# Princess Sumaya University for Technology

• Amman, Amman, Jordan
Recent publications
The world has been hit with several crises during the last decade and still suffering from emerged Corona Virus pandemic economic crisis. Jordanian firms, like other firms around the world faced a financial crisis due to decrease in consumers’ demand and obstacles with shipping goods around the world. The current study examines the impact of the Jordanian national defense law in providing a financial protection for firms during the pandemic. Also, we study the behavior of consumers and business owners using an online interview. The results show that business owners are more likely to keep using the same prices before the quarantine period and consumers are more likely to have a buying panic, which, in return, have a positive impact on cash flow. In addition, the study proposed an equation that would guide business owners in calculating the shortage of cash flow and liquidity (SCFL) during crisis periods that similar to that of the quarantine period of COVID-19.,The study provides recommendation to that governmental authorities on enactment of more regulations and policies that provide guidance on how to deal future economic crisis which should provide protection for the local economy and assist firms in getting the financial support they need go through future crisis.
In this paper, we study the norm and skew angular distances in a normed space $${\mathscr {X}}$$, where convex functions are used to obtain refinements and reverses of some outstanding results in the literature. For example, in this regard, we show that if $$a,b\in {\mathscr {X}}$$ are non-zero and if $$p,q>0$$ are such that $$\frac{1}{p}+\frac{1}{q}=1$$, then \begin{aligned} \begin{aligned} 2\lambda \left( \frac{{{p}^{r}}{{\left\| a \right\| }^{r}}+{{q}^{r}}{{\left\| b \right\| }^{r}}}{2}-{{\left\| \frac{pa+qb}{2} \right\| }^{r}} \right)&\le {{p}^{r-1}}{{\left\| a \right\| }^{r}}+{{q}^{r-1}}{{\left\| b \right\| }^{r}}-{{\left\| a+b \right\| }^{r}} \\&\le 2\mu \left( \frac{{{p}^{r}}{{\left\| a \right\| }^{r}}+{{q}^{r}}{{\left\| b \right\| }^{r}}}{2}-{{\left\| \frac{pa+qb}{2} \right\| }^{r}} \right) , \end{aligned} \end{aligned}where $$r\ge 1$$, $$\lambda =\min \left\{ {1}/{p},{1}/{q}\right\}$$ and $$\mu =\max \left\{ {1}/{p},{1}/{q} \right\}$$. Then we explain how this result extends some known results in the literature. Many other related results will be also shown. Then, with the theme of convexity, we employ a log-convex approach on certain matrix functions to obtain improvements and new sights of some matrix inequalities, including possible bounds of $$\Vert A^{t}XB^{1-t}\Vert ,$$ where A, B are positive definite matrices, X is an arbitrary matrix, $$\Vert \cdot \Vert$$ is a unitarily invariant norm and $$0\le t\le 1.$$ Many other results involving matrix and scalar log-convex functions will be presented too.
The proliferation of cloud services and the massive volume of traffic provided by content delivery networks are driving the present rapid growth of Internet traffic. This obviously exacerbates the congestion concerns in communication networks, with a focus on the core and backbone components in particular. In this paper, a multipath routing traffic grooming and adaptive modulation are combined to establish an integer linear programming model with the optimization objective of minimizing the spectrum resource occupation, and a novel algorithm is proposed to solve the problem of bandwidth allocation and holding-time-aware routing in software-defined networks (SDN) integrated with elastic optical networks. A single path single service allocation approach is utilized to establish the service connection for both instant reservation allocation and advance reservation allocation. If the single path single service allocation technique fails, the single path multi sub service allocation method is applied. If it fails, the multipath multi sub service allocation method is attempted. When establishing a service connection, the allocation method with the least spectrum resource is recommended in order to reduce spectrum resource occupancy and release spectrum resource as soon as feasible. When there are no differences in spectrum resources, the allocation technique with the fewest total occupied time slots is selected. The results of simulations suggest that this approach may minimize blocking rates while also increasing spectrum usage.
Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu’s method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.
Sustainability includes social, economic, and ecological responsibilities. The worldwide concern about sustainability is increasing, especially for those issues related to the ecological domain. Any organization wishing to survive and sustain its business should consider sustainability pillars within daily activities. Therefore, this study is directed to investigate how consumers’ identification, involvement, and commitment to sustainability affect entrepreneurship. This paper uses a quantitative cross-sectional method to collect the data from 400 respondents in Jordan. The results show a correlation between consumers’ level of involvement, identification, commitment, and sustainability components (economy-driven venture, society-driven venture, and ecology-driven venture). Moreover, commitment has the highest effect on customers’ intentions and behavior; identification has the second highest effect, while involvement does not significantly affect both customers’ intentions and behavior. The study recommends that all organizations, whatever they do and wherever they conduct their business, should consider sustainability pillars within their strategies and daily practices. The sustainability-driven ventures should not only attract the required customer segmentation via social media, but also enhance, strengthen, and engage their sense of identification, commitment, and belonging.
The aim of this study is to explore measures and options for utilizing the most abundant energy source in Jordan, which is solar energy. The study looks into the actions that Jordan can adopt that will give the stakeholders a higher return on investment. It emphasizes the importance of using such facilities in the applications of green building, which is growing in the country. The methodology is descriptive and analytical, using official data to support these options, such as the price of labor, land, oil, as well as equipment that is used in the building of energy infrastructure. The importance of the options stems from the fact that they minimize the energy gaps to form a thorough policy for any organization or ministry in the government. The study showed that the payback period for the installation of both solar water collectors and solar cell panels is approximately 2 and 2.5 years, respectively. This study has also arrived at several actions and recommendations to shrink the energy gap. The disparity between subsidies for low-energy users and high-energy users has slowed the development of alternative energies. Other kinds of renewable energy, such as hydroelectric and wind power, are underutilized in Jordan, although they can be utilized to reduce the volatility of oil and gas prices.
Cyber-physical systems (CPSs) are emergent systems that enable effective real-time communication and collaboration (C&C) of physical components such as control systems, sensors, actuators, and the surrounding environment through a cyber communication infrastructure. As such, autonomous vehicles (AVs) are one of the various fields that have significantly adopted the CPS approach to improving people's lives in smart cities by reducing energy consumption and air pollution. Therefore, autonomous vehicle-cyber physical system (AVs-CPSs) has attracted enormous investments from major corporations and is projected to use widely in the future. However, AV-CPS is vulnerable to cyber and physical threat vectors due to the deep integration of information technology (IT) with the communication process. CPS components such as sensors and control systems through network infrastructure are particularly vulnerable to cyber-attacks targeted by attackers using the communication system. This paper proposes an intelligent intrusion detection system (IIDS) for AVs-CPS using transfer learning to identify cyberattacks launched against connected physical components of AVs through a network infrastructure. First, AV-CPS was developed by implementing the controller area network (CAN) and integrating it into the AV simulation model. Second, the dataset was generated from the AV-CPS. The collected dataset was then preprocessed to be trained and tested via pre-trained CNNs. Third, eight pre-trained networks were implemented, namely, InceptionV3, ResNet-50, ShuffleNet, MobileNetV2, GoogLeNet, ResNet-18, SqueezeNet, and AlexNet. The performance of the implemented models was evaluated. According to the experimental evaluation results, GoogLeNet outperforms all other pre-rained networks scoring an F1- score of 99.47%.
