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In this study, a new computing model is developed using the strength of feedforward neural networks with the Levenberg–Marquardt method- (NN-BLMM-) based backpropagation technique. It is used to find a solution for the nonlinear system obtained from the governing equations of Falkner–Skan with heat transfer (FSE-HT). Moreover, the partial differential equations (PDEs) for the unsteady squeezing flow of heat and mass transfer of the viscous fluid are converted into ordinary differential equations (ODEs) with the help of similarity transformation. A dataset for the proposed NN-BLMM-based model is generated in different scenarios by a variation of various embedding parameters, Deborah number ( β ) and Prandtl number (Pr). The training (TR), testing (TS), and validation (VD) of the NN-BLMM model are evaluated in the generated scenarios to compare the obtained results with the reference results. For the fluidic system convergence analysis, a number of metrics such as the mean square error (MSE), error histogram (EH), and regression (RG) plots are utilized for measuring the effectiveness and performance of the NN-BLMM infrastructure model. The experiments showed that comparisons between the results of the proposed model and the reference results match in terms of convergence up to E-05 to E-10. This proves the validity of the NN-BLMM model. Furthermore, the results demonstrated that there is an increase in the velocity profile and a decrease in the thickness of the thermal boundary layer by increasing the Deborah number. Also, the thickness of the thermal boundary layer is decreased by increasing the Prandtl number.
Imran Khan; Hakeem Ullah; Hussain AlSalman; Mehreen Fiza; Saeed Islam; Asif Zahoor Raja; Mohammad Shoaib; Abdu H. Gumaei. Falkner–Skan Equation with Heat Transfer: A New Stochastic Numerical Approach. Mathematical Problems in Engineering 2021, 2021, 1 -17.
AMA StyleImran Khan, Hakeem Ullah, Hussain AlSalman, Mehreen Fiza, Saeed Islam, Asif Zahoor Raja, Mohammad Shoaib, Abdu H. Gumaei. Falkner–Skan Equation with Heat Transfer: A New Stochastic Numerical Approach. Mathematical Problems in Engineering. 2021; 2021 ():1-17.
Chicago/Turabian StyleImran Khan; Hakeem Ullah; Hussain AlSalman; Mehreen Fiza; Saeed Islam; Asif Zahoor Raja; Mohammad Shoaib; Abdu H. Gumaei. 2021. "Falkner–Skan Equation with Heat Transfer: A New Stochastic Numerical Approach." Mathematical Problems in Engineering 2021, no. : 1-17.
Technology is rapidly advancing and every aspect of life is being digitalized. Since technology has made life much better and easier, so organizations, such as businesses, industries, companies and educational institutes, etc., are using it. Despite the many benefits of technology, several risks and serious threats, called cyberattacks, are associated with it. The method of neutralizing these cyberattacks is known as cybersecurity. Sometimes, there are uncertainties in recognizing a cyberattack and nullifying its effects using righteous cybersecurity. For that reason, this article introduces interval-valued complex intuitionistic fuzzy relations (IVCIFRs). For the first time in the theory of fuzzy sets, we investigated the relationships among different types of cybersecurity and the sources of cyberattacks. Moreover, the Hasse diagram for the interval-valued complex intuitionistic partial order set and relation is defined. The concepts of the Hasse diagram are used to inspect different cybersecurity techniques and practices. Then, using the properties of Hasse diagrams, the most beneficial technique is identified. Furthermore, the best possible selection of types of cybersecurity is made after putting some restrictions on the selection. Lastly, the advantages of the proposed methods are illuminated through comparison tests.
Abdul Nasir; Naeem Jan; Abdu Gumaei; Sami Ullah Khan; Fahad R. Albogamy. Cybersecurity against the Loopholes in Industrial Control Systems Using Interval-Valued Complex Intuitionistic Fuzzy Relations. Applied Sciences 2021, 11, 7668 .
