Artificial neural networks based multimodal device for autism spectrum disorder.

Abstract

The neuro developmental condition known as Autism Spectrum Disorder (ASD) affects people on a lifetime basis and exhibits itself in a wide range of ways. In this research work a brand-new semi-supervised training method for the recognition of discrete multi-modal autism spectrum disorder is proposed. At the coarse-grained level, we consider that various methodologies are anticipated to explore equivalent information about child autism. To build DC AlexNet, this combines two small network branches and a large network (trunk network). The network trunk is programmed just to become familiar with the distinguishing characteristics shared by face images at different resolutions. It is built using recently suggested residential components. To project images to a place where their ranges are as little as possible, two branch networks are programmed to learn coupled-mappings (CMs) that are particular to a given resolution. The suggested technique is properly assessed utilizing the databases for the OMEGE and DIAEMO datasets by evaluating it to state-of-the-art techniques in terms of many parameters. Deep Coupled AlexNet is developed to obtain 98.13 % of accuracy, 95.1 % of precision, 94.3 % of recall and 95.4 of F1-score for OMEGE dataset. Moreover, 98.6 % of accuracy, 97.2 % of precision, 98.5 of recall and 97.5 % of F1-score for DIAEMO dataset (Tab. 8, Fig. 10, Ref. 16). Keywords: autism spectrum disorder, artificial neural networks, emotion recognition, interaction design, multimodal factors.