Sana Ibrahim Jawarneh

Scopus Publications

Design and development of an intelligent system based on artificial intelligence and machine learning using customs digital indicators
Ashraf I. A. Qahman, Malek Alzaqebah, Sana Jawarneh, Murad Ali Ahmad Al-Zaqeba, Attallah Hassan Mohamed Al-Taani, Ahmad Nader Aloqaily, Maryam A. Almatrooshi
International Journal of Data and Network Science, 2026
This paper aims to evaluate the role of AI and ML-driven innovative technologies in enhancing customs operations in Jordan. This research employed a quantitative approach to develop an overall conceptual model that encompasses both the technical and behavioral aspects of intelligent system adoption. The target population consisted of customs officers, border security personnel, and IT personnel responsible for customs clearance and trade facilitation in Jordan. The structured questionnaires were administered to the respondents to measure their perceptions of system effectiveness, satisfaction, performance outcomes, and evasion behavior and yielded a total of 358 valid responses. The research was conducted with proper statistical analysis, and the statistical techniques employed included primary data collected via SPSS Version 29 and advanced modeling using Structural Equation Modeling-Partial Least Squares (SEM-PLS) through the use of SmartPLS 4.0. The results indicated that the measurement model proved to be both valid and reliable, with Cronbach's alpha values exceeding 0.82 and AVE values above 0.50, indicating good internal consistency and convergent validity. Moreover, the structural model achieved good explanatory power, with R² values of 59% for Customs Evasion, 43% for User Satisfaction, and 100% for Digital Performance Indicators. These findings underscore the significance of user satisfaction as a key outcome of system effectiveness and a valuable tool for enhancing performance and deterrence. More specifically, the results shown how Intelligent System Effectiveness presents a positive and significant impact on User Satisfaction (β = 0.656, p < 0.001), which in turn has a high positive effect on both Digital Performance Indicators (β = 1.000, p < 0.001) and Customs Evasion reduction (β = 0.770, p < 0.001). The mediation analysis also confirmed that User Satisfaction fully mediates the relationship between system effectiveness and performance outcome, as well as between system effectiveness and evasion reduction. This research contributes to theory and practice by demystifying the design and implementation of AI-driven customs systems. It illustrates the importance of valuing both technical system quality and user-centric values in achieving and maintaining optimal performance in the digital space, as well as conformance with the law.
Optimized Dimensionality Reduction Using Metaheuristic and Class Separability
Eman Abdulazeem Ahmed, Malek Alzaqebah, Sana Jawarneh
International Journal of Advanced Computer Science and Applications, 2025
The high dimensionality of modern datasets presents significant challenges for machine learning, including increased computational cost, model complexity, and risk of overfitting. This study introduces a metaheuristic framework for optimized dimensionality reduction to identify the highly discriminative feature subsets. The proposed method (KDR-PSO) combines a Particle Swarm Optimization (PSO) algorithm with the K-Nearest Neighbors Distance Ratio (KDR) as a filter-based objective function. This metric quantitatively assesses class separability within a feature subspace by computing the ratio of the average distance from a sample to neighbors in other classes versus those in its own class. Maximizing this ratio with a penalty for model size, KDR-PSO automates the discovery of parsimonious feature sets that maximize inter-class discrimination. The method is computationally efficient, naturally lending itself to multi-class classification and avoiding the prohibitive cost associated with classifier-in-the-loop wrappers. Experimental results on benchmark gene expression and image datasets show that KDR-PSO can achieve better dimensionality reduction compared to baselines and other algorithms, such as winning a better or at least similar performing models with decreased features. This approach is a strong and pragmatic technique to improve the model interpretability and generalizability for high-dimensional regions.
Hybrid topic modeling method based on dirichlet multinomial mixture and fuzzy match algorithm for short text clustering
Mutasem K. Alsmadi, Malek Alzaqebah, Sana Jawarneh, Ibrahim ALmarashdeh, Mohammed Azmi Al-Betar, Maram Alwohaibi, Noha A. Al-Mulla, Eman AE Ahmed, Ahmad AL Smadi
Journal of Big Data, 2024
Topic modeling methods proved to be effective for inferring latent topics from short texts. Dealing with short texts is challenging yet helpful for many real-world applications, due to the sparse terms in the text and the high dimensionality representation. Most of the topic modeling methods require the number of topics to be defined earlier. Similarly, methods based on Dirichlet Multinomial Mixture (DMM) involve the maximum possible number of topics before execution which is hard to determine due to topic uncertainty, and many noises exist in the dataset. Hence, a new approach called the Topic Clustering algorithm based on Levenshtein Distance (TCLD) is introduced in this paper, TCLD combines DMM models and the Fuzzy matching algorithm to address two key challenges in topic modeling: (a) The outlier problem in topic modeling methods. (b) The problem of determining the optimal number of topics. TCLD uses the initial clustered topics generated by DMM models and then evaluates the semantic relationships between documents using Levenshtein Distance. Subsequently, it determines whether to keep the document in the same cluster, relocate it to another cluster, or mark it as an outlier. The results demonstrate the efficiency of the proposed approach across six English benchmark datasets, in comparison to seven topic modeling approaches, with 83% improvement in purity and 67% enhancement in Normalized Mutual Information (NMI) across all datasets. The proposed method was also applied to a collected Arabic tweet and the results showed that only 12% of the Arabic short texts were incorrectly clustered, according to human inspection.