Background: The COVID-19 pandemic was associated with extensive lockdown strategies which included universities, forcing educational administrations to implement online learning and acknowledging the countless consequences it would have on the educational process. Those prompt changes highlighted the importance of online learning effects on educational outcomes. Aim: To assess students’ learning preferences and the stress associated with online and face-to-face learning. Methods: This is a multi-center cross-sectional study, employing a web-based Google Forms, which was used among four universities in Jordan. The survey assessed students’ demographic characteristics, educational methods received, assessment of factors that may have influenced students’ stress, and assessment of ‘stress’ using the Perceived Stress Scale (PSS). Results: Among 1241 participating students, most of the students preferred face-to-face learning (43.3%), although the majority believed that online learning is less stressful (42.2%). The majority believed that face-to-face learning is efficient (42.7%), and that online learning is moderately efficient (38.4%), while many (35.3%) reported that the future of learning will be blended 50/50 between online and face-to-face learning. The mean score of PSS was 20.88, with 62.9% reported to have experienced moderate perceived stress, and 22.4% experienced high perceived stress. Conclusion: Although Jordanian university students prefer face-to-face learning over online learning, they believe that online learning can be less stressful. In addition to that, Jordanian students experienced a high mean of the PSS score, with more than 20% of students reporting high perceived stress.
Let G(n;θ2k+1) denote the class of non-bipartite graphs on n vertices containing no θ2k+1-graph and f(n;θ2k+1)=max{E(G):G∈G(n;θ2k+1)}. Let H(n;θ2k+1) denote the class of non-bipartite Hamiltonian graphs on n vertices containing no θ2k+1-graph and h(n;θ2k+1)=max{E(G):G∈H(n;θ2k+1)}. In this paper we determine h(n;θ2k+1) by proving that for sufficiently large odd n, h(n;θ2k+1)≤⌊(n−2k+3)24⌋+2k−3. Furthermore, the bound is best possible. Our results confirm the conjecture made by Bataineh in 2007.
Lingual ultrasound imaging is essential in linguistic research and speech recognition. It has been used widely in different applications as visual feedback to enhance language learning for non-native speakers, study speech-related disorders and remediation, articulation research and analysis, swallowing study, tongue 3D modelling, and silent speech interface. This article provides a comparative analysis and review based on quantitative and qualitative criteria of the two main streams of tongue contour segmentation from ultrasound images. The first stream utilizes traditional computer vision and image processing algorithms for tongue segmentation. The second stream uses machine and deep learning algorithms for tongue segmentation. The results show that tongue tracking using machine learning-based techniques is superior to traditional techniques, considering the performance and algorithm generalization ability. Meanwhile, traditional techniques are helpful for implementing interactive image segmentation to extract valuable features during training and postprocessing. We recommend using a hybrid approach to combine machine learning and traditional techniques to implement a real-time tongue segmentation tool.
The 802.11ah standard is a new energy-efficient, wireless networking protocol which allows thousands of indoor and outdoor devices to be connected to the same access point. The Centralized Authentication Control (CAC) method, described in the standard, enables up to 8000 stations to be authenticated and associated with the same access point. A baseline implementation of the CAC method has been reported; however, it suffers from a few limitations. In this paper, an efficient methodology is proposed to minimize the CAC method’s association time. The proposed methodology allows the association of a large number of stations by predicting the size of the network followed by selecting the best step size that will enable fast association between the access point and the stations of the network. The methodology consists of three stages. The first stage provides a baseline implementation of the 802.11ah standard, while the second stage eliminates the effect of too large or too small step sizes. The third stage finds the optimal step size and step repeat for each network size and predicts network size based on queue size. The performance of the proposed methodology is evaluated and compared in terms of total association time, number of total trials and percentage of ineffective trials. The methodology outperforms the baseline implementation by achieving a 30% reduction in the total association time, 30% reduction in the total number of trials and 45% reduction in the total number of ineffective trials.
The purpose of this research is to investigate the influence of corporate board and audit committee characteristics on firm performance as measured by accounting-based ratios (earnings per share, return on asset, and return on equity) as well as the market-based measure (Tobin's Q). In addition, this research introduces political connections to examine whether it can moderate the relationship between corporate board characteristics and firm performance. The study reveals that a higher proportion of independent directors and CEO duality positively affected firm performance. However, board meetings and board financial experience have no affected firm performance. The study also shows that audit committee independence, audit committee size, and audit committee expertise are positively related to firm performance. In contrast, it finds no discernible impact of audit committee meetings on firm performance. Our findings also suggest that the beneficial influences of the corporate board, in terms of higher firm performance, are greater in firms with political connections. To the best of the researchers’ knowledge, this is almost certainly the first analysis to examine the relation between the corporate board, audit committee characteristics, and firm performance, and the moderating effect of political connection of this relationship.
