Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Khekare, Ganesh
Unknown Affiliation
Computer Science & IT Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Contextual embedding generation of underwater images using deep learning techniques Kerai, Shivani; Khekare, Ganesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3111-3118

Abstract

This article delves into the cutting-edge realm of artificial intelligence, specifically focusing on its application in marine research via underwater image analysis. It introduces an innovative, integrated approach that combines object detection with image captioning tailored for the aquatic domain. Central to this approach is the advanced technique of image feature extraction, complemented by the strategic implementation of attention mechanisms within neural networks. These mechanisms are key in enhancing the precision and contextual understanding of underwater imagery. The efficacy of this method is underscored by extensive experiments on diverse underwater datasets. Results show notable improvements in detecting and describing complex underwater scenes, thereby providing invaluable insights for marine biologists, environmentalists, and the broader scientific community. This exploration marks a significant advancement in marine research, offering a new lens through which the underwater world can be understood and preserved.
Integrating blockchain, internet of things, and cloud for secure healthcare Kumaran, K Senthur; Khekare, Ganesh; Athitya M, Thanu; Arulmozhivarman, Aakash; Pranav M, Arvind; Chidambaram N, Hiritish
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.pp928-936

Abstract

This research paper shows a decentralized healthcare architecture using the integration of internet of things (IoT), blockchain, and cloud to improve speed up tuple broken security as well as scalability. Real time health information (e.g., pulse rate, sugar level) from patients is captured by IoT devices and preprocessed at the fog computing layer to securely send them to a cloud platform. Immutability and transparency Patient health records recorded by blockchain solutions are highly irreversible due to the underlying technology, while smart contracts take care of data integrity and privacy. The cloud layer delivers storage that scales and works, also including real-time analytics to access patient data from anywhere for healthcare providers while the core helps manage long-term information architecture. It does so by automating healthcare workflows and taking some of the manual interventional processes out such that care delivery becomes even more efficient. Together, these technologies provide a secure, efficient, patient-centered healthcare system whose architecture can easily support future needs in remote patient monitoring and inter-institutional collaboration, responding to emerging demands from modern healthcare systems.
A comprehensive artificial intelligence framework for reducing patient rehospitalizations Khekare, Ganesh; Janarthanan, Midhunchakkaravarthy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3827-3834

Abstract

The role of artificial intelligence (AI) in the healthcare sector is increasing daily. Readmissions of patients have become a significant challenge for the medical sector, adding unnecessary burden. Governments and public sectors are continuously working on the hospital readmissions reduction program (HRRP). In this research work, an AI framework has been developed to reduce patient readmissions. The accuracy of the framework has been increased by continuous refinement in feature engineering, incorporating several complex datasets. The framework analyses the different algorithms like bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and XGBoost for prediction. This framework has shown a 92% accuracy rate during testing, showing a 37% reduction in 40-day rehospitalization rates. This reduces the overburden on hospital systems by avoiding unnecessary readmissions of patients. The system’s real-time development, scalability, management of things in an ethical manner, and long-term viability will remain as future scope.
Co-Authors Arulmozhivarman, Aakash Athitya M, Thanu Chidambaram N, Hiritish Janarthanan, Midhunchakkaravarthy Kerai, Shivani Kumaran, K Senthur Pranav M, Arvind