no code implementations • 30 Dec 2023 • Mahshid Helali Moghadam, Mateusz Rzymowski, Lukasz Kulas
This study presents a deep learning-driven anomaly detection system augmented with interpretable machine learning models for identifying performance anomalies in an industrial sensorized vessel, called TUCANA.
no code implementations • 11 May 2023 • Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador, Hans Hansson
Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge.
1 code implementation • 16 Apr 2022 • Markus Borg, Jens Henriksson, Kasper Socha, Olof Lennartsson, Elias Sonnsjö Lönegren, Thanh Bui, Piotr Tomaszewski, Sankar Raman Sathyamoorthy, Sebastian Brink, Mahshid Helali Moghadam
We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system.
no code implementations • 22 Mar 2022 • Mahshid Helali Moghadam, Markus Borg, Mehrdad Saadatmand, Seyed Jalaleddin Mousavirad, Markus Bohlin, Björn Lisper
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system.
no code implementations • 19 Nov 2021 • Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin, Diego Oliva, Mahshid Helali Moghadam, Mehrdad Saadatmand
The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems.
no code implementations • 17 Oct 2021 • Seyed Vahid Moravvej, Seyed Jalaleddin Mousavirad, Mahshid Helali Moghadam, Mehrdad Saadatmand
To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem.
no code implementations • 20 Sep 2021 • Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin, Mahshid Helali Moghadam, Mehrdad Saadatmand, Mahdi Pedram
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems.
1 code implementation • 16 Sep 2021 • Hamid Ebadi, Mahshid Helali Moghadam, Markus Borg, Gregory Gay, Afonso Fontes, Kasper Socha
This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge.
1 code implementation • 5 Apr 2021 • Ali Sedaghatbaf, Mahshid Helali Moghadam, Mehrdad Saadatmand
On the other hand, we have a limited test budget to execute tests.
1 code implementation • 19 Aug 2019 • Mahshid Helali Moghadam, Mehrdad Saadatmand, Markus Borg, Markus Bohlin, Björn Lisper
On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible.