Selected scientific results, inventions, and discoveries of Vagan Terziyan et al. represented by short AI-generated videos
(Further details regarding these and other results: texts of the papers, PowerPoint presentations, supplementary material with codes and experiments, audio podcasts, etc., can be found in: https://ai.it.jyu.fi/vagan/papers.html or in https://www.jyu.fi/en/people/vagan-terziyan)
(0) General (incomplete) summary related to the content below (video, presentation, and podcast):
· Introductory video: https://ai.it.jyu.fi/SUMMARY.mp4
· Introductory presentation: https://ai.it.jyu.fi/SUMMARY.pdf
· Introductory podcast: https://ai.it.jyu.fi/SUMMARY.m4a
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(1) Multilevel knowledge representation (metamodels):
· Semantic metanetwork
Terziyan, V., & Puuronen, S. (2000). Reasoning with Multilevel Contexts in Semantic Metanetworks. In: P. Bonzon, M. Cavalcanti, & R. Nossun (Eds.), Formal Aspects in Context (Applied Logic Series, Vol. 20, pp. 107–126). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-9397-7_7
Video presentation by NotebookLM: https://ai.it.jyu.fi/Semantic_Metanetwork.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Metasemantic_Network.pdf
· Petri metanetwork
Savolainen, V., & Terziyan, V. (1999). Metapetrinets for Controlling Complex and Dynamic Processes. International Journal of Information and Management Sciences, 10(1), 13–32.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Petri_Metanetwork.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Petri_Metanetwork.pdf
Terziyan, V. (2011). Admiral: Military Logistics Execution Monitoring Engine based on Metapetrinets. “Immune” Project Presentations. https://ai.it.jyu.fi/NATO-Admiral.pdf
Video presentation by NotebookLM: https://ai.it.jyu.fi/Admiral.mp4
· Bayesian metanetwork
Terziyan, V. (2005). A Bayesian Metanetwork. International Journal on Artificial Intelligence Tools, 14(3), 371–384. World Scientific. https://doi.org/10.1142/S0218213005002156
Video presentation by NotebookLM: https://ai.it.jyu.fi/Bayesian_Metanetwork.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Bayesian_Presentation.pdf
(2) Framework for dynamic integration of trainable AI/ML predictors:
Terziyan, V. (2001). Dynamic Integration of Virtual Predictors. In: L. I. Kuncheva, F. Steimann, C. Haefke, M. Aladjem & V. Novak (Eds.), Proceedings of the International ICSC Congress on Computational Intelligence: Methods and Applications (CIMA’2001) (pp. 463-469). ICSC Academic Press, Canada/The Netherlands.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Dynamic_Integration_of_Predictors.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Dynamic_Ensemble_Teams_Presentation.pdf
(3) Public commerce and multilevel profiling framework:
Terziyan, V. (2001). Architecture for Mobile P-Commerce: Multilevel Profiling Framework. In: A. Preece (Ed.), Proceedings of the IJCAI-2001 International Workshop “E-Business and the Intelligent Web” (12 pp.).
Video presentation by NotebookLM: https://ai.it.jyu.fi/P-Commerce.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/P-Commerce_Presentation.pdf
(4) Distributed PIN verification for enhanced security:
Tang, J., Terziyan, V., & Veijalainen, J. (2003). Distributed PIN Verification Scheme for Improving Security of Mobile Devices. Mobile Networks and Applications, 8(2), 159–175. Kluwer Academic Publishers. https://doi.org/10.1023/A:1022289231864
Video presentation by NotebookLM: https://ai.it.jyu.fi/Distributed_PIN.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/PIN.pdf
(5) Agent models in space and time:
Jonker, C., Terziyan, V., & Treur, J. (2003). Temporal and Spatial Analysis to Personalize an Agent’s Dynamic Belief, Desire and Intention Profiles. In: Cooperative Information Agents VII: Proceedings of the 7-th International Workshop on Cooperative Information Agents (Lecture Notes in Computer Science, Vol. 2782, pp. 298-315). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45217-1_22
Video presentation by NotebookLM: https://ai.it.jyu.fi/BDI-Vagan-Jan-Catholine.mp4
(6) Making web services autonomous aka agents:
Ermolayev, V., Keberle, N., Plaksin, S., Kononenko, O., & Terziyan, V. (2004). Towards a Framework for Agent-Enabled Semantic Web Service Composition. International Journal of Web Services Research, 1(3), 63–87. IGI Global. https://doi.org/10.4018/jwsr.2004070
Video presentation by NotebookLM: https://ai.it.jyu.fi/Agents-Services.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Agents-Services.pdf
(7) Smart resources (agent-driven digital twins), semantic middleware, and Global Understanding Environment (more details to other relevant reports, papers, and other resources can be found here: https://ai.it.jyu.fi/SmartResource_UBIWARE.html):
· Smart Resources and Global Understanding Environment
Terziyan, V. (2007). Challenges of the “Global Understanding Environment” based on Agent Mobility. In: V. Sugumaran (Ed.), Application of Agents and Intelligent Information Technologies (pp. 121–152). IGI Global. https://doi.org/10.4018/978-1-59904-265-7.ch007
Video presentation by NotebookLM: https://ai.it.jyu.fi/Smart_Resources_and_GAN.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Smart_Resources_and_GAN.pdf
· Middleware for smart resources and agent programming language
Katasonov, A., Kaykova, O., Khriyenko, O., Nikitin, S., & Terziyan, V. (2008). Smart Semantic Middleware for the Internet of Things. In: J. Filipe, J. A. Cetto & J.-L. Ferrier (Eds.), Proceedings of the 5-th International Conference on Informatics in Control, Automation and Robotics (Vol. 1, pp. 169-178). SciTePress. https://doi.org/10.5220/0001489001690178
Nagy, M., Katasonov, A., Khriyenko, O., Nikitin, S., Szydlowski, M., & Terziyan, V. (2009). Challenges of Middleware for the Internet of Things. In: A. Rodic (Ed.), Automation and Control - Theory and Practice (pp. 247–270). IntechOpen. https://doi.org/10.5772/7869
Video presentation by NotebookLM: https://ai.it.jyu.fi/UBIWARE.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Middleware_Presentation.pdf
· UbiRoad: Advanced AI solution (agent-driven middleware) for smart roads
Terziyan, V., Kaykova, O., & Zhovtobryukh, D. (2010). UbiRoad: Semantic Middleware for Context-Aware Smart Road Environments. In: G.O. Bellot, H. Sasaki, M. Ehmann & C. Dini (Eds.), Proceedings of the Fifth International Conference on Internet and Web Applications and Services (ICIW-2010) (pp. 295-302). IEEE. https://doi.org/10.1109/ICIW.2010.50
Video presentation by NotebookLM: https://ai.it.jyu.fi/UbiRoad.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/UbiRoad_Presentation.pdf
(8) Knowledge Computing and Executable Reality:
Terziyan, V., & Kaykova, O. (2011). Towards “Executable Reality”: Business Intelligence on Top of Linked Data. In: Proceedings of the First International Conference on Business Intelligence and Technology (BUSTECH-2011) (pp. 26-33). IARIA XPS Press, ISBN: 978-1-61208-160-1
Terziyan, V., & Kaykova, O. (2012). From Linked Data and Business Intelligence to Executable Reality. International Journal on Advances in Intelligent Systems, 5(1&2), 194–208. IARIA XPS Press.
Terziyan, V., Shevchenko, O., & Golovianko, M. (2014). An Introduction to Knowledge Computing. Eastern-European Journal of Enterprise Technologies, 67(1/2), 2014, 27–40. https://doi.org/10.15587/1729-4061.2014.21830
Video presentation by NotebookLM: https://ai.it.jyu.fi/Executable_Reality.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Knowledge_Computing.pdf
Slide set by NotebookLM: https://ai.it.jyu.fi/Executable_Knowledge_Paradigm.pdf
(9) Understanding Big Data as a living and evolving biological organism:
Ermolayev, V., Akerkar, R., Terziyan, V., & Cochez M. (2013). Towards Evolving Knowledge Ecosystems for Big Data Understanding. In: Big Data Computing (pp. 3–56). Taylor & Francis. https://doi.org/10.1201/b16014-3
Video presentation by NotebookLM: https://ai.it.jyu.fi/Big_Data.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Knowledge_Ecosystem_for_Big_Data.pdf
(10) Emotions-enhanced AI:
· Emotional Business Intelligence
Terziyan, V., & Kaikova, O. (2015). The “Magic Square”: A Roadmap towards Emotional Business Intelligence. Journal of Decision Systems, 24(3), 255–272. Taylor & Francis. https://doi.org/10.1080/12460125.2015.969592
Video presentation by NotebookLM: https://ai.it.jyu.fi/Emotional_Business_Intelligence.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/EBI.pdf
· Emotional Neural Networks
Terziyan, V., Vitko, O., & Terziyan, O. (2025, submitted, under review). Emotional Neural Networks as a Hybrid of Learnable Reasoning and Feeling.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Emotional_Neural_Networks_1.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/Emotional_Neural_Networks_2.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/ENN.pdf
(11) Ontology-driven national portal to support quality assurance and promote trust in higher education:
Terziyan, V., Golovianko, M., & Shevchenko, O. (2015). Semantic Portal as a Tool for Structural Reform of the Ukrainian Educational System. Information Technology for Development, 21(3), 381–402. Taylor & Francis. https://doi.org/10.1080/02681102.2014.899955
Video presentation by NotebookLM: https://ai.it.jyu.fi/TRUST_QA_Portal.mp4
(12) Detailed ontology of time and temporal reasoning:
Terziyan, V., & Kaikova, O. (2016). Ontology for Temporal Reasoning based on Extended Allen’s Interval Algebra, International Journal of Metadata, Semantics and Ontologies, 11(2), 93–109. Inderscience Publishers. https://doi.org/10.1504/IJMSO.2016.080348
Video presentation by NotebookLM: https://ai.it.jyu.fi/Time_Ontology.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Time_Ontology.pdf
(13) Social distance metric (variations of neighborhood-based distance functions):
Terziyan, V. (2017). Social Distance Metric: From Coordinates to Neighborhoods, International Journal of Geographical Information Science, 31(12), 2401–2426. Taylor & Francis. https://doi.org/10.1080/13658816.2017.1367796
Video presentation by NotebookLM: https://ai.it.jyu.fi/Social_Distance.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Social_Distance.pdf
(14) Exploratory search using TB-Structure:
Terziyan, V., Golovianko, M., & Cochez, M. (2017). TB-Structure: Collective Intelligence for Exploratory Keyword Search. In: A. Calì, D. Gorgan & M. Ugarte (Eds.). Semantic Keyword-Based Search on Structured Data Sources (Lecture Notes in Computer Science, Vol. 10151, pp. 171-178). Springer, Cham. https://doi.org/10.1007/978-3-319-53640-8_15
Video presentation by NotebookLM: https://ai.it.jyu.fi/TB-Structure.mp4
(15) University for AI:
Golovianko, M., Gryshko, S. & Terziyan, V. (2018). From Deep Learning to Deep University: Cognitive Development of Intelligent Systems. In: J. Szymański & Y. Velegrakis (Eds.), Semantic Keyword-Based Search on Structured Data Sources (Lecture Notes in Computer Science, Vol. 10546, pp. 80-85). Springer, Cham. https://doi.org/10.1007/978-3-319-74497-1_8
Video presentation by NotebookLM: https://ai.it.jyu.fi/Deep_University.mp4
(16) Agile deep learning for unmanned aerial vehicles in smart cities:
Cochez, M., Periaux, J., Terziyan, V., & Tuovinen T. (2018). Agile Deep Learning UAVs Operating in Smart Spaces: Collective Intelligence vs. “Mission-Impossible”. In: P. Dies et al. (Eds.), Computational Methods and Models for Transport - New Challenges for the Greening of Transport Systems (Computational Methods in Applied Sciences, Vol. 45, pp. 31–53). Springer. https://doi.org/10.1007/978-3-319-54490-8_3
Video presentation by NotebookLM: https://ai.it.jyu.fi/Deep_Learning_UAVs.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/UAVs_in_Smart_Cities.pdf
(17) Digital cognitive cloning (including humans) and patented intelligence:
· Patented (digitally cloned) intelligence and its use in industry
Terziyan, V., Gryshko, S., & Golovianko, M. (2018). Patented Intelligence: Cloning Human Decision Models for Industry 4.0. Journal of Manufacturing Systems, 48 (Part C), 204–217. Elsevier. https://doi.org/10.1016/j.jmsy.2018.04.019
Video presentation by NotebookLM: https://ai.it.jyu.fi/Patented_Intelligence.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Patented_AI.pdf
· Digital cognitive cloning (PI-Mind technology)
Golovianko, M., Gryshko, S., Terziyan, V., & Tuunanen, T. (2022). Responsible Cognitive Digital Clones as Decision Makers: A Design Science Research Study. European Journal of Information Systems, 32(5), 879–901. https://doi.org/10.1080/0960085X.2022.2073278
Video presentation by NotebookLM: https://ai.it.jyu.fi/PI-Mind_Cognitive_Clones.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Cognitive_D_Clones.pdf
· Advanced cloning technologies
Terziyan, V., & Tiihonen, T. (2025). Digital Cloning as a Self-Adaptive Multicriteria Optimization Process. Procedia Computer Science, 253, 37–48. Elsevier. https://doi.org/10.1016/j.procs.2025.01.067
Terziyan, V., & Tiihonen, T. (2024). Using Cloning-GAN Architecture to Unlock the Secrets of Smart Manufacturing: Replication of Cognitive Models. Procedia Computer Science, 232, 890–902. Elsevier. https://doi.org/10.1016/j.procs.2024.01.089
Video presentation by NotebookLM: https://ai.it.jyu.fi/Cloning_Technologies.mp4
· 3D-printing of intelligent entities (including humans)
Terziyan, V. & Kaikova, O. (2024). Hybrid Additive Manufacturing: A Convergence of Physical, Digital, and Social Realms Driven by Generative AI. In: H. Fujita et al. (Eds.), Advances and Trends in Artificial Intelligence. Theory and Applications. (Lecture Notes in Computer Science, Vol. 14748, pp. 427–441). Springer. https://doi.org/10.1007/978-981-97-4677-4_35
Video presentation by NotebookLM: https://ai.it.jyu.fi/Printing_Intelligence.mp4
(18) Bridging human learning and machine learning for collective (hybrid and cellular) intelligence:
Gavriushenko, M., Kaikova, O., & Terziyan, V. (2020). Bridging Human and Machine Learning for the Needs of Collective Intelligence Development. Procedia Manufacturing, 42, 302–306. Elsevier. https://doi.org/10.1016/j.promfg.2020.02.092 (Best paper award).