AMA StyleAbdul Nasir, Naeem Jan, Abdu Gumaei, Sami Ullah Khan, Fahad R. Albogamy. Cybersecurity against the Loopholes in Industrial Control Systems Using Interval-Valued Complex Intuitionistic Fuzzy Relations. Applied Sciences. 2021; 11 (16):7668.
Chicago/Turabian StyleAbdul Nasir; Naeem Jan; Abdu Gumaei; Sami Ullah Khan; Fahad R. Albogamy. 2021. "Cybersecurity against the Loopholes in Industrial Control Systems Using Interval-Valued Complex Intuitionistic Fuzzy Relations." Applied Sciences 11, no. 16: 7668.
This paper aims to implement an analytical method, known as the Laplace homotopy perturbation transform technique, for the result of fractional-order Whitham–Broer–Kaup equations. The technique is a mixture of the Laplace transformation and homotopy perturbation technique. Fractional derivatives with Mittag-Leffler and exponential laws in sense of Caputo are considered. Moreover, this paper aims to show the Whitham–Broer–Kaup equations with both derivatives to see their difference in a real-world problem. The efficiency of both operators is confirmed by the outcome of the actual results of the Whitham–Broer–Kaup equations. Some problems have been presented to compare the solutions achieved with both fractional-order derivatives.
Kamsing Nonlaopon; Muhammad Naeem; Ahmed M. Zidan; Rasool Shah; Ahmed Alsanad; Abdu Gumaei. Numerical Investigation of the Time-Fractional Whitham–Broer–Kaup Equation Involving without Singular Kernel Operators. Complexity 2021, 2021, 1 -21.
AMA StyleKamsing Nonlaopon, Muhammad Naeem, Ahmed M. Zidan, Rasool Shah, Ahmed Alsanad, Abdu Gumaei. Numerical Investigation of the Time-Fractional Whitham–Broer–Kaup Equation Involving without Singular Kernel Operators. Complexity. 2021; 2021 ():1-21.
Chicago/Turabian StyleKamsing Nonlaopon; Muhammad Naeem; Ahmed M. Zidan; Rasool Shah; Ahmed Alsanad; Abdu Gumaei. 2021. "Numerical Investigation of the Time-Fractional Whitham–Broer–Kaup Equation Involving without Singular Kernel Operators." Complexity 2021, no. : 1-21.
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.
Muhammad Talha Waheed; Saif Ur Rehman; Naeem Jan; Abdu Gumaei; Mabrook Al-Rakhami. 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.
AMA StyleMuhammad Talha Waheed, Saif Ur Rehman, Naeem Jan, Abdu Gumaei, Mabrook Al-Rakhami. 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.
Chicago/Turabian StyleMuhammad Talha Waheed; Saif Ur Rehman; Naeem Jan; Abdu Gumaei; Mabrook Al-Rakhami. 2021. "Some New Coupled Fixed-Point Findings Depending on Another Function in Fuzzy Cone Metric Spaces with Application." Mathematical Problems in Engineering 2021, no. : 1-21.
Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.
Rachid Sammouda; Abdu Gumaei; Ali El-Zaart. Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions. Mathematical Problems in Engineering 2021, 2021, 1 -17.
AMA StyleRachid Sammouda, Abdu Gumaei, Ali El-Zaart. Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions. Mathematical Problems in Engineering. 2021; 2021 ():1-17.
Chicago/Turabian StyleRachid Sammouda; Abdu Gumaei; Ali El-Zaart. 2021. "Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions." Mathematical Problems in Engineering 2021, no. : 1-17.
Fall prevalence is high among elderly people, which is challenging due to the severe consequences of falling. This is why rapid assistance is a critical task. Ambient assisted living (AAL) uses recent technologies such as 5G networks and the internet of medical things (IoMT) to address this research area. Edge computing can reduce the cost of cloud communication, including high latency and bandwidth use, by moving conventional healthcare services and applications closer to end-users. Artificial intelligence (AI) techniques such as deep learning (DL) have been used recently for automatic fall detection, as well as supporting healthcare services. However, DL requires a vast amount of data and substantial processing power to improve its performance for the IoMT linked to the traditional edge computing environment. This research proposes an effective fall detection framework based on DL algorithms and mobile edge computing (MEC) within 5G wireless networks, the aim being to empower IoMT-based healthcare applications. We also propose the use of a deep gated recurrent unit (DGRU) neural network to improve the accuracy of existing DL-based fall detection methods. DGRU has the advantage of dealing with time-series IoMT data, and it can reduce the number of parameters and avoid the vanishing gradient problem. The experimental results on two public datasets show that the DGRU model of the proposed framework achieves higher accuracy rates compared to the current related works on the same datasets.
