Search Results (31)
Assessment of individual-level exposure to air pollutants using personal monitoring
A paradigm shift is occurring in the assessment of exposure to urban environmental stressors. Improved accuracy of personal monitors has enabled researchers to study exposure at the individual level. Monitoring stations are the primary reference point for air quality in urban environments, although there is a need to assess exposure …
Demand forecasting with machine learning methods
This thesis examines how machine learning can be applied in demand forecasting. In particular, it describes a novel approach toward lumpy and intermittent demand forecasting. It advocates using a two-fold model for forecasting lumpy (irregular demand occurrence, strong demand size variability) and intermittent (irregular demand occurrence, little demand size variability) …
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 …
Identification of indoor radio environment properties based on channel state information using machine learning approaches
Characterization of the indoor radio environment (RE) is a prerequisite for advances in the design and optimization of next-generation indoor wireless networks and for the construction of a digital twin of the building. The need for comprehensive and accurate indoor characterization will be evident in the future hyper-connected mixed real-virtual …
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 …
Annotation of semi-polar organic contaminants by using gas chromatography coupled to mass spectrometry and machine learning
Contaminants of emerging concern (CECs), representing a subgroup of organic compounds of natural or synthetic origin, and their degradation and transformation products (TPs), with potentially harmful effects on humans, biota, and the environment, are the eco-exposome (EE) constituents of utmost importance. Their identification, quantification, and continued investigation into their environmental …
Scalable neuro-symbolic machine learning
With the resurgence of neural network-based learning in the last decade, machine learning methods are becoming critical components of many real-life intelligent systems. However, while being able to learn effectively and at scale, such systems are often non-interpretable and unable to exploit existing symbolic background knowledge. The paradigm that offers …
Classification of wireless links using machine learning techniques
Due to the nature of the wireless transmission medium, wireless communications are characterised by notably larger losses of data packets than wired communications. The quality of wireless links is highly dependent on channel variations, interference and even transceiver imperfections. Such link uncertainty instigated the development of numerous techniques that can …
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 …
Detection of anomalous and suspicious behavior patterns from spatio-temporal agent traces
Many applications, including smart environments, surveillance, human-robot interaction, and ambient assisted living, involve the problem of learning patterns of agent behavior from sensor data. Deviant behavior is a pattern in the data that either does not conform to the expected behavior, that is, anomalous behavior, or matches previously defined unwanted …