Programa de Doctorat en Intel·ligència Artificial: Recent submissions
Now showing items 21-40 of 75
Document-level machine translation : ensuring translational consistency of non-local phenomena
In this thesis, we study the automatic translation of documents by taking into account cross-sentence phenomena. This document-level information is typically ignored by most of the standard state-of-the-art Machine ...
Knowledge aggregation in people recommender systems : matching skills to tasks
People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such ...
Learning latent variable models : efficient algorithms and applications
Learning latent variable models is a fundamental machine learning problem, and the models belonging to this class - which include topic models, hidden Markov models, mixture models and many others - have a variety of ...
A hybrid approach for item collection recommendations : an application to automatic playlist continuation
Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are ...
Analysis of class C G-protein coupled receptors using supervised classification methods
G protein-coupled receptors (GPCRs) are cell membrane proteins with a key role in regulating the function of cells. This is the result of their ability to transmit extracellular signals, which makes them relevant for ...
Human-smart rollator interaction for gait analysis and fall prevention using learning methods and the i-Walker
The ability to walk is typically related to several bio-mechanical components that are involved in the gait cycle (or stride), including free mobility of joints, particularly in the legs; coordination of muscle activity ...
A soft computing decision support framework for e-learning
Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break ...
Using natural language processing for question answering in closed and open domains
With regard to the growth in the amount of social, environmental, and biomedical information available digitally, there is a growing need for Question Answering (QA) systems that can empower users to master this new wealth ...
Low-rank regularization for high-dimensional sparse conjunctive feature spaces in information extraction
One of the challenges in Natural Language Processing (NLP) is the unstructured nature of texts, in which useful information is not easily identifiable. Information Extraction (IE) aims to alleviate it by enabling automatic ...
Knowledge-based and data-driven approaches for geographical information access
Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present ...
A computational intelligence analysis of G proteincoupled receptor sequinces for pharmacoproteomic applications
Arguably, drug research has contributed more to the progress of medicine during the past decades than any other scientific factor. One of the main areas of drug research is related to the analysis of proteins. The world ...
Exploiting word embeddings for modeling bilexical relations
There has been an exponential surge of text data in the recent years. As a consequence, unsupervised methods that make use of this data have been steadily growing in the field of natural language processing (NLP). Word ...
Bringing social reality to multiagent and service architectures : practical reductions for monitoring of deontic-logic and constitutive norms
As distributed systems grow in complexity, the interactions among individuals (agents, services) of such systems become increasingly more complex and therefore more difficult to constrain and monitor. We propose to view ...
A methodology for pre-post intervention studies : an application for a nutritional case study
Nowadays, it is widely accepted that many of modern lifestyle habits such as diet and exercise, together with genetics, play an important role in the development of many diseases such as cardiovascular disease, diabetes, ...
Towards more reliable feature evaluations for classification
In this thesis we study feature subset selection and feature weighting algorithms. Our aim is to make their output more stable and more useful when used to train a classifier. We begin by defining the concept of stability ...
A dynamic adaptive framework for improving case-based reasoning system performance
An optimal performance of a Case-Based Reasoning (CBR) system means, the CBR system must be efficient both in time and in size, and must be optimally competent. The efficiency in time is closely related to an efficient and ...
Unsupervised entity linking using graph-based semantic similarity
Nowadays, the human textual data constitutes a great proportion of the shared information resources such as World Wide Web (WWW). Social networks, news and learning resources as well as Knowledge Bases (KBs) are just the ...
Supporting the design of sequences of cumulative activities impacting on multiple areas through a data mining approach : application to design of cognitive rehabilitation programs for traumatic brain injury patients
Traumatic brain injury (TBI) is a leading cause of disability worldwide. It is the most common cause of death and disability during the first three decades of life and accounts for more productive years of life lost than ...
NoMoDEI : A framework for Norm Monitoring on Dynamic Electronic Institutions
With the growth of the Internet, computational systems have become more and more complex, often including complicate interconnected networks of autonomous components. The need to bring some organisational structure into ...
Multivariate methods for interpretable analysis of magnetic resonance spectroscopy data in brain tumour diagnosis
Malignant tumours of the brain represent one of the most difficult to treat types of cancer due to the sensitive organ they affect. Clinical management of the pathology becomes even more intricate as the tumour mass increases ...
