Esra'a Mahmoud Alhenawi
@zu.edu.jo
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Networks and Communications, Computer Science, Computer Science Applications
Scopus Publications
Scopus Publications
Shereen Hamadneh, Jehan Hamadneh, Esraa Alhenawi, Ruba Abu Khurma, and Abdelazim G. Hussien
Springer Science and Business Media LLC
AbstractTo identify risk factors for smoking among pregnant women, and adverse perinatal outcomes among pregnant women. A case–control study of singleton full-term pregnant women who gave birth at a university hospital in Jordan in June 2020. Pregnant women were divided into three groups according to their smoking status, active, passive, and non-smokers. They were interviewed using a semi-structured questionnaire that included demographic data, current pregnancy history, and neonatal outcomes. Low-level maternal education, unemployment, secondary antenatal care, and having a smoking husband were identified as risk factors for smoke exposure among pregnant women. The risk for cesarean section was ninefold higher in nulliparous smoking women. Women with low family income, those who did not receive information about the hazards of smoking, unemployed passive smoking women, and multiparty raised the risk of neonatal intensive care unit admission among active smoking women. This risk increased in active and passive women with lower levels of education, and inactive smoking women with low family income by 25 times compared to women with a higher level of education. Smoking is associated with adverse perinatal outcomes. Appropriate preventive strategies should address modifiable risk factors for smoking during pregnancy.
Esra’a Alhenawi, Ruba Abu Khurma, Robertas Damaševic̆ius, and Abdelazim G. Hussien
Springer Science and Business Media LLC
AbstractAccording to Moore’s law, computer processing hardware technology performance is doubled every year. To make effective use of this technological development, the algorithmic solutions have to be developed at the same speed. Consequently, it is necessary to design parallel algorithms to be implemented on parallel machines. This helps to exploit the multi-core environment by executing multiple instructions simultaneously on multiple processors. Traveling Salesman (TSP) is a challenging non-deterministic-hard optimization problem that has exponential running time using brute-force methods. TSP is concerned with finding the shortest path starting with a point and returning to that point after visiting the list of points, provided that these points are visited only once. Meta-heuristic optimization algorithms have been used to tackle TSP and find near-optimal solutions in a reasonable time. This paper proposes a parallel River Formation Dynamics Optimization Algorithm (RFD) to solve the TSP problem. The parallelization technique depends on dividing the population into different processors using the Map-Reduce framework in Apache Spark. The experiments are accomplished in three phases. The first phase compares the speedup, running time, and efficiency of RFD on 1 (sequential RFD), 4, 8, and 16 cores. The second phase compares the proposed parallel RFD with three parallel water-based algorithms, namely the Water Flow algorithm, Intelligent Water Drops, and the Water Cycle Algorithm. To achieve fairness, all algorithms are implemented using the same system specifications and the same values for shared parameters. The third phase compares the proposed parallel RFD with the reported results of metaheuristic algorithms that were used to solve TSP in the literature. The results demonstrate that the RFD algorithm has the best performance for the majority of problem instances, achieving the lowest running times across different core counts. Our findings highlight the importance of selecting the most suitable algorithm and core count based on the problem characteristics to achieve optimal performance in parallel optimization.
Zaher Salah, Esraa Elsoud, Waleed Al-Sit, Esraa Alhenawi, Fuad Alshraiedeh, and Nawaf Alshdaifat
Institute of Advanced Engineering and Science
The proliferation of internet of things (IoT) technologies has expanded the user base of the internet, but it has also exposed users to increased cyber threats. Intrusion detection systems (IDSs) play a vital role in safeguarding against cybercrimes by enabling early threat response. This research uniquely centers on the critical dimensionality aspects of wireless datasets. This study focuses on the intricate interplay between feature dimensionality and intrusion detection systems. We rely on the renowned IEEE 802.11 security-oriented AWID3 dataset to implement our experiments since AWID was the first dataset created from wireless network traffic and has been developed into AWID3 by capturing and studying traces of a wide variety of attacks sent into the IEEE 802.1X extensible authentication protocol (EAP) environment. This research unfolds in three distinct phases, each strategically designed to enhance the efficacy of our framework, using multi-nominal class, multi-numeric class, and binary class. The best accuracy achieved was 99% in the three phases, while the lowest accuracy was 89.1%, 60%, and 86.7% for the three phases consecutively. These results offer a comprehensive understanding of the intricate relationship between wireless dataset dimensionality and intrusion detection effectiveness.
