Dr. Mohammad Abboush

nach Vereinbarung

• https://www.researchgate.net/profile/Mohammad_Abboush
   
• https://orcid.org/0000-0002-5533-0029

• 2024: Promotion als (Dr. -Ing.) am Institut für Software und Systems Engineering an der Technischen Universität Clausthal, Thema: “Machine Learning-based Intelligent Fault Detection and Diagnosis for Real-Time Validation Process of Automotive Software Systems during the Development Phase“
• Seit 2019: Wissenschaftlicher Mitarbeiter an der Technischen Universität Clausthal
• 2018: Masterabschluss im Studiengang Mechatronics Engineering mit dem Titel 'Deep Learning Application in Fault Detection and Diagnosis of Mechatronics Systems, A Case Study: Demand-controlled Ventilation and Heating System based on Wireless Sensor and Actuator Networks'
• 2017 - 2018: Wissenschaftliche Hilfskraft mit Bachelorabschluss an der Universität Siegen
• 2015 - 2018: DAAD Stipendium "Programm Führung für Syrien"

• Entwicklung von Softwaresystemen für die Automobilindustrie
• Verifizierung und Validierung (V&V)
• Hardware-in-the-Loop (HIL)-basierte Echtzeit-Tests von Automotive-Software-System
• Zuverlässigkeits- und Sicherheitsanalyse
• Machine Learning, Deep Learning and LLMs

• Studentische Abschlussarbeiten (Betreuer und Gutachter)
  • Large Language Model-Anwendungen im Bereich Automotive Software System Testing mit HIL
• Vorlesung und Übung
  • Software System Engineering
  • Software and System Life-Cycle
  • Automatisierte Verkehrssysteme (Connected and Autonomous Automotive Engineering)
  • Programmierkurs 

1. M Abboush, FA Dzoupet Yimtichi, Ö Tan, H Ourrad, A Rausch, "Robust and Interpretable Anomaly Detection in Automotive Test Recordings Using Denoising Autoencoders with Adaptive Thresholding", Electronics 15 (12), 2723.
2. M Abboush, E Ghannoum, A Rausch, "An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation", Knowledge-Based Systems, 114922.
3. M Abboush, A Hatahet, A Rausch, "LLMs-Powered Real-Time Fault Injection: An Approach Toward Intelligent Fault Test Cases Generation", 2025 IEEE 28th International Conference on Intelligent Transportation …
4. M Abboush, C Knieke, A Rausch, "Advancing real-time validation of automotive software systems via continuous integration and intelligent failure analysis", Scientific Reports 15 (1), 32936.
5. M Abboush, C Knieke, A Rausch, "Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering", Results in Engineering 27, 105900.
6. M Abboush, C Knieke, A Rausch, "Machine learning-assisted test records analysis during the real-time validation of automotive software systems based on hil simulation"58th Hawaii International Conference on System Sciences (HICSS).
7. M Abboush, C Knieke, A Rausch, "Enhancing Clustering Performance of Failed Test Cases during HIL Simulation: A Study on Deep Auto-Encoder Structures and Hyperparameter Tuning", Applied Sciences 14 (19), 9064.
8. M Abboush, C Knieke, A Rausch, "A virtual testing framework for real-time validation of automotive software systems based on hardware-in-the-loop and fault injection", Sensors 24 (12), 3733.
9. A Amyan, M Abboush, C Knieke, A Rausch, "Automating fault test cases generation and execution for automotive safety validation via NLP and HIL simulation", Sensors 24 (10), 3145.
10. M Abboush, C Knieke, A Rausch, "Enhancing Clustering Performance of Failed Test Cases during HIL Simulation: A Study on Deep Auto-Encoder Structures and Hyperparameter Tuning", Applied Sciences 14 (19), 9064.
11. M Abboush, C Knieke, A Rausch, "Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems", Electronics 13 (2), 437.
12. M Abboush, C Knieke, A Rausch, "A Virtual Testing Framework for Real-Time Validation of Automotive Software Systems Based on Hardware-in-the-Loop and Fault Injection", Sensors 24 (12), 3733.
13. M Abboush, C Knieke, A Rausch, "Representative Dataset Generation Framework for AI-based Failure Analysis during real-time Validation of Automotive Software Systems", 57th Hawaii International Conference on System Sciences (HICSS).
14. M Abboush, C Knieke, A Rausch, "Intelligent Identification of Simultaneous Faults of Automotive Software Systems under Noisy and Imbalanced Data based on Ensemble LSTM and Random Forest", IEEE Access.
15. M Abboush, C Knieke, A Rausch, "GRU-based denoising autoencoder for detection and clustering of unknown single and concurrent faults during system integration testing of automotive software systems", Sensors 23 (14), 6606.
16. M Abboush, D Bamal, C Knieke, A Rausch, "Intelligent Fault Detection and Classification Based on Hybrid Deep Learning Methods for Hardware-in-the-Loop Test of Automotive Software Systems", Sensors 2022, 22, 4066.
17. M Abboush, D Bamal, C Knieke, A Rausch, "Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems", Sensors 2022, 22, 1360.
18. A Amyan, M Abboush, C Knieke, A Rausch,"Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation", Sensors 24 (10), 3145.
19. TM Ailane, M Abboush, C Knieke, A Lawendy, A Rausch, "Toward Formalizing The Emergent Behavior in Software Engineering", 2021 IEEE/ACM Joint 9th International Workshop on Software Engineering for Systems-of-Systems and 15th Workshop on Distributed Software Development, Software Ecosystems and Systems-of-Systems (SESoS/WDES).
20. A. Behravan, M. Abboush, R. Obermaisser, "Deep Learning Application in Mechatronics Systems’ Fault Diagnosis, a Case Study of the Demand-Controlled Ventilation and Heating System", The 2019 Internet of Things, Mechatronics and their Applications International Conference (IoTMAC’2019) in Advances in Science and Engineering Technology (ASET’2019) conference, Dubai, United Arab Emirates, 2019.
21. A. Behravan, M. Abboush, R. Obermaisser, "Fault Injection Framework for Demand-Controlled Ventilation and Heating Systems Based on Wireless Sensor and Actuator Networks", The 9th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON 2018) "BEST PAPER AWARD", University of British Columbia, Vancouver, Canada, 2018. Best Paper Award