Optimizing Deep Learning Algorithm for Tears Sensor Data Analysis: An Algorithmic Analysis
Dr. Sarbeswar HotaAssociate Professor, Department of Computer Applications, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India. sarbeswarahota@soa.ac.in0009-0006-0921-8323
Dr. Abhiraj MalhotraCentre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India. abhiraj.malhotra.orp@chitkara.edu.in0009-0005-1871-4807
Dr. R. Hannah Jessie RaniDepartment of Electrical and Electronics Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India. jr.hannah@jainuniversity.ac.in0000-0002-5449-104X
Dr. Vijay Jagdish UpadhyeDepartment of Microbiology, Research and Development Cell, Parul University, Vadodara, Gujarat, India. vijay.upadhye82074@paruluniversity.ac.in0000-0002-8821-1720
Prateek AggarwalChitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, India. prateek.aggarwal.orp@chitkar.edu.in0009-0001-8154-6018
Dr. Awakash MishraProfessor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Uttar Pradesh, India. awakash.mishra@muit.in0009-0009-8318-950X
Keywords: Deep Learning, Healthcare, High-Dimensional, Preprocessing Technique, Disease Detection, Monitoring.
Abstract
There have been significant developments in deep learning-based algorithms in various domains, including healthcare. One aspect that may benefit from diagnostics and monitoring is tear sensor data. Nevertheless, conventional data analysis methods struggle with extracting what is meaningful from tear sensor data, which are complex and high-dimensional. In the present study, we introduce a novel deep-learning model for the efficient analysis of tear sensor data. To process the data from the tear sensors, we employ sophisticated machine learning algorithms, such as Convolutional and Recurrent Neural Networks, for feature extraction and pattern recognition in our algorithm. Specifically, we employ a novel data preprocessing method to reduce noise and enhance data quality substantially. Improved methods, such as the one described above, yield more rapid and accurate analysis of tear sensor data as well as enhanced disease detection and monitoring.