International Journal of Computational Intelligence Studies
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International Journal of Computational Intelligence Studies (2 papers in press)
Wolf : A framework for digital workplace - Architecture and models - by Khadija ELAMRANI, Noureddine Chenfour, Mohamed LAHMER, Ghita Daoudi Abstract: The main purpose of the digital workplace (DW) is to ensure to the organizations different contributors or actors a portal of digital services, which are accessible through a virtual desktop covering all its business services. During our studies, we were able to identify five major problems. First of all, we note a great confusion in the related definitions because most of them are restricted to the teaching sector. Secondly, most existing DWs are summarized as a simple gateway to pre-existing digital tools collection that covers the organizations business domains, without any means of communication between them. Another problem is the lack of a reference architecture. Moreover, we could not identify any logical or physical model to represent the different DWs entities. Lastly, there is a total absence of a standard or even an appropriate vocabulary.rnFaced with these shortfalls, we propose in this paper a set of fundamentals that is composed by a definition encapsulating the different domains, as well as a naming system and a vocabulary that identify both the entities that compose the virtual desktop and their connections and flows. Based on these fundamentals, we also propose our framework WOLF (Digital Workplace based on Open and Light architecture Framework) that generate automatically customized digital workplaces, and is distinguished from other existing DWs solutions by its generic and extensible character. The generated DW encapsulates all of the organizations domains, services, flows and a collaboration system between the different actors. Our proposed frameworks architecture allows us to classify and organize the various entities into a tree representation whilst data nodes are modelled using XML files. Keywords: Digital workplace; Digital workspace; Collaboration; Digital work environment.
Alzheimer's disease prediction using Regression models and SVM by M. Rohini, D. Surendran Abstract: Alzheimer's disease (AD) and cognitive impairment due to aging are the recently prevailing diseases among aged inhabitants because of an increase in the aging population. Several demographic characters, structural and functional neuroimaging investigations, cardio-vascular studies, neuropsychiatric symptoms, cognitive performances and biomarkers in cerebrospinal fluids are the various predictors for AD. We can consider these input features for the prediction of symptoms whether they belong to AD or normal cognitive impairment for aging. In the proposed study, the hypothesis is derived for supervised learning methods such as multivariate linear regression, logistic regression, and SVM. We perform feature scaling and normalization with features as an initial step for applying the parameters to derive the hypothesis. We analyze performance metrics with the implementation results. The present work is applied to 1000 baseline assessment data from Alzheimers disease Neuro-Imaging Initiative studies (ADNI) that give conversion prediction. The comparison of results in literature studies suggests that the efficiency of the proposed study is highly helpful in differentiating AD pathology from cognitive impairment because of aging. Keywords: Multivariate linear regression; logistic regression; Support Vector Machine(SVM);Feature scaling; Normalization;ADNI.