Algorithmic material selection for wearable medical devices a genetic algorithm-based framework with multiscale modeling





Wearable medical devices, Material selection framework, Genetic algorithm, Multiscale modeling, Performance assessment, Computational material science

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  • Nitin J. Wange Department of Applied Mathematics and Humanities, Yashwantrao Chavan College of Engineering, Nagpur, Maharashtra, India.
  • Sachin V. Chaudhari Department of Electronics and Communication Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India.
  • Koteswararao Seelam Department of Electronics and Communication Engineering, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
  • S. Koteswari Department of Electronics and Communication Engineering, Pragati Engineering College, Surampalem, Kakinada, Andhra Pradesh, India.
  • T. Ravichandran Department of Computer Science and Engineering, Karpagam College of Engineering, Othakkal Madam, Coimbatore, India
  • Balamurugan Manivannan Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.


This research presents a novel algorithmic material selection framework for wearable medical devices, utilizing a genetic algorithm-based approach with multiscale modeling. The study employs a comprehensive research methodology encompassing computational modeling, data visualization, and performance assessment. Initially, a diverse set of materials is defined, and their performance scores are assigned to establish a baseline for evaluation. The ensuing data visualization includes a bar chart, a scatter plot, and a line chart, providing insights into material performance, cost-performance relationships, and the convergence of the genetic algorithm, respectively. Performance metrics such as accuracy, precision, and recall are calculated to gauge the algorithm’s efficacy, presented in a bar chart for a nuanced evaluation. Furthermore, a receiver operating characteristic (ROC) curve and a confusion matrix are employed for discriminative ability assessment and detailed classification performance analysis. The results showcase the algorithm’s proficiency in material selection, emphasizing the importance of accuracy, precision, and recall in the complex landscape of wearable medical device development. The abstract concludes with a summary of the implications drawn from each visualization, highlighting the potential of the proposed algorithmic framework to enhance the precision and efficiency of material selection processes for wearable medical devices. This research contributes to the advancement of materials science in healthcare applications, presenting a holistic approach that integrates computational techniques and data-driven methodologies for optimized material selection

How to Cite

Wange, N. J., Chaudhari, S. V., Seelam, K., Koteswari, S., Ravichandran, T., & Manivannan, B. (2024). Algorithmic material selection for wearable medical devices a genetic algorithm-based framework with multiscale modeling. The Scientific Temper, 15(01), 1581–1587.


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