Enhanced Phishing Transactions Detection on Ethereum Network with Tree-based Ensembles: An Empirical Study
SJ Alsunaidi, H Aljamaan Blockchain: Research and Applications
Adversarial Vulnerability Transcends Computational Paradigms: Feature Engineering Provides No Defense Against Neural Adversarial Transfer
A Hsain, A Abdelkader, EB Mbaya, H Aljamaan arXiv preprint
System and method for code smell detection using transformer-based code representations with self-supervision by predicting reserved words
A Alazba, H Aljamaan, M Alshayeb US Patent 12,591,499
Leveraging machine learning models to improve smart contract security: A survey of vulnerabilities and detection methods
SJ Alsunaidi, H Aljamaan, M Hammoudeh ACM Computing Surveys
Enhancing Python Code Smell Detection with Heterogeneous Ensembles
R Sandouka, H Aljamaan International Journal of Software Engineering and Knowledge Engineering
A Survey of Sentiment Analysis in Halal Tourism Using Machine Learning and Lexicon Approaches for Extracting Sentiment Polarity
B Jan, A Hamid, MF Muna, H Aljamaan Engineering Headway
Cort: Transformer-based code representations with self-supervision by predicting reserved words for code smell detection
A Alazba, H Aljamaan, M Alshayeb Empirical Software Engineering
An automated approach to aspect-based sentiment analysis of apps reviews using machine and deep learning
N Alturayeif, H Aljamaan, J Hassine Automated Software Engineering
Arabic cyberbullying detection using machine learning: State of the art survey
N Alsunaidi, S Aljbali, Y Yasin, H Aljamaan 27th International Conference on Evaluation and Assessment in Software Engineering (EASE)
A survey on botnets attack detection utilizing machine and deep learning models
D Alomari, F Anis, M Alabdullatif, H Aljamaan 27th International Conference on Evaluation and Assessment in Software Engineering (EASE)
Twitter spam accounts detection using machine learning models
SJ Alsunaidi, RT Alraddadi, H Aljamaan 14th International Conference on Computational Intelligence and Applications
Code smell detection using feature selection and stacking ensemble: An empirical investigation
A Alazba, H Aljamaan Information and Software Technology
Aware: Aspect-based sentiment analysis dataset of apps reviews for requirements elicitation
N Alturayeif, H Aljamaan, M Baslyman 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Bad smell detection using machine learning techniques: a systematic literature review
A Al-Shaaby, H Aljamaan, M Alshayeb Arabian Journal for Science and Engineering
Software defect prediction using tree-based ensemblesH Aljamaan, A Alazba 16th ACM International Conference on Predictive Models for Software Engineering (PROMISE)
MOTL: a textual language for trace specification of state machines and associationsH Aljamaan, TC Lethbridge, MA Garzón 25th Annual International Conference on Computer Science and Software Engineering (CASCON)
Umple: A framework for model driven development of object-oriented systems
MA Garzón, H Aljamaan, TC Lethbridge IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER)
Specifying Trace Directives for UML Attributes and State MachinesH Aljamaan, TC Lethbridge, O Badreddin, G Guest, A Forward 2nd International Conference on Model-Driven Engineering and Software Evolution (MODELSWARD)
Enhanced Code Generation from UML Composite State Machines
O Badreddin, TC Lethbridge, A Forward, M Elaasar, H Aljamaan, et al. 2nd International Conference on Model-Driven Engineering and Software Evolution (MODELSWARD)
Reverse engineering of object-oriented code into Umple using an incremental and rule-based approach
MA Garzón, TC Lethbridge, H Aljamaan, O Badreddin CASCON 2014
A Model-Driven Solution for Financial Data Representation Expressed in FIXML
V Abdelzad, H Aljamaan, O Adesina, M Garzón, T Lethbridge TTC@STAF
2013
An ensemble of computational intelligence models for software maintenance effort predictionH Aljamaan, MO Elish, I Ahmad International Work-Conference on Artificial Neural Networks (IWANN)
An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented softwareHI Aljamaan, MO Elish IEEE Symposium on Computational Intelligence and Data Mining (CIDM)