Work place: P.M. Platonov Educational and Scientific Institute of Computer Engineering, Automation, Robotics, and Computer Programming, Odesa National University of Technology, Оdesa, 65039, Ukraine
E-mail: zosimovvv@gmail.com
Website:
Research Interests:
Biography
Dr. Viacheslav Zosimov is a researcher at the P.M. Platonov Educational and Scientific Institute of Computer Engineering, Automation, Robotics, and Computer Programming of Odesa National University of Technology, Оdesa, Ukraine. He holds a DSc Degree in Computer Science (Taras Shevchenko National University of Kyiv in 2020). His research interests include ontologies and semantic web technologies, with a focus on structuring knowledge for intelligent web systems. He also explores object-oriented programming in the development of adaptive applications for e-commerce. Additionally, he is interested in inductive modeling methods for data-driven decision support and system optimization.
By Oleksandra Bulgakova Viacheslav Zosimov Victor Perederyi
DOI: https://doi.org/10.5815/ijisa.2025.04.05, Pub. Date: 8 Aug. 2025
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.
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