Zenun Kastrati is a Senior Lecturer at the Department of Informatics at Linnaeus University (LNU). Previously, he was associated with the Department of Computer Science and Media Technology at Linnaeus University as a Postdoctoral Research Fellow. Prior to joining LNU in 2018, he worked for more than 9 years as a Lecturer/Researcher at the University of Prishtina, Kosovo. He obtained his PhD from Norwegian University of Science and Technology (NTNU), Norway, in Computer Science in 2018 and a Master degree in Computer Science through the EU TEMPUS Programme developed and implemented jointly by University of Pristina, Kosovo, Université de La Rochelle, France, and Institute of Technology Carlow, Republic of Ireland.
Zenun is responsible for the following course:
- 4DV117 Applied Machine Learning (MSc)
He is also involved in teaching for the courses:
- 4ME310 Adaptive and Semantic Web (MSc)
- 1IK143 Introduction to Object Oriented Programming (BSc)
- 1IK153 Data structures, Databases and Graphical user interfaces with an Object Oriented Programming Language (BSc)
Zenun's research interests focus on the field of artificial intelligence with a special focus on NLP, machine/deep learning, semantic web, sentiment analysis, and learning technologies.
Article in journal (Refereed)
- Shaikh, S., Daudpota, S.M., Imran, A.S., Kastrati, Z. (2021). Towards Improved Classification Accuracy on Highly Imbalanced Text Dataset Using Deep Neural Language Models. Applied Sciences. 11. 1-20.
- Imran, A.S., Daudpota, S.M., Kastrati, Z., Batra, R. (2020). Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets. IEEE Access. 8. 181074-181090.
- Kastrati, Z., Kurti, A., Imran, A.S. (2020). WET : Word embedding-topic distribution vectors for MOOC video lectures dataset. Data in Brief. 28. 1-6.
- Kastrati, Z., Imran, A.S., Kurti, A. (2020). Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students' Reviews of MOOCs. IEEE Access. 8. 106799-106810.
- Kastrati, Z., Imran, A.S., Kurti, A. (2019). Integrating word embeddings and document topics with deep learning in a video classification framework. Pattern Recognition Letters. 128. 85-92.
- Kastrati, Z., Imran, A.S., Yayilgan, S.Y. (2019). The impact of deep learning on document classification using semantically rich representations. Information Processing & Management. 56. 1618-1632.
- Kastrati, Z., Imran, A.S. (2019). Performance analysis of machine learning classifiers on improved concept vector space models. Future generations computer systems. 96. 552-562.
- Kastrati, Z., Imran, A.S., Yildirim-Yayilgan, S. (2016). SEMCON : a semantic and contextual objective metric for enriching domain ontology concepts. International Journal on Semantic Web and Information Systems. 12. 1-24.
Conference paper (Refereed)
- Kastrati, Z., Arifaj, B., Lubishtani, A., Gashi, F., Nishliu, E. (2020). Aspect-Based Opinion Mining of Students’ Reviews on Online Courses. Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. 510-514.
- Ferati, M., Dalipi, F., Kastrati, Z. (2020). Open Government Data Through the Lens of Universal Design. Universal Access in Human-Computer Interaction. Applications and Practice : 14th International Conference, UAHCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II. 331-340.
- Imran, A.S., Kastrati, Z., Svendsen, T.K., Kurti, A. (2019). Text-Independent Speaker ID Employing 2D-CNN for Automatic Video Lecture Categorization in a MOOC Setting. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). 273-277.
- Kastrati, Z., Kurti, A., Hagelbäck, J. (2019). The Effect of a Flipped Classroom in a SPOC : Students' Perceptions and Attitudes. ICETC 2019: Proceedings of the 2019 11th International Conference on Education Technology and Computers. 246-249.
- Kastrati, Z., Imran, A.S., Kurti, A. (2019). Transfer Learning to Timed Text Based Video Classification Using CNN. Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics.
- Imran, A.S., Dalipi, F., Kastrati, Z. (2019). Predicting Student Dropout in a MOOC : An Evaluation of a Deep Neural Network Model. Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence. 190-195.
- Imran, A.S., Kastrati, Z., Svendsen, T.K., Kurti, A. (2019). Text-Independent Speaker ID for Automatic Video Lecture Classification Using Deep Learning. Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence, April 19-22, 2019, Bali, Indonesia. 175-180.
- Dalipi, F., Imran, A.S., Kastrati, Z. (2018). MOOC Dropout Prediction Using Machine Learning Techniques : Review and Research Challenges. Proceedings of 2018 IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education. 1007-1014.
- Kastrati, Z., Yayilgan, S.Y. (2017). Supervised Ontology-Based Document Classification Model. Proceedings of the International Conference on Compute and Data Analysis, ICCDA'17. 245-251.
- Kastrati, Z., Yayilgan, S.Y. (2017). Improving document classification effectiveness using knowledge exploited by ontologies. Natural language processing and information systems : 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017, Proceedings. 435-438.
- Imran, A.S., Kastrati, Z. (2016). Pedagogical document classification and organization using domain ontology. Learning and Collaboration Technologies : Third International Conference, LCT 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings. 499-509.
- Kastrati, Z., Yayilgan, S.Y., Hjelsvold, R. (2016). Automatically Enriching Domain Ontologies for Document Classification. WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics. 1-4.
- Kastrati, Z., Imran, A.S., Yayilgan, S.Y. (2015). An improved concept vector space model for ontology based classification. 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). 240-245.
- Kastrati, Z., Imran, A.S., Yayilgan, S.Y. (2015). A General Framework for Text Document Classification Using SEMCON and ACVSR. Human Interface and the Management of Information. Information and Knowledge Design. 310-319.
- Kastrati, Z., Imran, A.S. (2015). Document image classification using SEMCON. 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA). 1-6.
- Dalipi, F., Yayilgan, S.Y., Kastrati, Z. (2015). Enhancing the Learner’s Performance Analysis Using SMEUS Semantic E-learning System and Business Intelligence Technologies. Learning and Collaboration Technologies. LCT 2015. 208-217.
- Kastrati, Z., Imran, A.S., Yayilgan, S.Y. (2015). SEMCON : Semantic and contextual objective metric. Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015). 65-68.
- Kastrati, Z., Yayilgan, S.Y., Imran, A.S. (2015). Using Context-Aware and Semantic Similarity Based Model to Enrich Ontology Concepts. Natural Language Processing and Information Systems. 137-143.
- Kastrati, Z., Imran, A.S., Yayilgan, S.Y. (2014). Building Domain Ontologies for Hyperlinked Multimedia Pedagogical Platforms. HCI International 2014 - Posters’ Extended Abstracts. 95-100.
- Picovici, D., Denieffe, D., Kastrati, Z. (2010). Subjective-based Quality Assessment for Online Games. Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques. 10-1.
Chapter in book (Other academic)
- Kastrati, Z., Imran, A.S., Yildirim-Yayilgan, S. (2018). A Hybrid Concept Learning Approach to Ontology Enrichment. Innovations, Developments, and Applications of Semantic Web and Information Systems. IGI Global. 85-119.