Computer Science, Artificial Intelligence, Information Systems, Software
46
Scopus Publications
1455
Scholar Citations
22
Scholar h-index
34
Scholar i10-index
Scopus Publications
Resource efficient hybrid baseline for named entity recognition in classical Arabic Ramzi Salah, Muaadh Mukred, Lailatul Qadri binti Zakaria, Fuad Ali Mohammed Al-Yarimi, Ibrahim T. Nather Khasro, Ali Safaa Sadiq Scientific Reports, 2026 The major challenge manifesting in resource-efficient named entity recognition for Classical Arabic may be attributed to the language’s rich morphology, orthographic variation, and the limitation in computing budgets. Thus, this study develops and proposes a hybrid approach that is compact, integrating linguistically informed rules, genetic-algorithm (GA) feature selection, and a multinomial Naive Bayes tagger. The system is exposed to CANER with leakage-controlled splits and documented statistical procedures thereafter achieving micro-F1 93.0% (with a precision of 93.3% and recall of 92.7%), with macro-F1 88.7%. Based on per-class analysis, boundary-aware rules mainly increased precision for frequent entities, namely PER and LOC. On the other hand, GA-controlled sparsity determined recall in minority categories, as in SECT and DAY. The combined pipeline was confirmed to produce a balanced precision-recall profile compared to individual components, along with significant minimized feature dimensionality and training time. Notably, new transformer baselines are untrained owing to limitations in computations and data, but rather the contribution is generatable and interpretable reference baseline for Classical Arabic. This has transparent implementation details and evaluation safeguards for bringing about replication and accurate comparison. Even though higher macro-F1 is achieved by transformer systems, based on the findings, a meticulously engineered hybrid can produce competitive effectiveness in the face of limited settings and valuable diagnostic views through interpretative error patterns.
A MODEL FOR THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN INCLUSIVE EDUCATION: AN EXPLORATORY STUDY OF KEY FACTORS AND EXPERT INSIGHTS Kok Weng Ma, Rexado Pramudita Julianton, Xian Yang Chan, Yong Teng Chai, Muaadh Mukred, Mikkay Wong Ei Leen, Abdu H. Gumaei Journal of Information Technology Education Research, 2025 Aim/Purpose: This study adopts a mixed-method approach to examine the factors influencing the adoption of AI-based assistive technologies among students with special needs. Specifically, it explores the roles of social support, motivation, digital literacy, and self-efficacy in shaping students’ perceptions and behavioral intentions toward these technologies within inclusive education settings. Background: The integration of assistive technologies into inclusive learning environments has gained significant attention due to its potential to improve educational outcomes for students with disabilities. Despite this growing interest, limited empirical research has explored the determinants of such technologies’ adoption, particularly through the technology acceptance model lens. This study addresses this gap by extending the technology acceptance model framework to incorporate additional constructs such as social support, motivation, digital literacy, and self-efficacy, offering a more comprehensive understanding of user acceptance in inclusive contexts. Methodology: A mixed-method design was employed. The quantitative phase involved a survey of 118 students enrolled in six inclusive education programs across Saudi Arabian universities. Data were analyzed using structural equation modeling via SmartPLS to examine the relationships between the proposed constructs. Complementing this, the qualitative phase included in-depth interviews with eight AI experts specializing in assistive technologies for students with disabilities, providing contextual insights and validating the quantitative findings. Contribution: This study advances the technology acceptance model by integrating key psychological and social variables (social support, motivation, digital literacy, and self-efficacy) into the adoption framework. Doing so offers a more nuanced perspective on how students with special needs interact with and perceive AI-based assistive tools, addressing a critical gap in current inclusive education research. Findings: The findings from the quantitative analysis indicate that social support and motivation significantly enhance perceived usefulness, while digital literacy and self-efficacy significantly enhance perceived ease of use. Both usefulness and ease of use substantially affect intention toward adopting assistive technology. Additionally, the qualitative findings reveal key themes emphasizing user-friendly designs, contextual adaptability, ethical considerations, and AI’s potential to promote student autonomy. Recommendations for Practitioners: Expert interviews underscore the necessity for ongoing professional development among educators to effectively implement assistive AI tools. Furthermore, fostering partnerships between technology developers and educators is critical for designing tools that meet the real-world needs of inclusive classrooms. Recommendation for Researchers: By extending the technology acceptance model, this study opens new avenues for future research into other influencing factors, such as institutional readiness, cultural considerations, and policy support. Researchers are encouraged to apply this enriched model in various educational and geographical contexts to further validate its applicability. Impact on Society: Understanding the factors that influence the adoption of assistive technology is essential for fostering inclusive and equitable education systems. This study contributes to the development of policies and implementation strategies that ensure students with disabilities can fully benefit from emerging educational technologies. Future Research: Future studies can expand the scope of this research by exploring different educational settings, conducting longitudinal studies to assess long-term adoption trends, and incorporating additional stakeholders, such as parents and policymakers, to gain a more comprehensive understanding of assistive technology integration.
