International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 17, No. 4, 8 Aug. 2025
Cover page and Table of Contents: PDF (size: 620KB)
Author(s)
Oleksandra Bulgakova
1,*
Viacheslav Zosimov
1
Victor Perederyi
2
Index Terms
Adaptive Scheduling, Transformer Model, Behavioral Sequence Modeling, Time Management, Productivity Optimization
Abstract
This paper presents a comprehensive framework for intelligent and personalized task scheduling based on Transformer architectures and contextual-behavioral feature modeling. The proposed system processes sequences of user activity enriched with temporal, spatial, and behavioral information to generate structured task representations. Each predicted task includes six key attributes: task type, execution time window, estimated duration, execution context, confidence score, and priority level. By leveraging Transformer encoders, the model effectively captures long-range temporal dependencies while enabling parallel processing, which significantly improves both scalability and responsiveness compared to recurrent approaches.
The system is designed to support real-time adaptation by integrating diverse data sources such as device activity, location, calendar status, and behavioral metrics. A modular architecture enables input encoding, multi-head self-attention, and global behavior summarization for downstream task generation. Experimental evaluation using artificially generated user data illustrates the model’s ability to maintain high accuracy in task type and timing prediction, with consistent performance under varying contextual conditions. The proposed approach is applicable in domains such as digital productivity, cognitive workload balancing, and proactive time management, where adaptive and interpretable planning is essential.
Cite This Paper
Oleksandra Bulgakova, Viacheslav Zosimov, Victor Perederyi, "AI Scheduling with Contextual Transformers", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.4, pp.50-57, 2025. DOI:10.5815/ijisa.2025.04.05
Reference
[1]Kumar P., Kumar R., Nagesh B, et al .Advancing Digital Payment Systems: Combining AI, Big Data, and Biometric Authentication for Enhanced Security[J]. Engineering and Computer Science, 2022, 11(8). doi: 10.18535/ijecs/v11i08.4698.
[2]Mario Alvarez-Jimenez, Peter Koval, Lianne Schmaal，et al. The Horyzons project: a randomized controlled trial of a novel online social therapy to maintain treatment effects from specialist first-episode psychosis services[J]. World Psychiatry, 2021, 20(2). doi: 10.1002/wps.20858.
[3]Chelsea Arnold, John Farhall, Kristi-Ann Villagonzalo et al. Engagement with online psychosocial interventions for psychosis: A review and synthesis of relevant factors[J]. Internet Interventions, 2021, 25:100411. doi: 10.1016/j.invent.2021.100411
[4]André Barcaui, André Monat. Who is better in project planning?Generative artificial intelligence or project managers?[J]. Project Leadership and Society, 2023, 4(100101). doi: 10.1016/j.plas.2023.100101
[5]Muhammad Irfan Hashfi; Raharjo, Teguh. Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review[J]. Advanced Computer Science and Applications, 2023, 14(9): 366-376.
[6]Zosimov V., Bulgakova O. Calculation the Measure of Expert Opinions Consistency Based on Social Profile Using Inductive Algorithms. 15th International Scientific Conference on Intellectual Systems of Decision Making and Problems of Computational Intelligence, ISDMCI 2019, 2019, 1020: 622-636. doi: 10.1007/978-3-030-26474-1_43.
[7]C. Subakan, M. Ravanelli, S. Cornell, M. Bronzi and J. Zhong. Attention Is All You Need In Speech Separation. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 2021, pp. 21-25, doi: 10.1109/ICASSP39728.2021.9413901.
[8]V. Abhishek, S. Binny, T. Johan , et al. Federated Learning: Collaborative Machine Learning without Centralized Training Data[J]. Engineering Technology and Management Sciences, 2022, 5(6): 355-359.
[9]Das, S., Tariq, A., Santos, T., Kantareddy, S.S., Banerjee, I. (2023). Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research. In: Colliot, O. (eds) Machine Learning for Brain Disorders. Neuromethods, vol 197. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3195-9_4.
[10]V. Zosimov and O. Bulgakova. Application of Personalized Ranking Models Based on Expert Evaluations for Sorting Goods on E-commerce Web Resources. 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), Zbarazh, Ukraine, 2020, pp. 42-45, doi: 10.1109/CSIT49958.2020.9321902.
[11]Nosouhian, S., Nosouhian, F., & Kazemi Khoshouei, A. A Review of Recurrent Neural Network Architecture for Sequence Learning: Comparison between LSTM and GRU. Preprints. 2021. doi: 10.20944/preprints202107.0252.v1.
[12]Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4055-4064, 2018.
[13]G.D. Abowd, A.K. Dey, P.J. Brown, N. Davies, M. Smith, P. Steggles. Towards a Better Understanding of Context and Context-Awareness. In: Gellersen, HW. (eds) Handheld and Ubiquitous Computing. HUC 1999. Lecture Notes in Computer Science, vol 1707, 1999. Springer, Berlin, Heidelberg. doi: 10.1007/3-540-48157-5_29
[14]S. Hochreiter, J. Schmidhuber. Long Short-Term Memory. Neural Computation, 1997, 9(8), 1735–1780. doi: 10.1162/neco.1997.9.8.1735
[15]H. Zhou, S. Zhang, J. Peng, et al. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(12). doi: 10.1609/aaai.v35i12.17325
[16]G. Taye, S. Sharma, P. Shah and Y. G. Nuriye. Exploring the Role of Artificial Intelligence in Class Scheduling and Management: A Comprehensive Survey and Review. 2023 International Conference on Computer Science and Emerging Technologies (CSET), Bangalore, India, 2023, pp. 1-11, doi: 10.1109/CSET58993.2023.10346898
[17]A. Vaswani, N. Shazeer, N. Parmar, N. et al. Attention is All You Need. Advances in Neural Information Processing Systems, 2017, 30. doi: 10.48550/arXiv.1706.03762