Mabrook S. Al-Rakhami; Abdu Gumaei; Meteb Altaf; Mohammad Mehedi Hassan; Bader Fahad Alkhamees; Khan Muhammad; Giancarlo Fortino. FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks. IEEE Access 2021, 9, 94299 -94308.
AMA StyleMabrook S. Al-Rakhami, Abdu Gumaei, Meteb Altaf, Mohammad Mehedi Hassan, Bader Fahad Alkhamees, Khan Muhammad, Giancarlo Fortino. FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks. IEEE Access. 2021; 9 ():94299-94308.
Chicago/Turabian StyleMabrook S. Al-Rakhami; Abdu Gumaei; Meteb Altaf; Mohammad Mehedi Hassan; Bader Fahad Alkhamees; Khan Muhammad; Giancarlo Fortino. 2021. "FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks." IEEE Access 9, no. : 94299-94308.
In this paper, we establish some new generalized rational type common fixed point results for compatible three self-mappings in complex-valued b-metric space, in which a one self-map is continuous. In support of our results, we present some illustrative examples to verify the validity of our main work. Moreover, we present the application of two Urysohn integral type equations (UITEs) for the existence of a common solution to support our work. The UITEs are v 1 p = ∫ k 1 k 2 Q 1 p , r , v 1 r d r + ℏ 1 p and v 2 p = ∫ k 1 k 2 Q 2 p , r , v 2 r d r + ℏ 2 p , where p ∈ k 1 , k 2 , v 1 , v 2 , ℏ 1 , ℏ 2 ∈ V , where V = C k 1 , k 2 , ℝ n is the set of all real-valued continuous functions defined on k 1 , k 2 and Q 1 , Q 2 : k 1 , k 2 × k 1 , k 2 × ℝ n ⟶ ℝ n .
Shahid Mehmood; Saif Ur Rehman; Naeem Jan; Mabrook Al-Rakhami; Abdu Gumaei. Rational Type Compatible Single-Valued Mappings via Unique Common Fixed Point Findings in Complex-Valued b-Metric Spaces with an Application. Journal of Function Spaces 2021, 2021, 1 -14.
AMA StyleShahid Mehmood, Saif Ur Rehman, Naeem Jan, Mabrook Al-Rakhami, Abdu Gumaei. Rational Type Compatible Single-Valued Mappings via Unique Common Fixed Point Findings in Complex-Valued b-Metric Spaces with an Application. Journal of Function Spaces. 2021; 2021 ():1-14.
Chicago/Turabian StyleShahid Mehmood; Saif Ur Rehman; Naeem Jan; Mabrook Al-Rakhami; Abdu Gumaei. 2021. "Rational Type Compatible Single-Valued Mappings via Unique Common Fixed Point Findings in Complex-Valued b-Metric Spaces with an Application." Journal of Function Spaces 2021, no. : 1-14.
The researcher has been facing problems while handling imprecise and vague information, i.e., the problems of networking, decision-making, etc. For encountering such complicated data, the notion of fuzzy sets (FS) has been considered an influential tool. The notion was extended to its generalizations by a number of researchers in different ways which helps to understand and assess even more complex issues. This article characterizes imprecision with four kinds of values of membership. In this work, we aim to define and examine cubic picture fuzzy sets and give an application on averaging aggregation operators. We first introduce the notion of a cubic picture fuzzy set, which is a pair of interval-valued picture fuzzy set and a picture fuzzy set by giving examples. Then, we define two kinds of ordering on these sets and also discuss some set-theoretical properties. Moreover, we introduce three kinds of averaging aggregation operators based on cubic picture fuzzy sets and, at the end, we illustrate the results with a decision-making problem by using one of the provided aggregation operators.