Intrusion Detection Using an Improved Cuckoo Search Optimization Algorithm
Mutasem K. Alsmadi, Dr. Rami Mustafa A Mohammad, Malek Alzaqebah, Sana Jawarneh, Muath AlShaikh, Ahmad Al Smadi, Fahad A. Alghamdi, Jehad Saad Alqurni, Hayat Alfagham
Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 2024
These days, intelligent cybersecurity models based on machine learning and data mining techniques are prevalent. Several factors might affect the quality of these models, including the accuracy, the ability to train new models quickly, the quick decision-making process, the simplicity of the created models, and the model’s interpretability. Feature selection algorithms can help achieve all these characteristics by isolating the crucial features from the unimportant ones during the model creation phase. The current article proposes an intelligent intrusion detection model based on an improved cuckoo search algorithm which is a nature-inspired optimization algorithm. The improved cuckoo search algorithm proposed in this article may tolerate several bad steps toward determining the set of effective features that would preserve or maximize the capabilities of the produced classification models. The generalization ability of such an algorithm is examined by applying it to 10 benchmark datasets, and it showed superior outcomes compared with several nature-inspired attribute selection approaches. Later, the improved cuckoo search algorithm is used to develop intelligent intrusion detection systems using the well-known “NSL-KDD” dataset. The obtained outcomes are appealing regarding the general performance, the time required to develop the intelligent intrusion detection models, and the number of rules generated.
Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction
Fahad A. Alghamdi, Haitham Almanaseer, Ghaith Jaradat, Ashraf Jaradat, Mutasem K. Alsmadi, Sana Jawarneh, Abdullah S. Almurayh, Jehad Alqurni, Hayat Alfagham
Machine Learning and Knowledge Extraction, 2024
In the healthcare field, diagnosing disease is the most concerning issue. Various diseases including cardiovascular diseases (CVDs) significantly influence illness or death. On the other hand, early and precise diagnosis of CVDs can decrease chances of death, resulting in a better and healthier life for patients. Researchers have used traditional machine learning (ML) techniques for CVD prediction and classification. However, many of them are inaccurate and time-consuming due to the unavailability of quality data including imbalanced samples, inefficient data preprocessing, and the existing selection criteria. These factors lead to an overfitting or bias issue towards a certain class label in the prediction model. Therefore, an intelligent system is needed which can accurately diagnose CVDs. We proposed an automated ML model for various kinds of CVD prediction and classification. Our prediction model consists of multiple steps. Firstly, a benchmark dataset is preprocessed using filter techniques. Secondly, a novel arithmetic optimization algorithm is implemented as a feature selection technique to select the best subset of features that influence the accuracy of the prediction model. Thirdly, a classification task is implemented using a multilayer perceptron neural network to classify the instances of the dataset into two class labels, determining whether they have a CVD or not. The proposed ML model is trained on the preprocessed data and then tested and validated. Furthermore, for the comparative analysis of the model, various performance evaluation metrics are calculated including overall accuracy, precision, recall, and F1-score. As a result, it has been observed that the proposed prediction model can achieve 88.89% accuracy, which is the highest in a comparison with the traditional ML techniques.
Cyberbullying detection framework for short and imbalanced Arabic datasets
Malek Alzaqebah, Ghaith M. Jaradat, Dania Nassan, Rawan Alnasser, Mutasem K. Alsmadi, Ibrahim Almarashdeh, Sana Jawarneh, Maram Alwohaibi, Noha A. Al-Mulla, Nouf Alshehab, Suboh Alkhushayni
Journal of King Saud University Computer and Information Sciences, 2023
Cyberbullying detection has attracted many researchers to detect negative comments deployed on communication platforms as cyberbullying can take many forms: verbal, implicit, explicit, or even nonverbal. The successful growth of social media in recent years has opened new perspectives on the detection of cyberbullying, although related research still encounters several challenges, such as data imbalance and expression implicitness. In this paper, we propose an automated cyberbullying detection framework designed to produce satisfactory results, especially when imbalanced short text and different dialects exist in the Arabic text data. In the proposed framework a new method to solve the imbalance problem is suggested, where the modified simulated annealing optimization algorithm is used to find the optimal set of samples from the majority class to balance the training set. This method has been evaluated using traditional machine learning algorithms including support vector machine, and deep learning algorithms including Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). To generate a framework that can detect Arabic written cyberbullying on communication platforms, the accuracy, recall, specificity, sensitivity and mean squared error are used as the main performance indicators. The results indicate that the proposed framework can improve the performance of the tested algorithms, and Bi-LSTM outperforms other methods for cyberbullying classification.