, Ayat Mahmoud Al-Hinawi, Radwan A. Alelaimat, , Esraa Alhenawi, , Mohammad I. AlBiajawi, and
Engineered Science Publisher
Ayat Mahmoud Al-Hinawi 1*, Radwan A. Alelaimat , Esraa Alhenawi 2, Mohammad I. AlBiajawi 3, 1Department of Allied Engineering Sciences, Hashemite University, Zarqa 2 Faculty of Information Technology, Zarqa University, Zarqa, Jordan 3 Faculty of Civil Engineering Technology, Universiti Malaysia Pahang AL-Sultan Abdullah, Persiaran Tun Khalil Yaakob, 26300 Gambang, Pahang, Malaysia
Pedro A. Castillo-Valdivieso, Esraa Alhenawi, Shatha Awawdeh, Ruba Abu Khurma, Maribel García-Arenas, and Amjad Hudaib
Universidad Nacional de La Plata
Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and budget. Many factors affect requirement prioritization such as stakeholder expectations, complexity, dependency, scalability, risk and cost. Therefore, finding the proper order of requirements is a challenging process. Hence, different types of requirements prioritization techniques have been developed to support this task. In this survey we propose a novel classification that can classify the prioritization techniques under two major classes: relative and exact prioritization techniques class where each class is divided into two subclasses. We also provide an overview about fifteen different requirements prioritization techniques that are classified under our proposed classification. Moreover, we make a comparison between methods that are related to the same subclass to analyze their strengths and weakness. Based on the comparison results, the properties for each proposed subclass of techniques are identified. Depending on these properties, we present some recommendations to help project managers in the process of selection the most suitable technique to prioritize requirements based on their project characteristics.
Rizik Al-Sayyed, Esra’a Alhenawi, Hadeel Alazzam, Ala’a Wrikat, and Dima Suleiman
Springer Science and Business Media LLC
Ruba Abu Khurma, Esraa Alhenawi, Malik Braik, Fatma A Hashim, Amit Chhabra, and Pedro A Castillo
Oxford University Press (OUP)
Abstract It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature Selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, i.e., they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, SO is employed to put the evolutionary algorithms’ preservation of the best premise into practice. This is accomplished by utilizing three alternative selection systems – tournament, proportional, and linear – to improve the exploration phase. These are used in exploration to allow solutions to be found more thoroughly and in relation to a chosen solution than at random. These are Tournament Logarithmic Snake Optimizer (TLSO), Proportional Logarithmic Snake Optimizer, and Linear Order Logarithmic Snake Optimizer. A number of 22 reference medical datasets were used in experiments. The findings indicate that, among 86% of the datasets, TLSO attained the best accuracy, and among 82% of the datasets, the best feature reduction. In terms of the standard deviation, the TLSO also attained noteworthy reliability and stability. On the basis of running duration, it is, nonetheless, quite effective.
Ayat AlSayyed, Abdullah Mahmoud Taqateq, Rizik Al-Sayyed, Dima Suleiman, Sarah Shukri, Esraa Alhenawi, and Ayyoub Mahmoud Albsheish
Growing Science
Dental caries is arguably the most persistent dental condition that affects most people over their lives. Carious lesions are commonly diagnosed by dentists using clinical and visual examination along with oral radiographs. In many circumstances, dental caries is challenging to detect with photography and might be mistaken as shadows for various reasons, including poor photo quality. However, with the introduction of Artificial Intelligence and robotic systems in dentistry, photographs can be a helpful tool in oral epidemiological research for the assessment of dental caries prevalence among the population. It can be used particularly to create a new automated approach to calculate DMF (Decay, Missing, Filled) index score. In this paper, an autonomous diagnostic approach for detecting dental cavities in photos is developed using deep learning algorithms and ensemble methods. The proposed technique employs a set of pretrained models including Xception, VGG16, VGG19, and DenseNet121 to extract essential characteristics from photographs and to classify images as either normal or caries. Then, two ensemble learning methods, E- majority and E-sum, are employed based on majority voting and sum rule to boost the performances of the individual pretrained model. Experiments are conducted on 50 images with data augmentation for normal and caries images, the employed E-majority and E-sum achieved an accuracy score of 96% and 97%, respectively. The obtained results demonstrate the superiority of the proposed ensemble framework in the detection of caries. Furthermore, this framework is a step toward constructing a fully automated, efficient decision support system to be used in the dentistry area.
