The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the cons...

This study aimed to contribute the development of a research case definition for post-acute sequelae SARS CoV-2 infection (PASC) using PASC data set and experiences from definitions developed myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Our database included patients with who provided self-report symptomology during onset time survey completion (post-infection). We found that we...

Our aim is to use unsupervised, non-Gaussian clustering of Arctic glaciers for post-classification change detection. Firstly, we demonstrate the consistency of nonGaussian clustering algorithms for Envisat ASAR images by characterizing the expected random error level for different SAR acquisition conditions (such as incidence angle).This allows us to determine whether an observed variation is s...

In this paper we introduce a quality grading system with perceptron networks for ’Jonagold’ apples. The system is composed of pre-processing, classification, post-processing and decision-taking steps. It is tested by a database of 819 apple images and the resulting images are compared with a reference database which is manually segmented. Performances of 2, 3-class classifiers are compared wher...

We experiment with text classification of threads from Linux web user forums, in the context of improving information access to the problems and solutions described in the threads. We specifically focus on classifying threads according to: (1) them describing a specific problem vs. containing a more general discussion; (2) the completeness of the initial post in the thread; and (3) whether prob...

The goal of our project is to develop an accurate tagger for questions posted on Stack Exchange. Our problem is an instance of the more general problem of developing accurate classifiers for large scale text datasets. We are tackling the multilabel classification problem where each item (in this case, question) can belong to multiple classes (in this case, tags). We are predicting the tags (or ...