arXiv subjects

Raheleh Hadian

Publications and source records attributed to Raheleh Hadian.

3 recordsLinked to original sources

arXiv2025

On the Simplification of Neural Network Architectures for Predictive Process Monitoring

Predictive Process Monitoring (PPM) aims to forecast the future behavior of ongoing process instances using historical event data, enabling proactive decision-making. While recent advances rely heavily on deep learning models such as LSTMs and Transformers, their high computational cost hinders practical adoption. Prior work has explored data reduction techniques and alternative feature encodings, but the effect of simplifying model architectures themselves remains underexplored. In this paper, we analyze how reducing model complexity, both in terms of parameter count and architectural depth, impacts predictive performance, using two established PPM approaches. Across five diverse event logs, we show that shrinking the Transformer model by 85% results in only a 2-3% drop in performance across various PPM tasks, while the LSTM proves slightly more sensitive, particularly for waiting time prediction. Overall, our findings suggest that substantial model simplification can preserve predictive accuracy, paving the way for more efficient and scalable PPM solutions.

cs.LG↗

arXiv2024

Leveraging Generative AI for Extracting Process Models from Multimodal Documents

This paper presents an investigation of the capabilities of Generative Pre-trained Transformers (GPTs) to auto-generate graphical process models from multi-modal (i.e., text- and image-based) inputs. More precisely, we first introduce a small dataset as well as a set of evaluation metrics that allow for a ground truth-based evaluation of multi-modal process model generation capabilities. We then conduct an initial evaluation of commercial GPT capabilities using zero-, one-, and few-shot prompting strategies. Our results indicate that GPTs can be useful tools for semi-automated process modeling based on multi-modal inputs. More importantly, the dataset and evaluation metrics as well as the open-source evaluation code provide a structured framework for continued systematic evaluations moving forward.

cs.SE↗

arXiv2021

Dissecting functional degradation in NiTi shape-memory-alloys containing amorphous regions via atomistic simulations

Molecular dynamics simulations are performed to provide a detailed understanding of the functional degradation of shape memory alloys at small scale. The origin of the experimentally reported accumulation of plastic deformation and the anomalous sudden increase of the residual strain under cyclic mechanical loading are explained by detailed insights into the relevant atomic scale processes. Our work reveals that the mechanical response of shape-memory-alloy pillars under cyclic compression is significantly influenced by the presence of an amorphous-like surface region as experimentally induced by focused ion beam milling. The main factor responsible for the observed degradation of superelasticity under cyclic loading is the accumulated plastic deformation and the resultant retained martensite originating from a synergetic contribution of the amorphous and crystalline shape-memory-alloy regions. We show that the reported sudden diminishment of the stress plateaus and hysteresis under cyclic loading is caused by the increased stability of the martensite phase due to the presence of the amorphous phase. Based on the identified mechanism responsible for the degradation, we validate reported methods of recovering the superelasticity and propose a new method to prohibit the synergetic contribution of the amorphous and crystalline regions, such as to achieve a sustainable operation of shape memory alloys at small scale.

cond-mat.mtrl-sci↗