Artificial Intelligence
Many factors influence the efficiency of a classification model in Machine Learning, including the dataset’s size and the type of Machine Learning (ML) technique utilized in the classification process. Primarily, the accuracy varies among different machine learning methods. This paper develops a model that analyses and measures the impact of Tax Dataset Augmentation on the Accuracy of Machine Learning Models. The paper compares the performance of different models based on the most common machine learning techniques, namely: DT, RF, SVM, and ANN (MLP). Based on three datasets provided by Yemen’s Tax Authority. The first dataset contains 1083 records, while the second dataset is identical to the previous one, but with nearly five times the number of records, following data preprocessing, which resulted in approximately 5000 additional records. The last dataset is the same as the original dataset, but it has been duplicated nearly ten times, resulting in almost 10,000 records. The dataset partitioning technique utilized k-fold validation using the three datasets. Results show that the performance of ML classifiers such as ANN (MLP), DT, RF, and SVM is affected by dataset augmentation in terms of accuracy, recall, precision, and F-score. Results also show that the performance varies among the first three techniques; however, the SVM Classifier yields the lowest results. In general, despite some techniques leading to overfitting, it is found that most machine learning models utilizing tax datasets with five times the duplicates produced better outcomes than those using the original dataset. These findings provide practical guidance for tax authorities in selecting robust machine learning models under limited data availability and highlight risks associated with naïve dataset expansion.
Abeer Abdullah Shujaaaddeen, Ammar T.Zahary, Fadl Mutaher Ba-Alwi (2026). A Comparative Model to Analyze the Impact of Tax Dataset Augmentation on the Accuracy of Machine Learning Models. Sana'a University Journal of Applied Sciences and Technology, 1914-1923. DOI: https://doi.org/10.59628/jast.v4i4.2121
Abeer Abdullah Shujaaaddeen, Ammar T.Zahary, Fadl Mutaher Ba-Alwi . “A Comparative Model to Analyze the Impact of Tax Dataset Augmentation on the Accuracy of Machine Learning Models.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1914-1923. https://doi.org/10.59628/jast.v4i4.2121. Accessed 30 Oct. 2026.
Abeer Abdullah Shujaaaddeen, Ammar T.Zahary, Fadl Mutaher Ba-Alwi . 2026. “A Comparative Model to Analyze the Impact of Tax Dataset Augmentation on the Accuracy of Machine Learning Models.” Sana'a University Journal of Applied Sciences and Technology : 1914-1923. DOI: https://doi.org/10.59628/jast.v4i4.2121
Abeer Abdullah Shujaaaddeen, Ammar T.Zahary, Fadl Mutaher Ba-Alwi (2026) ‘A Comparative Model to Analyze the Impact of Tax Dataset Augmentation on the Accuracy of Machine Learning Models’, Sana'a University Journal of Applied Sciences and Technology, pp. 1914-1923. doi: 10.59628/jast.v4i4.2121
@article{jast2026_art2121,
author = {Abeer Abdullah Shujaaaddeen, Ammar T.Zahary, Fadl Mutaher Ba-Alwi },
title = {A Comparative Model to Analyze the Impact of Tax Dataset Augmentation on the Accuracy of Machine Learning Models},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {April}, pages = {1914-1923}, doi = {10.59628/jast.v4i4.2121},
url = {https://doi.org/10.59628/jast.v4i4.2121},}
A Comparative Model to Analyze the Impact of Tax Dataset Augmentation on the Accuracy of Machine Learning Models
The performance of a classification model in machine learning is affected by many factors, such as the type of machine learning technology used. Accuracy varies from method to method. This paper presents a comparison between the performance of different models in terms of the machine learning technique used (e.g. KNN, NB, SVM, DT, RF, MLP). Based on the data provided by the Tax Authority of Yemen, which is related to the commercial and industrial profits tax, which consists of 760 attributes, after the preprocessing of data. The dataset partition technique used k-fold validation. The paper shows that the e Naïve Bayes (NB) classifier gave the highest result in accuracy and other measures. Then KNN, SVM, and RF gave the same results in accuracy 99.87%, but in SVM, KNN the results were also the same in the rest measures, while in RF models the rest measures were 97.91%,99.95%, and 98.91% in Recall, Precision and F-score in order. MLP gave 98.42 in accuracy with 66.62%, 64.21%, and 64.40 in the recall, Precision, and F-score, then DT gave 97.76% in accuracy with 57.006% ,99.24% and 72.41% in the recall, precision, and F-score.
Abeer Abdullah Shujaaddeen, Fadl M.M. Ba-Alwi (2023). A Comparative Study of the Performance of Machine Learning Models on a Tax Dataset of Yemen to Detect Levels of Tax Evasion. Sana'a University Journal of Applied Sciences and Technology. DOI: https://doi.org/10.59628/jast.v1i4.528
Abeer Abdullah Shujaaddeen, Fadl M.M. Ba-Alwi . “A Comparative Study of the Performance of Machine Learning Models on a Tax Dataset of Yemen to Detect Levels of Tax Evasion.” Sana'a University Journal of Applied Sciences and Technology, 2023, https://doi.org/10.59628/jast.v1i4.528. Accessed 30 Oct. 2026.
Abeer Abdullah Shujaaddeen, Fadl M.M. Ba-Alwi . 2023. “A Comparative Study of the Performance of Machine Learning Models on a Tax Dataset of Yemen to Detect Levels of Tax Evasion.” Sana'a University Journal of Applied Sciences and Technology DOI: https://doi.org/10.59628/jast.v1i4.528
Abeer Abdullah Shujaaddeen, Fadl M.M. Ba-Alwi (2023) ‘A Comparative Study of the Performance of Machine Learning Models on a Tax Dataset of Yemen to Detect Levels of Tax Evasion’, Sana'a University Journal of Applied Sciences and Technology. doi: 10.59628/jast.v1i4.528
@article{jast2023_art528,
author = {Abeer Abdullah Shujaaddeen, Fadl M.M. Ba-Alwi },
title = {A Comparative Study of the Performance of Machine Learning Models on a Tax Dataset of Yemen to Detect Levels of Tax Evasion},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2023}, month = {December}, doi = {10.59628/jast.v1i4.528},
url = {https://doi.org/10.59628/jast.v1i4.528},}
A Comparative Study of the Performance of Machine Learning Models on a Tax Dataset of Yemen to Detect Levels of Tax Evasion
Personalized e-learning adapts educational materials and learning paths to individual learner characteristics. This adaptation is often supported by analyzing learner interactions through Web Usage Mining. The present survey reviews research from 2010 to 2025 that addresses dynamic web usage pattern mining for personalized e-learning environments. Four main contributions stand out. First, a hierarchical classification organizes approaches across five interconnected levels: data sources, preprocessing, pattern discovery, dynamic mining, and recommendation systems. A comparison then follows between static and dynamic mining approaches—tracing temporal and incremental methodologies, something earlier surveys often overlooked. Third, the analysis turns to recommendation mechanisms: collaborative filtering and content-based methods. Finally, ongoing challenges are identified and future directions suggested. Static algorithms such as Apriori and FP-Growth, according to the review’s conclusion, are increasingly giving way to dynamic techniques—incremental mining and sliding window analysis, for instance. Dynamic methods capture the evolution of learner behavior more effectively. Several research gaps remain. Despite their proven effectiveness, deploying these dynamic techniques in real-world educational settings continues to face obstacles, with scalability and latency being the most prominent. More critically, metrics for evaluating pattern interestingness based on educational impact remain underdeveloped.
Emad Mahmoud Al-Azazi, Ahmed Sultan Al-Hegami (2026). A A Dynamic Mining of Interesting Web Usage Patterns for Personalized E-Learning: A Systematic Review. Sana'a University Journal of Applied Sciences and Technology, 2265-2283. DOI: https://doi.org/10.59628/jast.v4i6.2980
Emad Mahmoud Al-Azazi, Ahmed Sultan Al-Hegami . “A A Dynamic Mining of Interesting Web Usage Patterns for Personalized E-Learning: A Systematic Review.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 2265-2283. https://doi.org/10.59628/jast.v4i6.2980. Accessed 30 Oct. 2026.