Tehreem; Abdu Gumaei; Amjad Hussain. New Operators of Cubic Picture Fuzzy Information with Applications. Journal of Mathematics 2021, 2021, 1 -16.
AMA StyleTehreem, Abdu Gumaei, Amjad Hussain. New Operators of Cubic Picture Fuzzy Information with Applications. Journal of Mathematics. 2021; 2021 ():1-16.
Chicago/Turabian StyleTehreem; Abdu Gumaei; Amjad Hussain. 2021. "New Operators of Cubic Picture Fuzzy Information with Applications." Journal of Mathematics 2021, no. : 1-16.
In this manuscript, the theory of constant picture fuzzy graphs (CPFG) is developed. A CPFG is a generalization of constant intuitionistic fuzzy graph (CIFG) and a special case of picture fuzzy graph (PFG). Additionally, the article includes some basic definitions of CPFG such as totally constant picture fuzzy graphs (TCPFGs), constant function, bridge of CPFG, and their related results. Also, an application of CPFG in Wi-Fi network system is discussed. Finally, a comparison of CPFG is established with that of the CIFG which exhibits the superiority of the proposed idea over the existing ones is discussed.
Rukhshanda Anjum; Abdu Gumaei; Abdul Ghaffar. Certain Notions of Picture Fuzzy Information with Applications. Journal of Mathematics 2021, 2021, 1 -8.
AMA StyleRukhshanda Anjum, Abdu Gumaei, Abdul Ghaffar. Certain Notions of Picture Fuzzy Information with Applications. Journal of Mathematics. 2021; 2021 ():1-8.
Chicago/Turabian StyleRukhshanda Anjum; Abdu Gumaei; Abdul Ghaffar. 2021. "Certain Notions of Picture Fuzzy Information with Applications." Journal of Mathematics 2021, no. : 1-8.
Fuzzy sets and fuzzy logics are used to model events with imprecise, incomplete, and uncertain information. Researchers have developed numerous methods and techniques to cope with fuzziness or uncertainty. This research intends to introduce the novel concepts of complex neutrosophic relations (CNRs) and its types based on the idea of complex neutrosophic sets (CNSs). In addition, these concepts are supported by suitable examples. A CNR discusses the quality of a relationship using the degree of membership, the degree of abstinence, and the degree of nonmembership. Each of these degrees is a complex number from the unit circle in a complex plane. The real part of complex-valued degrees represents the amplitude term, while the imaginary part represents the phase term. This property empowers CNRs to model multidimensional variables. Moreover, some interesting properties and useful results have also been proved. Furthermore, the practicality of the proposed concepts is verified by an application, which discusses the use of the proposed concepts in statistical decision-making. Additionally, a comparative analysis between the novel concepts of CNRs and the existing methods is carried out.
Abdul Nasir; Naeem Jan; Abdu Gumaei; Sami Ullah Khan; Mabrook Al-Rakhami. Evaluation of the Economic Relationships on the Basis of Statistical Decision-Making in Complex Neutrosophic Environment. Complexity 2021, 2021, 1 -18.
AMA StyleAbdul Nasir, Naeem Jan, Abdu Gumaei, Sami Ullah Khan, Mabrook Al-Rakhami. Evaluation of the Economic Relationships on the Basis of Statistical Decision-Making in Complex Neutrosophic Environment. Complexity. 2021; 2021 ():1-18.
Chicago/Turabian StyleAbdul Nasir; Naeem Jan; Abdu Gumaei; Sami Ullah Khan; Mabrook Al-Rakhami. 2021. "Evaluation of the Economic Relationships on the Basis of Statistical Decision-Making in Complex Neutrosophic Environment." Complexity 2021, no. : 1-18.
