Abstract: The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.
Abstract: Nowadays, the dissemination of information touches the distributed world, where selecting the relevant servers to a user request is an important problem in distributed information retrieval. During the last decade, several research studies on this issue have been launched to find optimal solutions and many approaches of collection selection have been proposed. In this paper, we propose a new collection selection approach that takes into consideration the number of documents in a collection that contains terms of the query and the weights of those terms in these documents. We tested our method and our studies show that this technique can compete with other state-of-the-art algorithms that we choose to test the performance of our approach.
Abstract: This paper discusses the use of a template-based
approach for documenting high-quality requirements as part of course
projects in an undergraduate Software Engineering course. In order
to ease some of the Requirements Engineering activities that are
performed when defining requirements by using the template, a new
CASE tool, RED:WIRE, was first developed and later tested by
students attending the course. Two questionnaires were conceived
around a study that aims to analyze the new tool’s learnability as
well as other obtained results concerning its usability in particular
and the Requirements Engineering skills developed by the students
in general.
Abstract: This paper presents a classifier ensemble approach for
predicting the survivability of the breast cancer patients using the
latest database version of the Surveillance, Epidemiology, and End
Results (SEER) Program of the National Cancer Institute. The system
consists of two main components; features selection and classifier
ensemble components. The features selection component divides the
features in SEER database into four groups. After that it tries to find
the most important features among the four groups that maximizes the
weighted average F-score of a certain classification algorithm. The
ensemble component uses three different classifiers, each of which
models different set of features from SEER through the features
selection module. On top of them, another classifier is used to give
the final decision based on the output decisions and confidence
scores from each of the underlying classifiers. Different classification
algorithms have been examined; the best setup found is by using the
decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the
underlying classifiers and Na¨ıve Bayes for the classifier ensemble
step. The system outperforms all published systems to date when
evaluated against the exact same data of SEER (period of 1973-2002).
It gives 87.39% weighted average F-score compared to 85.82% and
81.34% of the other published systems. By increasing the data size to
cover the whole database (period of 1973-2014), the overall weighted
average F-score jumps to 92.4% on the held out unseen test set.