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Dr. S.S. (Sahand) Mohammadi Ziabari PhD

Faculty of Science
Graduate School of Informatics
Visiting address
  • Science Park 904
Postal address
  • Postbus 94214
    1090 GE Amsterdam
Contact details
  • Publications

    2026

    • Bakker, S., Ma, Y., & Mohammadi Ziabari, S. S. (2026). Addressing Label Scarcity: Hybrid Anomaly Detection in Mental Healthcare Billing. In Information Integration and Web Intelligence: 27th International Conference, iiWAS 2025, Matsue, Japan, December 8–10, 2025 : proceedings (pp. 112–126). (Lecture Notes in Computer Science; Vol. 16330). Springer. Advance online publication. https://doi.org/10.1007/978-3-032-11976-6_8

    2025

    • Ashtar, D., Mohammadi Ziabari, S., & Alsahag, A. M. M. (2025). Hybrid Forecasting for Sustainable Electricity Demand in The Netherlands Using SARIMAX, SARIMAX-LSTM, and Sequence-to-Sequence Deep Learning Models. Sustainability, 17(16), Article 7192. https://doi.org/10.3390/su17167192
    • Braakman, J., Mohammadi Ziabari, S. S., & Korver, A. (2025). Enhancing Soil Pollution Prediction Through Expert-Defined Risk Zones and Machine Learning: A Case Study in the Netherlands. In P. Delir Haghighi, M. Greguš, G. Kotsis, & I. Khalil (Eds.), Information Integration and Web Intelligence: 26th International Conference, iiWAS 2024, Bratislava, Slovak Republic, December 2–4, 2024 : proceedings (Vol. II, pp. 219-225). (Lecture Notes in Computer Science; Vol. 15343). Springer. https://doi.org/10.1007/978-3-031-78093-6_19
    • Chen, J., Alsahag, A. M. M., & Mohammadi Ziabari, S. S. (2025). An analytics framework for interpretable subseasonal forecasting under decadal climate variability. Decision Analytics Journal, 17, Article 100660. https://doi.org/10.1016/j.dajour.2025.100660
    • Chen, X., Liu, H., & Mohammadi Ziabari, S. (2025). Efficient Sparse MLPs Through Motif-Level Optimization Under Resource Constraints. AI, 6(10), Article 266. https://doi.org/10.3390/ai6100266 [details]
    • Coolwijk, S., Mohammadi Ziabari, S. S., & Angileri , F. (2025). Vision Transformer Approach to Customer Churn Prediction Radar Chart Image Classification for Non-subscription Based E-commerce. In P. Delir Haghighi, M. Greguš, G. Kotsis, & I. Khalil (Eds.), Information Integration and Web Intelligence: 26th International Conference, iiWAS 2024, Bratislava, Slovak Republic, December 2–4, 2024 : proceedings (Vol. II, pp. 75–80). (Lecture Notes in Computer Science; Vol. 15343). Springer. https://doi.org/10.1007/978-3-031-78093-6_6
    • Curiël, R., Alsahag, A. M. M., & Mohammadi Ziabari, S. S. (2025). Integrating Climate and Economic Predictors in Hybrid Prophet–(Q)LSTM Models for Sustainable National Energy Demand Forecasting: Evidence from The Netherlands. Sustainability, 17(19), Article 8687. https://doi.org/10.3390/su17198687
    • Katona, Z., Mohammadi Ziabari, S. S., & Karimi Nejadasl, F. (2025). MARINE: A Computer Vision Model for Detecting Rare Predator-Prey Interactions in Animal Videos. In A. Dasgupta, R. U. Kiran, R. El Shawi, S. Srirama, & M. Adhikari (Eds.), Big Data and Artificial Intelligence: 12th International Conference, BDA 2024, Hyderabad, India, December 17–20, 2024, Proceedings (pp. 183–199). (Lecture Notes in Computer Science; Vol. 15526). Springer. https://doi.org/10.1007/978-3-031-81821-9_11
    • Mohammadi Ziabari, S. S. (2025). Explainable feature selection combining particle swarm optimisation with adaptive LASSO for MRI radiogenomics: Predicting HPV status in oropharyngeal cancer. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2025.109204
    • Mohammadi Ziabari, S. S., & Anwar, K. (2025). Attention to the Branches: A Comparative Analysis of FairMOT with Transformers on Fish Dataset. In Multi-disciplinary Trends in Artificial Intelligence: 17th International Conference, MIWAI 2024, Pattaya, Thailand, November 11–15, 2024 : proceedings (Vol. I, pp. 64–76). (Lecture Notes in Computer Science; Vol. 15431), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-981-96-0692-4_6
    • Tigchelaar, K., Mohammadi Ziabari, S. S., & Mulder, J. (2025). The Integration of Federated Learning Techniques in Predictive Aircraft Maintenance Using Cloud Services. In S. Wu, X. Su, X. Xu, & B. H. Kang (Eds.), Knowledge Management and Acquisition for Intelligent Systems: 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Kyoto, Japan, November 18–19, 2024 : proceedings (pp. 203-213). (Lecture Notes in Computer Science; Vol. 15372), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-981-96-0026-7_16
    • Van de Sype, L., Vert, M., Sharpanskykh, A., & Mohammadi Ziabari, S. S. (2025). Effects of Unplanned Incoming Flights on Airport Relief Processes After a Major Natural Disaster. Aerospace, 12(10), Article 857. https://doi.org/10.3390/aerospace12100857
    • Zhu, C., Mohammadi Ziabari, S. S., & Alsahag, A. M. M. (2025). Task-Adaptive Debiasing with SCM for Sentiment Analysis. Machine Learning for Computational Science and Engineering, 1, Article 41. https://doi.org/10.1007/s44379-025-00043-x

