triangular property of fuzzy cone metric.” To ensure the existence of our results, we present some illustrative unique coupled fixed-point examples. Furthermore, we present an application of a Lebesgue integral-type contraction mapping in fuzzy cone metric spaces and to prove a unique coupled fixed-point theorem.
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Affiliation(s)
- Saif Ur Rehman
- Department of Mathematics, Gomal University, Dera Ismail Khan 29050, Pakistan
| | | | - Naeem Jan
- Department of Mathematics, Gomal University, Dera Ismail Khan 29050, Pakistan
| | - Abdu Gumaei
- Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen
| | - Mabrook Al-Rakhami
- STC’s Artificial Intelligence Chair, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Waheed MT, Rehman SU, Jan N, Gumaei A, Al-Rakhami M. An Approach of Lebesgue Integral in Fuzzy Cone Metric Spaces via Unique Coupled Fixed Point Theorems. Journal of Function Spaces 2021; 2021:1-14. [DOI: 10.1155/2021/8766367] [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] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
In the theory of fuzzy fixed point, many authors have been proved different contractive type fixed point results with different types of applications. In this paper, we establish some new fuzzy cone contractive type unique coupled fixed point theorems (FP-theorems) in fuzzy cone metric spaces (FCM-spaces) by using “the triangular property of fuzzy cone metric” and present illustrative examples to support our main work. In addition, we present a Lebesgue integral type mapping application to get the existence result of a unique coupled FP in FCM-spaces to validate our work.
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Affiliation(s)
| | - Saif Ur Rehman
- Department of Mathematics, Gomal University, Dera Ismail Khan 29050, Pakistan
| | - Naeem Jan
- Department of Mathematics, Gomal University, Dera Ismail Khan 29050, Pakistan
| | - Abdu Gumaei
- Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen
| | - Mabrook Al-Rakhami
- STC’s Artificial Intelligence Chair, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Gumaei A, Sammouda R, Al-Rakhami M, AlSalman H, El-Zaart A. Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression. Health Informatics J 2021; 27:1460458221989402. [PMID: 33570011 DOI: 10.1177/1460458221989402] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.
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Affiliation(s)
- Abdu Gumaei
- Research Chair of Pervasive and Mobile Computing, King Saud University, Saudi Arabia.,Taiz University, Yemen
| | | | - Mabrook Al-Rakhami
- Research Chair of Pervasive and Mobile Computing, King Saud University, Saudi Arabia
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Waheed MT, Rehman SU, Jan N, Gumaei A, Al-Rakhami M. Some New Coupled Fixed-Point Findings Depending on Another Function in Fuzzy Cone Metric Spaces with Application. Mathematical Problems in Engineering 2021; 2021:1-21. [DOI: 10.1155/2021/4144966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
In this paper, we introduce the new concept of coupled fixed-point (FP) results depending on another function in fuzzy cone metric spaces (FCM-spaces) and prove some unique coupled FP theorems under the modified contractive type conditions by using “the triangular property of fuzzy cone metric.” Another function is self-mapping continuous, one-one, and subsequently convergent in FCM-spaces. In support of our results, we present illustrative examples. Moreover, as an application, we ensure the existence of a common solution of the two Volterra integral equations to uplift our work.
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Affiliation(s)
| | - Saif Ur Rehman
- Department of Mathematics, Gomal University, Dera Ismail Khan 29050, Pakistan
| | - Naeem Jan
- Department of Mathematics, Gomal University, Dera Ismail Khan 29050, Pakistan
| | - Abdu Gumaei
- Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen
| | - Mabrook Al-Rakhami
- STC’s Artificial Intelligence Chair, Department of Information Systems, King Saud University, Riyadh 11543, Saudi Arabia
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Ali Z, Mahmood T, Gumaei A. Order-α
CQ
Divergence Measures and Aggregation Operators Based on Complex q-Rung Orthopair Normal Fuzzy Sets and Their Application to Multi-Attribute Decision-Making. INT J COMPUT INT SYS 2021. [DOI: 10.2991/ijcis.d.210622.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
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Qazi A, Naseer K, Qazi J, AlSalman H, Naseem U, Yang S, Hardaker G, Gumaei A. Conventional to online education during COVID-19 pandemic: Do develop and underdeveloped nations cope alike. Child Youth Serv Rev 2020; 119:105582. [PMID: 33071406 PMCID: PMC7550864 DOI: 10.1016/j.childyouth.2020.105582] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/03/2020] [Accepted: 10/03/2020] [Indexed: 06/01/2023]
Abstract
