Journal of Polymer & Composites Original Research

Cognitive Polymer Composite Systems for Autonomous Biomedical Response

  1. S. A. Jadhav Department of Computer Science and Engineering, Krishna Institute of Science and Technology, Krishna Vishwa Vidyapeeth “Deemed to be University”, Karad, Satara
  2. Tanveer Ahmad Wani Department of Physics, Noida international University, Greater Noida
  3. Abhijeet Deshpande Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune
  4. Eswar S, Meenakshi , Department of Allied Health, College of Allied Health Sciences, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research, Kanchipuram
  5. B Pravallika Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Shamshabad, Hyderabad
  6. Kada Tulasi Department of Mechanical Engineering, Pragati Engineering College, Kakinada District

Abstract

Cognitive polymer composite systems: A novel form of intelligent biomaterials with the capability to sense, process and respond to real time physiological stimuli on their own. The present paper offers a comprehensive platform to integrate stimulus responsive polymers with intrinsic sensing networks and learning based decision models to offer adaptable bio-medical solutions. Mathematical formulations are available that are used to model stimulus response behavior, sensor signal processing and cognition decision processes. The proposed methodology will be a hybridization of the advanced fabrication and artificial intelligence models, including artificial neural networks and reinforcement learning, to provide dynamic adaptation in case of being exposed to alternative biological conditions. Experimental evaluation use has also shown more responsiveness, stability and accuracy compared to traditional systems and have also hinted possible uses in smart implants, drug delivery and even personalized healthcare systems. It has been demonstrated in this paper that the utilization of smart polymers containing embedded sensors and models based on learning can be helpful in offering autonomous biomedical functionality. The proposed system was more precise, sensitive, and faithful as compared to conventional biomaterials. Its capacity to cope with real-time physiological information and dynamically adjust explains good prospects in its application in smart implants, drug-delivery and personalized healthcare. The range of clinical use can be expanded, through future work, by increasing the range of scalability and a long-term biocompatibility, and improved integration with more complex artificial intelligence techniques.

Keywords

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