Terziyan, V., Gavriushenko, M., Girka, A., Gontarenko, A., & Kaikova, O. (2021). Cloning and Training Collective Intelligence with Generative Adversarial Networks. IET Collaborative Intelligent Manufacturing, 3(1), 64–74. John Wiley and Sons Ltd. https://doi.org/10.1049/cim2.12008
Video presentation by NotebookLM: https://ai.it.jyu.fi/Collective_Intelligence.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Cellular_AI.pdf
(19) Neural networks with disabilities, adversarial training of coolabilities, and new concept of Complementary Artificial Intelligence:
Terziyan, V., & Kaikova, O. (2021). Neural Networks with Disabilities: An Introduction to Complementary Artificial Intelligence. Neural Computation, 34(1), 255–290. MIT Press Direct. https://doi.org/10.1162/neco_a_01449
Video presentation by NotebookLM: https://ai.it.jyu.fi/Neural_Networks_with_Coolabilities.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Complementary_AI.pdf
(20) Ignorance learning (exploring empty spaces in data):
Terziyan, V., & Nikulin, A. (2021). Semantics of Voids within Data: Ignorance-Aware Machine Learning. ISPRS International Journal of Geo-Information, 10(4), 246. https://doi.org/10.3390/ijgi10040246
Video presentation by NotebookLM: https://ai.it.jyu.fi/Ignorance_Learning.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Black_Holes.pdf
(21) Deep Analytics for Evidence-Based Academic Decision-Making:
Semenets, V., Terziyan, V., Gryshko, S., & Golovianko, M. (2021). Assessment and Decision-Making in Universities: Analytics of the Administration-Staff Compromises. arXiv preprint arXiv:2105.10560.
Video presentation (English) by NotebookLM: https://ai.it.jyu.fi/Portal_Analytics_Video_ENG.mp4
Audio Podcast NotebookLM (English): https://ai.it.jyu.fi/Portal_Analytics_Podcast_ENG.m4a
Slide set by NotebookLM (English): https://ai.it.jyu.fi/Portal_Analytics_Presentation_ENG.pdf
Video presentation (Ukrainian) by NotebookLM: https://ai.it.jyu.fi/Portal_Analytics_Video_UKR.mp4
Audio Podcast NotebookLM (Ukrainian): https://ai.it.jyu.fi/Portal_Analytics_Podcast_UKR.m4a
Slide set by NotebookLM (Ukrainian): https://ai.it.jyu.fi/Portal_Analytics_Presentation_UKR.pdf
(22) Hyper-flexible neural networks based on learnable mathematics:
Terziyan, V., Malyk, D., Golovianko, M., & Branytskyi, V. (2022). Hyper-Flexible Convolutional Neural Networks Based on Generalized Lehmer and Power Means. Neural Networks, 155, 177–203. Elsevier. https://doi.org/10.1016/j.neunet.2022.08.017
Video presentation by NotebookLM: https://ai.it.jyu.fi/Hyper-Flexible_AI.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Hyper-Flexible_Presentation.pdf
(23) Artificial General Intelligence in Industry 4.0:
Kumpulainen, S., & Terziyan, V. (2022). Artificial General Intelligence vs. Industry 4.0: Do They Need Each Other? Procedia Computer Science, 200, 140–150. Elsevier. https://doi.org/10.1016/j.procs.2022.01.213
Video presentation by NotebookLM: https://ai.it.jyu.fi/AGI.mp4
(24) Digital immunity, digital vaccination, and adversarial learning (for AI, for humans, and for hybrid collective intelligence):
· AI under attack and adversarial machine learning
Terziyan, V., Golovianko, M., & Gryshko, S. (2018). Industry 4.0 Intelligence under Attack: From Cognitive Hack to Data Poisoning. In: K. Dimitrov (Ed.), Cyber Defence in Industry 4.0 Systems and Related Logistics and IT Infrastructure (NATO Science for Peace and Security Series D: Information and Communication Security, Vol. 51, pp. 110–125). IOS Press. https://doi.org/10.3233/978-1-61499-888-4-110
Video presentation by NotebookLM: https://ai.it.jyu.fi/AI_Under_Attack.mp4
· Digital immunity and digital vaccination vs. digital cloning
