Thi Thuy Chau Tran

• Tran, Thi Thuy Chau; Nguyen, Hoa Thi; Magnusson, Lars Vidar & Olsson, Roland (2026). How Well Do Automatically Evolved Neurons Perform? A Comparative Study in Human Activity Recognition. ESREL 2026. doi: https:/cmsweb.com.sg/rps2prod/esrel2026/epro/pdf/esrel26-p26301.pdf. Full text in Research Archive Show summary

  Neuroevolution, a subfield of AI leveraging evolutionary algorithms, offers an effective method for automatically generating data-adaptive equations that optimize fit for specific datasets while minimizing human design bias. This study investigates the efficacy of the WISDM-ARN, a specialized Adaptive Recurrent Neuron (ARN) synthesized via the Automatic Design of Algorithms through Evolution (ADATE) system, for Human Activity Recognition (HAR). In safety-critical applications, such as healthcare monitoring and industrial safety, the reliability of locomotion classification is paramount to prevent system failures and ensure user protection. We benchmark the evolved WISDM-ARN against three established architectures: Simple RNN, Long Short-Term Memory (LSTM), and Transformers. Experimental results demonstrate that the WISDM-ARN successfully adapts to the unique temporal characteristics of gyroscope data, achieving a testing accuracy of 97\%, which is comparable to the baseline models with resulting 70\%, 95\%, and 92\%, respectively. These findings highlight the potential of neuroevolutionary synthesis as a dependable alternative to manually engineered architectures. By integrating neuroevolution with modern deep learning, the ADATE-generated neuron represents a significant step toward automated reliable neural modeling for human activity recognition tasks and safety-enhanced time-series analysis.

• Heir, Åsmund; Aghayev, Samir; Tran, Thi Thuy Chau & Mölder, Anicka (2024). Inversion with stratigraphy-guided deep learning. Geophysics. ISSN 0016-8033. 89(4). doi: 10.1190/geo2023-0586.1. Full text in Research Archive
• Magnusson, Lars Vidar; Olsson, Roland & Tran, Thi Thuy Chau (2023). Recurrent Neural Networks for Oil Well Event Prediction. IEEE Intelligent Systems. ISSN 1541-1672. 38(2), p. 73–80. doi: 10.1109/MIS.2023.3252446. Full text in Research Archive Show summary

  We have conducted a comparison between three types of recurrent neural networks and their ability to predict anomalies occurring in oil wells using a publicly available dataset. We have included two types of well-known state-of-the-art recurrent neural networks and a new type with neurons evolved specifically for the dataset using automatic programming. We show that the new type of recurrent neuron offers a massive improvement over the state of the art. The overall test accuracy of the new network type is 94.6%, which is an improvement by 18.3%, or 14.6 percentage points. We also show that a network with the new neuron performs better than any other solution proposed for the dataset.

View all works in NVA

• Tran, Thi Thuy Chau; Erdem, Cagri; Yazidi, Anis; Kjølerbakken, Kai Morgan & Griwodz, Carsten (2026). Research Proposal: Non-intrusive Stress Recognition using Multimodality Deep Learning. doi: 10.1145/3793853.3798410. Full text in Research Archive
• Chandler, David; Chandnani, Krisha; Goswami, Aditi; Marinelli, Ryan Anthony; Ness, Preben Monteiro & O’Donovan, Brian [Show all 20 contributors for this article] (2026). Data Study Group Final Report: CEFAS - Automated identification of sea pens using OpenCV and machine learning. The Alan Turing Institute. doi: https:/zenodo.org/records/20447259. Full text in Research Archive

View all works in NVA