Deep learning assisted cognitive diagnosis for the D-Riska application

Abstract

In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 % of Accuracy, 94 % of Precision, 91 %, of Recall and 92% of F1-Score.

Models of Electricity Price Forecasting: Bibliometric Research

“…There is also a growing importance of transforming the economy [38,39] and economic growth model into one "that includes the environmental as well as the social dimension and establishing a relationship between the environment and development" [40]. Such a change is possible only if the EPF models are classified or their risks are recognized and assessed [41]. The accuracy of the EPF influences the volume of the generated electricity [42,43] and reduces the operational risk in business [44,45].…”