Deep Biblio: Bibliometric Analysis in Augmented Text Deep Learning and Classification Techniques Tiong Yew Tang, Muaadh Mukred, Mohammed I. Nofal 2025 1st International Conference on Computational Intelligence Approaches and Applications Icciaa 2025 Proceedings, 2025 This research aims to study the impacts of augmented text deep learning and machine learning classification techniques for bibliometric analysis. The study specifically focused on the identification of publication names using keywords. The study built upon existing research by exploring innovative data augmentation and machine learning strategies to achieve enhanced accuracy in dense academic datasets. The successful implementation of augmentation approaches effectively overcame generalizability issues caused by class imbalance, leading to significant improvements in classification scores. Semantic enrichment played a vital role in enhancing the datasets, with the skilful incorporation of tools such as WordNet, FastText, GloVe, RandomSwap, Random Deletion, and transformer substitution methods like GPT-2, DistilBERT, and All-Mini. These techniques substantially increased the data volume from 520 to 100,800 instances. The research findings indicated that Linear Regression (LR) stood out prominently among the tested algorithms. Compared to a baseline performance of 1.27%, the strategic augmentation strategies and model optimizations resulted in a significant improvement, with LR achieving an outstanding classification score of 10.15% - an approximate increase of 800%.
Recommended Machine Learning and Deep Learning Models in Improving Sales Forecasting Across Diverse Industries: A Review Analysis Michelle Lee Yee Lin, Teo Mong Hao, Muaadh Mukred, Mohammed I. Nofal 2025 1st International Conference on Computational Intelligence Approaches and Applications Icciaa 2025 Proceedings, 2025 Sales forecasting is essential for business strategy, impacting inventory, finance, and customer satisfaction. Traditional forecasting methods struggle with complex, dynamic datasets and modern transformations. Nonetheless, this paper investigates the role of machine learning (ML) and deep learning (DL) in improving sales forecasting accuracy across industries. A systematic review of recent studies (2020–2024) reveals advancements in hybrid forecasting methods that integrate classical techniques with ML/DL to tackle challenges such as seasonality and non-linear relationships. The review emphasizes the necessity of industry-specific customization, model scalability, and external variable integration for enhanced forecasting precision. In fact, by analyzing methodologies, performance metrics and case studies, this paper offers insights into the efficacy of ML/DL models in diverse business scenarios. It advocates customized, data-driven forecasting strategies to secure competitive advantages in contemporary markets.
AI-Powered Reviewer Assignment: A Machine Learning and Social Network-Based Framework for Automated Peer Review Optimization Geetha Nadarajan, Ramesh Balakrishnan, Muaadh Mukred, Noridayu Adnan, Waleed Abdulkafi Ahmed 2025 IEEE 1st International Conference on Emerging Innovation and Digital Technology Iceidt 2025, 2025 The peer review process is essential for ensuring the quality and credibility of academic research, yet traditional reviewer selection methods remain time-consuming and subjective. This study proposes an advanced, machine learning-driven system to enhance reviewer selection by leveraging big data analytics. The system aggregates and standardizes reviewer information from multiple sources, starting from a database of existing credible reviewers of an academic journal and exploring their respective social networks to identify other potential quality journal reviewers, ensuring data integrity through structured citation metrics such as the h-index and citation history. Additionally, novel indicators—such as citation recency and impact growth—are introduced to dynamically assess reviewer expertise and relevance. Machine learning algorithms are then employed to predict reviewer suitability, assigning relevance scores and using natural language processing (NLP) techniques to align reviewers with specific journal topics. Automated reports provide journal editors with data-driven insights, streamlining decision-making and reducing bias. Future iterations will enhance interoperability with editorial management systems for broader adoption. This approach optimizes reviewer selection, making the peer review process more efficient, transparent, and data-driven.
