Application of machine learning to design low noise propellers

Over 90% of international trade is carried out over seas. Shipping is currently the cheapest mode of transoceanic transport. The traffic density of shipping lanes on seas, oceans, and also rivers is likely to increase. Consequently, the GHG, NOx, SOx and noise emissions from shipping will rise making it more difficult to meet stricter emission...

Declarative Image Generation

Generating synthetic images has wide applications in several fields such as creating datasets for machine learning or using these images to investigate the behaviour of machine learning models. An essential requirement when generating images is to control aspects such as the entities or objects in the image. Controlling this helps in creating...

Investigation and Comparison of Evaluation Methods of Model-Agnostic Explainable AI Models

Many artificial intelligence (AI) systems are built using black-box machine learning (ML) algorithms. The lack of transparency and interpretability reduces their trustworthiness. In recent years, research into explainable AI (XAI) has increased. These systems are designed to tackle common ML issues such as trust, accountability, and transparency...

Alert-driven Attack Graph Generation using S-PDFA

Ideal cyber threat intelligence (CTI) includes insights into attacker strategies that are specific to a network under observation. Such CTI currently requires extensive expert input for obtaining, assessing, and correlating system vulnerabilities into a graphical representation, often referred to as an attack graph (AG). Instead of deriving AGs...

Differentiating Task-Based Functional Ultrasound Signals via Data-Driven Decompositions

At the Center for Ultrasound and Brain imaging at Erasmus MC in Rotterdam, a mouse's visual cortex had been imaged using the fUS technique. The mouse had been exposed to different visual stimuli. The stimuli varied in position, size, and shape. We investigate how the measured task-based fUS signals differ depending on the visual stimuli...

Local Explanation Methods for Isolation Forest: Explainable Outlier Detection in Anti-Money Laundering

Machine learning methods like outlier detection are becoming increasingly more popular as tools in the fight against money laundering. In this thesis, we analyse the Isolation Forest outlier detection algorithm in detail and introduce a new local explanation method for Isolation Forest, the MI-Local-DIFFI (Multiple Indicator Local-DIFFI) method....

Explanatory Techniques for Machine Learning Models

Since the last decade, we are assisting a widespread use of “black box” Machine Learning algorithms, these are algorithms with excellent performance but whose outcomes are hard to understand to a human agent. However, there are some situation when it is important to understand why a certain output is given,<br/>and the field of explanability in...

Towards Understanding of Deep Learning in Profiled Side-Channel Analysis: Similarity of predictors measured and explained

Side-channel attacks (SCA) aim to extract a secret cryptographic key from a device, based on unintended leakage. Profiled attacks are the most powerful SCAs, as they assume the attacker has a perfect copy of the target device under his control. In recent years, machine learning (ML) and deep learning (DL) techniques have became popular as...

DeepSleep: A sensor-agnostic approach towards modelling a sleep classification system

Sleep is a natural state of our mind and body during which our muscles heal and our memories are consolidated. It is such a habitual phenomenon that we have been viewing it as another ordinary task in our day-to-day life. However, owing to the current fast-paced, technology-driven generation, we are letting ourselves be sleep-deprived, giving...

Example and Feature importance-based Explanations for Black-box Machine Learning Models

Machine Learning (ML) is a rapidly growing field. There has been a surge of complex black-box models with high performance. On the other hand, the application of these models especially in high-risk domains is more stagnant due to lack of transparency and trust in these black-box models. There is a disconnect between the black-box character of...