Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

The choice of academic itineraries and/or optional subjects to attend is not usually an easy decision since, in most cases, students lack the information, maturity, and knowledge required to make right decisions. This paper evaluates the support of Collaborative Systems for helping and guiding students in this decision-making process, considering the behavior and impact of these systems on the use of data different from the formal information the students usually use. For this purpose, the research applied the clustering based Multi-dimension Tensor Factorization approach to build a recommendation system and confirm that the increment in tensors improves the recommendation accuracy. As a result, this approach permits the user to take advantage of the contextual information to reduce the sparsity issue and increase the recommendation accuracy

Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis

In the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students

Monitoring the early signs of cognitive decline in elderly by computer games: an MRI study

BACKGROUND: It is anticipated that current and future preventive therapies will likely be more effective in the early stages of dementia, when everyday functioning is not affected. Accordingly the early identification of people at risk is particularly important. In most cases, when subjects visit an expert and are examined using neuropsychological tests, the disease has already been developed. Contrary to this cognitive games are played by healthy, well functioning elderly people, subjects who should be monitored for early signs. Further advantages of cognitive games are their accessibility and their cost-effectiveness. PURPOSE: The aim of the investigation was to show that computer games can help to identify those who are at risk. In order to validate games analysis was completed which measured the correlations between results of the 'Find the Pairs' memory game and the volumes of the temporal brain regions previously found to be good predictors of later cognitive decline. PARTICIPANTS AND METHODS: 34 healthy elderly subjects were enrolled in the study. The volume of the cerebral structures was measured by MRI. Cortical reconstruction and volumetric segmentation were performed by Freesurfer. RESULTS: There was a correlation between the number of attempts and the time required to complete the memory game and the volume of the entorhinal cortex, the temporal pole, and the hippocampus. There was also a correlation between the results of the Paired Associates Learning (PAL) test and the memory game. CONCLUSIONS: The results gathered support the initial hypothesis that healthy elderly subjects achieving lower scores in the memory game have increased level of atrophy in the temporal brain structures and showed a decreased performance in the PAL test. Based on these results it can be concluded that memory games may be useful in early screening for cognitive decline

Environmental factors are stronger predictors of primate species’ distributions than basic biological traits

Understanding the neutral, biological and environmental processes driving species distributions is valuable in informing conservation efforts because it will help us predict how species will respond to changes in environmental conditions. Environmental processes affect species differently according to their biological traits, which determine how they interact with their environment. Therefore, functional, trait-based modelling approaches are considered important for predicting distributions and species responses to change but even for data-rich primate communities our understanding of the relationships between traits and environmental conditions is limited. Here we use a large-scale, high-resolution dataset of African diurnal primate distributions, biological traits and environmental conditions to investigate the role of biological traits and environmental trait filtering in primate distributions. We collected data from published sources for 354 sites, and 14 genera with 57 species across Sub-Saharan Africa. We then combined a three-table ordination method, RLQ, with the Fourth Corner approach to test relationships between environmental variables and biological traits and used a mapping approach to visually assess patterning in primate genus and species’ distributions. We found no significant relationships between any groups of environmental variables and biological traits, despite a clear role of environmental filtering in driving genus and species’ distributions. The most important environmental driver of species distributions was temperature seasonality, followed by rainfall. We conclude that the relative flexibility of many primate genera means that not any one particular set of traits drives their species-environment associations, despite the clear role of such associations in their distribution patterns