Emad Mahmoud Al-Azazi, Ahmed Sultan Al-Hegami . 2026. “A A Dynamic Mining of Interesting Web Usage Patterns for Personalized E-Learning: A Systematic Review.” Sana'a University Journal of Applied Sciences and Technology : 2265-2283. DOI: https://doi.org/10.59628/jast.v4i6.2980
Emad Mahmoud Al-Azazi, Ahmed Sultan Al-Hegami (2026) ‘A A Dynamic Mining of Interesting Web Usage Patterns for Personalized E-Learning: A Systematic Review’, Sana'a University Journal of Applied Sciences and Technology, pp. 2265-2283. doi: 10.59628/jast.v4i6.2980
@article{jast2026_art2980,
author = {Emad Mahmoud Al-Azazi, Ahmed Sultan Al-Hegami },
title = {A A Dynamic Mining of Interesting Web Usage Patterns for Personalized E-Learning: A Systematic Review},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {June}, pages = {2265-2283}, doi = {10.59628/jast.v4i6.2980},
url = {https://doi.org/10.59628/jast.v4i6.2980},}
A A Dynamic Mining of Interesting Web Usage Patterns for Personalized E-Learning: A Systematic Review
Microarray technology produces data with a large number of dimensions. The abundance of dimensions in the data makes it challenging for machine learning algorithms to extract meaningful information from it. To overcome this limitation, feature selection (FS) techniques can be applied to reduce the dimensionality of the data. FS is a crucial preprocessing step that allows for the handling of high-dimensional data and facilitates more effective and efficient processing. The primary objective of this paper is to develop an efficient FS method that can effectively reduce the dimensionality of microarray data. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized in the first filtering stage to extract informative features. The best feature subset is then determined by using a Particle Swarm Optimization (PSO) algorithm as a wrapper feature selector to identify ideal features. The proposed approach is evaluated using microarray datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared with other algorithms. The experimental findings indicate that the proposed approach outperforms the benchmark methods in terms of classification accuracy.
Heba Al.Marwai, Ghaleb H AL-Gaphari (2024). A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification. Sana'a University Journal of Applied Sciences and Technology, 375-380. DOI: https://doi.org/10.59628/jast.v2i4.1023
Heba Al.Marwai, Ghaleb H AL-Gaphari . “A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification.” Sana'a University Journal of Applied Sciences and Technology, 2024, pp. 375-380. https://doi.org/10.59628/jast.v2i4.1023. Accessed 30 Oct. 2026.
Heba Al.Marwai, Ghaleb H AL-Gaphari . 2024. “A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification.” Sana'a University Journal of Applied Sciences and Technology : 375-380. DOI: https://doi.org/10.59628/jast.v2i4.1023
Heba Al.Marwai, Ghaleb H AL-Gaphari (2024) ‘A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification’, Sana'a University Journal of Applied Sciences and Technology, pp. 375-380. doi: 10.59628/jast.v2i4.1023
@article{jast2024_art1023,
author = {Heba Al.Marwai, Ghaleb H AL-Gaphari },
title = {A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2024}, month = {September}, pages = {375-380}, doi = {10.59628/jast.v2i4.1023},
url = {https://doi.org/10.59628/jast.v2i4.1023},}
A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification
Using Internet of Things (IoT) and deep learning techniques in the healthcare sector has gained significant momentum in recent years. These technologies have the potential to transform traditional healthcare monitoring systems into real-time data collection, analysis, and decision-making capabilities. While several models have been developed to assist people with heart diseases, numerous obstacles still impede the effectiveness of the current solutions, such as power consumption, latency, accuracy, and scalability. Therefore, this study aims to develop a promising smart healthcare monitoring model that integrates IoT and deep learning techniques for saving patient lives. In addition, it clarifies the current research gap. The methodology used in this study was a literature review, which was conducted to identify relevant studies on IoT and deep learning applications in healthcare and find the gaps in each. This model consists of three main components: data acquisition through IoT devices, data processing using deep learning algorithms, and decision-making based on analyzed data. Moreover, it showed an unstable rate of accuracy in the current studies, which were taken from 2021 to 2023. In the future, our proposed smart healthcare monitoring model will solve that gap, which is already available in the current studies, and that will be proven using real-time materials such as Arduino and heart disease sensors.
Marwa Abdulrahman Al-Hadi, Ghaleb Hamoud Al-Gaphari, Ibrahim Ahmed Al-Baltah, Francisco Bacallao Julian (2024). A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques. Sana'a University Journal of Applied Sciences and Technology, 147-153. DOI: https://doi.org/10.59628/jast.v2i2.811
Marwa Abdulrahman Al-Hadi, Ghaleb Hamoud Al-Gaphari, Ibrahim Ahmed Al-Baltah, Francisco Bacallao Julian . “A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques.” Sana'a University Journal of Applied Sciences and Technology, 2024, pp. 147-153. https://doi.org/10.59628/jast.v2i2.811. Accessed 30 Oct. 2026.
Marwa Abdulrahman Al-Hadi, Ghaleb Hamoud Al-Gaphari, Ibrahim Ahmed Al-Baltah, Francisco Bacallao Julian . 2024. “A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques.” Sana'a University Journal of Applied Sciences and Technology : 147-153. DOI: https://doi.org/10.59628/jast.v2i2.811
Marwa Abdulrahman Al-Hadi, Ghaleb Hamoud Al-Gaphari, Ibrahim Ahmed Al-Baltah, Francisco Bacallao Julian (2024) ‘A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques’, Sana'a University Journal of Applied Sciences and Technology, pp. 147-153. doi: 10.59628/jast.v2i2.811
@article{jast2024_art811,
author = {Marwa Abdulrahman Al-Hadi, Ghaleb Hamoud Al-Gaphari, Ibrahim Ahmed Al-Baltah, Francisco Bacallao Julian },
title = {A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2024}, month = {May}, pages = {147-153}, doi = {10.59628/jast.v2i2.811},
url = {https://doi.org/10.59628/jast.v2i2.811},}
A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques
Generative AI Applications have become revolutionary technologies that can help with academic evaluation, educational decision-making, and individualized learning. However, despite their technological potential, many higher education institutions especially those in poor nations continue to struggle with faculty and student acceptance of these applications. This study offers in order to determine crucial success variables impacting the acceptance, use and difficulties of generative AI applications, a thorough analysis of the literature. Research from databases including Google Scholar, Scopus, and IEEE Explore that were published between 2021 and 2025 was chosen. A conceptual model based on organizational, individual/psychological, legal/cultural/social, and technical variables is proposed in this study. It also generates hypotheses and evaluates the importance of crucial success variables. Additionally, it outlines the main obstacles to the acceptance of generative AI applications, as well as their limitations and potential future developments, which present a plethora of chances for the creation of various solutions.
Wafa A. Hageb , Ammar T. Zahary (2026). A Systematic Review of Factors Influencing the Acceptance of Generative Artificial Intelligence Applications in Yemeni Higher Education. Sana'a University Journal of Applied Sciences and Technology, 1890-1902. DOI: https://doi.org/10.59628/jast.v4i3.2594
Wafa A. Hageb , Ammar T. Zahary . “A Systematic Review of Factors Influencing the Acceptance of Generative Artificial Intelligence Applications in Yemeni Higher Education.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1890-1902. https://doi.org/10.59628/jast.v4i3.2594. Accessed 30 Oct. 2026.
Wafa A. Hageb , Ammar T. Zahary . 2026. “A Systematic Review of Factors Influencing the Acceptance of Generative Artificial Intelligence Applications in Yemeni Higher Education.” Sana'a University Journal of Applied Sciences and Technology : 1890-1902. DOI: https://doi.org/10.59628/jast.v4i3.2594
Wafa A. Hageb , Ammar T. Zahary (2026) ‘A Systematic Review of Factors Influencing the Acceptance of Generative Artificial Intelligence Applications in Yemeni Higher Education’, Sana'a University Journal of Applied Sciences and Technology, pp. 1890-1902. doi: 10.59628/jast.v4i3.2594
@article{jast2026_art2594,
author = {Wafa A. Hageb , Ammar T. Zahary },
title = {A Systematic Review of Factors Influencing the Acceptance of Generative Artificial Intelligence Applications in Yemeni Higher Education},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {March}, pages = {1890-1902}, doi = {10.59628/jast.v4i3.2594},
url = {https://doi.org/10.59628/jast.v4i3.2594},}
A Systematic Review of Factors Influencing the Acceptance of Generative Artificial Intelligence Applications in Yemeni Higher Education
This paper investigates the application of Artificial Intelligence (AI) in Self-Organizing Networks (SON) for 5G networks, focusing on coverage enhancement and reduction of Operational Expenditures (OPEX). A conceptual AI-Self Organizing Networks (SON) framework integrated with O RAN architecture is proposed, and an illustrative Python-based simulation is conducted to demonstrate potential trends in coverage probability, energy consumption, and estimated OPEX savings. The simulation results indicate that AI-SON can achieve near-optimal coverage (coverage probability 0.9985) while reducing energy usage and maintenance costs, with an estimated OPEX reduction of 2030% compared to baseline strategies. The study clarifies that the simulation is illustrative and not experimentally validated, providing a foundation for future rigorous evaluations.
