Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children

The application of Artificial Neural Networks (ANNs) to different domains become stronger everyday. In this accepted paper, we used ANNs to Detect Fetal Alcohol Spectrum Disorder in Children. The paper was accepted in high impact journal named Applied Sciences (IF = 2.458).

Abstract: Fetal alcohol spectrum disorder (FASD) is an umbrella term for children’s conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject’s quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer-based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children’s images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75\% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. Model obtained obtained an accuracy of 88.46% (psycometric, 74.07% (Antisaccadic), 72.24% (Prosaccadic), 88% (Memory guide saccade) and, 75% (DTI). These results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. These results are an improved from Zhang’s 2019 model which used the same data with less accuracy level.

Defining SPL Scope in Small Companies

A high impact article named “A Collaborative Method for Scoping Software Product Lines: a Case Study in a Small Software Company” was accepted in indexed journal Applied Science (IF = 2.458). This work was developed with members of Colombian and Chile.  

Abstract: SPL scoping is the activity for bounding Software Product Lines (SPL), gathering heterogeneous knowledge from diverse sources. For achieving an agreement among different stakeholders, a commonalty scope must be understood and committed to. However, gathering this knowledge from stakeholders with individual interests is a complex task. This paper reports the experience of scoping the SPL of a small Colombian software company, applying and evaluating a collaborative method called CoMeS-SPL. The company was looking to develop a set of products from a product previously developed with great potential to be adapted and sold to different customers. From a collaborative relationship university–enterprise model, the research groups that developed CoMeS-SPL proposed to use it answering to the company needs for defining an organization-suitable reuse scope around its platform called CORA. Both parties joined in the scoping co-production of the first SPL of the company. This method implied that the company would perform new tasks and involve other roles different for those who are used to defining the scope of a single product. The company actors considered that they obtained a useful scope and perceived the collaboration as valuable because they shared different knowledge and perspectives. The researchers were able to provide feedback on their proposed model, identifying successes and aspects to improve. The experience allowed strengthening the ties of cooperation with the company, and new projects and consultancies are being carried out.