Exploring the Potential of Metaverse Adoption in Higher Education: A Diffusion of Innovation Model Approach to Enhancing Student Engagement Muaadh Mukred, Umi Asma’ Mokhtar, Burkan Hawash, Hussain AlSalman, Muhammad Zohaib, Yousef Ibrahim Abuzawayda Sage Open, 2025 In the virtual realm, also referred to as the metaverse, users generally communicate using avatars. This metaverse is viewed as a prospective tool in different industry types, and in this regard, researchers have examined its significance in education owing to its increasing reach. In fact, it has been widely accepted that Information System (IS) model assessments in light of user’s reactions and their use of the metaverse systems is a worthwhile research branch. This is because metaverse adoption can open several avenues of enhanced student engagement in terms of collaboration, accessibility, new opportunities, personalization, creativity, and future preparedness of students. Therefore, in this study, the Diffusion of Innovation (DOI) model is employed to explain the successful adoption of the metaverse. The study tested the model using Partial Least Squares-Structural Equation Modeling (PLS-SEM) on data obtained from 1,700 students, respondents from Saudi Arabia, Yemen, and Libya, through an online survey questionnaire. Based on the findings, all the extended variables positively affected trust, relative advantage, and behavioral intention toward metaverse adoption. The study revealed that adopting metaverse technologies positively enhances students’ engagement by providing immersive, interactive, and personalized learning experiences. This positive impact underscores the potential of metaverse technologies to transform educational environments, making learning more engaging and effective. The findings highlighted the contribution of the study to practice and theory, particularly to developers, designers, and decision-makers in promoting metaverse use. More importantly, the study enables institutions of higher learning to adopt metaverse by integrating it into other tools like mobile learning in blended learning technologies.
Automated Machine Learning (AutoML): Transforming Data Science Workflows in Big Data Analytics Mohammed I. Nofal, Banan Mohammad Alfalah, Fayiz Momani, Muaadh Mukred, Omar Zughoul, Fathye Mohammed 2025 1st International Conference on Computational Intelligence Approaches and Applications Icciaa 2025 Proceedings, 2025 The introduction of Automated Machine Learning (AutoML) can be considered a game-changing development in the field of data science and more specifically, in the area of big data analytics. AutoML brings an expert-level approach to non-experts, allowing them to conduct intricate data analysis because it simplifies complicated tasks such as data preprocessing, feature engineering, model testing, hyper parameter adjustment of the models, and finally deploying the trained models. This advancement lessens reliance on qualified personnel, improves efficiency and helps cut down any associated lag in time from when an organization begins to utilize large amounts of data. This paper illustrates AutoML facilitates progress in multiple components for modern times: evolution, accessibility, integration into numerous sectors, and decision making. Respecting AutoML's various roles, fostering innovation while promoting inclusivity comes with certain challenges such as ethical considerations and algorithmic transparency. To summarize, AutoML will most likely disrupt analytical maturity and strengthen competitive advantages deemed sustainable today.
Electronic records management System invisibility in the agriculture sector Burkan Hawash, Umi Asma Mokhtar, Muaadh Mukred, Mikkay Wong Ei Leen, Wan aida nadia binti Wan Abdullah, Ibrahim T. Nather Khsroo 2024 7th International Conference on Internet Applications Protocols and Services Netapps 2024, 2024
Understanding older adults’ vulnerabilities to digital investment fraud: a conceptual model and instrument development F Mohammed, M Mukred, M Munir, ITN Khasro The Journal of Adult Protection, 1-17 , 2026 2026
Resource efficient hybrid baseline for named entity recognition in classical Arabic R Salah, M Mukred, LQ Zakaria, FAM Al-Yarimi, ITN Khasro, AS Sadiq Scientific Reports , 2026 2026 Citations: 1
Metaverse and augmented reality in e-commerce: Bibliometric analysis and thematic exploration F Mohammed, YH Yi, JBJ Ni, M Mukred, NH Al-Kumaim, AA Hagar J. Res. Innov. Technol 5 (1), 22-46 , 2026 2026
