Fuzzy Temporal Rule-Based Natural Language Processing forMedical Text Analysis for Caring for Geriatric People

Abstract

The development of robotics applications heavily relies on artificial intelligence and machine learning, which are prerequisites for creating ‎intelligent robotic systems. Furthermore, the development of intelligent robots requires feature selection, classification, and fuzzy rule-based ‎decision making. Because they immediately aid the elderly in receiving medical care, allowing them to go about their daily lives more ‎quickly and effectively, medical assistive robots are beneficial to society. By removing characteristics that don't contribute, such as noise, ‎null values in databases, and properties unrelated to classification problems, feature selection lowers the dimension of the data. Natural ‎language processing can be utilized in medical applications, such as providing medical assistance through robot arm control, to handle the ‎challenging task of directing the robot arm using elderly instructions or orders. To effectively convert speech to text and conduct ‎morphological, syntactic, and semantic analysis on the converted text to more precisely detect the commands issued to robots by older ‎adults, the Fuzzy Temporal Rule-based Semantic Analysis Algorithm (FTRSAA) is suggested in this study. Additionally, the efficient ‎design and execution of the system that helps the elderly are made possible by this voice-activated robot control system, which utilizes the ‎latest intelligent robot technology. By using these recently suggested algorithms to comprehend natural language texts generated from ‎discussions, it communicates with older people.