Recently, brain–computer interface (BCI) based systems have become an emerging technology facilitating smart living. Accurate identification of eye states (open or closed) via an EEG-based BCI interface has many applications in a smart living environment, such as controlling devices and monitoring health status. Artificial neural networks (ANNs), including deep neural networks, are currently quite popular in many applications. In this study, a robust and unique ANN-based ensemble method is developed in which multiple ANNs are trained individually using different parts of the training data. The outcomes of each ANN are then combined using another ANN to enhance the predictive intelligence. The outcome of this ANN is considered the ultimate prediction of the user’s eye state. The proposed ensemble method requires minimal training time and yields highly accurate eye state classification. An extensive analysis of bias and variance was used to assess the generalization ability of the proposed model while applying it to a real BCI environment and dataset. The proposed model outperforms traditional ANNs and other machine learning tools for eye state classification.
Mohammad Mehedi Hassan; Rafiul Hassan; Shamsul Huda; Zia Uddin; Abdu Gumaei; Ahmed Alsanad. A predictive intelligence approach to classify brain–computer interface based eye state for smart living. Applied Soft Computing 2021, 108, 107453 .
AMA StyleMohammad Mehedi Hassan, Rafiul Hassan, Shamsul Huda, Zia Uddin, Abdu Gumaei, Ahmed Alsanad. A predictive intelligence approach to classify brain–computer interface based eye state for smart living. Applied Soft Computing. 2021; 108 ():107453.
Chicago/Turabian StyleMohammad Mehedi Hassan; Rafiul Hassan; Shamsul Huda; Zia Uddin; Abdu Gumaei; Ahmed Alsanad. 2021. "A predictive intelligence approach to classify brain–computer interface based eye state for smart living." Applied Soft Computing 108, no. : 107453.
Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP-SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.
Muhammad Mazhar Bukhari; Bader Fahad Alkhamees; Saddam Hussain; Abdu Gumaei; Adel Assiri; Syed Sajid Ullah. An Improved Artificial Neural Network Model for Effective Diabetes Prediction. Complexity 2021, 2021, 1 -10.
AMA StyleMuhammad Mazhar Bukhari, Bader Fahad Alkhamees, Saddam Hussain, Abdu Gumaei, Adel Assiri, Syed Sajid Ullah. An Improved Artificial Neural Network Model for Effective Diabetes Prediction. Complexity. 2021; 2021 ():1-10.
Chicago/Turabian StyleMuhammad Mazhar Bukhari; Bader Fahad Alkhamees; Saddam Hussain; Abdu Gumaei; Adel Assiri; Syed Sajid Ullah. 2021. "An Improved Artificial Neural Network Model for Effective Diabetes Prediction." Complexity 2021, no. : 1-10.
Complex fuzzy coverings (CFCs) are the natural mixture of the complex fuzzy sets (CFSs) and coverings, which are the modified versions of the coverings by replacing crisp sets with CFSs. This manuscript aims to explore the complex fuzzy neighborhood operators (CFNOs) by introducing the notions such as β-neighborhood system (β-NO), complex fuzzy β-minimal description (CFβ-MND), and complex fuzzy β-maximal description (CFβ-MXD). First, we explore the complex fuzzy β-covering approximation space (CFβ-CAS) and then we propose the above notions and investigate their properties. Additionally, we construct the CFNOs based on the complex fuzzy β-coverings (CFβ-Cs). Finally, the CFβ-Cs were derived by using CFNOs, and their properties are considered. These all notions are also verified with the help of suitable examples to show that the presented approaches are extensive, reliable, and proficient techniques.
Tahir Mahmood; Zeeshan Ali; Abdu Gumaei. Interdependency of Complex Fuzzy Neighborhood Operators and Derived Complex Fuzzy Coverings. IEEE Access 2021, 9, 73506 -73521.
AMA StyleTahir Mahmood, Zeeshan Ali, Abdu Gumaei. Interdependency of Complex Fuzzy Neighborhood Operators and Derived Complex Fuzzy Coverings. IEEE Access. 2021; 9 (99):73506-73521.
