Studies included in the thesis

  1. V. Boeva, M. Angelova, V. M. Devagiri, and E. Tsiporkova. “Bipartite Split-Merge Evolutionary Clustering”. In: Agents and Artificial Intelligence. Ed. by J. van den Herik, A. P. Rocha, and L. Steels. Cham: Springer International Publishing, 2019, pp. 204–223. DOI: 10.1007/978-3-030-37494-5_11
  2. V. M. Devagiri, V. Boeva, and E. Tsiporkova. “Split-Merge Evolutionary Clustering for Multi-View Streaming Data”. In: Procedia Computer Science 176 (2020). Knowledge-Based and Intelligent Information Engineering Systems: Proceedings of the 24th International Conference KES2020, pp. 460–469. ISSN: 1877-0509. DOI: 10.1016/j.procs.2020.08.048
  3. V. M. Devagiri, V. Boeva, and S. Abghari. “A Multi-view Clustering Approach for Analysis of Streaming Data”. In: Artificial Intelligence Applications and Innovations. Ed. by I. Maglogiannis, J. Macintyre, and L. Iliadis. Cham: Springer International Publishing, 2021, pp. 169–183. ISBN: 978-3-030-79150-6. DOI: 10.1007/978-3-030-79150-6_14
  4. V. M. Devagiri, V. Boeva, S. Abghari, F. Basiri, and N. Lavesson. “Multi-View Data Analysis Techniques for Monitoring Smart Building Systems”. In: Sensors 21.20 (2021). ISSN: 1424-8220. DOI: 10.3390/s21206775
  5. C. Åleskog, V. M. Devagiri, and V. Boeva. “A Graph-Based Multi-view Clustering Approach for Continuous Pattern Mining”. In: Recent Advancements in Multi-View Data Analytics. Ed. by W. Pedrycz and S.-M. Chen. Cham: Springer International Publishing, 2022, pp. 201–237. ISBN: 978-3-030-95239-6. DOI: 10.1007/978-3-030-95239-6_8
  6. V. M. Devagiri, V. Boeva, and S. Abghari. “Domain Adaptation Through Cluster Integration and Correlation”. In: 2022 IEEE International Conference on Data Mining Workshops (ICDMW). 2022, pp. 1–8. DOI: 10.1109/ICDMW58026.2022.00025
  7. V. M. Devagiri, V. Boeva, and S. Abghari. "A Domain Adaptation Technique through Cluster Boundary Integration". Evolving Systems. (under publication).
  8. V. M. Devagiri, P. Dagnely, V. Boeva and E. Tsiporkova. "Putting Sense into Multi-source Heterogeneous Data with Hypergraph Clustering Analysis". Accepted for the Symposium on Intelligent Data Analysis (IDA), Stockholm, Sweden, April 2024. DOI: 10.1007/978-3-031-58553-1_10
  1. M. Angelova, V. M. Devagiri, V. Boeva, P. Linde and N. Lavesson. "An Expertise Recommender System based on Data from Institutional Repository (DiVA)". Leslie Chan and Pierre Mounier (Eds.): Connecting the Knowledge Commons – from projects to sustainable infrastructure. OpenEdition Press, pp.135-149, 2019. DOI: 10.4000/books.oep.9078
  2. V. Boeva, M. Angelova, V. M. Devagiri, E. Tsiporkova, A Split-Merge Framework for Evolutionary Clustering, 31th Swedish AI Society Workshop SAIS 2019, Umeå, Sweden, June 2019.
  3. V. Boeva, E. Casalicchio, S. Abghari, A.A. Al-Saedi, V.M. Devagiri, A. Petef, P. Exner, A. Isberg. and M. Jasarevic. 2022. "Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN)". Position Papers of the 17th Conference on Computer Science and Intelligence Systems, Annals of Computer Science and Information Systems 31 (2022): 71-78. DOI: 10.15439/2022F267

Posters

  1. V. Boeva, M. Angelova, V. M. Devagiri and E. Tsiporkova. "Patient Profiling Using Evolutionary Clustering". ACM Celebration of Women in Computing: womENcourage 2019, Rome, Italy, September 2019.
  2. V. M. Devagiri, V. Boeva and S. Abghari. "A Domain Integration Bi-correlation Clustering". PhD Track, DSAA 2022

Other studies

  1. V. M. Devagiri and A. Cheddad. “Splicing forgery detection and the impact of image resolution”. In: 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2017, pp. 1–6. DOI: 10.1109/ECAI.2017.8166431