Mohamed Hankal (2026). AI-Driven Self-Organizing Networks (SON) for 5G OPEX Reduction: A Comprehensive Survey and Conceptual Framework. Sana'a University Journal of Applied Sciences and Technology, 1496-1506. DOI: https://doi.org/10.59628/jast.v4i1.2291
Mohamed Hankal . “AI-Driven Self-Organizing Networks (SON) for 5G OPEX Reduction: A Comprehensive Survey and Conceptual Framework.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1496-1506. https://doi.org/10.59628/jast.v4i1.2291. Accessed 30 Oct. 2026.
Mohamed Hankal . 2026. “AI-Driven Self-Organizing Networks (SON) for 5G OPEX Reduction: A Comprehensive Survey and Conceptual Framework.” Sana'a University Journal of Applied Sciences and Technology : 1496-1506. DOI: https://doi.org/10.59628/jast.v4i1.2291
Mohamed Hankal (2026) ‘AI-Driven Self-Organizing Networks (SON) for 5G OPEX Reduction: A Comprehensive Survey and Conceptual Framework’, Sana'a University Journal of Applied Sciences and Technology, pp. 1496-1506. doi: 10.59628/jast.v4i1.2291
@article{jast2026_art2291,
author = {Mohamed Hankal },
title = {AI-Driven Self-Organizing Networks (SON) for 5G OPEX Reduction: A Comprehensive Survey and Conceptual Framework},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {January}, pages = {1496-1506}, doi = {10.59628/jast.v4i1.2291},
url = {https://doi.org/10.59628/jast.v4i1.2291},}
AI-Driven Self-Organizing Networks (SON) for 5G OPEX Reduction: A Comprehensive Survey and Conceptual Framework
Artificial intelligence (AI) is reshaping academic practice across research and learning. To address fragmented evidence, this review aims to (i) map commonly used academic AI tools, (ii) synthesize reported effects on research and learning performance, and (iii) outline risks and governance needs. We conducted a qualitative content analysis using publicly available “materials,” including official tool documentation and websites, major academic databases , and user reports; records were screened for relevance to higher education and research, data were charted on features, use-cases, and ethical considerations, and tools were categorized into five domains: AI writing assistants; literature-review & citation tools; data analysis & visualization; productivity & organization; and learning & tutoring. Main results show comparative feature tables that support tool selection and consistent benefit signals: higher research efficiency and writing support, and within education—greater engagement and personalization; persistent risks include academic integrity, privacy, bias, and over-reliance, implying the need for clear governance and assessment design. Verification relied on human fact-checking against primary sources and consistency checks across categories; the review is exploratory and descriptive and doesnot include benchmarking or controlled experiments.
Jehan Wajeh Almaqtari, Emad Alramada, Ayman Alsabry (2026). An Overview and Impact Analysis of AI Tools in Academic Research, Learning, and Educational Performance. Sana'a University Journal of Applied Sciences and Technology, 1665-1681. DOI: https://doi.org/10.59628/jast.v4i2.1921
Jehan Wajeh Almaqtari, Emad Alramada, Ayman Alsabry . “An Overview and Impact Analysis of AI Tools in Academic Research, Learning, and Educational Performance.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1665-1681. https://doi.org/10.59628/jast.v4i2.1921. Accessed 30 Oct. 2026.
Jehan Wajeh Almaqtari, Emad Alramada, Ayman Alsabry . 2026. “An Overview and Impact Analysis of AI Tools in Academic Research, Learning, and Educational Performance.” Sana'a University Journal of Applied Sciences and Technology : 1665-1681. DOI: https://doi.org/10.59628/jast.v4i2.1921
Jehan Wajeh Almaqtari, Emad Alramada, Ayman Alsabry (2026) ‘An Overview and Impact Analysis of AI Tools in Academic Research, Learning, and Educational Performance’, Sana'a University Journal of Applied Sciences and Technology, pp. 1665-1681. doi: 10.59628/jast.v4i2.1921
@article{jast2026_art1921,
author = {Jehan Wajeh Almaqtari, Emad Alramada, Ayman Alsabry },
title = {An Overview and Impact Analysis of AI Tools in Academic Research, Learning, and Educational Performance},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {February}, pages = {1665-1681}, doi = {10.59628/jast.v4i2.1921},
url = {https://doi.org/10.59628/jast.v4i2.1921},}
An Overview and Impact Analysis of AI Tools in Academic Research, Learning, and Educational Performance
Linguistic annotation provides additional information associated with a particular purpose in a document or another piece of information. It is widely used in various fields, ranging from computing and bioinformatics to psychology, law, and linguistics. This study aimed to develop a linguistic annotator for nominal Arabic sentences. The annotator detects both the Inchoative (I) and Predicate (P) phrases within nominal sentences in Arabic text. This is based on a shallow parsing method for chunking sentence constituents. The motivation behind this study is to produce a tool that enables further analysis, such as rhetorical parsing. This annotator can help improve the accuracy of linguistic parsers and enhance natural language understanding. The experiments were conducted on a standard dataset supported by a competitive case study. These results are promising and encouraging.
Saeed AL-Dobai, Bakeel Azman, Ghalib AL-Gaphari, Monier Ana’am (2025). Annotation of Inchoative and Predicate in Arabic Nominal Sentence. Sana'a University Journal of Applied Sciences and Technology, 1178-1186. DOI: https://doi.org/10.59628/jast.v3i5.1468
Saeed AL-Dobai, Bakeel Azman, Ghalib AL-Gaphari, Monier Ana’am . “Annotation of Inchoative and Predicate in Arabic Nominal Sentence.” Sana'a University Journal of Applied Sciences and Technology, 2025, pp. 1178-1186. https://doi.org/10.59628/jast.v3i5.1468. Accessed 30 Oct. 2026.
Saeed AL-Dobai, Bakeel Azman, Ghalib AL-Gaphari, Monier Ana’am . 2025. “Annotation of Inchoative and Predicate in Arabic Nominal Sentence.” Sana'a University Journal of Applied Sciences and Technology : 1178-1186. DOI: https://doi.org/10.59628/jast.v3i5.1468
Saeed AL-Dobai, Bakeel Azman, Ghalib AL-Gaphari, Monier Ana’am (2025) ‘Annotation of Inchoative and Predicate in Arabic Nominal Sentence’, Sana'a University Journal of Applied Sciences and Technology, pp. 1178-1186. doi: 10.59628/jast.v3i5.1468
@article{jast2025_art1468,
author = {Saeed AL-Dobai, Bakeel Azman, Ghalib AL-Gaphari, Monier Ana’am },
title = {Annotation of Inchoative and Predicate in Arabic Nominal Sentence},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2025}, month = {October}, pages = {1178-1186}, doi = {10.59628/jast.v3i5.1468},
url = {https://doi.org/10.59628/jast.v3i5.1468},}
Annotation of Inchoative and Predicate in Arabic Nominal Sentence
Breast cancer (BC) is a major health concern worldwide. It is a complex and multifactorial disease, and identifying its risk factors is crucial for early detection and effective treatment. This review article provides an overview of the literature on breast cancer risk factors, data sources, and machine learning algorithms for prediction. The paper discusses the various risk factors associated with breast cancer, including age, family history, lifestyle choices, and environmental factors. Additionally, the article explores the different data sources used in breast cancer research, including clinical data, genomic data, and lifestyle data. The paper then reviews the different machine-learning algorithms used for breast cancer prediction, including supervised and unsupervised learning. The performance of ML algorithms in predicting BC risk using different data sources was assessed. This review provides valuable insights into the current state of research on BC risk factors and prediction using ML algorithms and data sources, and the findings will be useful for healthcare professionals and researchers working in the field of BC.
Ayman Alsabry, Malek Algabri, Amin Mohamed Ahsan (2023). Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature. Sana'a University Journal of Applied Sciences and Technology. DOI: https://doi.org/10.59628/jast.v1i2.361
Ayman Alsabry, Malek Algabri, Amin Mohamed Ahsan . “Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature.” Sana'a University Journal of Applied Sciences and Technology, 2023, https://doi.org/10.59628/jast.v1i2.361. Accessed 30 Oct. 2026.