Breast cancer is one of the leading causes of death among women worldwide. Many methods have been proposed for automatic breast cancer diagnosis. One popular technique utilizes a classification-based association called Association Classification (AC). However, most AC algorithms suffer from considerable numbers of generated rules. In addition, irrelevant and redundant features may affect the measures used in the rule evaluation process. As such, they could severely affect the accuracy rates in rule mining. Feature selection identifies the optimal subset of features representing a problem in almost the same context as the original features. Feature selection is a critical preprocessing step for data mining as it tends to increase the prediction speed and accuracy of the classification model and thereby increase performance. In this research, an ensemble filter feature selection method and a wrapper feature selection algorithm in conjunction with the AC approach are proposed for undertaking breast cancer classification. The proposed approach employs optimal discriminative feature subsets for breast cancer prediction. Specifically, it first utilizes a new bootstrapping search strategy that effectively selects the most optimal feature subset that considers the overall weighted average of the relative frequency-based evaluation criteria function. We employ a Weighted Average of Relative Frequency (WARF)-based filter method to compute discriminative features from the ensemble results. The adopted filter algorithms utilize the prioritization ranking technique for selecting a subset of informative features that are used for subsequent AC-based disease classification. Another wrapper feature selection method, namely a hybrid Particle Swarm Optimization (PSO)-WARF filter-based wrapper method, is also proposed for feature selection. Two classification models, i.e., WARF-Predictive Classification Based on Associations (PCBA) and hybrid PSO-WARF-PCBA, are subsequently constructed based on the above filter and wrapper-based feature selection methods for breast cancer prediction. The proposed approach of the two models is evaluated using UCI breast cancer datasets. The empirical results indicate that our models achieve impressive performance and outperform a variety of well-known benchmark AC algorithms consistently for breast cancer diagnosis.
This study aims to recognize the sustainability independence of the Jordanian Association of Certified Public Accountants (JACPA/JCPA) and its impact on the credibility gap of the accounting information of companies operating in Jordan. This study demonstrates the effects of the apparent and intellectual sustainability independence on the credibility gap of accounting information. A total of 93 online questionnaires were analyzed using multiple regressions. The results revealed an impact of the apparent independence of the JCPA on the quality of the information credibility gap related to service fees, and no statistically significant impact for both consulting and accounting service fees was found. This study also concludes research regarding the impact of intellectual independence of the JCPA on the information credibility gap regarding the code of professional ethics and the commitment of auditing offices to their customers.
Internet-of-Things (IoT)-based cyber-physical systems are increasingly being adopted because of the recent technological advancements in sensor technology, edge computing, machine learning, and big data. Integrating machine learning into designing IoT-based cyber-physical systems is essential. However, it is considered a challenging problem. This stems from the fact that IoT devices generate extensive data that requires extensive processing to achieve adequate learning. Relying on local learning by each IoT device is not feasible in most cases due to its limited resources. On the contrary, relying on all cloud-based learning requires transmitting a large amount of data to the cloud to perform the learning process, which is inefficient in large-scale IoT deployments. Therefore, this paper proposes a novel edge-computing architecture that employs the concept of distributed multi-task learning over EC networks in large-scale IoT-based cyber–physical systems. The architecture develops multiple distributed learning algorithms, a data placement architecture, task allocation algorithms, and a network protocol. In addition, it considers the problem of learning model parameters from IoT data distributed over different edge nodes in a large geographical area without sending raw data to the cloud. The architecture supports several distributed machine models that are trained using a combination of machine learning algorithms and population-based search algorithms to optimize the learning process. Population-based search algorithms allow for maintaining a set of candidate solutions, with each solution corresponding to a unique point in the search space for an optimal solution. Having the dataset distributed over several edge nodes, with each node having its own unique set of candidate solutions, increases the chance of finding a solution that generalizes well for the overall dataset combined. Simulation experiments with real IoT datasets are conducted to evaluate the accuracy of the proposed learning models. Results show the ability to achieve high-accuracy results that are close to single-machine models but with significantly efficient edge computing resource utilization.
