The effect of recentness of consumer-grade wearable training data on the ability of a DNN to identify users

Heart rate data and other data collected by consumer-grade wearable devices can give away quite useful information about the user. It can for example be used by machine learning algorithms such as Deep Neural Networks (DNN) to learn patterns about cardiovascular disease and fitness, or be used for identification. Heart rate patterns can also...

Smart Home Energy Efficiency: Application of machine learning on smart meter power data to improve efficient electric energy usage in households

Nonintrusive Load Monitoring (NILM) can be used to disaggregate household energy<br/>usage collected from a central meter to the level of individual appliances, but has so far mostly been applied in controlled, small-scale settings. Further potential such as the application for energy efficiency classification and management have remained...

Developing a diagnostic assessment tool to evaluate damage in buildings

Mining activities at the Groningen gas field are causing earthquakes which result in building damage. This has started a discussion on what type of damage is caused by earthquakes. These discussions are typical in the forensic engineering field, especially in complex cases. The problem is the lack of regulations, in terms of standardizations and...

Go Deep or Go Home?

Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that...

Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks

Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained...

Sampling settings in active learning for investigating inconsistency

Active learning has the potential to reduce labeling costs in terms of time and money. In practical use, active learning works as an efficient data labeling strategy. Another point of view to look at active learning is to consider active learning as a learning problem, where the training data is queried by the active learner. Under this...

Conversation Quality: Modeling in Free-Standing Conversational Groups

Social interactions in general are multifaceted and there exists an wide set of factors and events that influence them. Hence, interactions as a social phenomena have been studied by researchers in the fields of psychology and social signal processing from different stand points. The common trend in literature is to perform an in-depth study on...

Automatic Cattle Lameness Assessment with Markerless Cattle Pose Estimation

Lameness is characterized by abnormal gait and is an indicator of various hoof diseases in cattle. Not only does this raise animal welfare issues, it also causes significant economic loss from reduced milk yield and fertility. Despite that, prevalence of lameness in dairy farms is high because farmers are unable to dedicate time and labor to...

Face recognition using traditional machine learning algorithms and deep neural networks with application to face verification

Biometrics authentication has been very useful and necessary nowadays due to the great developments in technology and the transaction of huge amounts of sensitive data on a daily basis. Traditionally, access to some data or service is achieved by means of some documents or a password. However, these methods are not very convenient. Alternatively...

Domain Adaptation in Acoustic Rainfall Sensors

Rainfall is increasing in frequency and intensity due to climate change. Hydrological models exist that can report bottlenecks in urban infrastructures. However, these require accurate rainfall estimations with high temporal and spatial resolution. The fulfillment of these requirements is challenged due to high costs. This can be solved with...

One-Class Classification: for high-dimensional data

This M.Sc. thesis report investigates the application of one-class classification techniques to complex high-dimensional data. The aim of a one-class classifier is to separate target data from non-target data, but only a dataset containing target data is available for training. The issue with high-dimensional data is that it is difficult to...

Automated report generation from agricultural robot log data

Note: some parts of the abstract have been censored to hide company secrets.<br/><br/>This project was performed for Orchid N.V., an innovator in agriculture. The project focuses on their Scalar system. This is a system that consists out of multiple machines that automatically feed cows. While farmers are generally pleased with these systems, --...

Robust Automatic Object Detection in a Maritime Environment: Polynomial background estimation and the reduction of false detections by means of classification

Robust automatic detection of surface and air objects in a maritime environment is a problem that is of growing importance to the Royal Netherlands Navy (RNLN). Due to a shift in the field of operation from the open oceans towards the littoral waters, the RNLN is forced to operate in complex environments with cluttered backgrounds and the...

Multimodal Interface for DNA Alignment of Sequences

Major research efforts in Bioinformatics include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution. To perform these specific tasks different tools are used. Using these tools separately is a...