Ayman Alsabry, Malek Algabri, Amin Mohamed Ahsan . 2023. “Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature.” Sana'a University Journal of Applied Sciences and Technology DOI: https://doi.org/10.59628/jast.v1i2.361
Ayman Alsabry, Malek Algabri, Amin Mohamed Ahsan (2023) ‘Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature’, Sana'a University Journal of Applied Sciences and Technology. doi: 10.59628/jast.v1i2.361
@article{jast2023_art361,
author = {Ayman Alsabry, Malek Algabri, Amin Mohamed Ahsan },
title = {Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2023}, month = {April}, doi = {10.59628/jast.v1i2.361},
url = {https://doi.org/10.59628/jast.v1i2.361},}
Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature
Polycystic ovary syndrome (PCOS) is a serious hormonal disorder that affects women and significantly impacts their quality of life. In modern times, women are increasingly susceptible to this syndrome, which is a major cause of numerous health problems, most notably infertility. Early detection of PCOS significantly reduces complications, making an early and accurate diagnosis system crucial.
Among all diagnostic techniques, machine learning (ML) has demonstrated superior performance due to its ability to extract features and patterns from data. Therefore, this field has received widespread attention from researchers, and numerous studies have been conducted to detect PCOS using machine learning techniques. These methods have included convolutional neural networks (CNN), support vector machines (SVM), k-nearest neighbors (KNN), random forests, logistic regression, decision trees, and the Naive Bayes algorithm, among others.
This paper aims to shed light on all current techniques used in PCOS detection using machine learning algorithms, providing a comprehensive descriptive and contextual review. It also provides a detailed analysis of how various ML techniques have been used in this field over the past decades, with an in-depth discussion of these approaches. A comprehensive review of the various datasets used in PCOS diagnosis is also provided, comparing the performance of algorithms from both quantitative and qualitative perspectives. Finally, the paper discusses the most prominent challenges facing this field, in addition to exploring future research prospects.
Sawsan Mohammed Al-Sharsi, Ghalab Hamoud Al-Gaphari (2025). Comprehensive Review of Polycystic Ovary Syndrome Detection Techniques. Sana'a University Journal of Applied Sciences and Technology, 1033-1042. DOI: https://doi.org/10.59628/jast.v3i4.1776
Sawsan Mohammed Al-Sharsi, Ghalab Hamoud Al-Gaphari . “Comprehensive Review of Polycystic Ovary Syndrome Detection Techniques.” Sana'a University Journal of Applied Sciences and Technology, 2025, pp. 1033-1042. https://doi.org/10.59628/jast.v3i4.1776. Accessed 30 Oct. 2026.
Sawsan Mohammed Al-Sharsi, Ghalab Hamoud Al-Gaphari . 2025. “Comprehensive Review of Polycystic Ovary Syndrome Detection Techniques.” Sana'a University Journal of Applied Sciences and Technology : 1033-1042. DOI: https://doi.org/10.59628/jast.v3i4.1776
Sawsan Mohammed Al-Sharsi, Ghalab Hamoud Al-Gaphari (2025) ‘Comprehensive Review of Polycystic Ovary Syndrome Detection Techniques’, Sana'a University Journal of Applied Sciences and Technology, pp. 1033-1042. doi: 10.59628/jast.v3i4.1776
@article{jast2025_art1776,
author = {Sawsan Mohammed Al-Sharsi, Ghalab Hamoud Al-Gaphari },
title = {Comprehensive Review of Polycystic Ovary Syndrome Detection Techniques},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2025}, month = {August}, pages = {1033-1042}, doi = {10.59628/jast.v3i4.1776},
url = {https://doi.org/10.59628/jast.v3i4.1776},}
Comprehensive Review of Polycystic Ovary Syndrome Detection Techniques
Fingerprint recognition has widespread security applications owing to its uniqueness, permanence, and simplicity in capture. However, conventional fingerprint authentication systems face high false acceptance and rejection rates and susceptibility to spoofing attacks. To address these issues, this study proposes a deep learning-based fingerprint authentication system using a Convolutional Neural Network (CNN) with five convolution layers to derive robust spatial features. The model was trained and cross-validated on the SOCOFing dataset with regularization and data augmentation to enhance generalization and spoof resistance. Experimental results show that the proposed CNN achieved a training accuracy of 99.10% with a loss of 0.0223 and a validation accuracy of 98.89% with a loss of 0.0114. Moreover, the model maintained a low false acceptance rate of 0.33% and false rejection rate of 0.25%, demonstrating its efficacy and credibility for secure and real-time biometric authentication. A rigorous comparison with conventional CNN models and DCCN architectures confirmed that the proposed model provides higher accuracy, lower computational cost, and stronger resistance to spoofing attacks. These findings indicate that the proposed system successfully addresses existing limitations and offers a practical, scalable, and reliable solution for fingerprint verification using deep CNN architectures.
Ghaleb H. Aljafary , Abdulrahman Hussian (2025). Deep Convolutional Neural Networks for Fingerprint Classification. Sana'a University Journal of Applied Sciences and Technology, 1145-1154. DOI: https://doi.org/10.59628/jast.v3i5.1831
Ghaleb H. Aljafary , Abdulrahman Hussian . “Deep Convolutional Neural Networks for Fingerprint Classification.” Sana'a University Journal of Applied Sciences and Technology, 2025, pp. 1145-1154. https://doi.org/10.59628/jast.v3i5.1831. Accessed 30 Oct. 2026.
Ghaleb H. Aljafary , Abdulrahman Hussian . 2025. “Deep Convolutional Neural Networks for Fingerprint Classification.” Sana'a University Journal of Applied Sciences and Technology : 1145-1154. DOI: https://doi.org/10.59628/jast.v3i5.1831
Ghaleb H. Aljafary , Abdulrahman Hussian (2025) ‘Deep Convolutional Neural Networks for Fingerprint Classification’, Sana'a University Journal of Applied Sciences and Technology, pp. 1145-1154. doi: 10.59628/jast.v3i5.1831
@article{jast2025_art1831,
author = {Ghaleb H. Aljafary , Abdulrahman Hussian },
title = {Deep Convolutional Neural Networks for Fingerprint Classification},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2025}, month = {October}, pages = {1145-1154}, doi = {10.59628/jast.v3i5.1831},
url = {https://doi.org/10.59628/jast.v3i5.1831},}
Deep Convolutional Neural Networks for Fingerprint Classification
The widespread adoption of face masks during the COVID-19 pandemic created new requirements for facial recognition technology, driving focused innovation in Masked Face Recognition (MFR) systems. This survey synthesizes and analyzes key research developments from 2020 to 2024, examining more than twenty pivotal studies in the field. The review provides a structured comparison of contemporary deep learning methodologies, with particular attention to Convolutional Neural Network (CNN) implementations including ResNet, VGG, and MobileNet architectures. It further evaluates the characteristics and performance outcomes associated with principal publicly available datasets employed in MFR model development. The analysis highlights important considerations for advancing MFR systems, particularly regarding data quality and model generalization. Current research directions emphasize improving the alignment between training data characteristics and real-world application environments. Among available resources, the MaskedFace-Net dataset demonstrates particularly strong performance in benchmark evaluations, with reported recognition rates exceeding 99%, attributed to its specialized design and substantial scale. For practical implementation, the findings support utilizing established CNN-based frameworks, with opportunities for enhancement through complementary approaches such as integration with traditional classifiers. Looking forward, this survey identifies promising pathways for continued progress in MFR technology, centered on evolutionary advancements within proven architectural paradigms, sophisticated data utilization strategies, and optimizations for deployment efficiency. This comprehensive review offers valuable guidance for researchers and engineers working to create effective and adaptable masked face recognition solutions.
Haron M . Ahmed Omer, Fadl Ba-Alwi (2026). Deep Learning Approaches for Masked Face Recognition: A Survey of Research During (2020-2024). Sana'a University Journal of Applied Sciences and Technology, 2130-2138. DOI: https://doi.org/10.59628/jast.v4i5.2643
Haron M . Ahmed Omer, Fadl Ba-Alwi . “Deep Learning Approaches for Masked Face Recognition: A Survey of Research During (2020-2024).” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 2130-2138. https://doi.org/10.59628/jast.v4i5.2643. Accessed 30 Oct. 2026.
