Index
Improvements in SSL image-text learnings on CXR images
Deep Learning based Collimator Detection
Understanding Odor Descriptors through Advanced NLP Models and Semantic Scores
Explainable Predictive Maintenance: Forecasting and Anomaly Detection of Diagnostic Trouble Codes for Truck Fleet Management
Abstract:
Predictive Maintenance involves monitoring a vehicle’s Diagnostic Trouble Codes (DTCs) to identify potential anomalies before they escalate into major problems, enabling maintenance teams to proactively conduct necessary repairs or maintenance and prevent critical breakdowns.
This thesis aims to explore and compare various approaches of data analytics and machine learning methods for finding patterns and abnormalities to forecast the next DTC (with a specific emphasis on predicting Suspect Parameter Number (SPN) and Failure Mode Identifier (FMI) codes) in the sequence and using anomaly detection methods to understand how dangerous the predicted DTC is. It also aims to make the forecasted model interpretable using Explainable AI techniques for maintenance professionals to have a clear understanding of the underlying factors influencing predictions.
The dataset is provided by Elektrobit Automotive GmbH and contains tabular time series data.
Research Objectives
- Investigating strategies for enhancing predictive maintenance models through effective data pre-processing, feature selection, and handling an imbalanced dataset.
- Comparing various model architectures for effective forecasting of the DTC.
- Designing and evaluating anomaly detection strategies to distinguish between dangerous and non-dangerous forecasted DTC.
- Assessing Explainable AI approaches in improving the explainability of forecasted DTC prediction models.
Thesis Outline
The thesis involves the following key steps:
- Step 1: Literature review and theoretical framework development.
- Step 2: Data pre-processing, and analysis.
- Step 3: Design and develop model architectures for our use case.
- Step 4: Build Explainable AI based framework for the models.
- Step 5: Evaluate and compare the results of the models.
- Step 6: Thesis writing and final presentation preparation.
Through an in-depth exploration of data analytics and machine learning, this thesis seeks to elevate predictive maintenance by investigating effective strategies, model architectures, anomaly detection, and Explainable AI for Diagnostic Trouble Codes. The theoretical framework, grounded in a comprehensive literature review, will guide the study’s key steps, leading to actionable insights for proactive vehicle maintenance.
References
- A. B. Hafeez, E. Alonso, and A. Ter-Sarkisov. Towards sequential multivariate fault prediction for vehicular predictive maintenance. In 2021 20th IEEE International Conference on Ma chine Learning and Applications (ICMLA), pages 1016–1021, Pasadena, CA, USA, 2021. doi: 10.1109/ICMLA52953.2021.00167.
- Abdul Basit Hafeez, Eduardo Alonso, and Atif Riaz. DTCEncoder: A Swiss Army Knife Architecture for DTC Exploration, Prediction, Search and Model Interpretation. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pages 519– 524, 2022.
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention Is All You Need. CoRR, abs/1706.03762, 2017.
- M. Yang, J. Moon, S. Yang, H. Oh, S. Lee, Y. Kim, and J. Jeong. Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers. Appl. Sci., 12:6390, 2022. doi: 10.3390/app12136390.
Attention Artifact! Misalignment and artifact detection using deep learning and augmentation
MA_misalignment_detectionDeep Learning-Driven Approaches for Optimizing Accuracy and Inference Speed in Compact Segmentation Models on Edge Devices
Automatic detection of Bronchoscopes on x-ray images
Scamming Scammers using Large Language Models
This Master Thesis is a cooperation with the Chair of Applied Cryptography.
Work description
In the digital age, scam emails have become a serious threat. These fraudulent emails aim to steal sensitive information or cause financial damage. This thesis aims to better understand the problem of scam emails and develop effective solutions to reduce their success. We will address several aspects, including the vulnerability of email addresses to scammers, the differentiation of scam emails from other dubious messages, the automation of responses through Large Language Models (LLMs), the detection of the usage of LLMs by the scammers, and the evaluation of the economic damage to the scammers based on the data obtained. We aim to strengthen the security of digital communication and help minimize the risks for users and organizations.
The following questions should be considered:
- How can an email address be made vulnerable to scammers?
- How can emails from scammers be distinguished from other dubious emails?
- How can LLM responses be automated and customized?
- How quickly do scammers recognize automated responses?
- How can we accurately assess the extent of the economic harm caused by the scammer using our collected data?
Prerequisites
Prerequisites for this task include good knowledge of Deep Learning and IT Security, familiarity with Python and PyTorch, and the capability to work independently.
For your application, please send your transcript of record.