J. Fiosina, P. Sievers, M. Drache, and S. Beuermann. (2024) AI-Based Forecasting of Polymer Properties for High-Temperature Butyl Acrylate Polymerizations, ACS Polymers Au. DOI: 10.1021/acspolymersau.4c00047

J. Fiosina, P. Sievers, G. Kanagaraj, M. Drache, S. Beuermann. (2024) Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization. Polymers, 16, 945. DOI: 10.3390/polym16070945

J. Fiosina, P. Sievers, M. Drache, S. Beuermann (2023). Polymer Reaction Engineering meets Explainable Machine Learning. Computers & Chemical Engineering, Volume 177, 108356, DOI: 10.1016/j.compchemeng.2023.

J. Fiosina. (2022). Computationally intensive, distributed and decentralised machine learning: from theory to applications. Cumulative Habilitation thesis. TU Clausthal.

J. Fiosina. (2022) Interpretable Privacy-Preserving Collaborative Deep Learning for Taxi Trip Duration Forecasting, Communications in Computer and Information Science, vol 1612. Springer, Cham, pp. 392-411.

C. Koetsier, J. Fiosina, J. N. Gremmel, M. Sester, J. P. Müller, and D. Woisetschläger. (2022). Detection of anomalous vehicle trajectories using federated learning, ISPRS Open Journal of Photogrammetry and Remote Sensing 4, Elsevier, 100013.

C. Koetsier, J. Fiosina, J. N. Gremmel, M. Sester, J. P. Müller, and D. Woisetschläger.(2021) Federated cooperative detection of anomalous vehicletrajectories at intersections, In Proc. of . 29th ACM Int. Conf. on Advances in Geographic  Information Systems (ACM SIGSPATIAL 2021). ACM, New York, NY, USA, 10 pages.

J. Fiosina. (2021)  Explainable federated learning for taxi travel time prediction, In Proc. of the7th Int. Conf. on Vehicle Technology and Intelligent Transport Systems - VEHITS 2021, pages 670-677, INSTICC, SciTEPress

S. Schleibaum, M. Greve, T.B. Lembcke, A. Azaria, J. Fiosina, N. Hazon, L. M. Kolbe, S. Kraus, J. P. Müller, M. Vollrath. (2020) How Did You Like This Ride? An Analysis of User Preferences in Ridesharing Assignments, In Proc. of the 6th Int. Conf. on Vehicle Technology and Intelligent Transport Systems, VEHITS 2020, Prague, Czech Republic, May 2-4, 157-168, 2020

S. Kraus, A. Azaria, J. Fiosina, M. Greve, N. Hazon, L. Kolbe, T.Lembcke, J. P. Müller, S. Schleibaum, M. Vollrath. (2020) AI for Explaining Decisions in Multi-Agent Environments, Blue Sky Paper, AAAI 2020 : The 34th AAAI Conf. on AI, New York, USA, February 7-12,  2020, pages 13534-13538. AAAI Press.

J. Fiosina, M. Fiosins, and S. Bonn. (2019) Deep learning and random forest-based augmentation of sRNA expression profiles. In Z. Cai, P. Skums, and M. Li, editors, Bioinformatics Research and Applications, pages 159–170, Cham, 2019. Springer International Publishing  [Preprint]

J. Fiosina, M. Fiosins, and S. Bonn. (2020) Explainable deep learning for augmentation of sRNA expression profiles. Journal of Computational Biology, 27(2). pages 234-247, 2020

J. Fiosina, M. Fiosins (2017). Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting. Applied Computational Intelligence and Soft Computing, 2017, 13. [Bib]

Fiosina, J. and Fiosins, M. (2014). Resampling based Modelling of Individual Routing Preferences in a Distributed Traffic Network. International Journal of Artificial Intelligence 12(1), 79-103. [Preprint]

Fiosina, J., Fiosins, M. (2013). Density-Based Clustering in Cloud-Oriented Collaborative Multi-Agent Systems. (HAIS2013), Lecture Notes in Computer Science, Vol. 8073, pp 639-648 [Preprint]