Haron M . Ahmed Omer, Fadl Ba-Alwi . 2026. “Deep Learning Approaches for Masked Face Recognition: A Survey of Research During (2020-2024).” Sana'a University Journal of Applied Sciences and Technology : 2130-2138. DOI: https://doi.org/10.59628/jast.v4i5.2643
Haron M . Ahmed Omer, Fadl Ba-Alwi (2026) ‘Deep Learning Approaches for Masked Face Recognition: A Survey of Research During (2020-2024)’, Sana'a University Journal of Applied Sciences and Technology, pp. 2130-2138. doi: 10.59628/jast.v4i5.2643
@article{jast2026_art2643,
author = {Haron M . Ahmed Omer, Fadl Ba-Alwi },
title = {Deep Learning Approaches for Masked Face Recognition: A Survey of Research During (2020-2024)},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {May}, pages = {2130-2138}, doi = {10.59628/jast.v4i5.2643},
url = {https://doi.org/10.59628/jast.v4i5.2643},}
Deep Learning Approaches for Masked Face Recognition: A Survey of Research During (2020-2024)
Recent advancements in Natural Language Processing (NLP) and Text-to-SQL systems have enabled easier interaction with relational databases. However, most solutions focus on English, leaving Arabic underrepresented. This study addresses that gap by fine-tuning the Llama-3-SQLCoder model to convert Arabic text into correct and executable SQL, enabling non-technical users to work with databases without learning SQL syntax. We enhanced the model using Low-Rank Adaptation (LoRA) and Unsloth, training it on Arabic questions paired with SQL queries from the Northwind database. To support low-resource environments, the model was converted to the GGUF format, reducing computational requirements while preserving performance. Evaluation results showed an execution accuracy of 90.24% and a validity rate of 97.56%, outperforming the zero-shot baseline (44% and 80%). The model also achieved an Exact Match score of 32% and an F1 score of 0.83, compared to 12% and 0.61 for the baseline. These findings demonstrate that LoRA and Unsloth are effective for adapting SQL-specialized models to Arabic. Despite these improvements, the system still struggles with complex nested queries and dialectal variations, indicating areas for future work. Overall, this study contributes to narrowing the gap between Arabic and other languages in Text-to-SQL research and improves database accessibility for non-technical users.
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar (2026). Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models. Sana'a University Journal of Applied Sciences and Technology, 1507-1519. DOI: https://doi.org/10.59628/jast.v4i1.2248
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar . “Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1507-1519. https://doi.org/10.59628/jast.v4i1.2248. Accessed 30 Oct. 2026.
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar . 2026. “Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models.” Sana'a University Journal of Applied Sciences and Technology : 1507-1519. DOI: https://doi.org/10.59628/jast.v4i1.2248
Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar (2026) ‘Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models’, Sana'a University Journal of Applied Sciences and Technology, pp. 1507-1519. doi: 10.59628/jast.v4i1.2248
@article{jast2026_art2248,
author = {Mohammed Taj, Mohammed Zayed, Abdulwahid Alhetar, Mohammed Rajeh, Mohammed Abbas Al-Sharafi, Basem Abdulrhman Munassar },
title = {Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {January}, pages = {1507-1519}, doi = {10.59628/jast.v4i1.2248},
url = {https://doi.org/10.59628/jast.v4i1.2248},}
Enabling Arabic Database Querying via Parameter-Efficient Fine-Tuning of Large Language Models
Intrusion detection is crucial for securing Industrial Internet of Things (IIoT) networks, especially within edge computing environments. Traditional Intrusion Detection Systems (IDSs) struggle with the complexity and dynamic nature of IIoT networks, where increasing intrusion classes make classification tasks more challenging. While the Asynchronous Advantage Actor-Critic (A3C) algorithm has shown promise in reinforcement learning-based IDSs, previous A3C implementations suffer from slow convergence, high variance, unstable gradient updates, and inefficient parameter synchronization. These issues limit their ability to accurately classify diverse attack patterns, particularly underrepresented intrusion types. To address these challenges, this research introduces an Enhanced A3C (EA3C) using an enhanced convolutional neural network (CNN) structure to significantly improve feature representation compared to traditional fully connected networks from the dataset before passing them to the policy and value networks. Additionally, gradient clipping will be applied to prevent exploding gradients, and bootstrapping-based reward handling will be used to enhance policy and value estimation for better long-term learning and improved intrusion detection performance. The proposed approach is evaluated using the X-IIoTID dataset, which is a comprehensive benchmark that is effective in detecting a wide range of cyber threats in IIoT environments. Experimental results indicate that the EA3C algorithm significantly outperforms Decision Tree (DT), Adversarial Environment Reinforcement Learning (AERL), Double Deep Q-Network (DDQN), and traditional A3C algorithms, particularly in identifying underrepresented attack classes. The results of EA3C show that its weighted Accuracy, Precision, Recall, and F1-score exceeded 0.98, making it suitable for practical use with the increasing number of labeled classes of cyber-attacks. Although these results are promising, this algorithm needs further improvement, especially for attacks with very small samples or attacks that occur for the first time, such as zero-day attacks.
Abdulssalm M Khako, Sharaf A Alhomdy (2025). Enhanced Asynchronous Advantage Actor-Critic (EA3C) Algorithm for Intrusion Detection in IIoT Environment. Sana'a University Journal of Applied Sciences and Technology, 898-914. DOI: https://doi.org/10.59628/jast.v3i3.1681
Abdulssalm M Khako, Sharaf A Alhomdy . “Enhanced Asynchronous Advantage Actor-Critic (EA3C) Algorithm for Intrusion Detection in IIoT Environment.” Sana'a University Journal of Applied Sciences and Technology, 2025, pp. 898-914. https://doi.org/10.59628/jast.v3i3.1681. Accessed 30 Oct. 2026.
Abdulssalm M Khako, Sharaf A Alhomdy . 2025. “Enhanced Asynchronous Advantage Actor-Critic (EA3C) Algorithm for Intrusion Detection in IIoT Environment.” Sana'a University Journal of Applied Sciences and Technology : 898-914. DOI: https://doi.org/10.59628/jast.v3i3.1681
Abdulssalm M Khako, Sharaf A Alhomdy (2025) ‘Enhanced Asynchronous Advantage Actor-Critic (EA3C) Algorithm for Intrusion Detection in IIoT Environment’, Sana'a University Journal of Applied Sciences and Technology, pp. 898-914. doi: 10.59628/jast.v3i3.1681
@article{jast2025_art1681,
author = {Abdulssalm M Khako, Sharaf A Alhomdy },
title = {Enhanced Asynchronous Advantage Actor-Critic (EA3C) Algorithm for Intrusion Detection in IIoT Environment},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2025}, month = {June}, pages = {898-914}, doi = {10.59628/jast.v3i3.1681},
url = {https://doi.org/10.59628/jast.v3i3.1681},}
Enhanced Asynchronous Advantage Actor-Critic (EA3C) Algorithm for Intrusion Detection in IIoT Environment
Artificial intelligence (AI) significantly enhances our daily lives, driving many service and financial institutions to seek optimal utilization of it. This paper addresses the problem, which is the inability of some institutions, such as banks, to satisfy customers using outdated methods and solely focusing on acquiring new customers instead of prioritizing customer retention that would be more effective and profitable to them. The main object of this paper is to enhance the quality of service in banks by proposing an intelligent model that leverages customer data to improve services and foster customer loyalty. This paper was motivated by the limited research on customer churn in Yemen and the scarcity of simple intelligent models globally. The methodology employed in this paper includes an in-depth analysis of existing literature on customer churn. Finally, this paper provides a comprehensive and concise conclusion based on the findings.
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah (2024). Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study. Sana'a University Journal of Applied Sciences and Technology, 348-354. DOI: https://doi.org/10.59628/jast.v2i4.1059
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah . “Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study.” Sana'a University Journal of Applied Sciences and Technology, 2024, pp. 348-354. https://doi.org/10.59628/jast.v2i4.1059. Accessed 30 Oct. 2026.
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah . 2024. “Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study.” Sana'a University Journal of Applied Sciences and Technology : 348-354. DOI: https://doi.org/10.59628/jast.v2i4.1059
Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah (2024) ‘Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study’, Sana'a University Journal of Applied Sciences and Technology, pp. 348-354. doi: 10.59628/jast.v2i4.1059
@article{jast2024_art1059,
author = {Sultan Yahya AL-SULTAN, Ibrahim Ahmed Al-Baltah },
title = {Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2024}, month = {September}, pages = {348-354}, doi = {10.59628/jast.v2i4.1059},
url = {https://doi.org/10.59628/jast.v2i4.1059},}
Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study
Strong cybersecurity measures are becoming vitally crucial as cloud computing utilization rises. Advanced and dynamic cyberthreats are frequently difficult for traditional intrusion detection systems (IDS), which rely on preset signatures and rules, to identify. This study improves the detection of known and unknown intrusions in cloud systems using Machine Learning (ML) methods. The UNSW-NB15 dataset was used to train and assess a num- ber of ML classifiers, including Random Forest (RF), Decision Tree (DT), XGBoost, Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Gradient Boosting (GB). It uses full feature training across several classifiers and also investigates the implications of feature reduction, in contrast to many other studies that mainly employ full feature sets to train RF alone. Critical metrics including accuracy (ACC), precision, recall, and F1-score are used to analyze classifier performance and provide a thorough evaluation of their efficacy in in- trusion detection. The findings show that using all characteristics the RF and DT obtained perfect accuracy (1.00). In the case of less characteristics when using feature selection techniques (RF-based selection, information gain, or mutual information), the RF retained the best accuracy (0.94), whereas NB performed the worst overall. This study emphasizes the significance of feature selection in enhancing IDS performance and shows that ML-based techniques may greatly increase threat detection in cloud settings, even when feature dimensionality is lowered.