AI-Powered Reviewer Assignment: A Machine Learning and Social Network-Based Framework for Automated Peer Review Optimization G Nadarajan, R Balakrishnan, M Mukred, N Adnan, WA Ahmed 2025 1st International Conference on Emerging Innovation and Digital … , 2025 2025
Exploring the potential of metaverse adoption in higher education: A diffusion of innovation model approach to enhancing student engagement M Mukred, UA Mokhtar, B Hawash, H AlSalman, M Zohaib, ... SAGE Open 15 (3), 21582440251363668 , 2025 2025 Citations: 15
A model for the adoption of artificial intelligence in inclusive education: An exploratory study of key factors and expert insights KW Ma, RP Julianton, XY Chan, YT Chai, M Mukred, MWE Leen, ... Journal of Information Technology Education: Research 24, 027 , 2025 2025 Citations: 4
Recommended machine learning and deep learning models in improving sales forecasting across diverse industries: A review analysis MLY Lin, TM Hao, M Mukred, MI Nofal 2025 1st International Conference on Computational Intelligence Approaches … , 2025 2025 Citations: 2
Deep Biblio: Bibliometric Analysis in Augmented Text Deep Learning and Classification Techniques TY Tang, M Mukred, MI Nofal 2025 1st International Conference on Computational Intelligence Approaches … , 2025 2025
Automated Machine Learning (AutoML): Transforming Data Science Workflows in Big Data Analytics MI Nofal, BM Alfalah, F Momani, M Mukred, O Zughoul, F Mohammed 2025 1st International Conference on Computational Intelligence Approaches … , 2025 2025 Citations: 2
Hybrid Deep Learning for Detecting Hate Speech Across Social Media Platforms L Kevin, CT Yee, CZ Liang, S Yong, YC Han, M Mukred, F Mohammed Current and Future Trends on AI Applications: Volume 1, 289-304 , 2025 2025 Citations: 2
AI-driven Smart City security and surveillance system: A bibliometric analysis WQ Fan, AS Ismail, F Mohammed, M Mukred Current and Future Trends on AI Applications: Volume 1, 305-328 , 2025 2025 Citations: 8
A study on the use of enterprise resource planning to improve the sustainability of Malaysian SMEs SP Sivaganthan, M Mukred, F Mohammed, MWE Leen Current and Future Trends on AI Applications: Volume 1, 395-409 , 2025 2025 Citations: 5
Gamification in Agile Development Methodology: A Systematic Literature Review F Mohammed, M Mukred, MA Bazel, MSB Sajat 2024 7th International Conference on Internet Applications, Protocols, and … , 2024 2024
Electronic Records Management System Invisibility in the Agriculture Sector B Hawash, UA Mokhtar, M Mukred, MWE Leen, ITN Khsroo 2024 7th International Conference on Internet Applications, Protocols, and … , 2024 2024
Environmental, social and governance (esg) scores automation in global reporting initiative (gri) with natural language processing HQ Ngee, A Ganesh, MAN Azmi, TY Tang, M Mukred, F Mohammed, ... 2024 7th International Conference on Internet Applications, Protocols, and … , 2024 2024 Citations: 6
Leveraging AI-Driven Topic Modeling for Business Analytics: A Bibliometric Analysis of Trends in Antidepressant Efficacy M Mukred, ST Lee, TY Tang, F Mohammed, A Ahmad 2024 7th International Conference on Internet Applications, Protocols, and … , 2024 2024 Citations: 1
The roots of digital aggression: Exploring cyber-violence through a systematic literature review M Mukred, U Asma'Mokhtar, FA Moafa, A Gumaei, AS Sadiq, ... International Journal of Information Management Data Insights 4 (2), 100281 , 2024 2024 Citations: 48
A Machine Learning Approach for Named Entity Recognition in Classical Arabic Natural Language Processing R Salah, M Mukred, LQ binti Zakaria, FAM Al-Yarimi KSII Transactions on Internet and Information Systems 18 (10), 2895-2919 , 2024 2024 Citations: 4
Advanced neural network-based model for predicting court decisions on child custody M Abrar, A Salam, F Ullah, M Nadeem, H AlSalman, M Mukred, F Amin PeerJ Computer Science 10, e2293 , 2024 2024 Citations: 9
Geographic information systems adoption model: A partial least square-structural equation modeling analysis approach NA Alzahrani, SNHS Abdullah, N Adnan, KAZ Ariffin, M Mukred, ... Heliyon 10 (15) , 2024 2024 Citations: 10
MOST CITED SCHOLAR PUBLICATIONS
The key factors in adopting an electronic records management system (ERMS) in the educational sector: a UTAUT-based framework M Mukred, ZM Yusof, FM Alotaibi, U Asma’Mokhtar, F Fauzi IEEE Access 7, 35963-35980 , 2019 2019 Citations: 161