Fiosina, J., Fiosins, M., Müller, J.P. (2013). Decentralised Cooperative Agent-based Clustering in Intelligent Traffic Clouds. (MATES2013), Lecture Notes in Computer Science Vol. 8076 pp 59-72 [Preprint]

Fiosina, J. and Fiosins, M. (2013). Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network. In. Proc. of the 10th Int. Conf. on Distributed Computing and Artificial Intelligence (DCAI 2013), Advances in Intelligent Systems and Computing, Vol. 217, Springer Verlag, Berlin / Heidelberg, pp. 103-110, DOI: 10.1007/978-3-319-00551-5_13 [Preprint]

Andronov A. and Fiosina J., (2013). Resampling-based nonparametric statistical inferences about the distributions of order statistics. Journal of Mathematical Sciences, 191(4): 485-491, Springer US.

Fiosina, J. and Fiosins, M. (2013). Chapter 1: ‘Cooperative Regression-Based Forecasting in Distributed Traffic Networks’. Qurban A. Memon, ed.,"Distributed Network Intelligence, Security and Applications". CRC Press, Taylor & Francis Group, pp. 3-37.

Fiosina, J., Fiosins, M., Müller, J.P. (2013). Big Data Processing and Mining for Next Generation Intelligent Transportation Systems. Jurnal Teknologi (Sciences & Engineering), 63(3): 23–38, Penerbit UTM Press, Universiti Teknologi Malaysia

Fiosina, J. and Fiosins, M. (2012). Cooperative Kernel-Based Forecasting in Decentralized Multi-Agent Systems for Urban Traffic Networks. In Proc. of Ubiquitous Data Mining (UDM) Workshop at the 20th European Conf. on Artificial Intelligence, CEUR-WS.org, Vol. 960, pp. 3-7. 

Fiosina, J. (2012). Decentralised Regression Model for Intelligent Forecasting in Multi-agent Traffic Networks. In Proc. of the 9th Int. Conf. on Distributed Computing and Artificial Intelligence (DCAI’12), 28-30 Mar. 2012, Salamanca, Spain, Springer Verlag, S. Omatu et al. (Eds.): Advances in Intelligent and Soft Computing, 151, Springer, pp. 255-263. DOI: 10.1007/978-3-642-28765-7_30, Online ISBN: 978-3-642-24800-9 [Preprint]

Fiosins, M., Fiosina, J. and Müller, J. P. (2012). Change Point Analysis for Intelligent Agents in City Traffic. In Cao. L., Bazzan. A., Symeonidis. A., Gorodetsky. V., Weiss. G. and Yu. P. (eds.). Agents and Data Mining Interaction. Lecture Notes in Artificial Intelligence 7103, Springer, pp. 195-210, DOI: 10.1007/978-3-642-27609-5_13, Online ISBN: 978-3-642-27609-5 [Preprint]

Fiosina J. and Fiosins M. (2011). Resampling-based Change Point Estimation. In Proc. of the 10th Int. Sym. on Intelligent Data Analysis (IDA'11), 29-31 Oct. 2011,Porto, Portugal, Springer Verlag, Lecture Notes in Computer Science, 7014: 150-161 [Preprint]

Fiosina, J. and Fiosins, M. (2011). Resampling Approach to Comparison of Two Routes in Stochastic Graph. In Proceedings of the 14th Int. Conf. Applied Stochastic Models and Data Analysis (ASMDA2011), Rome, Italy, pp. 457-464.

Fiosins, M.; Fiosina, J.; Müller, J .P. and Görmer, J. (2011). Reconciling Strategic and Tactical Decision Making in Agent-Oriented Simulation of Vehicles in Urban Traffic. In Proc. of 4th Int. ICST Conf. on Simulation Tools and Techniques (SimuTools'2011), CD Proceedings and to appear in ACM Digital Library, 8 pages. [Preprint]

Fiosins, M.; Fiosina, J.; Müller, J. and Görmer, J. (2011). Agent-Based Integrated Decision Making for Autonomous Vehicles in Urban Traffic. In Demazeau, Y.; Pechoucek, M., Corchado, J. and Pérez, J. (eds.): Advances on Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, Springer Berlin / Heidelberg, 88:173-178. [Preprint]

Fiosina J. and Fiosins M. (2011). Statistical Estimation for Reliability Model Based on Shot-Noise Processes in a Case of Small Samples. Quality Technology and Quantitative Management, 8(4): 451-462. [Preprint]

Andronov A., Fioshina H. (Fiosina, J.) and Fioshin M., (2009). Statistical Estimation for a Failure Model with Damage Accumulation in a Case of Small Samples. Journal of Statistical Planning and Inference, Elsevier, 139(5):1685-1692. 

