Fatima Barakat Al-Shannaq
@aau.edu.jo
Software Enginerring Department/College of Computer Sciences and Informatics
Fatima Shannaq
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Husam Ahmad Alhamad, Mohammad Shehab, Mohd Khaled Y. Shambour, Muhannad A. Abu-Hashem, Ala Abuthawabeh, Hussain Al-Aqrabi, Mohammad Sh. Daoud, and Fatima B. Shannaq
MDPI AG
Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.
Areej AlShorman, Fatima Shannaq, and Mohammad Sheha
Growing Science
Smart contracts offer automation for various decentralized applications but suffer from vulnerabilities that cause financial losses. Detecting vulnerabilities is critical to safeguarding decentralized applications before deployment. Automatic detection is more efficient than manual auditing of large codebases. Machine learning (ML) has emerged as a suitable technique for vulnerability detection. However, a systematic literature review (SLR) of ML models is lacking, making it difficult to identify research gaps. No published systematic review exists for ML approaches to smart contract vulnerability detection. This research focuses on ML-driven detection mechanisms from various databases. 46 studies were selected and reviewed based on keywords. The contributions address three research questions: vulnerability identification, machine learning model approaches, and data sources. In addition to highlighting gaps that require further investigation, the drawbacks of machine learning are discussed. This study lays the groundwork for improving ML solutions by mapping technical challenges and future directions.
Fatima Shannaq, Areej Alshorman, Riziq Al- Sayyed, Mohammad Shehab, and Walaa Alomari
Slovenian Association Informatika
Mohammad Shehab, Mohd Khaled Yousef Shambour, Muhannad A. Abu Hashem, Husam Ahmad Al Hamad, Fatima Shannaq, Manar Mizher, Ghaith Jaradat, Mohammad Sh. Daoud, and Laith Abualigah
Springer Science and Business Media LLC
Fatima Shannaq, Maria Habib, Hossam Faris, and Mahmoud M. Hammad
IEEE
Text classification has many applications in various fields; such as news categorization, sentiment analysis, E-mail spam filtering, and others. However, handling textual data is a challenging task owing to the potentially massive number of features (words). The presence of redundant irrelevant features deteriorates the performance of a learning algorithm and makes the process of text classification more complex. This research conducts a comparison study of several filtering-based feature se-lection methods in the context of Arabic text classification. Arabic is a highly complex language syntactically and morphologically which leads to more complicated learning tasks. Proposing a ro-bust classification model is demanding. Remarkably, integrating filtering approaches results in significant improvements in the performance of classification algorithms.
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.
Fatima Shannag, Bassam H. Hammo, and Hossam Faris
Springer Science and Business Media LLC
Mohammad Shehab, Omar Tarawneh, Hani AbuSalem, Fatima Shannag, and Walaa Al-Omari
IEEE
Gradient-based optimizer (GBO) is one of the most promising metaheuristic algorithms, where it proved its efficiency in various fields. GBO combine two major search mechanisms population-based and gradient-based Newton. Thus, it has a strong ability in global search. However, it suffers from dealing with local search problems. In this paper, a new version introduces which integrates the feature of Simulating annealing method (SA) with the GBO (GBOSA) to enhance the local search technique. The proposed GBOSA has been compared with various popular algorithms and improved variants on a set of real-world engineering problems. The experiment results show that GBOSA outperformed the other algorithms in the literature.
Fatima Shannaq, Bassam Hammo, Hossam Faris, and Pedro A. Castillo-Valdivieso
Institute of Electrical and Electronics Engineers (IEEE)
Social networks facilitate communication between people from all over the world. Unfortunately, the excessive use of social networks leads to the rise of antisocial behaviors such as the spread of online offensive language, cyberbullying (CB), and hate speech (HS). Therefore, abusive\\offensive and hate detection become a crucial part of cyberharassment. Manual detection of cyberharassment is cumbersome, slow, and not even feasible in rapidly growing data. In this study, we addressed the challenges of automatic detection of the offensive tweets in the Arabic language. The main contribution of this study is to design and implement an intelligent prediction system encompassing a two-stage optimization approach to identify and classify the offensive from the non-offensive text. In the first stage, the proposed approach fine-tuned the pre-trained word embedding models by training them for several epochs on the training dataset. The embeddings of the vocabularies in the new dataset are trained and added to the old embeddings. While in the second stage, it employed a hybrid approach of two classifiers, namely XGBoost and SVM, and a genetic algorithm (GA) to mitigate the drawback of the classifiers in finding the optimal hyperparameter values to run the proposed approach. We tested the proposed approach on Arabic Cyberbullying Corpus (ArCybC), which contains tweets collected from four Twitter domains: gaming, sports, news, and celebrities. The ArCybC dataset has four categories: sexual, racial, intelligence, and appearance. The proposed approach produced superior results, in which the SVM algorithm with the Aravec SkipGram word embedding model achieved an accuracy rate of 88.2% and an F1-score rate of 87.8%.
Hasan Rawashdeh, Shatha Awawdeh, Fatima Shannag, Esraa Henawi, Hossam Faris, Nadim Obeid, and Jon Hyett
Elsevier BV
Fatima B. Shannag and Bassam H. Hammo
IEEE
Event detection is essential for decision makers to understand the events surrounding their real world. Social media microblogging platforms play a significant role in our life. One of these platforms is Twitter, which has an extreme high exchange rate and accordingly has become a valuable and relevant source for many political and social events. Event detection from social media attracted the attention of researchers in different natural languages. Extracting and detecting events from Arabic tweets is still under investigation. In this paper, we have used the Python Natural Language Toolkit (NLTK) library to develop two classifiers for filtering and detecting extracted events from Arabic tweets. The first classifier filters the collected tweets using two passes. The first pass identifies the hashtags while the second pass does a shallow analysis on the tweets content. The second classifier analyzes the text extracted from the tweets. As a case study, we present the tragic events of the Jordan flash floods near the Dead Sea. The model successfully filtered all the collected tweets and picked the ones describing the incidents within that region. Analyzed data revealed important information to learn from this lesson for the future. The solution can be generalized and adapted to other problems.