BACKGROUND Educational institutes around the globe are facing challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Online learning is being carried out to avoid face to face contact in emergency scenarios such as coronavirus infectious disease 2019 (COVID-19) pandemic. Students need to adapt to new roles of learning through information technology to succeed in academics amid COVID-19. OBJECTIVE However, access and use of online learning resources and its link with satisfaction of students amid COVID-19 are critical to explore. Therefore, in this paper, we aimed to assess and compare the access & use of online learning of Bruneians and Pakistanis amid enforced lockdown using a five-items satisfaction scale underlying existing literature. METHOD For this, a cross-sectional study was done in the first half of June 2020 after the pandemic situation among 320 students' across Pakistan and Brunei with a pre-defined questionnaire. Data were analyzed with statistical software package for social sciences (SPSS) 2.0. RESULTS The finding showed that there is a relationship between students' satisfaction and access & use of online learning. Outcomes of the survey suggest that Bruneian are more satisfied (50%) with the use of online learning amid lockdown as compared to Pakistanis (35.9%). Living in the Urban area as compared to a rural area is also a major factor contributing to satisfaction with the access and use of online learning for both Bruneian and Pakistanis. Moreover, previous experience with the use of online learning is observed prevalent among Bruneians (P = .000), while among friends and family is using online learning (P = .000) were encouraging factors contributed to satisfaction with the use of online learning among Pakistanis amid COVID-19. Correlation results suggest that access and use factors of online learning amid COVID-19 were positively associated with satisfaction among both populations amid COVID-19 pandemic. However, Bruneian is more satisfied with internet access (r = 0.437, P < .000) and affordability of gadgets (r = 0.577, P < .000) as compare to Pakistanis (r = 0.176, P < .050) and (r = 0.152, P < .050). CONCLUSION The study suggested that it is crucial for the government and other policymakers worldwide to address access and use of online learning resources of their populace amid pandemic.
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Affiliation(s)
- Atika Qazi
- Centre for Lifelong Learning, Universiti Brunei Darussalam, Brunei Darussalam
| | - Khulla Naseer
- Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Javaria Qazi
- Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Hussain AlSalman
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Usman Naseem
- School of Computer Science, University of Sydney, Australia
| | - Shuiqing Yang
- School of Information Management and Engineering, Zhejiang University of Finance and Economics, China
| | - Glenn Hardaker
- Centre for Lifelong Learning, Universiti Brunei Darussalam, Brunei Darussalam
| | - Abdu Gumaei
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Gumaei A, Hassan MM, Huda S, Hassan MR, Camacho D, Del Ser J, Fortino G. A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106658] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Akbar MA, Shameem M, Mahmood S, Alsanad A, Gumaei A. Prioritization based Taxonomy of Cloud-based Outsource Software Development Challenges: Fuzzy AHP analysis. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106557] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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15
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Nawaz A, Huang Z, Wang S, Akbar A, AlSalman H, Gumaei A. GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism. Sensors (Basel) 2020; 20:E5143. [PMID: 32916967 PMCID: PMC7570549 DOI: 10.3390/s20185143] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 08/05/2020] [Revised: 08/27/2020] [Accepted: 09/01/2020] [Indexed: 11/30/2022]
Abstract
GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods.
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Affiliation(s)
- Asif Nawaz
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; (Z.H.); (S.W.); (A.A.)
| | - Zhiqiu Huang
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; (Z.H.); (S.W.); (A.A.)
- Key Laboratory of Safety-Critical Software, Nanjing University of Aeronautics and Astronautics, Ministry of Industry and Information Technology, Nanjing 211106, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210093, China
| | - Senzhang Wang
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; (Z.H.); (S.W.); (A.A.)
| | - Azeem Akbar
- Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; (Z.H.); (S.W.); (A.A.)
| | - Hussain AlSalman
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;
| | - Abdu Gumaei
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;
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Akbar MA, Mahmood S, Shafiq M, Alsanad A, Alsanad AAA, Gumaei A. Identification and prioritization of DevOps success factors using fuzzy-AHP approach. Soft comput 2020. [DOI: 10.1007/s00500-020-05150-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Hassan MM, Gumaei A, Alsanad A, Alrubaian M, Fortino G. A hybrid deep learning model for efficient intrusion detection in big data environment. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.10.069] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Gumaei A, Al-Rakhami M, AlSalman H, Md. Mizanur Rahman S, Alamri A. DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing. ACTA ACUST UNITED AC 2020. [DOI: 10.32604/cmc.2020.011740] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Gumaei A, Sammouda R, Al-Salman AM, Alsanad A. An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images. Sensors (Basel) 2018; 18:s18051575. [PMID: 29762519 PMCID: PMC5982524 DOI: 10.3390/s18051575] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [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: 04/18/2018] [Revised: 05/10/2018] [Accepted: 05/11/2018] [Indexed: 11/18/2022]
Abstract
Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.
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Affiliation(s)
- Abdu Gumaei
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
| | - Rachid Sammouda
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
| | - Abdul Malik Al-Salman
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
| | - Ahmed Alsanad
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
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