Search Results (5)
Explainable machine learning techniques for applications in life sciences
As ecological, agricultural, and biological disciplines face mounting challenges like biodiversity loss, food chain disruption, and climate change, leveraging machine learning (ML) to process complex and heterogeneous data becomes increasingly vital. This dissertation explores the potential of ML in combination with explainability approaches for enhancing research in life sciences, specifically …
An intelligent cognitive system for computational psychotherapy with a conversational agent for attitude and behavior change in stress, anxiety and depression
The increasing prevalence of mental health issues worldwide has amplified the significance of computational psychotherapy, which includes creating computational tools for the mental healthcare and tools to support existing mental health professionals. This work presents a computational psychotherapy system that predicts and forecasts mental health issues in users, and utilizes …
Exploiting domain knowledge in predictive learning from food and nutrition data
Human knowledge about food and nutrition has evolved drastically with time. With food and nutrition-related data being mass produced and easily accessible, the next step is to use Artificial Intelligence (AI) to translate data into knowledge. The majority of AI research is model-driven, and classical Machine Learning (ML) pipelines concentrate …
Food and drink image detection and recognition using deep convolutional neural networks
A healthy diet is becoming increasingly relevant as recognizing dietary deficiencies often leads to actionable results that can improve the individual’s overall health. However, to identify areas of potential improvement, tracking food intake is necessary. Manual methods have traditionally been used to perform this tracking, but these methods have a …
Complex nodes in trees for structured output prediction
In this thesis, we integrate complex nodes into predictive clustering trees (PCTs). PCTs are well-established machine learning models that are very flexible in terms of the machine learning tasks that they can address, including structured output prediction and semisupervised learning. Like standard decision trees, they are learned with a greedy …