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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Srinivasan, Chelliah
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Computer Science & IT Electrical & Electronics Engineering

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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.
Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms Gurulakshmanan, Gurumoorthi; Amarnath, Raveendra N.; Lebaka, Sivaprasad; Reddy, Munnangi Koti; Mohankumar, Nagarajan; Muthumarilakshmi, Surulivelu; Srinivasan, Chelliah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2427-2435

Abstract

The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.
Co-Authors Amarnath, Raveendra N. Gurulakshmanan, Gurumoorthi Karthi, Govindharaju Krishnamoorthi, Suresh Kumar Lebaka, Sivaprasad Mohankumar, Nagarajan Muthumarilakshmi, Surulivelu Pinjarkar, Latika Radhika, Moorthy Raju, Ayalapogu Ratna Rathinam, Anantha Raman Reddy, Munnangi Koti