    2024

    • de Bosscher, B. C. D., Mohammadi Ziabari, S. S., & Sharpanskykh, A. (2024). Towards a Better Understanding of Agent-Based Airport Terminal Operations Using Surrogate Modeling. In L. G. Nardin, S. Mehryar, & S. Mehryar (Eds.), Multi-Agent-Based Simulation XXIV: 24th International Workshop, MABS 2023, London, UK, May 29–June 2, 2023 : revised selected papers (pp. 16-29). (Lecture Notes in Computer Science; Vol. 14558), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-031-61034-9_2
    • van Beveren, I., Sergidou, E., & Mohammadi Ziabari, S. S. (2024). Evaluating Deep Learning-Based Speaker Verification Systems: A Comparative Study Across Open-Source and Forensic Datasets. In Evaluating Deep Learning-Based Speaker Verification Systems: A Comparative Study Across Open-Source and Forensic Datasets
    • van de Sande, S. N. P., Alsahag, A. M. M., & Mohammadi Ziabari, S. S. (2024). Enhancing the Predictability of Wintertime Energy Demand in The Netherlands Using Ensemble Model Prophet-LSTM. Processes, 12(11), Article 2519. https://doi.org/10.3390/pr12112519

    2023

    • Chikhi, A., Mohammadi Ziabari, S. S., & van Essen, J. W. (2023). A Comparative Study of Traditional, Ensemble and Neural Network-Based Natural Language Processing Algorithms. Journal of Risk and Financial Management, 16(7), Article 327. https://doi.org/10.3390/jrfm16070327
    • De Bosscher, B. C. D., Mohammadi Ziabari, S. S., & Sharpanskykh, A. (2023). A comprehensive study of agent-based airport terminal operations using surrogate modeling and simulation. Simulation Modelling Practice and Theory, 128, Article 102811. https://doi.org/10.1016/j.simpat.2023.102811
    • De Leeuw, B., Mohammadi Ziabari, S. S., & Sharpanskykh, A. (2023). Surrogate Modeling of Agent-Based Airport Terminal Operations. In F. Lorig, & E. Norling (Eds.), Multi-Agent-Based Simulation XXIII - 23rd International Workshop, MABS 2022, Revised Selected Papers (pp. 82-94). (Lecture Notes in Computer Science; Vol. 13743), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-031-22947-3_7
    • Deshamudre, R., Mohammadi Ziabari, S. S., & van Houten, M. (2023). Enhancing AI Adoption in Healthcare: A Data Strategy for Improved Heart Disease Prediction Accuracy Through Deep Learning Techniques. In P. Delir Haghighi, E. Pardede, G. Dobbie, V. Yogarajan, N. A. S. ER, G. Kotsis, & I. Khalil (Eds.), Information Integration and Web Intelligence: 25th International Conference, iiWAS 2023, Denpasar, Bali, Indonesia, December 4–6, 2023 : proceedings (pp. 13-19). (Lecture Notes in Computer Science; Vol. 14416). Springer. https://doi.org/10.1007/978-3-031-48316-5_2
    • Hooftman, D., Mohammadi Ziabari, S. S., & Snijder, J. (2023). Exploring CycleGAN for Bias Reduction in Gender Classification: Generative Modelling for Diversifying Data Augmentation. In H. Lu, M. Blumenstein, S.-B. Cho, C.-L. Liu, Y. Yagi, & T. Kamiya (Eds.), Pattern Recognition: 7th Asian Conference, ACPR 2023, Kitakyushu, Japan, November 5–8, 2023 : proceedings (pp. 26-40). (Lecture Notes in Computer Science; Vol. 14408). Springer. https://doi.org/10.1007/978-3-031-47665-5_3

    2022

    • Janssen, S., Sharpanskykh, A., & Mohammadi Ziabari, S. S. (2022). Using Causal Discovery to Design Agent-Based Models. In K. H. Van Dam, & N. Verstaevel (Eds.), Multi-Agent-Based Simulation XXII - 22nd International Workshop, MABS 2021, Revised Selected Papers (pp. 15-28). (Lecture Notes in Computer Science; Vol. 13128), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-94548-0_2

    2021

    • Andrianov, A., Ziabari, S. S. M., & Gerritsen, C. (2021). A brain-inspired cognitive support model for stress reduction based on an adaptive network model. Cognitive Systems Research, 65, 151-166. https://doi.org/10.1016/j.cogsys.2020.10.010
    • Mekić, A., Mohammadi Ziabari, S. S., & Sharpanskykh, A. (2021). Systemic agent-based modeling and analysis of passenger discretionary activities in airport terminals. Aerospace, 8(6), Article 162. https://doi.org/10.3390/aerospace8060162
    • Mohammadi Ziabari, S. S., Sanders, G., Mekic, A., & Sharpanskykh, A. (2021). Demo Paper: A Tool for Analyzing COVID-19-Related Measurements Using Agent-Based Support Simulator for Airport Terminal Operations. In F. Dignum, J. M. Corchado, & F. De La Prieta (Eds.), Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection - 19th International Conference, PAAMS 2021, Proceedings (pp. 359-362). (Lecture Notes in Computer Science; Vol. 12946), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-85739-4_32
    • Sanders, G., Mohammadi Ziabari, S. S., Mekić, A., & Sharpanskykh, A. (2021). Agent-Based Modelling and Simulation of Airport Terminal Operations Under COVID-19-Related Restrictions. In F. Dignum, J. M. Corchado, & F. De La Prieta (Eds.), Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection - 19th International Conference, PAAMS 2021, Proceedings (pp. 214-228). (Lecture Notes in Computer Science; Vol. 12946), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-85739-4_18
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