Comparison of specific segmentation methods used for copy move detection
Eman Abdulazeem Ahmed, Malek Alzaqebah, Sana Jawarneh, Jehad Saad Alqurni, Fahad A. Alghamdi, Hayat Alfagham, Lubna Mahmoud Abdel Jawad, Usama A. Badawi, Mutasem K. Alsmadi, Ibrahim Almarashdeh
International Journal of Electrical and Computer Engineering, 2023
In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.
Robust watermarking based on modified Pigeon algorithm in DCT domain
Muath AlShaikh, Malek Alzaqebah, Sana Jawarneh
Multimedia Tools and Applications, 2023
Improved Whale Optimization with Local-Search Method for Feature Selection
Malek Alzaqebah, Mutasem K. Alsmadi, Sana Jawarneh, Jehad Saad Alqurni, Mohammed Tayfour, Ibrahim Almarashdeh, Rami Mustafa A. Mohammad, Fahad A. Alghamdi, Nahier Aldhafferi, Abdullah Alqahtani, Khalid A. Alissa, Bashar A. Aldeeb, Usama A. Badawi, Maram Alwohaibi, Hayat Alfagham
Computers Materials and Continua, 2023
Various feature selection algorithms are usually employed to improve classification models’ overall performance. Optimization algorithms typically accompany such algorithms to select the optimal set of features. Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics. The present paper presents two Stages of Local Search models for feature selection based on WOA (Whale Optimization Algorithm) and Great Deluge (GD). GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search. Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm. In addition, disruptive selection (DS) is employed to select the solutions from the population for local search. DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions. Fifteen (15) standard benchmark datasets provided by the University of California Irvine (UCI) repository were used in evaluating the proposed approaches’ performance. Next, a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature. The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods. Hence, the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.
Cuckoo algorithm with great deluge local-search for feature selection problems
Mutasem K. Alsmadi, Malek Alzaqebah, Sana Jawarneh, Sami Brini, Ibrahim Almarashdeh, et al.
International Journal of Electrical and Computer Engineering, 2022
Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.
Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem
Malek Alzaqebah, Sana Jawarneh, Maram Alwohaibi, Mutasem K. Alsmadi, Ibrahim Almarashdeh, Rami Mustafa A. Mohammad
Journal of King Saud University Computer and Information Sciences, 2022
Hybrid feature selection method based on particle swarm optimization and adaptive local search method
Malek Alzaqebah, Sana Jawarneh, Rami Mustafa A. Mohammad, Mutasem K. Alsmadi, Ibrahim Al-marashdeh, Eman A. E. Ahmed, Nashat Alrefai, Fahad A. Alghamdi
International Journal of Electrical and Computer Engineering, 2021
Improved Multi-Verse Optimizer Feature Selection Technique with Application to Phishing, Spam, and Denial of Service Attacks
International Journal of Communication Networks and Information Security, 2021
Self-adaptive bee colony optimisation algorithm for the flexible job-shop scheduling problem
Malek Alzaqebah, Salwani Abdullah, Rami Malkawi, Sana Jawarneh
International Journal of Operational Research, 2021
Moth optimisation algorithm with local search for the permutation flow shop scheduling problem
Anmar Abuhamdah, Malek Alzaqebah, Sana Jawarneh, Ahmad Althunibat, Mustafa Banikhalaf
International Journal of Computer Applications in Technology, 2021
Memory based cuckoo search algorithm for feature selection of gene expression dataset
Malek Alzaqebah, Khaoula Briki, Nashat Alrefai, Sami Brini, Sana Jawarneh, Mutasem K. Alsmadi, Rami Mustafa A. Mohammad, Ibrahim ALmarashdeh, Fahad A. Alghamdi, Nahier Aldhafferi, Abdullah Alqahtani
Informatics in Medicine Unlocked, 2021
Neighborhood search methods with moth optimization algorithm as a wrapper method for feature selection problems
Malek Alzaqebah, Nashat Alrefai, Eman A. E. Ahmed, Sana Jawarneh, Mutasem K. Alsmadi
International Journal of Electrical and Computer Engineering, 2020
Bees algorithm for vehicle routing problems with time windows
Malek Alzaqebah, , Sana Jawarneh, Hafiz Mohd Sarim, Salwani Abdullah
International Journal of Machine Learning and Computing, 2018
Modified artificial bee colony for the vehicle routing problems with time windows
Malek Alzaqebah, Salwani Abdullah, Sana Jawarneh
Springerplus, 2016
Sequential insertion heuristic with adaptive bee colony optimisation algorithm for vehicle routing problem with time windows
Sana Jawarneh, Salwani Abdullah
Plos One, 2015