Dr Waddah Saeed

 Waddah Saeed is a Senior Lecturer in Data Analytics and Program Lead for the MSc Data Analytics at the School of Computer Science and Informatics, De Montfort University (DMU). He earned his Ph.D. in Information Technology from Universiti Tun Hussein Onn Malaysia in 2019, receiving the Gold Award in the Publication Category for his thesis. Prior to joining DMU, he served as a Postdoctoral Research Fellow at the University of Agder in Norway and as a Lecturer at Asia Pacific University of Technology & Innovation (APU) in Malaysia. His research focuses on time series analysis and forecasting, data mining, machine learning, and explainable AI.

In addition to his academic roles, he actively contributes to global scholarship as an External Examiner, Advisor, and Assessor for some UK universities, including the University of Nottingham, Cranfield University, Abertay University, Birmingham City University, and University of Salford. He also serves as an External Grant Reviewer, Guest Editor, and peer reviewer for leading publishers such as Elsevier and Springer, reflecting his commitment to advancing research and educational excellence.

• Digital Future Institute

Selected Publications:

• Thinn, K. T., & Saeed, W. (2026). Consensus and Divergence in Explainable AI (XAI): Evaluating Global Feature-Ranking Consistency with Empirical Evidence from Solar Energy Forecasting. Mathematics, 14(2), 297. (Impact Factor: 2.2)
• Saeed, W., & Omlin, C. (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-based systems, 263, 110273. (Impact Factor: 8.0)
• Mohammed, M. S., Al-Dhamari, A., Saeed, W., Al-Aswadi, F. N., Saleh, S. A. M., & Marsono, M. N. (2023). Motion pattern-based scene classification using adaptive synthetic oversampling and fully connected deep neural network. IEEE Access, 11, 119659-119675. (Impact Factor: 3.9)
• Kiguchi, M., Saeed, W., & Medi, I. (2022). Churn prediction in digital game-based learning using data mining techniques: Logistic regression, decision tree, and random forest. Applied Soft Computing, 118, 108491. (Impact Factor: 6.7)
• Saeed, W. (2022). Frequency-based ensemble forecasting model for time series forecasting. Computational and Applied Mathematics, 41(2), 66. (Impact Factor: 2.2)
• Waheeb, W., & Ghazali, R. (2020). A novel error-output recurrent neural network model for time series forecasting. Neural Computing and Applications, 32(13), 9621-9647. (Impact Factor: 4.7)
• Waheeb, W., Ghazali, R., & Hussain, A. J. (2018). Dynamic ridge polynomial neural network with Lyapunov function for time series forecasting. Applied Intelligence, 48(7), 1721-1738. (Impact Factor: 1.9)
• Waheeb, W., Ghazali, R., & Herawan, T. (2016). Ridge polynomial neural network with error feedback for time series forecasting. PloS one, 11(12), e0167248. (Impact Factor: 3.1)

A full and updated list of publications can be found in the Google Scholar link below.
https://scholar.google.com/citations?user=2pqJUFEAAAAJ

• Time series analysis and forecasting
• Machine learning
• Data Science
• Explainable AI
• Energy Analytics

• Module leader, Data Mining Techniques and Applications
• Module leader, Advanced Data Analytics
• Module leader, Data Visualization
• Module leader, Business Intelligence

• PhD in Information Technology (Gold Award in the Publication Category)
• Master in Computer Science (Soft Computing).
• BSc in Computer Science.

• MSc Data Analytics
• BSc (Hons) Business Data Analytics
• BSc (Hons) Applied Computing

• Elseiver Knowledge-Based Systems – KBS Outstanding Paper Award, 2025
• Gold Award (Publication Category), Universiti Tun Hussein Onn Malaysia, Malaysia, 2019.
• Best Paper Award, 3rd international conference of reliable information and communication technology, Malaysia, 2018.
• Best Paper Award, 2nd international conference on soft computing in data science, Malaysia, 2016.

Fellow of the Higher Education Academy (FHEA), Advance HE-UK, 2023  - Present.

• Highfield Level 3 Award in Undertaking End-Point Assessment (RQF), 2025.
• University Pedagogy Course, University of Agder, Norway, 2022.
• NVIDIA DLI Certificate – Fundamentals of Deep Learning for Multi-GPUs, NVIDIA Deep Learning Institute, 2021.

• CEM Faculty Travel & Conference Fund, PI, 2025.
• Divergence and Consensus in Explainable AI Feature Importance Ranking Methods for Solar Forecasting, CEM Faculty Research Fund, PI, 2025.

• Guest Editor, Special Issue "Advances in Time Series Forecasting with Applications", Mathematics (IF = 2.2) , MDPI (https://www.mdpi.com/journal/mathematics/special_issues/4P1Y8C5G5D)
• Guest Editor, Special Issue "Intelligent Energy Forecasting Solutions: Machine Learning Driving Renewable Energy Advancements", Applied Sciences (IF = 2.7) , MDPI (https://www.mdpi.com/journal/applsci/special_issues/T5G3U361F1)

• External Examiner for University of Salford.
• External Examiner for Abertay University.
• Independent Assessor for the Level 6 Data Scientist Degree Apprenticeship Programme at the University of Nottingham.
• External Academic Advisor for Birmingham City University.
• External Grant Reviewer, Dutch Research Council.
• Reviewer for various publishers such as Elsevier, Springer, MDPI, Hindawi, and IET.
• Technical Program Committee member for various international conferences worldwide, including those held in Spain, Finland, Norway, Malaysia, United Arab Emirates, Bahrain, and Egypt.

Prospective students with their own funding are encouraged to reach out to me via waddah.saeed@dmu.ac.uk. Please provide a brief overview of your research interests and academic background. I am particularly interested in the following areas:

• Energy Analytics and Forecasting
• Disease Forecasting
• Forecasting Large Collections of Time Series Data
• Explainable AI for Time Series Forecasting
• Hierarchical Time Series Forecasting
• Graph Neural Networks for Time Series Forecasting

If you have a specific project or concept in mind that aligns with my research interests, please include it in your introduction. 

While I have a particular interest in time series forecasting and its various aspects, I am open to exploring other related topics within the field of machine learning.