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BRAIN: Social Media as Medical Validator

The electronic word of mouth (eWOM) is a communication form adapted to the digitalized world of today where persons that never met communicate in an impersonal manner. The paper Social Media as Medical Validator written by Laura Broasca, Versavia-Maria Ancusa and Horia Ciocarlie aims to explore the receptiveness towards a negative bias in health-related electronic Word of Mouth.

The researchers point out that when evaluating the credibility of the eWOM we should take into account that social factors play an unexpected role, as most people use personal details and peripheral cues, not a clear, logical, fact-based judgement to reach a conclusion. And yet, the credibility of the traditional information sources is surpassed by that of eWOM.

Google trends comparison between vaccine-related search words
Google trends comparison between vaccine-related search words

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BRAIN: A Robust Approach of Facial Orientation Recognition from Facial Features

In the field of computer vision and pattern recognition, face orientation recognition stands as a significant topic. In the paper A Robust Approach of Facial Orientation Recognition from Facial Features, the authors Stefan Andrei, Kishor Datta Gupta, Md Manjurul Ahsan and Kazi Md. Rokibul Alam introduce us to an image mapping technique for face analysis.

The methodology of the study consists of two main phases: Face Feature Extraction and creating the graph image, and Matching Graph image with stored images. The Face Feature Extraction presents four steps that include Face Detection, Feature Extraction, Obtaining Feature Data and Creating image with these data.

The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The technique of this study relies on the four main features of the face: left eye, right eye, nose, and mouth. It is mandatory to obtain the positions, size, height, width, and angle of these features respective to each face. By acquiring the data from the features, a new picture including the model and shape of the face is created.

Experiment Results: Sample image data 1
Experiment Results: Sample image data 1

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BRAIN: Sentiment Analysis on Embedded Systems Blended Courses

The paper written by Răzvan Bogdan from the Department of Computers and Information Technology, Politehnica University of Timisoara, includes a presentation of a modality of integrating Embedded Systems Massive Open Online Courses (MOOCs) into blended courses. More than that, it also provides an evaluation of this approach: the sentiment analysis technique.

Twitter sentiment analysis results
Twitter sentiment analysis results

Starting with the explanation of MOOCs, the author insists on one type of courses which is still underrepresented in the field of blended courses – that of embedded systems. Consequently, we can understand that the aim of this paper is to understand, with the help of sentiment analysis, the way in which students react to blending embedded systems MOOCs into embedded system courses. We also find out that the blending variant is applied to Embedded Systems course at “Politehnica” University of Timisoara in Romania, third year of study.

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