Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Moghadam, Mahshid Helali"'
The integration of sensorized vessels, enabling real-time data collection and machine learning-driven data analysis marks a pivotal advancement in the maritime industry. This transformative technology not only can enhance safety, efficiency, and sust
Externí odkaz:
http://arxiv.org/abs/2401.00112
Over the past few decades, Industrial Control Systems (ICSs) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is
Externí odkaz:
http://arxiv.org/abs/2305.09678
Autor:
Abbas, Muhammad, Hamayouni, Ali, Moghadam, Mahshid Helali, Saadatmand, Mehrdad, Strandberg, Per Erik
Processing and reviewing nightly test execution failure logs for large industrial systems is a tedious activity. Furthermore, multiple failures might share one root/common cause during test execution sessions, and the review might therefore require r
Externí odkaz:
http://arxiv.org/abs/2301.03450
Autor:
Dehlaghi-Ghadim, Alireza, Balador, Ali, Moghadam, Mahshid Helali, Hansson, Hans, Conti, Mauro
Publikováno v:
Computers in Industry 148 (2023): 103906
With the advent of smart industry, Industrial Control Systems (ICS) are increasingly using Cloud, IoT, and other services to meet Industry 4.0 targets. The connectivity inherent in these services exposes such systems to increased cybersecurity risks.
Externí odkaz:
http://arxiv.org/abs/2210.13325
Autor:
Borg, Markus, Henriksson, Jens, Socha, Kasper, Lennartsson, Olof, Lönegren, Elias Sonnsjö, Bui, Thanh, Tomaszewski, Piotr, Sathyamoorthy, Sankar Raman, Brink, Sebastian, Moghadam, Mahshid Helali
Integration of Machine Learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guidelines are under development to support the safety of ML-based sy
Externí odkaz:
http://arxiv.org/abs/2204.07874
Autor:
Moghadam, Mahshid Helali, Borg, Markus, Saadatmand, Mehrdad, Mousavirad, Seyed Jalaleddin, Bohlin, Markus, Lisper, Björn
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. In the newly proposed version, we utili
Externí odkaz:
http://arxiv.org/abs/2203.12026
Autor:
Mousavirad, Seyed Jalaleddin, Schaefer, Gerald, Korovin, Iakov, Oliva, Diego, Moghadam, Mahshid Helali, Saadatmand, Mehrdad
The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems. It is based on three main operators: mental search, grouping, a
Externí odkaz:
http://arxiv.org/abs/2111.10188
Autor:
Moravvej, Seyed Vahid, Mousavirad, Seyed Jalaleddin, Moghadam, Mahshid Helali, Saadatmand, Mehrdad
Plagiarism is one of the leading problems in academic and industrial environments, which its goal is to find the similar items in a typical document or source code. This paper proposes an architecture based on a Long Short-Term Memory (LSTM) and atte
Externí odkaz:
http://arxiv.org/abs/2110.08771
Autor:
Mousavirad, Seyed Jalaleddin, Schaefer, Gerald, Korovin, Iakov, Moghadam, Mahshid Helali, Saadatmand, Mehrdad, Pedram, Mahdi
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the mutation operator. In this paper, we propose a novel DE algorithm, Clu-
Externí odkaz:
http://arxiv.org/abs/2109.09351
Autor:
Ebadi, Hamid, Moghadam, Mahshid Helali, Borg, Markus, Gay, Gregory, Fontes, Afonso, Socha, Kasper
With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of simulation-based prototyping pla
Externí odkaz:
http://arxiv.org/abs/2109.07960