Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Sani, Mohammadreza Fani"'
Large language models (LLMs) hold promise for generating plans for complex tasks, but their effectiveness is limited by sequential execution, lack of control flow models, and difficulties in skill retrieval. Addressing these issues is crucial for imp
Externí odkaz:
http://arxiv.org/abs/2410.12870
The current cybersecurity landscape is increasingly complex, with traditional Static Application Security Testing (SAST) tools struggling to capture complex and emerging vulnerabilities due to their reliance on rule-based matching. Meanwhile, Large L
Externí odkaz:
http://arxiv.org/abs/2409.15735
Large Language Models (LLMs) have shown significant promise in plan generation. Yet, existing datasets often lack the complexity needed for advanced tool use scenarios - such as handling paraphrased query statements, supporting multiple languages, an
Externí odkaz:
http://arxiv.org/abs/2409.09191
The continuous flow of data collected by Internet of Things (IoT) devices, has revolutionised our ability to understand and interact with the world across various applications. However, this data must be prepared and transformed into event data befor
Externí odkaz:
http://arxiv.org/abs/2409.03478
Autor:
Sroka, Michal, Sani, Mohammadreza Fani
One of the main use cases of process mining is to discover and analyze how users follow business assignments, providing valuable insights into process efficiency and optimization. In this paper, we present a comprehensive dataset consisting of 50 rea
Externí odkaz:
http://arxiv.org/abs/2308.12211
Autor:
Ghahfarokhi, Anahita Farhang, Mansouri, Taha, Moghadam, Mohammad Reza Sadeghi, Bahrambeik, Nila, Yavari, Ramin, Sani, Mohammadreza Fani
Publikováno v:
journal={Kybernetes}, volume={51}, number={9}, pages={2852--2876}, year={2022}, publisher={Emerald Publishing Limited}
As the number of credit card users has increased, detecting fraud in this domain has become a vital issue. Previous literature has applied various supervised and unsupervised machine learning methods to find an effective fraud detection system. Howev
Externí odkaz:
http://arxiv.org/abs/2306.01008
Autor:
Sani, Mohammadreza Fani, Vazifehdoostirani, Mozhgan, Park, Gyunam, Pegoraro, Marco, van Zelst, Sebastiaan J., van der Aalst, Wil M. P.
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2301.07624
Autor:
Sani, Mohammadreza Fani, Vazifehdoostirani, Mozhgan, Park, Gyunam, Pegoraro, Marco, van Zelst, Sebastiaan J., van der Aalst, Wil M. P.
Publikováno v:
ICPM Workshops (2021) 154-166
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for
Externí odkaz:
http://arxiv.org/abs/2204.01470
Autor:
Elkoumy, Gamal, Fahrenkrog-Petersen, Stephan A., Sani, Mohammadreza Fani, Koschmider, Agnes, Mannhardt, Felix, von Voigt, Saskia Nuñez, Rafiei, Majid, von Waldthausen, Leopold
Privacy and confidentiality are very important prerequisites for applying process mining in order to comply with regulations and keep company secrets. This paper provides a foundation for future research on privacy-preserving and confidential process
Externí odkaz:
http://arxiv.org/abs/2106.00388
Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware recommender sy
Externí odkaz:
http://arxiv.org/abs/2103.16654