One of the challenges in Attribute-Based Access Control (ABAC) implementation is acquiring sufficient metadata against entities and attributes. Intelligent mining and extracting ABAC policies and attributes make ABAC implementation more feasible and cost-effective. This research paper focuses on attribute extraction from an existing enterprise relational database management system – RDBMS. The proposed approach tends to first classify entities according to some aspects of RDBMS systems. By reverse engineering, some metadata elements and ranking values are calculated for each part. Then entities and attributes are assigned a final rank that helps to decide what attribute subset is a candidate to be an optimal input for ABAC implementation. The proposed approach has been tested and implemented against an existing enterprise RDBMS, and the results are then evaluated. The approach enables the choice to trade-off between accuracy and overhead. The results score an accuracy of up to 80% with no overhead or 88% of accuracy with 65% overhead.
This study examines critical factors influencing Omani entrepreneurs’ adoption of the internet of things (IoT) by expanding the constructs at the unified theory of acceptance and use of technology (UTAUT2) with entrepreneurs’ innovativeness, IT knowledge (ITK), and trust. A cross-sectional survey questionnaire was used to collect data from 158 entrepreneurs in Oman. Data were analyzed through the structural equation modeling technique using SmartPLS. The results indicated that performance expectancy, habit, social influence, trust (TR), ITK, and entrepreneurs’ innovativeness (PI) significantly affect Omani entrepreneurs’ intention to adopt IoT. Nonetheless, the results show that there is no significant relationship between hedonic motivation, effort expectancy, price value, and facilitating conditions to adopt IoT. This study contributes to previous literature by incorporating entrepreneurs’ innovativeness, ITK, and trust into UTAUT2. Furthermore, this study was conducted in a Middle Eastern country with solid support from the government for entrepreneurs; also, there is a gap in such studies in this area. This study helps practitioners in the field better understand how to influence entrepreneurs, push them toward using IoT applications further, and encourage non-users to start using them.
: Lithium-ion batteries are commonly used in electric vehicles, embedded systems, and portable devices, including laptops and mobile phones. Electrochemical models are widely used in battery diagnostics and charging/discharging control, considering their high extractability and physical interpretability. Many artificial intelligence charging algorithms also use electrochemical models for to enhance operation efficiency and maintain a higher state of health. However, the parameter identification of electrochemical models is challenging due to the complicated model structure and the high count of physical parameters to be considered. In this manuscript, a comprehensive electrochemical lithium-ion battery model is proposed for the charging and discharging process-es. The proposed model accounts for all dynamic characteristics of the battery, including the cell open-circuit voltage, cell voltage, internal battery impedance, charging/discharging current, and temperature. The key novelty of the proposed model is the use of simulated open-circuit voltage and simulated changes in entropy data instead of experimental data to provide battery voltage and temperature profiles during charging and discharging cycles in the development of the final model. An available experimental dataset at NASA for an LCO 18650 battery was utilized to test the proposed model. The mean absolute error for the simulated charging cell voltage and tem-perature values were 0.05 V and 0.3 °C, compared with 0.14 V and 0.65 °C for the discharging profile. The simulation results proved the effectiveness and accuracy of the proposed model, while simplicity was the key factor in developing the final model, as shown in the subsequent sections of the manuscript.
Keyless systems have replaced the old-fashioned methods of inserting physical keys into keyholes to unlock the door, which are inconvenient and easily exploited by threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, keyless systems are also susceptible to being compromised by a threat actor who intercepts the transmitted signal and performs a replay attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.
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994 members
• Department of Communications Engineering
• Department of Computer Engineering
• Computer Science Dept.
• King Hussein Faculty for Computing Sciences
• King Abdullah II for Engineering
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