Branytskyi, V., Golovianko, M., Gryshko, S., Malyk, D., Terziyan, V., & Tuunanen, T. (2022). Digital Clones and Digital Immunity: Adversarial Training Handles Both. International Journal of Simulation and Process Modelling, 18(2), 124–139. Inderscience Publishers. https://doi.org/10.1504/IJSPM.2022.10048910 . https://dx.doi.org/10.1504/IJSPM.2022.126106
Video presentation by NotebookLM: https://ai.it.jyu.fi/Digital_Immunity.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Dual_Solution_Presentation.pdf
· Adversarial training of AI’s and human resilience
Kaikova, O., Terziyan, V., Tiihonen, T., Golovianko, M., Gryshko, S., & Titova, L. (2022). Hybrid Threats against Industry 4.0: Adversarial Training of Resilience. E3S Web of Conferences, 353, 03004. https://doi.org/10.1051/e3sconf/202235303004
Video presentation by NotebookLM: https://ai.it.jyu.fi/Adversarial_Training_of_Resilience.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/WARN_and_Immune_Presentation.pdf
· WARN project (“Academic response to hybrid threats”) and related immunity training for students to counter hybrid threats
WARN Project Team (2025). Final Report on WARN Project (“Academic Response to Hybrid Threats”). Erasmus+ Project Report, EU.
Video presentation by NotebookLM: https://ai.it.jyu.fi/WARN.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/WARN-2.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/WARN_Report_Presentation.pdf
· Adversarial games for hybrid collective intelligence
Terziyan, V. (2024). Adversarial Learning, Adversarial Games, and Adversarial Collective Intelligence: Exploring the Potential of AI for Strategic Superiority. In: O. Kaikova & T. Tiihonen (Eds.), Presentations of the WARN Project Workshop. Cergy, France, May 15, 2024. https://ai.it.jyu.fi/Adversarial_Games.pptx
Video presentation by NotebookLM: https://ai.it.jyu.fi/Adversarial_Games_1.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/Adversarial_Games_2.mp4
· Adversarial maintenance in smart manufacturing
Terziyan, V., & Kaikova, O. (2025). Guardians of Reliability, Robustness, and Resilience: Adversarial Maintenance in the Era of Industry 4.0 and 5.0. Procedia Computer Science, 253, 13–24. Elsevier. https://doi.org/10.1016/j.procs.2025.01.065
Video presentation by NotebookLM: https://ai.it.jyu.fi/Adversarial_Maintenance.mp4
(25) Bio-inspired advanced Generative Adversarial Network architecture:
Branytskyi, V., Golovianko, M., Malyk, D., & Terziyan, V. (2022). Generative Adversarial Networks with Bio-Inspired Primary Visual Cortex for Industry 4.0. Procedia Computer Science, 200, 418–427. Elsevier. https://doi.org/10.1016/j.procs.2022.01.240
Video presentation by NotebookLM: https://ai.it.jyu.fi/V1-GAN.mp4
(26) Moving from Industry 4.0 to Industry 5.0 with autonomous cognitive clones of humans:
Golovianko, M., Terziyan, V., Branytskyi, V., & Malyk, D. (2023). Industry 4.0 vs. Industry 5.0: Co-existence, Transition, or a Hybrid. Procedia Computer Science, 217, 102–113. Elsevier. https://doi.org/10.1016/j.procs.2022.12.206.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Industry_4_0_vs_5_0.mp4
(27) Causality-Aware Neural Networks:
Terziyan, V., & Vitko, O. (2023). Causality-Aware Convolutional Neural Networks for Advanced Image Classification and Generation. Procedia Computer Science, 217, 495–506. Elsevier. https://doi.org/10.1016/j.procs.2022.12.245.
Streltsov, O., Terziyan, V., & Vitko, O. (2025). Discover and Explore Weak Causality and Causal Disposition in Images for Smart Manufacturing Tasks. Procedia Computer Science, 253, 189–200. Elsevier. https://doi.org/10.1016/j.procs.2025.01.082
Video presentation by NotebookLM: https://ai.it.jyu.fi/Causality-Aware_CNN.mp4
(28) Homeomorphic encryption for Privacy-Preserving Machine Learning:
Terziyan, V., Malyk, D., Golovianko, M., & Branytskyi, V. (2023). Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing. Procedia Computer Science, 217, 91–101. Elsevier. https://doi.org/10.1016/j.procs.2022.12.205.
