Chugh, R., Turnbull, D., Kutty, S., Sabrina, F., Rashid, M. M., Morshed, A., . . . Subramani, S. (2025). Generative AI as a learning assistant in ICT education: Student perspectives and educational implications. Education and Information Technologies, 30(16), 23693-23728. doi:10.1007/s10639-025-13686-3

Jensen, A., Brown, P., Groves, K., & Morshed, A. (2025). Next generation crop protection: A systematic review of trends in modelling approaches for disease prediction. Computers and Electronics in Agriculture, 234, 1-17. doi:10.1016/j.compag.2025.110245

Awasthi, T. R., Morshed, A., & Swain, D. L. (2025). A machine learning approach to simulate cattle growth at pasture using remotely collected walk-over weights. Agricultural Systems, 226, 1-12. doi:10.1016/j.agsy.2025.104332

Chugh, R., Turnbull, D., Morshed, A., Sabrina, F., Azad, S., Rashid, M. M., . . . Subramani, S. (2025). The promise and pitfalls: A literature review of generative artificial intelligence as a learning assistant in ICT education. Computer Applications in Engineering Education, 33(2), 1-18. doi:10.1002/cae.70002

Awasthi, T. R., Morshed, A., Williams, T., & Swain, D. L. (2024). Simulation approaches used for management and decision making in the beef production sector: A systematic review. Animals, 14(11), 1-26. doi:10.3390/ani14111632

Woodbright, M. D., Morshed, A., Browne, M., Ray, B., & Moore, S. (2024). Toward transparent AI for neurological disorders: A feature extraction and relevance analysis framework. IEEE Access, 12, 37731-37743. doi:10.1109/ACCESS.2024.3375877

Murad, N. Y., Mahmood, T., Forkan, A. R. M., Morshed, A., Jayaraman, P. P., & Siddiqui, M. S. (2023). Weed Detection Using Deep Learning: A Systematic Literature Review. Sensors, 23(7), 1-45. doi:10.3390/s23073670

Joachim, S., Forkan, A. R. M., Jayaraman, P. P., Morshed, A., & Wickramasinghe, N. (2022). A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes. Sensors, 22(12), 1-24. doi:10.3390/s22124620

Regino, A. G., dos Reis, J. C., Bonacin, R., Morshed, A., & Sellis, T. (2021). Link maintenance for integrity in linked open data evolution: Literature survey and open challenges. Semantic Web, 12(3), 517-541. doi:10.3233/sw-200398

Imran, S., Mahmood, T., Morshed, A., & Sellis, T. (2021). Big data analytics in healthcare A systematic literature review and roadmap for practical implementation. IEEE/CAA Journal of Automatica Sinica, 8(1), 1-22. doi:10.1109/jas.2020.1003384

Islam, N., Rashid, M. M., Wibowo, S., Xu, C. -Y., Morshed, A., Wasimi, S. A., . . . Rahman, S. M. (2021). Early weed detection using image processing and machine learning techniques in an Australian chilli farm. Agriculture, 11(5), 1-13. doi:10.3390/agriculture11050387

Jayaraman, P. P., Forkan, A. R. M., Morshed, A., Haghighi, P. D., & Kang, Y. B. (2020). Healthcare 4.0: A review of frontiers in digital health. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), 1-23. doi:10.1002/widm.1350

Zahid, H., Mahmood, T., Morshed, A., & Sellis, T. (2020). Big data analytics in telecommunications: Literature review and architecture recommendations. IEEE/CAA Journal of Automatica Sinica, 7(1), 18-38. doi:10.1109/JAS.2019.1911795

Morshed, A., Jayaraman, P. P., Sellis, T., Georgakopoulos, D., Villari, M., & Ranjan, R. (2017). Deep Osmosis: Holistic Distributed Deep Learning in Osmotic Computing. IEEE Cloud Computing, 4(6), 22-32. doi:10.1109/mcc.2018.1081070

Dutta, R., Morshed, A., Aryal, J., D'Este, C., & Das, A. (2014). Development of an intelligent environmental knowledge system for sustainable agricultural decision support. Environmental Modelling & Software, 52, 264-272. doi:10.1016/j.envsoft.2013.10.004

Chugh, R., Morshed, A., Sabrina, F., Azad, S., Rashid, M. D. M., Kaisar, S., . . . Turnbull, D. (2025). AI-powered pedagogy: Generative AI as a learning assistant in the ICT discipline. Rockhampton, QLD: CQUniversity Australia.