Khawla Ali Maodah, Fursan Thabit, Sharaf Alhomdy (2025). Enhancing Intrusion Detection System in cloud computing Using Machine Learning Techniqueschine Learning Techniques. Sana'a University Journal of Applied Sciences and Technology, 1203-1221. DOI: https://doi.org/10.59628/jast.v3i5.1777
Khawla Ali Maodah, Fursan Thabit, Sharaf Alhomdy . “Enhancing Intrusion Detection System in cloud computing Using Machine Learning Techniqueschine Learning Techniques.” Sana'a University Journal of Applied Sciences and Technology, 2025, pp. 1203-1221. https://doi.org/10.59628/jast.v3i5.1777. Accessed 30 Oct. 2026.
Khawla Ali Maodah, Fursan Thabit, Sharaf Alhomdy . 2025. “Enhancing Intrusion Detection System in cloud computing Using Machine Learning Techniqueschine Learning Techniques.” Sana'a University Journal of Applied Sciences and Technology : 1203-1221. DOI: https://doi.org/10.59628/jast.v3i5.1777
Khawla Ali Maodah, Fursan Thabit, Sharaf Alhomdy (2025) ‘Enhancing Intrusion Detection System in cloud computing Using Machine Learning Techniqueschine Learning Techniques’, Sana'a University Journal of Applied Sciences and Technology, pp. 1203-1221. doi: 10.59628/jast.v3i5.1777
@article{jast2025_art1777,
author = {Khawla Ali Maodah, Fursan Thabit, Sharaf Alhomdy },
title = {Enhancing Intrusion Detection System in cloud computing Using Machine Learning Techniqueschine Learning Techniques},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2025}, month = {October}, pages = {1203-1221}, doi = {10.59628/jast.v3i5.1777},
url = {https://doi.org/10.59628/jast.v3i5.1777},}
Enhancing Intrusion Detection System in cloud computing Using Machine Learning Techniqueschine Learning Techniques
In today's world, social media has become one of the most accessible sources of news for people worldwide due to its low cost, easy accessibility, and rapid dissemination. However, the harmful effects of fake news on individuals and society have been evident for a long time. Despite the challenges in detecting false information, numerous scholars are working to understand the issue and its characteristics.
The purpose of this paper is to analyze contemporary frameworks that make use of different machine learning techniques in order to further our understanding of false news identification. The efficiency of these frameworks in recognizing and halting the spread of false information may be compared by examining various methods. A particular technique was used in the review process to provide a thorough and instructive assessment.
Even though a lot of research has been done to identify false news, more can be done in the future. These studies have many limitations such as biased data, adaptability, credibility analysis and the news is only categorized as legitimate or unauthentic by their solutions. Nevertheless, a system of ratings or scores to assess the reliability of news is necessary for a workable approach.
Malek Algabri, Ebtsam Nasser Ali Abu Huliqah, Mossa Ghurab, Abdualmajed A. G. Al-Khulaidi, Ghaleb H. Al Gaphari (2024). Fake News Detection On Social Media:Review of Literature. Sana'a University Journal of Applied Sciences and Technology, 7 - 15. DOI: https://doi.org/10.59628/jast.v2i1.369
Malek Algabri, Ebtsam Nasser Ali Abu Huliqah, Mossa Ghurab, Abdualmajed A. G. Al-Khulaidi, Ghaleb H. Al Gaphari . “Fake News Detection On Social Media:Review of Literature.” Sana'a University Journal of Applied Sciences and Technology, 2024, pp. 7 - 15. https://doi.org/10.59628/jast.v2i1.369. Accessed 30 Oct. 2026.
Malek Algabri, Ebtsam Nasser Ali Abu Huliqah, Mossa Ghurab, Abdualmajed A. G. Al-Khulaidi, Ghaleb H. Al Gaphari . 2024. “Fake News Detection On Social Media:Review of Literature.” Sana'a University Journal of Applied Sciences and Technology : 7 - 15. DOI: https://doi.org/10.59628/jast.v2i1.369
Malek Algabri, Ebtsam Nasser Ali Abu Huliqah, Mossa Ghurab, Abdualmajed A. G. Al-Khulaidi, Ghaleb H. Al Gaphari (2024) ‘Fake News Detection On Social Media:Review of Literature’, Sana'a University Journal of Applied Sciences and Technology, pp. 7 - 15. doi: 10.59628/jast.v2i1.369
@article{jast2024_art369,
author = {Malek Algabri, Ebtsam Nasser Ali Abu Huliqah, Mossa Ghurab, Abdualmajed A. G. Al-Khulaidi, Ghaleb H. Al Gaphari },
title = {Fake News Detection On Social Media:Review of Literature},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2024}, month = {February}, pages = {7 - 15}, doi = {10.59628/jast.v2i1.369},
url = {https://doi.org/10.59628/jast.v2i1.369},}
Fake News Detection On Social Media:Review of Literature
Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recognition and identity verification systems. Deep learning-based approaches have been shown through cross-sectional studies to improve recognition accuracy under diverse environmental and demographic conditions. Anti-counterfeiting (Anti-Spoofing) and real presence detection features integrated into systems have likewise enhanced system security against advanced attacks such as 3D masks, false images and videos, and Deepfake technology. Future trends point to the need to develop deep, multi-sensory and interpretable learning models, and adopt learning strategies based on limited data, while adhering to legal and ethical frameworks to ensure fairness and
transparency.
Rusul Hussein Hasan , Rasha Majid Hassoon, Inaam Salman Aboud (2025). From Algorithms to Applications: A Review of AI-Based Face Recognition and Identity Verification. Sana'a University Journal of Applied Sciences and Technology, 1452-1459. DOI: https://doi.org/10.59628/jast.v3i6.2286
Rusul Hussein Hasan , Rasha Majid Hassoon, Inaam Salman Aboud . “From Algorithms to Applications: A Review of AI-Based Face Recognition and Identity Verification.” Sana'a University Journal of Applied Sciences and Technology, 2025, pp. 1452-1459. https://doi.org/10.59628/jast.v3i6.2286. Accessed 30 Oct. 2026.
Rusul Hussein Hasan , Rasha Majid Hassoon, Inaam Salman Aboud . 2025. “From Algorithms to Applications: A Review of AI-Based Face Recognition and Identity Verification.” Sana'a University Journal of Applied Sciences and Technology : 1452-1459. DOI: https://doi.org/10.59628/jast.v3i6.2286
Rusul Hussein Hasan , Rasha Majid Hassoon, Inaam Salman Aboud (2025) ‘From Algorithms to Applications: A Review of AI-Based Face Recognition and Identity Verification’, Sana'a University Journal of Applied Sciences and Technology, pp. 1452-1459. doi: 10.59628/jast.v3i6.2286
@article{jast2025_art2286,
author = {Rusul Hussein Hasan , Rasha Majid Hassoon, Inaam Salman Aboud },
title = {From Algorithms to Applications: A Review of AI-Based Face Recognition and Identity Verification},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2025}, month = {December}, pages = {1452-1459}, doi = {10.59628/jast.v3i6.2286},
url = {https://doi.org/10.59628/jast.v3i6.2286},}
From Algorithms to Applications: A Review of AI-Based Face Recognition and Identity Verification
Generative adversarial networks (GANs) represent a ground-breaking advancement in artificial intelligence, revolutionizing data generation and content creation. This systematic review critically analyses literature from 2019-2024, sourced from major databases like IEEE Xplore and Scopus, to go beyond descriptive surveys and provide a synthesized, critical evaluation. We identify a pivotal gap in the existing literature: a frequent disconnect between reporting technical advancements and examining their profound ethical and societal implications. Our analysis yields several integrated insights. First, while GAN applications predominantly excel in visual content generation with architectures like StyleGAN and BigGAN achieving remarkable progress in photorealistic image and video synthesis their efficacy is highly application-specific, revealing significant trade-offs in stability and diversity in domains such as medical imaging and real-time video processing. Second, persistent core challenges like training instability and mode collapse continue to drive architectural innovations; however, we critically examine how solutions like Wasserstein loss or minibatch discrimination demonstrate varying effectiveness and limitations across different tasks and datasets. Third, and most critically, we argue that ethical concerns, particularly regarding deepfakes and data bias, are not peripheral issues but are intrinsically linked to architectural choices and the quality of training data. The key contribution of this review is its novel, integrated framework for evaluating GANs, which concurrently assesses architectural efficacy, application-specific maturity, and associated ethical risks. This holistic and critical synthesis provides researchers with a nuanced reference and outlines clear, responsible directions for future GAN development, emphasizing the need for models that are not only more powerful but also more robust, fair, and accountable.
