AI-Driven Assessment of Students: Current Uses and Research Trends

During the last decade, the use of AIs is being incorporated into the educational field whether to support the analysis of human behavior in teachinglearning contexts, as didactic resource combined with other technologies or as a tool for the assessment of the students. This proposal presents a Systematic Literature Review and mapping study on the use of AIs for the assessment of students that aims to provide a general overview of the state of the art and identify the current areas of research by answering 6 research questions related with the evolution of the field, and the geographic and thematic distribution of the studies. As a result of the selection process this study identified 20 papers focused on the research topic in the repositories SCOPUS and Web of Science from an initial amount of 129. The analysis of the papers allowed the identification of three main thematic categories: assessment of student behaviors, assessment of student sentiments and assessment of student achievement as well as several gaps in the literature and future research lines addressed in the discussion

A spectral conjugate gradient method with sufficient descent property for neural network training.

Abstract. Conjugate gradient methods constitute an excellent choice for efficiently training large neural networks since they don't require the evaluation of the Hessian matrix neither the impractical storage of an approximation of it. Despite the theoretical and practical advantages of these methods their main drawback is the use of restarting procedures in order to guarantee convergence, abandoning second order derivative information keywords. Neural networks, descent spectral conjugate gradient methods, sufficient descent property, truncate strategy

Special Issue: Regularization Techniques for Machine Learning and Their Applications

Over the last decade, learning theory performed significant progress in the development of sophisticated algorithms and their theoretical foundations. The theory builds on concepts that exploit ideas and methodologies from mathematical areas such as optimization theory. Regularization is probably the key to address the challenging problem of overfitting, which usually occurs in high-dimensional learning. Its primary goal is to make the machine learning algorithm “learn” and not “memorize” by penalizing the algorithm to reduce its generalization error in order to avoid the risk of overfitting. As a result, the variance of the model is significantly reduced, without substantial increase in its bias and without losing any important properties in the data. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

DTCo: An Ensemble SSL Algorithm for X-ray Classification

In the last decades, the classification of images was established as a typical method for diagnosing many abnormalities and diseases. The purpose of an efficient classification method is considered essential in modern diagnostic medicine in order to increase the number of diagnosed patients and decrease the analysis time. The significant storage capabilities of electronic media have enabled research centers to accumulate repositories of classified (labeled) images and mostly of a large number of unclassified (unlabeled) images. Semi-supervised learning algorithms have become a hot topic of research as an alternative to traditional classification methods, seeing as they exploit the explicit classification information of labeled data with the knowledge hidden in the unlabeled data resulting in the creation of powerful and effective classifiers. In this work, we propose a new ensemble self-labeled algorithm, called DTCo, for X-ray classification. The efficacy of the presented algorithm is illustrated by a series of experiments against other state-of-the-art self-labeled methods. © Springer Nature Switzerland AG 2020

Employing Constrained Neural Networks for Forecasting New Product’s Sales Increase

Part 2: 8th Mining Humanistic Data WorkshopInternational audienceAn intelligent sales forecasting system is considered a rather significant objective in the food industry, since a reasonably accurate prediction has the possibility of gaining significant profits and better stock management. Many food companies and restaurants strongly rely on their previous data history for predicting future trends in their business operations and strategies. Undoubtedly, the area of retail food analysis has been dramatically changed from a rather qualitative science based on subjective or judgemental assessments to a more quantitative science which is also based on knowledge extraction from databases. In this work, we evaluate the performance of weight-constrained neural networks for forecasting new product’s sales increase. These new prediction models are characterized by the application of conditions on the weights of the network in the form of box-constraints, during the training process. The preliminary numerical experiments demonstrate the classification efficiency of weight-constrained neural networks in terms of accuracy, compared to state-of-the-art machine learning prediction models