Terziyan, V., Bilokon, B., & Gavriushenko, M. (2024). Deep Homeomorphic Data Encryption for Privacy Preserving Machine Learning. Procedia Computer Science, 232, 2201–2212. Elsevier. https://doi.org/10.1016/j.procs.2024.02.039
Video presentation by NotebookLM: https://ai.it.jyu.fi/Homeomorphic_Encryption.mp4
(29) How to discover more information from the image by smartly focusing on its parts:
Terziyan, V., Kaikova, O., Malyk, D., & Branytskyi, V. (2023). The Truth is Out There: Focusing on Smaller to Guess Bigger in Image Classification. Procedia Computer Science, 217, 1323–1334. Elsevier. https://doi.org/10.1016/j.procs.2022.12.330.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Smart_Focusing.mp4
(30) Adversarial cellular automata for simulating resilience:
Terziyan, V., Terziian, A., & Vitko, O. (2024). Creative and Adversarial Cellular Automata for Simulating Resilience in Industry 5.0. Procedia Computer Science, 232, 1491–1502. Elsevier. https://doi.org/10.1016/j.procs.2024.01.147
Video presentation by NotebookLM: https://ai.it.jyu.fi/Cellular_Automata_and_Resilience.mp4
(31) Taxonomy-informed neural networks:
Terziyan, V., & Vitko, O. (2024). Taxonomy-Informed Neural Networks for Smart Manufacturing. Procedia Computer Science, 232, 1388–1399. Elsevier. https://doi.org/10.1016/j.procs.2024.01.137
Video presentation by NotebookLM: https://ai.it.jyu.fi/Taxonomy-Informed_NN.mp4
(32) AI-as-a-user-of-AI, AI-as-a-designer-of-AI, responsible AI autonomy, and AI evolution:
· AI as a user of AI (responsible autonomy)
Shukla, A.K., Terziyan, V., & Tiihonen, T. (2024). AI as a User of AI: Towards Responsible Autonomy. Heliyon, 10(11), e31397. Cell Press. https://doi.org/10.1016/j.heliyon.2024.e31397
Video presentation by NotebookLM: https://ai.it.jyu.fi/AI_as_a_User_of_AI.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/AI_as_a_User_of_AI-2.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/AI-as-a-user-of-AI_Presentation.pdf
· AI as a designer of AI (self-design methodology)
Verma, D., Terziyan, V., Tuunanen, T., & Shukla, A.K. (2026). Toward an Artifact that Designs Itself: Generative Design Science Research Approach. AI and Ethics, 6, 104. https://doi.org/10.1007/s43681-025-00965-5
Video presentation by NotebookLM: https://ai.it.jyu.fi/AI-as-a-Designer_of_AI.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/AI-as-a-designer-of-AI_Presentation.pdf
· Ethical evolution of autonomous AI (preserving human values across generations)
Terziyan, V., Tiihonen, T., & Shukla, A.K., Gryshko, S., Golovianko, M., Terziyan, O., & Vitko, O. (2025). Towards Ethical Evolution: Responsible Autonomy of Artificial Intelligence across Generations. AI and Ethics, 5, 5165–5190. Springer. https://doi.org/10.1007/s43681-025-00759-9
Video presentation by NotebookLM: https://ai.it.jyu.fi/Ethical_Evolution.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Ethical_Presentation.pdf
(33) Resilience training in higher education via adversarial learning:
Gryshko, S., Terziyan, V., & Golovianko, M. (2025). Resilience Training in Higher Education: AI-Assisted Collaborative Learning. In: Auer, M.E. & Rüütmann, T. (Eds.), Futureproofing Engineering Education for Global Responsibility. (Lecture Notes in Networks and Systems, Vol. 1281, pp. 126–138). Springer, Cham. https://doi.org/10.1007/978-3-031-83520-9_12
Video presentation by NotebookLM: https://ai.it.jyu.fi/Resilience_in_HE.mp4
(34) “European Virtual Venture” (summary of long collaboration with French universities and industry regarding advanced education for masters):
Golovianko, M., Gryshko, S., Kaikova, O., Shevchenko, O., Terziyan, V., & Absi, R. (2025). Exploring the Social Impact of Multidisciplinary, Multicultural Hybrid Collaborative Learning across Diverse Domains. In: Auer, M.E. & Rüütmann, T. (Eds.), Futureproofing Engineering Education for Global Responsibility. (Lecture Notes in Networks and Systems, Vol. 1260, pp. 141–148). Springer, Cham. https://doi.org/10.1007/978-3-031-85652-5_15
Video presentation by NotebookLM: https://ai.it.jyu.fi/European_Virtual_Venture.mp4
(35) Context-Aware AI:
Terziyan, V., & Vitko, O. (2025). Context-Aware Machine Learning for Smart Manufacturing. Procedia Computer Science, 253, 25–36. Elsevier. https://doi.org/10.1016/j.procs.2025.01.066