Burgos, D., Morshed, A., Rashid, M. M., & Mandala, S. (2024). A comparison of machine learning models to deep learning models for cancer image classification and explainability of classification. In 2024 International Conference on Data Science and Its Applications, ICoDSA 2024 (pp. 386-390). Piscataway, NJ: IEEE. doi:10.1109/ICoDSA62899.2024.10651790

Awasthi, T. R., Morshed, A., Chang, A. Z., Mandala, S., & Swain, D. L. (2024). Impact of weighing frequency on the determination of daily weights of cattle. In 2024 International Conference on Data Science and Its Applications (ICoDSA) (pp. 522-526). Piscataway, NJ: IEEE. doi:10.1109/ICoDSA62899.2024.10652207

Woodbright, M. D., Morshed, A., Browne, M., Ray, B., & Moore, S. (2024). Optimised hybrid-model selection for the autonomous relevance technique explainable framework. In 2024 International Conference on Data Science and Its Applications Icodsa 2024 (pp. 380-385). Piscataway, NJ: IEEE. doi:10.1109/ICoDSA62899.2024.10651906

Humayun, S., Mahmood, T., & Morshed, A. (2024). A multi-objective feature selection framework for breast cancer data using evolutionary methods. In IEEE Conference on Engineering Informatics (ICEI) (pp. 210-216). Piscataway, NJ: IEEE. doi:10.1109/ICEI64305.2024.10912222

Chakkappan, G., Morshed, A., & Rashid, M. M. (2024). Explainable AI and big data analytics for data security risk and privacy issues in the financial industry. In 2024 IEEE Conference on Engineering Informatics, ICEI 2024 (pp. 177-184). Piscataway, NJ: IEEE. doi:10.1109/ICEI64305.2024.10912422

Rashid, M. D. M., Sabrina, F., Imam, T., Ray, B., Gordon, S., Morshed, A., & Wibowo, S. (2023). Online machine learning-based anomaly detection in Internet of Things applications. In Proceedings of The IEEE CSDE (pp. 306-310). Piscataway, NJ: IEEE. doi:10.1109/CSDE59766.2023.10487703

Awasthi, T. R., Morshed, A., & Swain, D. (2023). A comparative study of machine learning methods: A case study of weight and growth of livestock. In 2023 IEEE Engineering Informatics Ei 2023 (pp. 142-148). Piscataway, NJ: IEEE. doi:10.1109/IEEECONF58110.2023.10520498

Murad, N. Y., Mahmood, T., & Morshed, A. (2023). Weed classification for smart farming in field crops: A machine learning evaluation. In 2023 IEEE Engineering Informatics Ei 2023 (pp. 89-95). Piscataway, NJ: IEEE. doi:10.1109/IEEECONF58110.2023.10520463

Rashid, M. M., Sabrina, F., Ray, B., Morshed, A., Steven, G., & Wibowo, S. (2022). Anomaly Detection in IoT Applications Using Deep Learning with Class Balancing. In Proceedings of the IEEE CSDE (pp. 643-648). Piscataway, NJ: IEEE. Retrieved from https://ieeexplore.ieee.org/document/10089252

Joachim, S., Jayaraman, P. P., Forkan, A. R. M., Morshed, A., & Wickramasinghe, N. (2021). Design and development of a diabetes self-management platform: A case for responsible information system development. In T. X. Bui (Ed.), Proceedings of the 54th Annual Hawaii International Conference on System Sciences Vol. 2020-January (pp. 3784-3793). Honolulu, HI: HICSS Conference Office. doi:10.24251/HICSS.2021.459

Madithiyagasthenna, D., Jayaraman, P. P., Morshed, A., Forkan, A. R. M., Georgakopoulos, D., Kang, Y. B., & Piechowski, M. (2020). A solution for annotating sensor data streams-An industrial use case in building management system. In Proceedings - IEEE International Conference on Mobile Data Management (pp. 194-201). Piscataway, NJ: IEEE. doi:10.1109/MDM48529.2020.00042

Forkan, A. R. M., Jayaraman, P. P., Kang, Y. B., & Morshed, A. (2020). ECHO: A tool for empirical evaluation cloud chatbots. In Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020 (pp. 669-672). Piscataway, NJ: IEEE. doi:10.1109/CCGrid49817.2020.00-26

Borundiya, A. P., Banerjee, A., Khargharia, H. S., Morshed, A., Azad, S., Ali, A. B. M. S., & Wasimi, S. (2019). Digital marketing through physical context awareness. In 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2019 (pp. 475-479). Piscataway, NJ: IEEE. doi:10.1109/CSDE48274.2019.9162392