Fioshina, H. (Fiosina, J.), (2009). Resampling Approach to the Estimation of a Reliability Model Based on Shot-Noise Processes. In Proc. of the 9th Workshop on Stochastic Models and Their Applications,– Aachen, Germany, pp. 12-13. 

Fioshina, H. (Fiosina, J.) and Fioshin, M., (2008). Statistical Estimation for Reliability Model Based on Shot-Noise Processes in a Case of Small Samples. In Proc. of the 2nd Int. Conf. On Accelerated Life Testing in Reliability and Quality Control, IMB, University Victor Segalen Bordeaux 2, Bordeaux, France, pp. 57-63. 

Fioshin, M and Fioshina, H. (Fiosina, J.), (2007). Resampling Approach to the Estimation of Reliability Systems. In Proc. of the Int. Conf. on Mathematical Methods in Reliability, Glasgow, Scotland. [Preprint]

Andronov, A.; Afanasyeva, H. (Fiosina, J.) and Fioshin, M., (2006). Statistical Estimation for a Failure Model  with the Accumulation of Damages. In Proc. of the Int. Conf. on     Degradation, Damage, Fatigue and Accelerated Life Models in Reliability Testing, Angers, France, pp. 75-81. 

Afanasyeva , H. (Fiosina, J.) and  Andronov, A., (2006). On robustness of resampling estimators for linear regression models. Communications in Dependability and Quality Management: An international Journal, 9(1): 5-11. 

Afanasyeva, H. (Fiosina, J.), (2005). A task of the storage control theory in transport systems using resampling-method. In Proc. of 5-th Int. Conf. “Transport Systems Telematics”, Katowice-Ustron, Poland, pp. 13-21. [Preprint]

Afanasyeva, H. (Fiosina, J.), (2005). Resampling-approach to a task of comparison of two renewal processes. In Proc. of 12th Int. Conf. on Analytical and Stochastic Modelling Techniques and Applications, -Riga: RTU, pp. 94-100. [Preprint]

Afanasyeva, H. (Fiosina, J.) and  Andronov, A., (2005). On robustness of resampling estimators for linear regression models. In Proc. of the Int. Symposium on Stochastic Models  in Reliability, Safety and Logistics, Beer Sheva, Israel, 2005, pp. 6-11. 

Afanasyeva, H. (Fiosina, J.), (2005). Resampling Median Estimators for Linear Regression Model. Transport and Telecommunication, 6(1): 90-94, Transport and Telecommunication Institute, Riga.

Andronov, A. and Afanasyeva, H. (Fiosina, J.), (2004). Resampling Based Non-Parametric Statistical Inferences about Distribution, Moments and Quantiles of Order Statistics. In Transactions of XXIV Int. Seminar on Stability Problems for Stochastic Models, -Riga: TTI, pp. 300-307

Afanasyeva, H. (Fiosina, J.), (2002). Fuzzy Learning Classifiers Systems for Classification Task. Transport and Telecommunication, 3(3): 43-51, Transport and Telecommunication Institute, Riga

Afanasyeva, H. (Fiosina, J.), (2002). The Resampling-estimator of Queuing Length Nonstationary Distribution for the Queuing System M/G/Infinity. Transport and Telecommunication, 3(1): 89-94, Transport and Telecommunication Institute, Riga

Afanasyeva, H. (Fiosina, J.), (2001). Genetics-based Machine Learning Systems for Classification Task. In Scientific Proc. of Riga Technical University. – Riga: RTU, pp. 8-16.