Video presentation by NotebookLM: https://ai.it.jyu.fi/Context-Aware_AI.mp4
(36) Smart mathematics for AI:
Terziyan, V., & Vitko, O. (2025). Adaptive Generalized Mean and Social Distance Metric for Smart Manufacturing Tasks. Procedia Computer Science, 253, 136–147. Elsevier. https://doi.org/10.1016/j.procs.2025.01.077
Video presentation by NotebookLM: https://ai.it.jyu.fi/Mean_and_Metric.mp4
(37) Hybrid AI models for smart manufacturing:
Kaikova, O., & Terziyan, V. (2024). Deep Neural Networks, Cellular Automata and Petri Nets: Useful Hybrids for Smart Manufacturing. Procedia Computer Science, 232, 2334–2346. Elsevier. https://doi.org/10.1016/j.procs.2024.02.052
Video presentation by NotebookLM: https://ai.it.jyu.fi/Hybrid_AI.mp4
(38) Evaluating impact of published articles using ChatGPT:
Terziyan, V., Kaikova, O., Golovianko, M., & Vitko, O. (2024). Can ChatGPT Challenge the Scientific Impact of Published Research, Particularly in the Context of Industry 4.0 and Smart Manufacturing?. Procedia Computer Science, 232, 2540–2550. Elsevier. https://doi.org/10.1016/j.procs.2024.02.072
Video presentation by NotebookLM: https://ai.it.jyu.fi/ChatGPT_Research.mp4
(39) Enhancing AI agents with large language models:
Terziyan, V., Vitko, O., & Terziyan, O. (2025). A Conceptual Design of Industrial Asset Maintenance System by Autonomous Agents Enhanced with ChatGPT. Intelligent and Sustainable Manufacturing, 2(1), 10008. https://doi.org/10.70322/ism.2025.10008
Terziyan, V., Vitko, O., Terziyan, O., & Terziian, A. (2025). Hybrid Intelligence for Industry 4.0: Integrating Large Language Models and Reinforcement Learning. International Journal of Computer Information Systems and Industrial Management Applications, 17, 610‒627. https://doi.org/10.70917/ijcisim-2025-0038
Video presentation by NotebookLM: https://ai.it.jyu.fi/OPRA_and_OPRA-RL.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Basic_OPRA_Framework.pdf
Slide set by NotebookLM: https://ai.it.jyu.fi/OPRA-RL-Presentation.pdf
(40) AI vs. Information Systems domains (proving AI dominance):
Shukla, A.K., Terziyan, V., & Tiihonen, T. (2025). Exploring Semantic Relationships and Cross-Disciplinary Influences: Case Study of Information Systems and Artificial Intelligence. Quality & Quantity, 26 pp. Springer. https://doi.org/10.1007/s11135-025-02437-8
Video presentation by NotebookLM: https://ai.it.jyu.fi/AI_vs_IS__Who_is_in_Charge.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/AI-vs-IS-Presentation.pdf
(41) Neural networks with malignant neurons:
Terziyan, V., Terziyan, O., & Vitko, O. (2025, to appear). Neural Networks with Malignant Neurons: Robust Models for Smart Manufacturing. Procedia Computer Science. Elsevier.
Video presentation by NotebookLM: https://ai.it.jyu.fi/ISM-2025-Malignant.mp4
(42) Autonomy protecting AI from human misuse (DieHard framework):
Terziyan, V., Bukovsky, I., Kaikova, O., Sobieczky, F., & Tiihonen, T. (2025, to appear). DieHard: Human-Centric Responsible and Resilient Autonomy for Mission-Critical Smart Systems. Procedia Computer Science. Elsevier.
Video presentation by NotebookLM: https://ai.it.jyu.fi/ISM-2025-DieHard.mp4
(43) Designing self-conscious AI:
Terziyan, V., Shukla, A. K., Gryshko, S., Golovianko, M., Terziyan, O., & Vitko, O. (2025, submitted, under review). Recursive Epiphany: A Bottom-Up Framework for Artificial Self-Consciousness in AI.
Video presentations by NotebookLM: https://ai.it.jyu.fi/Artificial_Self-Consciousness_1.mp4 and https://ai.it.jyu.fi/Artificial_Self-Consciousness_2.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Self-Consciousness_Presentation.pdf
(44) Time travel, parallel worlds and temporal cloning in AI systems:
Shukla, A. K., Terziyan, V., & Tiihonen, T. (2025, to be submitted). Time-Traveling Agents and Temporal Cloning: A Framework with Implications for Eco-Digital Transitions.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Time_Travel_and_Temporal_Cloning.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Temporal_Cloning_Presentation.pdf
(45) Showing how autonomous AI will destroy human industry:
Terziyan, V., Gryshko, S., Kaikova, O., & Golovianko, M. (2025, to appear). Can Artificial Intelligence Destroy Future Industry? Procedia Computer Science. Elsevier.
