Belief Hierarchical Clustering

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool

A Belief Approach for Detecting Spammed Links in Social Networks

International audienceNowadays, we are interconnected with people whether professionally or personally using different social networks. However, we sometimes receive messages or advertisements that are not correlated to the nature of the relation established between the persons. Therefore, it became important to be able to sort out our relationships. Thus, based on the type of links that connect us, we can decide if this last is spammed and should be deleted. Thereby, we propose in this paper a belief approach in order to detect the spammed links. Our method consists on modelling the belief that a link is perceived as spammed by taking into account the prior information of the nodes, the links and the messages that pass through them. To evaluate our method, we first add some noise to the messages, then to both links and messages in order to distinguish the spammed links in the network. Second, we select randomly spammed links of the network and observe if our model is able to detect them. The results of the proposed approach are compared with those of the baseline and to the k-nn algorithm. The experiments indicate the efficiency of the proposed model

Internet interventions for mental health in university students:A systematic review and meta-analysis

OBJECTIVES: Mental health disorders are highly prevalent among university students. Universities could be an optimal setting to provide evidence-based care through the Internet. As part of the World Mental Health International College Student initiative, this systematic review and meta-analysis synthesizes data on the efficacy of Internet-based interventions for university students' mental health. METHOD: A systematic literature search of bibliographical databases (CENTRAL, MEDLINE, and PsycINFO) for randomized trials examining psychological interventions for the mental health (depression, anxiety, stress, sleep problems, and eating disorder symptoms), well-being, and functioning of university students was performed through April 30, 2018. RESULTS: Forty-eight studies were included. Twenty-three studies (48%) were rated to have low risk of bias. Small intervention effects were found on depression (g = 0.18, 95% confidence interval [CI; 0.08, 0.27]), anxiety (g = 0.27, 95% CI [0.13, 0.40]), and stress (g = 0.20, 95% CI [0.02, 0.38]). Moderate effects were found on eating disorder symptoms (g = 0.52, 95% CI [0.22-0.83]) and role functioning (g = 0.41, 95% CI [0.26, 0.56]). Effects on well-being were non-significant (g = 0.15, 95% CI [-0.20, 0.50]). Heterogeneity was moderate to substantial in many analyses. After adjusting for publication bias, effects on anxiety were not significant anymore. DISCUSSION: Internet interventions for university students' mental health can have significant small-to-moderate effects on a range of conditions. However, more research is needed to determine student subsets for which Internet-based interventions are most effective and to explore ways to increase treatment effectiveness. © 2018 John Wiley & Sons, Ltd. KEYWORDS: Internet; college; mental disorders; meta-analysis; psychotherap

Knowledge Bases and Neural Network Synthesis

We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (NN) representation, run, and the results from run time and (eventually) from learning are decompiled to a symbolic description of the knowledge contained in the network. After inspecting this recovered knowledge, a designer would be able to modify the KB and go through the whole cycle of compiling, running, and decompiling again. The central question with which this project is concerned is, therefore, How do we go from a KB to an NN, and back again? We are investigating this question by building tools consisting of a repertoire of language/translation/network types, and trying them on problems in a variety of domains

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains