Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Development of artificial neural network-based classifiers to identify military impulse noise

Noise monitoring stations are in place around some military installations, to provide records that assist in processing noise complaints and damage claims. However, they are known to produce false positives and miss many impulse events. In this thesis, classifiers based on artificial neural networks were developed to improve the accuracy of military impulse noise identification. Two time-domain metrics, kurtosis and crest factor, and two custom frequency-domain metrics, spectral slope and weighted square error, were selected as inputs to the artificial neural networks. A separate effort attempted to identify military impulse noise by the shape of the recorded waveform. The classification algorithm achieved up to 100% accuracy on the training data and the validation data

Mining Large-Scale News Articles for Predicting Forced Migration via Machine Learning Techniques

Many people are being displaced every day from all around the globe. Many of them are forced to leave their homes because of socio-political conflicts, human-made or natural disasters. In order to develop an early warning system for forced migration in the context of humanitarian crisis, it is essential to study the factors that cause forced migration, and build a model to predict the future number of displaced people. In this research, we focus on studying forced migration due to socio-political conflicts for which violence is the main reason. In particular, we investigate whether the degree of violence in a specific region can be detected from news articles related to that region and whether the detected violence scores can be used to improve the prediction accuracy. We investigate three techniques to extract the degree of violence from a corpus of news articles: ED-FE, TD-FE and SWSW. SWSW measures the semantic similarity between documents and a set of seed-words representing violence. ED-FE extracts violent events from news articles, which are the incidents related to attacks or the ones resulting in casualties. TD-FE uses topic modeling techniques to reduce the size of the information for easier analysis and filtering the violent incidents. Experiments indicate that ED-FE and TD-FE provide accurate violence scores which are very effective features for making forced displacement forecasts and using them in prediction models improves the prediction accuracy