FedCSIS

Drawing attention to the #privacy issues associated with #RFID schemes: “Analyzing the Privacy of a #Healthcare RFID #Authentication Protocol” by AM Nica, Ș. Gheorghiță. ACSIS Vol. 41 p. 117–124; tinyurl.com/7sjahv62

Reviewing #AI and #ML technologies that have been used to describe, analyze and predict the mechanics of cells: “Integrating Artificial Intelligence Techniques in #CellMechanics” by M. Naeem, M. Fiorino, P. Addabbo, A. Coronato. ACSIS Vol. 41 p. 105–110; tinyurl.com/mw7afp4z

Six more days to contribute a POSITION paper to FedCSIS 2026. Strict deadline --> May 19th. Looking forward to your work. fedcsis.org

Proposing enhancements to GNN-specific few-shot learning technique for the toxicity prediction task: “#ToxicMoleculeClassification Using #GraphNeuralNetworks and #FewShotLearning.” by B. Mehta, K. Kothari, R. Nambiar, S. Shrawne. ACSIS Vol. 41 p. 105–110; tinyurl.com/2zw7wnr2

Integrating data sources, models, and tools to forecast #wateravailability: “#PredictiveSystem of the #WaterDeficit Analysis for the Black Sea Lowland (an example of the Kherson Region)” by L. Kuzmych, Y. Kyrylov, M. Voloshyn. ACSIS Vol. 41 p. 95–104; tinyurl.com/mve9epcu

@MarekBolanowski We may not be the best (one of these idolized venues ;-), but it is not bad to contribute to our conference ;-)

@FedCSIS AI-powered early Parkinson’s detection research from FedCSIS 2020 has now reached 49 citations in Scopus. Authors: R. Damaševičius, O. Abayomi-Alli, R. Maskeliūnas, A. Abayomi-Alli. Read the full paper: annals-csis.org/Volume_21/drp/…

Investigating the differences in #OJAs and their impact on the transferability of #NLP: “Exploring Linguistic and Cultural Differences in Online Job Advertisement Analysis for NLP Applications” by K. Krüger, L. Grüner. ACSIS Vol. 41 p. 83–94; tinyurl.com/27ndbx82

Combining data from local weather stations with global forecasts and reanalysis data: “Combining Local and Global #WeatherData to Improve Forecast Accuracy for #Agriculture” by F. Koutenský, J. Pihrt, M. Čepek, et al. ACSIS Vol. 41 p. 77–82; tinyurl.com/2xxccm4dC

Studying heuristic algorithms for job shop scheduling problems: “#JobShopScheduling with #IntegerProgramming, Shifting Bottleneck, and Decision Diagrams: A #ComputationalStudy” by Brannon King, Robert Hildebrand. ACSIS Vol. 41 p. 69–76; tinyurl.com/yv8ytar3

@sheriyuo Send work to @FedCSIS , we are IEEE, we are boutique, we are old style, we appreciate quality work!

Happy May Day! ;-) A few people started following us. Welcom! Please not only follow, but also --> (1) like, and (2) repost... Thank you!

Analyzing the types of changes in APIs and using this analysis to build a detailed model that shows the relationships between #API consumers and providers: “Towards #EvolvableAPIs through #OntologicalAnalysis” by N. Jíša, R. Pergl. ACSIS Vol. 41 p. 61–68; tinyurl.com/mkam9br4

@MarekBolanowski In general, Riga is a cool city! Interesting mixture of cultures and languages and..... Riga Black Balsam. We know that young people do not smoke, do not drink... but for those about to ROCK! Riga Black Balzam RULEZ!

@FedCSIS 2026 participants: planning your stay in Riga? Check the list of recommended hotels near the venue, including special discounted rates for attendees: fedcsis2026.jordan.pl/en/accommodati… Note: participants arrange accommodation individually. #FedCSIS2026 #Riga #Conference

@MariaGanzha307 @annals_csis @FedCSIS publishes all of its accepted contributions in @annals_csis -- consider sending us your work ;-)

Very Promising numbers for @annals_csis - 2000 citations more during just 4 month. All volumes are open source, see our website annals-csis.org

Title and abstract of FedCSIS 2026 keynote presentation of Professor Prodan, Radu, from Department of Computer Science, University of Innsbruck, Austria (scholar.google.com/citations?user…) is available here (this is hot of the press news ;-): 2026.fedcsis.org/keynotes/Prodan

Exploring the performance of #GBDT under a #FSL schema in order to provide strong baselines: “Gradient Boosting Trees and #LargeLanguageModels for #TabularData Few-Shot Learning” by Carlos Huertas. ACSIS Vol. 41 p. 53–59; tinyurl.com/2h84f7fz

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