Chicago/Turabian StyleTahir Mahmood; Zeeshan Ali; Abdu Gumaei. 2021. "Interdependency of Complex Fuzzy Neighborhood Operators and Derived Complex Fuzzy Coverings." IEEE Access 9, no. 99: 73506-73521.
Software industry is adopting global software development (GSD) due to its potential to produce quality products at a lower cost. However, the GSD firms face many challenges that make development activities more complicated, especially related to the requirements engineering (RE) process. The objectives of this article are to investigate and prioritize the barriers faced by the GSD organizations during the RE process. First, we identified 17 barriers related to the RE process in the GSD projects. Next, the identified barriers were further validated with real‐world GSD practitioners using a questionnaire survey. Finally, we applied the analytical hierarchy process to prioritize the investigated barriers with respect to their significance for the RE process in the GSD domain. The results show that coordination is the most significant barrier category for the RE process in GSD projects. Lack of standard and procedure for RE in GSD, lack of synchronized communication infrastructure, and lack of mutual understanding between the overseas RE teams are also high‐ranked barriers for the RE process in GSD. The authors believe that the findings of this study will assist practitioners and researchers in developing effective strategies and plans for the successful implementation of the RE process in the GSD context.
Muhammad Azeem Akbar; Wishal Naveed; Sajjad Mahmood; Saima Rafi; Ahmed Alsanad; Abeer Abdul‐Aziz Alsanad; Abdu Gumaei; Abdulrahman Alothaim. Prioritization of global software requirements' engineering barriers: An analytical hierarchy process. IET Software 2021, 1 .
AMA StyleMuhammad Azeem Akbar, Wishal Naveed, Sajjad Mahmood, Saima Rafi, Ahmed Alsanad, Abeer Abdul‐Aziz Alsanad, Abdu Gumaei, Abdulrahman Alothaim. Prioritization of global software requirements' engineering barriers: An analytical hierarchy process. IET Software. 2021; ():1.
Chicago/Turabian StyleMuhammad Azeem Akbar; Wishal Naveed; Sajjad Mahmood; Saima Rafi; Ahmed Alsanad; Abeer Abdul‐Aziz Alsanad; Abdu Gumaei; Abdulrahman Alothaim. 2021. "Prioritization of global software requirements' engineering barriers: An analytical hierarchy process." IET Software , no. : 1.
Nowadays, there is a digital era, where social media sites like Facebook, Google, Twitter, and YouTube are used by the majority of people, generating a lot of textual content. The user-generated textual content discloses important information about people’s personalities, identifying a special type of people known as psychopaths. The aim of this work is to classify the input text into psychopath and nonpsychopath traits. Most of the existing work on psychopath’s detection has been performed in the psychology domain using traditional approaches, like SRPIII technique with limited dataset size. Therefore, it motivates us to build an advanced computational model for psychopath’s detection in the text analytics domain. In this work, we investigate an advanced deep learning technique, namely, attention-based BILSTM for psychopath’s detection with an increased dataset size for efficient classification of the input text into psychopath vs. nonpsychopath classes.
Junaid Asghar; Saima Akbar; Muhammad Zubair Asghar; Bashir Ahmad; Mabrook S. Al-Rakhami; Abdu Gumaei. Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model. Computational and Mathematical Methods in Medicine 2021, 2021, 1 -10.
AMA StyleJunaid Asghar, Saima Akbar, Muhammad Zubair Asghar, Bashir Ahmad, Mabrook S. Al-Rakhami, Abdu Gumaei. Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model. Computational and Mathematical Methods in Medicine. 2021; 2021 ():1-10.
Chicago/Turabian StyleJunaid Asghar; Saima Akbar; Muhammad Zubair Asghar; Bashir Ahmad; Mabrook S. Al-Rakhami; Abdu Gumaei. 2021. "Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model." Computational and Mathematical Methods in Medicine 2021, no. : 1-10.
The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait.
Nahla F. Omran; Sara F. Abd-El Ghany; Hager Saleh; Abdelmgeid A. Ali; Abdu Gumaei; Mabrook Al-Rakhami. Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia. Complexity 2021, 2021, 1 -13.
