Improving real-time emotion recognition system in assistive communication technologies for disabled persons using deep learning with equilibrium algorithm

A disability is one of the significant problems which has been introduced and leads to current problems. Disability is and continues to be a basis of frustration as it is observed as a limitation, a physical, cognitive, and mental handicap, which limits the individual's growth and involvement. Therefore, considerable effort is put into removing this type of limitation. The problems that disabled individuals face are tackled in the initiative. Disabled individuals must depend on others to meet their requirements. Machine learning (ML) is performing great work to make a smart city and provide a safe presence for disabled people. Emerging as a smart city is possible when we can perceptively treat people with disabilities. This paper introduces a novel Sustainable Emotion Recognition System for Disabled Persons Using Deep Learning and Equilibrium Optimiser for Real-Time Communication Enhancement (SERDP-DLEOCE) approach. The SERDP-DLEOCE approach is designed as an advanced approach for emotion recognition in text to facilitate enhanced communication for people with disabilities. The process begins with text pre-processing, which involves multiple distinct stages to convert the raw text into a more suitable format for analysis. Furthermore, Word2Vec is employed for word embedding to capture semantic meaning by mapping words to dense vector representations. Moreover, the Elman neural network (ENN) model is used for emotion recognition in the test. Finally, the equilibrium optimizer (EO) model adjusts the hyperparameter values of the ENN model optimally and results in greater classification performance. The experimental validation of the SERDP-DLEOCE methodology is performed under the Emotion detection from text dataset. The comparison study of the SERDP-DLEOCE methodology portrayed a superior accuracy value of 95.15% over existing techniques.