Data Driven Approximations Of PDEs: On Robustness of Reduced Order Mappings between Function Spaces Against Noise

This paper presents a comprehensive exploration of a novel method combining Principal Component Analysis (PCA) and Neural Networks (NN) to efficiently solve Partial Differential Equations (PDEs), a fundamental challenge in modeling a wide range of real-world phenomena. Our research extends the work of Bhattacharya et al. by focusing on PCA for...

Finding biological markers for the prediction of colorectal cancer: Using machine learning methods to identify functional biomarkers in the human gut microbiome

Colorectal cancer (CRC), one of the leading causes of mortality, is challenging to diagnose. By using metagenomic analysis with machine learning methods, this can be done in a non-invasive manner. In this research, a neural network has been trained on relative pathway abundance data, a way to measure the functional potential of a microbiome, in...

Extending CTAB-GAN+ with StackGAN architecture

As privacy regulations (e.g. European General Data Protection Regulation) often prevent valuable flows of data between stakeholders, data synthesis can play a crucial role in sharing captured value in data sets without sharing personal details. Different attempts have been made at solving this problem with Generative Adversarial Networks (GAN)...

Improving accuracy of sound reflection estimation using neural networks

In this paper, we present a study to improve using neural networks for acoustic reflection localization. Our study focuses on the reimplementation of the proposed neural network model by Bologni et al. and investigates the effect of adding a third microphone to the microphone array. We reimplemented and trained the neural network using the same...

Evaluating Feature Attribution Methods: an Usecase on a Neural Fact-checking Model

In today's society, claims are everywhere, in the online and offline world. Fact-checking models can check these claims and predict if a claim is true or false, but how can these models be checked? Post-hoc XAI feature attribution methods can be used for this. These methods give scores indicating the influence of the individual tokens on the...

Error Estimates for Finite Element Simulations Using Neural Networks

In this work residual error estimates are constructed using Neural Networks for Finite Element Method. These can be used to do adaptive mesh refinement. Two neural networks are developed the Multilayer Perceptron and the Transformer model. The error estimates are made for 1d poisson equations but the idea will generalise to higher dimensions as...

Comparing multichannel mixed CNN-RNN to individual models for earthquake prediction

Earthquakes can do great harm to the environment and people's daily lives. Being able to predict an earthquake moments before it happens could therefore reduce harm and save human lives. Traditional methods have not been successful yet, but with the rise of techniques focused on deep learning, there is a growing interest to apply them to the...

Enabling Log Recommendation Through Machine Learning on Source Code

Logging is a common practice in software development that assists developers with the maintenance of software. Logging a system optimally is a challenging task, thus Li et al. have proposed a state-of-the-art log recommendation model. However, no further attempts exist to improve the model or reproduce their results using different training data...

Where to Score in AI World Cup Football?

To push the boundaries of technology, the world cup football for robots, RoboCup, is organized on a yearly basis since 1997. To push the boundaries of artificial intelligence, a simulated version of the RoboCup, AI World Cup Football, is arranged yearly from 2017. This requires skillful attackers, defenders and goalkeeper. A large part of having...

Evaluation of phoneme recognition through TDNN-OPGRU on Mandarin speech

This research expands past research on implementing the TDNN-OPGRU network for Automatic Phoneme Recognition on Dutch speech by implementing and testing the TDNN-OPGRU network on Mandarin speech. The goal of this research is to investigate the performance of the TDNN-OPGRU architecture when decoding phonemes in Mandarin prepared and spontaneous...

Freeform lens predictions by a Neural Network and B-splines: Utilizing the Fraunhofer approximation to train a Neural Network unsupervised

There exist a lot of methods to find the electromagnetic field behind a lens, so called the forward lens problem. In contrast very few methods can do the inverse, namely finding a lens that would produce a given image or light distribution for a known source. Here a solution to the forward problem, the Fraunhofer approximation, is used to find...

The application of Deep Learning to improving low count SPECT imaging

Preclinical SPECT systems such as the U-SPECT have been able to achieve sub-half-millimetre spatial resolution with the use of cylindrical pinhole collimators. Utilising this type of collimator comes at the cost of a reduction in the total number of detection events that take place. In order to compensate for this either the activity of the...

Real Time Threat Detection Through Network Analysis

Intermax Cloudsourcing B.V. designs, implements and manages critical IT-infrastructures for Dutch clients from the medical, public and financial sectors. The information that passes over these IT-infrastructures is highly confidential and privacy-sensitive, therefore it is essential that these infrastructures are secure. To improve the security...

BobRossNet: Accelerating rendering by partial inpainting

Before you lies the report that resulted from the Individual Research Project of Vera Hoveling. For this project, a neural network has been developed to complete partially rendered images: BobRossNet. In this document you will find the details of the project, the architecture of BobRossNet, its training, results and a reflection on its future.