Sarah E. Shukri, Rizik Al-Sayyed, Hamed Al-Bdour, Esraa Alhenawi, Tamara Almarabeh, and Hiba Mohammad
Growing Science
Technological advancements affect everyday life; they benefited our daily routines, habits, and activities. Underwater diving is one of the most interesting and attractive activities for tourists worldwide but could be risky and challenging. When paths are not clear, diving might take additional time and effort leading to some health problems. Thus, providing divers with proper direction information to surf underwater can be useful and helpful. Also, monitoring diverse health statuses and alerting them in case of any undesirable condition can increase their safety. Smart devices such as mobiles, watches, sensor devices, cellular networks along with the Internet of Things (IoT) can all provide location-based services. Such services can help in providing the best path for the divers and monitor their health status during diving. This paper proposes a new underwater routing approach, called Underwater Routing for Smart Diving “URSD”, which provides divers with routing information to visit underwater cultural or natural resources and monitors their health status during the diving period. The URSD approach was simulated and compared with the shortest path. Results showed that the URSD helped divers to route within paths that have a larger number of nodes, furthermore, it could enhance and improve divers experience and help them mitigate underwater risks.
Esra’a Alhenawi, Rizik Al-Sayyed, Amjad Hudaib, and Seyedali Mirjalili
Elsevier BV
Omar Khair Alla Alidmat, Kalsom Yusof Umi, Esraa Alhenawi, Hazem Jihad Badarneh, Raed Alazaidah, and Lara Al-Rbabah
IEEE
An agent evacuation under fire-spreading conditions is simulated using an improved two-dimensional cellular automaton model to characterize exit-choosing behavior inside a room with multiple exits. The proposed model incorporates non-static fire spreading behavior to avoid major discrepancies between reality and simulation. A new fire spreading parameter is introduced to simulate agents’ intelligent decision-making regarding movement, judgment of surrounding conditions, and action choices during fire evacuation. The overall agent behaviors during fire evacuation are also investigated, mainly to demonstrate the key role of the proposed fire spreading parameter in enabling agents to determine the fire location and size of the burning area in the early evacuation stage. The effects of agent distribution, density, fire location, spread speed, death toll, and total evacuation time on the evacuation process are also discussed in detail. Comparative analysis with previous works reveals notable results: the influence of the proposed fire parameter is markedly noticeable in reducing the average number of agents caught and killed by the spreading fire during evacuation. This reduction, in turn, could contribute to faster and safer escape during fire evacuation. The improved model can be applied to various fire evacuation scenarios and settings where multi-exit evacuation is required, such as stadiums, airports, or shopping malls.
Esra’a Alhenawi, Ruba Abu Khurma, Pedro A. Castillo, Maribel G. Arenas, and Ayat Mahmoud Al-Hinawi
IEEE
Text classification is a technique for grouping articles into pre-defined categories. Prior to categorization, text documents have to be set up and organised in a form that is compatible with the data mining techniques. That has produced many term weighting techniques have been developed in the literature to improve the functionality of text categorization systems. This paper compares techniques for text classification based on when stop words are eliminated from the documents once and when they are not. We used an Arabic data set of 322 papers grouped into six subjects (health, politics, science, education, agriculture and finance) to research which is the influence of prior weighting of features approaches on classification outcomes with regard to accuracy, recall, precision, and F-measure values. Each document subset includes 50 documents, except the health one that includes 61. We carried out four experiments and checked results getting that only for precision, the term frequency feature weighting approach with stop word removal are more or less the same than for binary method. Moreover the conclusion for accuracy, recall and F-measure is that Feature weighting approach with stop word outperforms the Binary one. Additionally, if the phrase for weighting is the same for both methods, to remove stop word increases classification accuracy for all the experiments.
Esra'a Alhenawi, Ruba Abu Khurma, Ahmad A. Sharieh, Omar Al-Adwan, Areej Al Shorman, and Fatima Shannaq
Institute of Electrical and Electronics Engineers (IEEE)
The problem of finding the shortest path between two nodes is a common problem that requires a solution in many applications like games, robotics, and real-life problems. Since its deals with a large number of possibilities. Therefore, parallel algorithms are suitable to solve this optimization problem that has attracted a lot of researchers from both industry and academia to find the optimal path in terms of runtime, speedup, efficiency, and cost compared to sequential algorithms. In mountain climbing, finding the shortest path from the start node under the mountain to reach the destination node is a fundamental operator, and there are some interesting issues to be studied in mountain climbing that cannot be found in a traditional two-dimensional space search. We present a parallel Ant Colony Optimization (ACO) to find the shortest path in the mountain climbing problem using Apache Spark. The proposed algorithm guarantees the security of the selected path by applying some constraints that take into account the secure slope angle for the path. A generated dataset with variable sizes is used to evaluate the proposed algorithm in terms of runtime, speedup, efficiency, and cost. The experimental results show that the parallel ACO algorithm significantly $(p < 0.05)$ outperformed the best sequential ACO. On the other hand, the parallel ACO algorithm is compared with one of the most recent research from the literature for finding the best path for mountain climbing problems using the parallel A* algorithm with Apache Spark. The parallel ACO algorithm with Spark significantly outperformed the parallel A* algorithm.
Orieb AbuAlghanam, Hadeel Alazzam, Esra’a Alhenawi, Mohammad Qatawneh, and Omar Adwan
Springer Science and Business Media LLC
Hadeel Alazzam, Aryaf Al-Adwan, Orieb Abualghanam, Esra’a Alhenawi, and Abdulsalam Alsmady
MDPI AG
Recently, the proliferation of smartphones, tablets, and smartwatches has raised security concerns from researchers. Android-based mobile devices are considered a dominant operating system. The open-source nature of this platform makes it a good target for malware attacks that result in both data exfiltration and property loss. To handle the security issues of mobile malware attacks, researchers proposed novel algorithms and detection approaches. However, there is no standard dataset used by researchers to make a fair evaluation. Most of the research datasets were collected from the Play Store or collected randomly from public datasets such as the DREBIN dataset. In this paper, a wrapper-based approach for Android malware detection has been proposed. The proposed wrapper consists of a newly modified binary Owl optimizer and a random forest classifier. The proposed approach was evaluated using standard data splits given by the DREBIN dataset in terms of accuracy, precision, recall, false-positive rate, and F1-score. The proposed approach reaches 98.84% and 86.34% for accuracy and F-score, respectively. Furthermore, it outperforms several related approaches from the literature in terms of accuracy, precision, and recall.
Esra’a Alhenawi, Hadeel Alazzam, Rizik Al-Sayyed, Orieb AbuAlghanam, and Omar Adwan
Walter de Gruyter GmbH
Abstract A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.
Hadeel Alazzam, Orieb AbuAlghanam, Qusay M. Al-zoubi, Abdulsalam Alsmady, and Esra’a Alhenawi
Walter de Gruyter GmbH
Abstract The Internet of Things (IoT) is widespread in our lives these days (e.g., Smart homes, smart cities, etc.). Despite its significant role in providing automatic real-time services to users, these devices are highly vulnerable due to their design simplicity and limitations regarding power, CPU, and memory. Tracing network traffic and investigating its behavior helps in building a digital forensics framework to secure IoT networks. This paper proposes a new Network Digital Forensics approach called (NDF IoT). The proposed approach uses the Owl optimizer for selecting the best subset of features that help in identifying suspicious behavior in such environments. The NDF IoT approach is evaluated using the Bot IoT UNSW dataset in terms of detection rate, false alarms, accuracy, and f-score. The approach being proposed has achieved 100% detection rate and 99.3% f-score and outperforms related works that used the same dataset while reducing the number of features to three features only.
Hadeel Alazzam, Orieb AbuAlghanam, Abdulsalam Alsmady, and Esra'a Alhenawi
IEEE
Text clustering is a popular data mining process used in data indexing and information retrieval. However, Existing clustering techniques suffer from several shortcomings such as sensitivity to the initial value, slow convergence, etc... In this paper, the Bond Energy Algorithm (BEA) has been used for clustering Arabic documents. The bond energy algorithm has been modified with the help of a Genetic algorithm to enhance the accuracy of the results. The efficiency of modified BEA has been compared to BEA baseline and K-means clustering in terms of precision, recall, and F-Score. The results show that Modified BEA can be used for clustering Arabic Documents, and the efficiency of the algorithm can be enhanced in the future to give a better result.
Esra'a Alhenawi, Rizik Al-Sayyed, Amjad Hudaib, and Seyedali Mirjalili
Elsevier BV
Hasan Rawashdeh, Shatha Awawdeh, Fatima Shannag, Esraa Henawi, Hossam Faris, Nadim Obeid, and Jon Hyett
Elsevier BV
Hadeel Alazzam, Esraa Alhenawi, and Rizik Al-Sayyed
Springer Science and Business Media LLC
Mobile Ad-hoc Network (MANET) is infrastructure-less network that consists of a set of mobile nodes. These nodes have limited power based on their batteries. Network lifetime is one of the most important challenges facing this type of networks; motivating many researchers to investigate alternatives that prolong the network lifetime. This paper proposes a new path selection metric that considers the ratio between the minimum residual energy of all route nodes and hop count value to select a rout in MDSR routing protocol. The discovered paths are checked periodically for ensuring their availabilities using special packets called DTC. Glomosim simulator is used to compare the modified MDSR protocol with the traditional MDSR and other existing protocols as well. Simulation results showed that the proposed routing protocol outperformed the traditional MDSR protocol in terms of network lifetime, packet delivery ratio and end to end delay. Moreover, it showed improved performance over other existing protocols in terms of packet delivery ratio and network lifetime.