AMA StyleNahla F. Omran, Sara F. Abd-El Ghany, Hager Saleh, Abdelmgeid A. Ali, Abdu Gumaei, Mabrook Al-Rakhami. Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia. Complexity. 2021; 2021 ():1-13.
Chicago/Turabian StyleNahla F. Omran; Sara F. Abd-El Ghany; Hager Saleh; Abdelmgeid A. Ali; Abdu Gumaei; Mabrook Al-Rakhami. 2021. "Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia." Complexity 2021, no. : 1-13.
The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this article, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning. Three parallel CNN pathways have been implemented in the representation learning phase to learn the coarse and fine-grained features from the PCG and to explore the salient features from variable receptive fields involving 2D-CNN based squeeze-expansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase, because of the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features without performing any feature extraction on the signal. The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.60% accuracy, 99.56% precision, 99.52% recall and 99.68% F1- score on an average while being computationally comparable. This model outperforms any previous works using the same database by a considerable margin. Moreover, the proposed model was tested on PhysioNet/CinC 2016 challenge dataset achieving an accuracy of 86.57%. Finally the model was evaluated on a merged dataset of Github PCG dataset and PhysioNet dataset achieving excellent accuracy of 88.09%. The high accuracy metrics on both primary and secondary dataset combined with a significantly low number of parameters and end-to-end prediction approach makes the proposed network especially suitable for point of care CVD screening in low resource setups using memory constraint mobile devices.
Samiul Based Shuvo; Shams Nafisa Ali; Soham Irtiza Swapnil; Mabrook S. Al-Rakhami; Abdu Gumaei. CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings. IEEE Access 2021, 9, 36955 -36967.
AMA StyleSamiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil, Mabrook S. Al-Rakhami, Abdu Gumaei. CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings. IEEE Access. 2021; 9 ():36955-36967.
Chicago/Turabian StyleSamiul Based Shuvo; Shams Nafisa Ali; Soham Irtiza Swapnil; Mabrook S. Al-Rakhami; Abdu Gumaei. 2021. "CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings." IEEE Access 9, no. : 36955-36967.
Recently, Named Data Networking (NDN) has emerged as a popular and active Internet architecture that addresses the issues of current host-centric communication. NDN is well suited for Internet of Things (IoT) which possesses massive applications that dominate the Internet today. It intends to provide named-based routing, in-networking caching, built-in mobility and multicast support as part of its design which leads to a substantial improvement in content delivery/retrieval. Though, this new architecture aches from some new challenges in terms of security. In this article, we seek our attention towards Content Poisoning Attack (CPA). The purpose of CPA is to inject poisoned content with an invalid signature into the NDN-based IoT networks. Unfortunately, none of the existing proposals work effectively when malicious attackers compromise the caches of NDN routers. To prevent this, we proposed a certificateless signature scheme for the preservation of CPA in NDN-based IoT networks. The proposed scheme is formally secure under the security hardness of Hyperelliptic Curve Discrete Logarithm Problem (HCDLP) with a security simulation/validation in “Automated Validation of Internet Security Protocols and Applications (AVISPA).” Besides, the formal proof we also compared the designed scheme with some existing solutions to show the cost-efficiency in terms of communication overhead and computation cost. To conclude, a robust deployment on NDN-based IoT networks is shown.
Saddam Hussain; Syed Sajid Ullah; Abdu Gumaei; Mabrook Al-Rakhami; Ijaz Ahmad; Syed Muhammad Arif. A Novel Efficient Certificateless Signature Scheme for the Prevention of Content Poisoning Attack in Named Data Networking-Based Internet of Things. IEEE Access 2021, 9, 40198 -40215.
AMA StyleSaddam Hussain, Syed Sajid Ullah, Abdu Gumaei, Mabrook Al-Rakhami, Ijaz Ahmad, Syed Muhammad Arif. A Novel Efficient Certificateless Signature Scheme for the Prevention of Content Poisoning Attack in Named Data Networking-Based Internet of Things. IEEE Access. 2021; 9 ():40198-40215.
