Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Veena Kalludi Narasimhaiah
Reva University
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Insights on assessing image processing approaches towards health status of plant leaf using machine learning Harsha Raju; Veena Kalludi Narasimhaiah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp884-891

Abstract

With the advancement of digital image processing in agriculture and crop cultivation, imaging techniques are adopted to acquire real-time health status. Out of all the parts of plants, the leaf is the direct indicator of its health status, and hence applying various image processing approaches could benefit the process of yielding informative cases of plant health. At present, there are various approaches, e.g., feature extraction, segmentation, identification, the classification being evolved up with more dependencies being found in using machine learning; the studies show many contributions towards this challenge. However, it is not yet conclusive to understand the optimal approach. Hence, this paper highlights an explicit strength and weakness associated with the existing approaches existing imaging processing techniques to identify the disease condition from an input of plant leaves' image. The study also contributes to highlighting open-end research problems to have conclusive remarks about effectiveness. 
ResNet based deep learning approach for chronic obstructive pulmonary disease prediction using lung sound analysis Babitha Sudhakar Ullal; Veena Kalludi Narasimhaiah; Rithul Kamesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1733-1745

Abstract

Chronic obstructive pulmonary disease (COPD) affects around 300-400 million people worldwide representing a critical healthcare challenge that requires early detection for effective intervention. This work introduces chronic lung analysis via audio signal prediction (CLASP), a novel framework achieving 97.90% accuracy in predicting COPD automatically through respiratory audio signal analysis. This method integrates advanced signal processing and deep learning architectures, comparing long short-term memory (LSTM), convolutional neural networks (CNN), and residual networks (ResNet) models for optimal performance. The ResNet architecture exhibits superior diagnostic capability with precision of 98.72%, recall of 96.86%, and 0.9937 area under the curve (AUC), as compared to existing methods by significant margins. These results establish a new benchmark for noninvasive COPD detection, thus enabling practical deployment in clinical settings thereby dramatically improving the patient outcomes by early detection and also reduce healthcare costs.
Co-Authors Babitha Sudhakar Ullal Harsha Raju Rithul Kamesh