Research ArticleOpen accessIn the abstractBoosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM ModelsHamid Sadeq Mahdi Alsultani, Ahmed H. AliwyNamed-Entity-Recognition(NER) is one of the most important Information-Extraction (IE) use cases, whichis used to improve the performance of Natural Languages Processing (NLP) tasks,such as Relation-Extraction (RE), Question-Answering (QA). Recently, Arabic NER is tackled in differentways by researchers. In this study, we assess the performance of two widelyused models, namely, LSTM and Bi-LSTM on the NER task in the Arabic languageand … …Journal of Computer Science, Vol. 18, Issue 9 · 6 Sep 2022 · 6,321 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Soft Voting Ensemble of XGBoost and DNN for At-Risk Student Performance PredictionEugene Wan, Po Chan Chiu, Mohammad bin Hossin et al.Early identification of at-risk students in higher education is important for timely academic intervention, yet conventional prediction methods often struggle with data imbalance and limited model precision. This study proposes a hybrid soft voting ensemble model that integrates Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) to enhance multi-class student grade prediction (A-F classification) and at-risk student identification. This … …Journal of Computer Science, Vol. 22, Issue 5 · 28 May 2026 · 1,404 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comprehensive Survey on Fake News Detection Using Machine LearningShraddha Shah, Sachin PatelIn the age of information data surplus and social media impact, the propagation of fake news has developed a significant societal concern. This challenge requires robust tools and methodologies for addressing, with machine learning emerging as a promising approach. The paper reviews different machine learning techniques in fake news detection, including supervised, unsupervised and semi-supervised methods. Supervised methods utilize labelled … …Journal of Computer Science, Vol. 21, Issue 4 · 20 Mar 2025 · 9,229 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassifying and Predicting Students’ Performance using Improved Decision Tree C4.5 in Higher Education InstitutesMohammed Hikmat Sadiq, Nawzat Sadiq AhmedStudents’ information in higher education institutions increases yearly. It is hard for them to extract meaningful information from the huge amount of data manually. Such information can support academic staff to stop students from dropping out at the end of courses. This can be done by evaluating the students’ performance for the course and also by predicting their performance in … …Journal of Computer Science, Vol. 15, Issue 9 · 12 Sep 2019 · 9,176 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Great Deluge Algorithm with Bi-Decay Rate for Efficient Task Scheduling in Grid ComputingKaiLun Eng, Abdullah Muhammed, Sazlinah Hasan, Mohamad Afendee MohamedTo realise the utmost idea of global collaborative resource sharing with Grid computing, the fundamental scheduling process is playing a critical role. However, scheduling in Grid computing environment is a well-known NP-complete problem. In this study, we propose a new extension of Great Deluge algorithm with an effective diversification strategy for the Grid scheduling problem. The proposed approach, namely BiGD, … …Journal of Computer Science, Vol. 15, Issue 3 · 7 Mar 2019 · 7,727 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Data Security Approach Based on GAN Teganographyand AES-RSA Encryption in a Distributed SystemDerrick Méthode Bagaza, Abraham Walake, Mamoudou Kourouma, Blaise Omer YenkeData security in distributed environments has become a major challenge due to the dispersion of resources, the expansion of the attack surface, and the increasing sophistication of cyber threats. Although traditional mechanisms such as data encryption, multi-factor authentication, and access control systems help to strengthen information protection, these approaches still have certain limitations when faced with advanced attacks and human … …Journal of Computer Science, Vol. 22, Issue 8 · 28 Jul 2026 · 1,068 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intrusion Detection Framework for MEC-Enabled IoT NetworksRofaida Tawfik, Abdelfattah Hegazy, Hesham Dahshan, Ahmed Gaber AbuabdallahMobile Edge Computing (MEC) has emerged as a promising paradigm for supporting latency-sensitive Internet of Things (IoT) applications by bringing computational resources closer to data sources. However, the distributed nature of MEC environments increases exposure to network-based security threats, particularly network intrusions that can impact both system reliability and task offloading efficiency. This paper proposes a security-aware framework that integrates … …Journal of Computer Science, Vol. 22, Issue 4 · 1 Apr 2026 · 1,927 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredictive Mathematical Modeling and Classification of Retail Sales Orders Using AI Machine Learning TechniquesMohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Hamzeh ZureigatThis study presents a systematic mathematical model known as a low-code approach to classifying retail sales orders by size using AI machine learning techniques within the Orange Data Mining platform. Leveraging a real-world sales dataset sourced from Kaggle, we implemented and evaluated ten classification models, including ensemble learners (AdaBoost, Gradient Boosting), probabilistic classifiers (Naïve Bayes), distance-based methods (kNN), and interpretable … …Journal of Computer Science, Vol. 22, Issue 3 · 10 Mar 2026 · 1,573 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comparative Study of Resampling, Cost-Sensitive, and Ensemble Techniques for Handling Class Imbalance in Indonesian Financial DataGunawan Kurnia, Ditdit Nugeraha UtamaHandling class imbalance is a critical challenge in machine learning applications, particularly in financial domains where minority instances often represent significant anomalies such as fraud or audit risks. Various oversampling and undersampling methods were tested, alongside cost-sensitive adjustments and ensemble models including Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The evaluation, based on 10-fold stratified cross-validation and performance metrics such … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,882 accessesView PDFCite