Chicago/Turabian StyleSaddam Hussain; Syed Sajid Ullah; Abdu Gumaei; Mabrook Al-Rakhami; Ijaz Ahmad; Syed Muhammad Arif. 2021. "A Novel Efficient Certificateless Signature Scheme for the Prevention of Content Poisoning Attack in Named Data Networking-Based Internet of Things." IEEE Access 9, no. : 40198-40215.
Spherical fuzzy set (SFS) is a modified version of fuzzy set (FS) to cope with uncertainty and complicated data in real-decision theory. In this article, some similarity measures, called cosine similarity measure (CSM), weighted cosine similarity measure (WCSM), set-theoretic similarity measure (STSM), weighted set-theoretic similarity measure (WSTSM), gray similarity measure (GSM), and weighted gray similarity measure (WGSM) are utilized in the setting of SFSs. Further, the information energy, correlation co-efficient (CC) and weighted correlation co-efficient (WCC) of SFSs are also introduced in this manuscript. The established measures based on SFSs are utilized in the setting of pattern recognition and medical diagnosis to express the validity and reliability of the explored measures with the help of some numerical examples. The projected measures based on SFSs are compared with existing measures, to show that the established measures for SFSs are more generalized than existing measures. The advantages and sensitive analysis of the investigated measures are also discussed in detail.
Tahir Mahmood; Muhammad Ilyas; Zeeshan Ali; Abdu Gumaei. Spherical Fuzzy Sets-Based Cosine Similarity and Information Measures for Pattern Recognition and Medical Diagnosis. IEEE Access 2021, 9, 25835 -25842.
AMA StyleTahir Mahmood, Muhammad Ilyas, Zeeshan Ali, Abdu Gumaei. Spherical Fuzzy Sets-Based Cosine Similarity and Information Measures for Pattern Recognition and Medical Diagnosis. IEEE Access. 2021; 9 ():25835-25842.
Chicago/Turabian StyleTahir Mahmood; Muhammad Ilyas; Zeeshan Ali; Abdu Gumaei. 2021. "Spherical Fuzzy Sets-Based Cosine Similarity and Information Measures for Pattern Recognition and Medical Diagnosis." IEEE Access 9, no. : 25835-25842.
Recently, the concept of a soft rough fuzzy covering (briefly, SRFC) by means of soft neighborhoods was defined and their properties were studied by Zhan’s model. As a generalization of Zhan’s method and in order to increase the lower approximation and decrease the upper approximation, the present work aims to define the complementary soft neighborhood and hence three types of soft rough fuzzy covering models (briefly, 1-SRFC, 2-SRFC, and 3-SRFC) are proposed. We discuss their axiomatic properties. According to these results, we investigate three types of fuzzy soft measure degrees (briefly, 1-SMD, 2-SMD, and 3-SMD). Also, three kinds of ψ -soft rough fuzzy coverings (briefly, 1- ψ -SRFC, 2- ψ -SRFC, and 3- ψ -SRFC) and three kinds of D -soft rough fuzzy coverings (briefly, 1- D -SRFC, 2- D -SRFC, and 3- D -SRFC) are discussed and some of their properties are studied. Finally, the relationships among these three models and Zhan’s model are presented.
Mohammed Atef; Shokry Nada; Abdu Gumaei; Ashraf S. Nawar. On Three Types of Soft Rough Covering-Based Fuzzy Sets. Journal of Mathematics 2021, 2021, 1 -9.
AMA StyleMohammed Atef, Shokry Nada, Abdu Gumaei, Ashraf S. Nawar. On Three Types of Soft Rough Covering-Based Fuzzy Sets. Journal of Mathematics. 2021; 2021 ():1-9.
Chicago/Turabian StyleMohammed Atef; Shokry Nada; Abdu Gumaei; Ashraf S. Nawar. 2021. "On Three Types of Soft Rough Covering-Based Fuzzy Sets." Journal of Mathematics 2021, no. : 1-9.