Forkan, A. R. M., Montori, F., Georgakopoulos, D., Jayaraman, P. P., Yavari, A., & Morshed, A. (2019). An industrial IoT solution for evaluating workers' performance via activity recognition. In Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems Vol. 2019-July (pp. 1393-1403). Piscataway, NJ: IEEE. doi:10.1109/ICDCS.2019.00139

Forkan, A. R. M., Kimm, G., Morshed, A., Jayaraman, P. P., Banerjee, A., & Huang, W. (2019). AqVision: A tool for air quality data visualisation and pollution-free route tracking for smart city. In T. G. Wyeld, E. Banissi, A. Ursyn, M. Bannatyne, N. Datia, & M. Sarfraz (Eds.), Proceedings - 2019 23rd International Conference in Information Visualization - Part II, IV-2 2019 (pp. 47-51). Piscataway, NJ: IEEE. doi:10.1109/IV-2.2019.00018

Huang, W., Jayaraman, P. P., Morshed, A., Blackburn, S., Redpath, C., Guerney, T., . . . Mui, R. (2019). Sens-e-motion: Capturing and visualising emotional status of computer users in real time. In T. G. Wyeld, E. Banissi, A. Ursyn, M. Bannatyne, N. Datia, & M. Sarfraz (Eds.), Proceedings - 2019 23rd International Conference in Information Visualization - Part II, IV-2 2019 (pp. 96-99). Piscataway, NJ: IEEE. doi:10.1109/IV-2.2019.00028

Morshed, A., Tsai, P. W., Jayaraman, P. P., Sellis, T., Georgakopoulos, D., Burke, S., . . . Jenkins, C. (2019). Viscrime: A crime visualisation system for crime trajectory from multi-dimensional sources. In WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining (pp. 802-805). New York, NY: The Association for Computing Machinery. doi:10.1145/3289600.3290617

Regino, A. G., Rodrigues Matsoui, J. K., dos Reis, J. C., Bonacin, R., Morshed, A., & Sellis, T. (2019). Understanding link changes in LOD via the evolution of life science datasets. In A. Hasnain, V. Novacek, M. Dumontier, & D. Rebholz-Schuhmann (Eds.), Proceedings of the Workshop on Semantic Web Solutions for Large-Scale Biomedical Data Analytics co-located with 18th International Semantic Web Conference (ISWC 2019) Vol. 2477 (pp. 40-54). Online: CEUR-WS. Retrieved from https://ceur-ws.org/Vol-2477/

Morshed, A., Forkan, A. R. M., Tsai, P. W., Jayaraman, P. P., Sellis, T., Georgakopoulos, D., . . . Ranjan, R. (2019). Viscrimepredict: A system for crime trajectory prediction and visualisation from heterogeneous data sources. In SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing Vol. Part F147772 (pp. 1099-1106). New York, NY: Association for Computing Machinery. doi:10.1145/3297280.3297388

Morshed, A., Forkan, A. R. M., Shah, T., Jayaraman, P. P., Ranjan, R., & Georgakopoulos, D. (2018). Visual analytics ontology-guided I-DE system: A case study of head and neck cancer in Australia. In Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018 (pp. 424-429). USA: IEEE. doi:10.1109/CIC.2018.00064

Joachim, S., Jayaraman, P. P., Forkan, A. R. M., Morshed, A., & Wickramasinghe, N. (2021). Developing a personalised diabetic platform using a design science research methodology. In N. Wickramasinghe (Ed.), Optimizing health monitoring systems with wireless technology (pp. 71-87). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-6067-8.ch007

Islam, N., Rashid, M. M., Wibowo, S., Wasimi, S., Morshed, A., Xu, C., & Moore, S. T. (2021). Machine learning based approach for weed detection in chilli field using RGB images. In H. Meng, T. Lei, M. Li, K. Li, N. Xiong, & L. Wang (Eds.), Advances in natural computation, fuzzy systems and knowledge discovery (Vol. 88, pp. 1097-1105). Cham, Switzerland: Springer. doi:10.1007/978-3-030-70665-4_119

Dutta, R., Morshed, A., & Aryal, J. (2014). Recommending environmental big data using semantically guided machine learning. In S. Sakr, & M. M. Gaber (Eds.), Large Scale and Big Data: Processing and Management (1st ed., pp. 463-494). Boca Raton, FL: CRC Press. doi:10.1201/b17112