Video presentation by NotebookLM: https://ai.it.jyu.fi/ISM-2025-Provocative.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/ISM-2025-Provocative-2.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/AI_confronts_Industry.mp4
(46) From black box to explainable AI via semantics and Large Language Models:
Terziyan, V., Vitko, O., & Terziyan, O. (2025, to appear). Semantic AI for Future Industries: Bridging Explainability and Integration in Black Box Models. Procedia Computer Science. Elsevier.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Semantic_AI.mp4
(47) Making neural networks smarter:
Terziyan, V., Vitko, O., & Terziyan, O. (2025, to appear). Beyond Backpropagation: Smarter Neural Networks for Smart Manufacturing. Procedia Computer Science. Elsevier.
Terziyan, V., Vitko, O., & Terziyan, O. (2025, to appear). Balancing Exploration and Exploitation for Efficient Black-Box Cloning in Smart Manufacturing. Procedia Computer Science. Elsevier. (Best paper award).
Video presentation by NotebookLM: https://ai.it.jyu.fi/Smarter_NNs.mp4
(48) Future-Informed Neural Networks:
Shukla, A. K., Terziyan, V., & Tiihonen, T. (2025, to be submitted). Future-Informed Neural Networks: Bridging Reality and Imagination.
Video presentation by NotebookLM: https://ai.it.jyu.fi/FINN.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/FINN_Presentation.pdf
(49) Fuzzy-Knowledge-Informed Machine Learning:
Terziyan, V., Shukla, A. K., & Kaikova, O. (2025, to be submitted). Introduction to Fuzzy-Knowledge-Informed Machine Learning.
Video presentation by NotebookLM: https://ai.it.jyu.fi/FKIML.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/FKIML_Presentation.pdf
(50) Recursive evolution of intelligence: Human Intelligence ‒ AI ‒ Post-AI
Terziyan, V., Gryshko, S., Shukla, A., & Terziian, A. (2025, to be submitted). AI Evolution: The Day After Tomorrow .
Video presentation by NotebookLM: https://ai.it.jyu.fi/AI__The_Day_After_Tomorrow.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/The_Day_After_Tomorrow-2.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/After_Tomorrow-3.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/After_Tomorrow_Presentation.pdf
(51) Self-coordinated swarms of autonomous AI agents
Terziyan, V., Tiihonen, T., Terziian, A., & Vitko, O. (2026, submitted, under review). Distributed Intelligence in Motion: Self-Organizing Cognitive Swarms for Synchronous Multi-Target Missions.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Swarms.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Swarms_Presentation.pdf
Video presentation by NotebookLM: https://ai.it.jyu.fi/Swarms-2.mp4
(52) Context-Aware “Social” Clustering
Terziyan, V., Terziyan, O., & Vitko, O. (2026, submitted, under review). Enhancing Clustering with Context-Aware Distances.
Video presentation by NotebookLM: https://ai.it.jyu.fi/Social_Clustering.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/Social_Clustering_Presentation.pdf
(53) Structural-Evolution-Recurrent-Neural-Networks (consider all data attributes as time axis)
Terziyan, V., Terziian, A., & Vitko, O. (2026, submitted, under review). Rethinking Order: Recurrent Neural Networks over Multiple Structural Projections.
Video presentation by NotebookLM: https://ai.it.jyu.fi/SE-RNNs-1.mp4
Video presentation by NotebookLM: https://ai.it.jyu.fi/SE-RNNs-2.mp4
Slide set by NotebookLM: https://ai.it.jyu.fi/SE-RNNs_Presentation-1.pdf
Slide set by NotebookLM: https://ai.it.jyu.fi/SE-RNNs_Presentation-2.pdf
(54) How to organize AI hallucinations within semantic graphs as interconnected imaginable worlds
Terziyan, V., Gryshko, S., Shukla, A., Terziyan, O., & Vitko, O. (2026, to be submitted). Making Sense of Hallucinations: Ontologies and Knowledge Graphs for Imaginable Reality.
Video presentation by NotebookLM (English): https://ai.it.jyu.fi/Guardrails-ENG.mp4
Video presentation by NotebookLM (Ukrainian): https://ai.it.jyu.fi/Guardrails-UKR.mp4
Slide set by NotebookLM (English): https://ai.it.jyu.fi/Guardrails_Presentation-ENG.pdf
Slide set by NotebookLM (Ukrainian): https://ai.it.jyu.fi/Guardrails_Presentation-UKR.pdf
Audio by NotebookLM (Ukrainian): https://ai.it.jyu.fi/Guardrails_Podcast.m4a
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