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Rathinam, Anantha Raman
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Advancing airway management for ventilation optimization in critical healthcare with cloud computing and deep learning Krishnamoorthi, Suresh Kumar; Karthi, Govindharaju; Radhika, Moorthy; Rathinam, Anantha Raman; Raju, Ayalapogu Ratna; Pinjarkar, Latika; Srinivasan, Chelliah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1053-1063

Abstract

Improving patient outcomes in critical care settings is significantly connected to effective ventilation control. This research introduces a new method for improving ventilation methods in critical healthcare utilizing a long short-term memory (LSTM) network hosted in the cloud. Ventilators, pulse oximeters, and capnography are just a few examples of medical equipment that input data into the system, which then uploads the data to the cloud for analysis. The LSTM network can learn from data patterns and correlations, drawing on respiratory parameters' time dynamics, to provide real-time suggestions and predictions for ventilation settings. The system aims to improve clinical results and reduce the risk of ventilator-induced lung damage by tailoring ventilation techniques according to each patient's requirements and by forecasting potential issues. Due to remote monitoring technology, medical professionals can quickly analyze their patient's conditions and act accordingly. The system allows for continuous improvement using iterative learning of more data and feedback. With the ability to optimize breathing and enhance patient care in critical healthcare situations, a hopeful development in airway management is needed.
Enhancing mobility with customized prosthetic designs driven by genetic algorithms Seeni, Senthil Kumar; Harshitha, Ganadamoole Madhava; Rathinam, Anantha Raman; Venkatara, Nagaiyanallur Lakshminarayanan; Sasirekha, Venkatesan; Tidke, Bharat; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp876-886

Abstract

Using genetic algorithms, this research intends to usher in a new era of prosthetic design that is redefining mobility. Through repeated evolutionary processes influenced by natural selection, the goal is to optimize prosthetic design parameters including material composition, structure, and control systems. The objective is to create prosthetic limbs that are more personalized to each user's requirements, improving their efficiency, comfort, and functioning via the application of genetic algorithms. The goal of this study is to show that the suggested strategy may improve mobility and user happiness more than standard ways by simulating and testing prosthetic devices in real-world settings. The end goal is to create conditions for a new age of prosthetic technology, where amputees' quality of life is greatly enhanced by devices that are individually designed to meet their biomechanical needs. The impact of prosthetic design and individual patient factors patient dataset derived from a random 5-sample with the following characteristics: ages 32–68, weight 65–90, height 155–180, crossover rate 0.6–0.9, mutation rate 0.05–0.2, population size 70–120, generations 30–60.
Co-Authors Harshitha, Ganadamoole Madhava Karthi, Govindharaju Krishnamoorthi, Suresh Kumar Murugan, Subbiah Pinjarkar, Latika Radhika, Moorthy Raju, Ayalapogu Ratna Sasirekha, Venkatesan Seeni, Senthil Kumar Srinivasan, Chelliah Tidke, Bharat Venkatara, Nagaiyanallur Lakshminarayanan