University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

Variable expression of the autism broader phenotype: findings from extended pedigrees.

Abstract: Factors influencing the rate, form, and severity of phenotypic expression among relatives of autistic probands are examined. Family history data on 3095 first- and second-degree relatives and cousins from 149 families with a child with autism and 36 families with a child with Down syndrome are studied. The results provide further evidence of an increased risk among autism relatives for the broadly defined autism phenotype. Of proband characteristics, severity of autism and obstetric optimality were confirmed as being related to familial loading for probands with speech. There was little variation in loading among probands lacking speech. The type of phenotypic profile reported in relatives appeared little influenced by characteristics of the relative or the proband, except for variation by degree of relative, parental status of relative, and perhaps proband's birth optimality score. Phenotypic rates among parents suggested reduced fitness for the severest and more communication-related forms of expression but not for the more mild and social forms of expression. Patterns of expression within the families did not support a simple X-linked nor an imprinted X-linked mode of inheritance. The basis for sex differences in rates of expression is discussed.

Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques

Abstract: Fake news is a phenomenon which is having a significant impact on our social life, in particular in the political world. Fake news detection is an emerging research area which is gaining interest but involved some challenges due to the limited amount of resources (i.e., datasets, published literature) available. We propose in this paper, a fake news detection model that use n-gram analysis and machine learning techniques. We investigate and compare two different features extraction techniques and six different machine classification techniques. Experimental evaluation yields the best performance using Term Frequency-Inverted Document Frequency (TF-IDF) as feature extraction technique, and Linear Support Vector Machine (LSVM) as a classifier, with an accuracy of 92%.