Samah Ahmed Mohammed AlSarori, Ammar T. Zahary (2026). Generative Adversarial Networks for Visual Content: A Comprehensive Review of Image and Video Synthesis, Challenges, and Ethical Implications. Sana'a University Journal of Applied Sciences and Technology, 1552-1579. DOI: https://doi.org/10.59628/jast.v4i1.2100
Samah Ahmed Mohammed AlSarori, Ammar T. Zahary . “Generative Adversarial Networks for Visual Content: A Comprehensive Review of Image and Video Synthesis, Challenges, and Ethical Implications.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1552-1579. https://doi.org/10.59628/jast.v4i1.2100. Accessed 30 Oct. 2026.
Samah Ahmed Mohammed AlSarori, Ammar T. Zahary . 2026. “Generative Adversarial Networks for Visual Content: A Comprehensive Review of Image and Video Synthesis, Challenges, and Ethical Implications.” Sana'a University Journal of Applied Sciences and Technology : 1552-1579. DOI: https://doi.org/10.59628/jast.v4i1.2100
Samah Ahmed Mohammed AlSarori, Ammar T. Zahary (2026) ‘Generative Adversarial Networks for Visual Content: A Comprehensive Review of Image and Video Synthesis, Challenges, and Ethical Implications’, Sana'a University Journal of Applied Sciences and Technology, pp. 1552-1579. doi: 10.59628/jast.v4i1.2100
@article{jast2026_art2100,
author = {Samah Ahmed Mohammed AlSarori, Ammar T. Zahary },
title = {Generative Adversarial Networks for Visual Content: A Comprehensive Review of Image and Video Synthesis, Challenges, and Ethical Implications},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {January}, pages = {1552-1579}, doi = {10.59628/jast.v4i1.2100},
url = {https://doi.org/10.59628/jast.v4i1.2100},}
Generative Adversarial Networks for Visual Content: A Comprehensive Review of Image and Video Synthesis, Challenges, and Ethical Implications
Accurate brain stroke prediction requires models that can detect high-risk cases while adapting to newly arriving clinical data. This study proposes an interpretable incremental tree-based learning approach for brain stroke prediction using Decision Tree, Random Forest, and Gradient Boosting. Experiments were conducted using the Cerebral Stroke Dataset, a public imbalanced stroke prediction dataset with 43,400 records and 12 attributes. Since the dataset does not provide verified patient-level timestamps, the data were divided into three sequential simulated cohorts to evaluate model behavior under progressive data availability. Decision Tree was used as a refitted interpretable baseline, while Random Forest and Gradient Boosting were updated using a warm-start strategy by appending additional estimators at later stages. Rule extraction and rule classification were used to translate tree-based decision logic into readable rules and categorize them as conforming, generalized, specialized, unexpected, or novel, enabling analysis of rule evolution across cohorts. Experimental results showed that the incremental approach improved sensitivity oriented performance and reduced update time compared with traditional retraining. At the final stage, incremental Gradient Boosting achieved 98.044% accuracy, 75.32% F1-score, 91.03% recall, and 94.66% ROC AUC, with only 40 false negatives. Therefore, incremental Gradient Boosting provided the most clinically suitable trade-off between stroke-case detection, update efficiency, and interpretable rule behavior.
Majid A. Aslan, Ahmed A. AL-Shalabi, Ahmed S. Al-Hegami (2026). Interpretable Incremental Tree-Based Learning for Brain Stroke Prediction with Rule Evolution. Sana'a University Journal of Applied Sciences and Technology, 2653-2667. DOI: https://doi.org/10.59628/jast.v4i10.3271
Majid A. Aslan, Ahmed A. AL-Shalabi, Ahmed S. Al-Hegami . “Interpretable Incremental Tree-Based Learning for Brain Stroke Prediction with Rule Evolution.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 2653-2667. https://doi.org/10.59628/jast.v4i10.3271. Accessed 30 Oct. 2026.
Majid A. Aslan, Ahmed A. AL-Shalabi, Ahmed S. Al-Hegami . 2026. “Interpretable Incremental Tree-Based Learning for Brain Stroke Prediction with Rule Evolution.” Sana'a University Journal of Applied Sciences and Technology : 2653-2667. DOI: https://doi.org/10.59628/jast.v4i10.3271
Majid A. Aslan, Ahmed A. AL-Shalabi, Ahmed S. Al-Hegami (2026) ‘Interpretable Incremental Tree-Based Learning for Brain Stroke Prediction with Rule Evolution’, Sana'a University Journal of Applied Sciences and Technology, pp. 2653-2667. doi: 10.59628/jast.v4i10.3271
@article{jast2026_art3271,
author = {Majid A. Aslan, Ahmed A. AL-Shalabi, Ahmed S. Al-Hegami },
title = {Interpretable Incremental Tree-Based Learning for Brain Stroke Prediction with Rule Evolution},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {October}, pages = {2653-2667}, doi = {10.59628/jast.v4i10.3271},
url = {https://doi.org/10.59628/jast.v4i10.3271},}
Interpretable Incremental Tree-Based Learning for Brain Stroke Prediction with Rule Evolution
Recently, the number of Yemeni users has been expanding quickly on social media platforms. Most research in Arabic sentiment analysis has gained on Modern Standard Arabic (MSA) and some specific dialects, such as Egyptian, Levantine, and Gulf. However, there is a noticeable gap in Yemeni dialect sentiment analysis research. The reason for that is the lack of reliable Yemeni lexical and corpus and a real dataset for social media sentiment analysis. This research addresses this lack by presenting the Yemeni Dialect sentiment lexicon and corpus. This lexicon and corpus provide valuable resources for researchers and practitioners seeking to analyze sentiment in Yemeni dialect social media content, contributing to a better understanding of Yemeni public opinion, social media monitoring, marketing, cultural understanding, and assisting in efforts to respond to crises in Yemen. The Yemeni Dialect sentiment lexicon is enriched with a reasonable number of words and phrases categorized according to their positive and negative sentiment tendencies. Moreover, we constructed a corpus dataset of more than 54,000 comments built from the Facebook platform. A large dataset of unlabeled comments from the main Yemeni telecommunications companies in Yemen (Yemen Telecom, Yemen Mobile, YOU, and Sabafon), are people commenting on a public issue related to the services provided by those companies. The lexicon-based approach is used to extract the sentiment’s polarity and label each of the provided comments to formulate a corpus dataset as being either positive, negative, or neutral. The evaluation metrics of experiments are accuracy, recall, precision, f-measure, and the confusion matrix. The accuracy result of the lexicon-based labeling approach was calculated through a comparison between the achieved results and the ones achieved through manually labeled comments by three Yemeni experts. Evaluation results using a lexicon-based approach achieved an accuracy of 90.05%.
Alaa Abdulkareem Hameed Brihi, Mossa Ghurab (2024). Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network. Sana'a University Journal of Applied Sciences and Technology, 422-431. DOI: https://doi.org/10.59628/jast.v2i5.1058
Alaa Abdulkareem Hameed Brihi, Mossa Ghurab . “Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network.” Sana'a University Journal of Applied Sciences and Technology, 2024, pp. 422-431. https://doi.org/10.59628/jast.v2i5.1058. Accessed 30 Oct. 2026.
Alaa Abdulkareem Hameed Brihi, Mossa Ghurab . 2024. “Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network.” Sana'a University Journal of Applied Sciences and Technology : 422-431. DOI: https://doi.org/10.59628/jast.v2i5.1058
Alaa Abdulkareem Hameed Brihi, Mossa Ghurab (2024) ‘Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network’, Sana'a University Journal of Applied Sciences and Technology, pp. 422-431. doi: 10.59628/jast.v2i5.1058
@article{jast2024_art1058,
author = {Alaa Abdulkareem Hameed Brihi, Mossa Ghurab },
title = {Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2024}, month = {October}, pages = {422-431}, doi = {10.59628/jast.v2i5.1058},
url = {https://doi.org/10.59628/jast.v2i5.1058},}
Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network
The rapid advancement of Generative Artificial Intelligence (GAI) has led to the proliferation of deepfake media, posing significant threats to digital security, privacy, and information integrity. To overcome this challenge, substantial research efforts have been directed toward developing automated detection techniques using deep learning methodologies. This study presents a comprehensive survey of deep learning-based deepfake detection methods, emphasizing lightweight and hybrid architectures designed for real-time deployment. The survey systematically categorizes the landscape of deepfake generation techniques and evaluates state-of-the-art detection frameworks, including: classical CNNs, efficient backbone architectures (MobileNet, EfficientNet), and spatiotemporal models (CNN LSTM/GRU). Furthermore, this study examines model compression techniques— including pruning and quantization — essential for resource-constrained deployment, and provides a structured analysis of benchmark datasets, major detection architecture categories, and persistent research gaps. By critically examining the trade-off between detection accuracy and computational latency, this paper identifies key open challenges and concludes by highlighting a research gap for probabilistically robust, lightweight frameworks, offering a roadmap for future research toward reliable, real-time deepfake forensics in unconstrained environments.
Azhar Abdulmughni, Nagi AL-Shaibany (2026). Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey. Sana'a University Journal of Applied Sciences and Technology, 2379-2389. DOI: https://doi.org/10.59628/jast.v4i7.3069
Azhar Abdulmughni, Nagi AL-Shaibany . “Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 2379-2389. https://doi.org/10.59628/jast.v4i7.3069. Accessed 30 Oct. 2026.
Azhar Abdulmughni, Nagi AL-Shaibany . 2026. “Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey.” Sana'a University Journal of Applied Sciences and Technology : 2379-2389. DOI: https://doi.org/10.59628/jast.v4i7.3069
Azhar Abdulmughni, Nagi AL-Shaibany (2026) ‘Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey’, Sana'a University Journal of Applied Sciences and Technology, pp. 2379-2389. doi: 10.59628/jast.v4i7.3069
@article{jast2026_art3069,
author = {Azhar Abdulmughni, Nagi AL-Shaibany },
title = {Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {July}, pages = {2379-2389}, doi = {10.59628/jast.v4i7.3069},
url = {https://doi.org/10.59628/jast.v4i7.3069},}
Lightweight Hybrid Deep Learning Models for Real-Time Deepfake Video Detection: A Comprehensive Survey
Autonomous vehicle (AV) trajectory planning in dense urban environments requires models that generalize to rare or previously unseen scenario types. Deep neural networks (DNNs) trained on large datasets perform poorly in such data-scarce conditions, while large language models (LLMs) generalize well but lack the numerical precision required for low-level vehicle control. This paper presents a hybrid trajectory planning framework that fuses a finetuned LLM with a multi-branch DNN at the trajectory level and applies a Model Predictive Control (MPC) controller as a final optimization stage. Gemini 1.5 Flash, fine-tuned on 479 labeled scenario-trajectory pairs, generates 15- aypoint trajectory proposals from structured textual scene descriptions. A modular DNN processes the same scene as a numerical input vector and produces a parallel trajectory proposal. The two proposals are fused by a learned trajectory refinement network, and the output is passed to an MPC controller that enforces vehicle dynamics constraints while minimizing a multi-objective cost function. Training relies on a three-step distortion- based augmentation technique, curve distortion, scale distortion, and Gaussian smoothing, that generates semi-realistic trajectory variants from a base dataset of over 70,000 labeled rows, reducing the data volume required for effective DNN training. Evaluation across 200 runs in the CARLA simulator on unseen urban scenarios shows that the MPC-enhanced hybrid model achieves an Average Displacement Error (ADE) of 0.66 m and a Final Displacement Error (FDE) of 1.94 m, corresponding to improvements of 37.7% and 34.3% over the DNN-only baseline. The collision rate (CR) decreases from 3.71% to 2.90%, a 21.8% reduction. These results establish that trajectory-level fusion of LLM semantic reasoning with DNN-based feature extraction delivers measurable accuracy and safety gains in data-efficient autonomous driving conditions. The ADE/FDE improvements are statistically significant (p < 0.001). The collision-rate reduction is a consistent trend across ablations, but it is not statistically significant at n = 200 runs (see Section 4.4).
Yusuf Al-jamaly, Khalil Al-Hatab, Adel Rawea (2026). LLM-DNN Fusion for Data-Efficient Trajectory Generation in Autonomous Driving: A Hybrid Approach with MPC Optimization. Sana'a University Journal of Applied Sciences and Technology, 2679-2691. DOI: https://doi.org/10.59628/jast.v4i10.3391
Yusuf Al-jamaly, Khalil Al-Hatab, Adel Rawea . “LLM-DNN Fusion for Data-Efficient Trajectory Generation in Autonomous Driving: A Hybrid Approach with MPC Optimization.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 2679-2691. https://doi.org/10.59628/jast.v4i10.3391. Accessed 30 Oct. 2026.
Yusuf Al-jamaly, Khalil Al-Hatab, Adel Rawea . 2026. “LLM-DNN Fusion for Data-Efficient Trajectory Generation in Autonomous Driving: A Hybrid Approach with MPC Optimization.” Sana'a University Journal of Applied Sciences and Technology : 2679-2691. DOI: https://doi.org/10.59628/jast.v4i10.3391
Yusuf Al-jamaly, Khalil Al-Hatab, Adel Rawea (2026) ‘LLM-DNN Fusion for Data-Efficient Trajectory Generation in Autonomous Driving: A Hybrid Approach with MPC Optimization’, Sana'a University Journal of Applied Sciences and Technology, pp. 2679-2691. doi: 10.59628/jast.v4i10.3391
@article{jast2026_art3391,
author = {Yusuf Al-jamaly, Khalil Al-Hatab, Adel Rawea },
title = {LLM-DNN Fusion for Data-Efficient Trajectory Generation in Autonomous Driving: A Hybrid Approach with MPC Optimization},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {October}, pages = {2679-2691}, doi = {10.59628/jast.v4i10.3391},
url = {https://doi.org/10.59628/jast.v4i10.3391},}
LLM-DNN Fusion for Data-Efficient Trajectory Generation in Autonomous Driving: A Hybrid Approach with MPC Optimization
The rapid evolution of 5G networks introduces unprecedented high-speed communication and massive connectivity, but it also intensifies security challenges. Ensuring confidentiality, integrity, and availability of data requires robust encryption mechanisms. This study evaluates the performance and overhead of Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), and Elliptic Curve Cryptography (ECC) in 5G networks using simulation-based analysis. Metrics considered include latency (ms), throughput (%), encryption/decryption time per MB, and computational cost on UE-class hardware, providing quantitative comparison across typical 5G scenarios (high mobility eMBB, URLLC, and mMTCIoT devices).
Key findings: AES achieves low latency and high throughput for bulk data; ECC provides secure, lightweight key exchange; RSA is suitable only for session key establishment due to higher computational overhead. Overall, hybrid AES+ECC provides the best tradeoff between security and performance in realistic 5G environments.
Mohamed Hankal (2026). Performance of Encryption schemes in 5G Networks. Sana'a University Journal of Applied Sciences and Technology, 1959-1966. DOI: https://doi.org/10.59628/jast.v4i4.2317
Mohamed Hankal . “Performance of Encryption schemes in 5G Networks.” Sana'a University Journal of Applied Sciences and Technology, 2026, pp. 1959-1966. https://doi.org/10.59628/jast.v4i4.2317. Accessed 30 Oct. 2026.
Mohamed Hankal . 2026. “Performance of Encryption schemes in 5G Networks.” Sana'a University Journal of Applied Sciences and Technology : 1959-1966. DOI: https://doi.org/10.59628/jast.v4i4.2317
Mohamed Hankal (2026) ‘Performance of Encryption schemes in 5G Networks’, Sana'a University Journal of Applied Sciences and Technology, pp. 1959-1966. doi: 10.59628/jast.v4i4.2317
@article{jast2026_art2317,
author = {Mohamed Hankal },
title = {Performance of Encryption schemes in 5G Networks},
journal = {Sana'a University Journal of Applied Sciences and Technology}, year = {2026}, month = {April}, pages = {1959-1966}, doi = {10.59628/jast.v4i4.2317},
url = {https://doi.org/10.59628/jast.v4i4.2317},}
Performance of Encryption schemes in 5G Networks
Nothing has been published in this category yet.
Sana'a University Journal
of Applied Sciences and Technology
A peer-reviewed, open-access journal publishing high-quality research in applied sciences, engineering, and technology since 2023.