Electronic records management system adoption readiness framework for higher professional education institutions in Yemen M Mukred, ZM Yusof, UA Mokhtar, NA Manap International Journal on Advanced Science, Engineering and Information … , 2016 2016 Citations: 85
Improving the decision-making process in the higher learning institutions via electronic records management system adoption M Mukred, ZM Yusof, UA Mokhtar, AS Sadiq, B Hawash, WA Ahmed KSII Transactions on Internet and Information Systems 15 (1), 90 - 113 , 2021 2021 Citations: 78
The adoption of electronic records management system (ERMS) in the Yemeni oil and gas sector B Hawash, U Asma’Mokhtar, ZM Yusof, M Mukred Records Management Journal 30 (1), 1-22 , 2020 2020 Citations: 68
The DeLone–McLean information system success model for electronic records management system adoption in higher professional education institutions of Yemen M Mukred, ZM Yusof International Conference of Reliable Information and Communication … , 2017 2017 Citations: 68
The impact of intention of use on the success of big data adoption via organization readiness factor A Haddad, AA Ameen, M Mukred International Journal of Management and Human Science (IJMHS) 2 (1), 43-51 , 2018 2018 Citations: 66
Ensuring the productivity of higher learning institutions through electronic records management system (ERMS) M Mukred, ZM Yusof, FM Alotaibi IEEE Access 7, 97343-97364 , 2019 2019 Citations: 59
Electronic records management systems and the competency of educational institutions: Evidence from Yemen M Mukred, ZM Yusof, WA Al-Moallemi, UA Mokhtar, B Hawash Information Development 38 (1), 125-148 , 2022 2022 Citations: 55
Factors affecting the cyber violence behavior among Saudi youth and its relation with the suiciding: A descriptive study on university students in Riyadh city of KSA NB Alotaibi, M Mukred Technology in Society 68, 101863 , 2022 2022 Citations: 55
Taxonomic framework for factors influencing ERMS adoption in organisations of higher professional education M Mukred, ZM Yusof, UA Mokhtar, F Fauzi Journal of Information Science 45 (2), 139-155 , 2018 2018 Citations: 55
Factors affecting Internet of Things (IoT) adoption in the Yemeni oil and gas sector B Hawash, UA Mokhtar, ZM Yusof, M Mukred, ASA Gaid 2021 International Conference of Technology, Science and Administration … , 2021 2021 Citations: 53
The roots of digital aggression: Exploring cyber-violence through a systematic literature review M Mukred, U Asma'Mokhtar, FA Moafa, A Gumaei, AS Sadiq, ... International Journal of Information Management Data Insights 4 (2), 100281 , 2024 2024 Citations: 48
Digital Transformation in The Oil and Gas Sector During Covid-19 Pandemic B Hawash, YI Abuzawayda, UA Mokhtar, ZM Yusof, M Mukred International Journal of Management (IJM) 11 (12), 725 - 735 , 2020 2020 Citations: 48
The performance of educational institutions through the electronic records management systems: factors influencing electronic records management system adoption M Mukred, ZM Yusof Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications … , 2020 2020 Citations: 45
The role of electronic records management (ERM) for supporting decision making process in Yemeni higher professional education (HPE): a preliminary review M Mukred, ZM Yusof Jurnal teknologi 73 (2), 117-122 , 2015 2015 Citations: 42
The adoption and use of learning analytics tools to improve decision making in higher learning institutions: An extension of technology acceptance model M Mukred, U Asma’Mokhtar, B Hawash, H AlSalman, M Zohaib Heliyon 10 (4) , 2024 2024 Citations: 37
Exploring the Acceptance of ChatGPT as a Learning Tool among Academicians: A Qualitative Study M Mukred, UA Mokhtar, B Hawash Jurnal Komunikasi: Malaysian Journal of Communication 39 (4), 306-323 , 2023 2023 Citations: 29
A framework for electronic records management system adoption in the higher professional education: Individual, technological and environmental factors M Mukred, ZM Yusof, UA Mokhtar, F Fauzi International Conference of Reliable Information and Communication … , 2018 2018 Citations: 28
Intention to Adopt Electronic Records Management System in the Oil and Gas Sector in Yemen B Hawash, M Umi Asma, ZM Yusof, M Mukred, W Ali International Journal of Advanced Trends in Computer Science and Engineering … , 2020 2020 Citations: 27
Enterprise resource planning adoption model for well-informed decision in higher learning institutions M Mukred, FM Alotaibi, ZM Yusof, UA Mokhtar, B Hawash, WA Ahmed Journal of Information Science 49 (3), 792–813 , 2023 2023 Citations: 26
