Fuzzy Temporal Rule-Based Natural Language Processing for‎Medical Text Analysis for Caring for Geriatric People - Sheelavant, Dr. Kumaresh; Lekshmy, Dr. P.L.; Madhurya, J.A.; Rahman, Dr. F.; Mishra, Dr. Nidhi (International Journal of Basic and Applied Sciences, 2025)
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.
Keywords: Medical Application; Artificial Intelligence; Deep Learning; Fuzzy Temporal Rule-Based Semantic Analysis Algorithm