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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Power Engineering (IJAPE)
Bandreddi Venkata Seshukumari
VNR Vignana Jyothi Institute of Engineering and Technology
Computer Science & IT Electrical & Electronics Engineering Engineering

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Artificial intelligence framework for multi-stage lung disease detection with audio signals Bandreddi Venkata Seshukumari; Jyothirmayi Tayi; Rajeshkhanna Bhuthkuri; Bhavani Madireddy; Jhansi Yellapu; Bodapati Venkata Rajanna; Nitalaksheswara Rao Kolukula; Siva Sairam Prasad Kodali; Jayasree Pinajala; James Stephen Meka; Chilakala Rami Reddy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp106-115

Abstract

Automated diagnostic systems are increasingly pivotal in advancing the accuracy and efficiency of medical diagnostics. Due to abnormal changes in human life and pollution, lung disease and cancer cases increasing in huge number. Identification and prediction of lung diseases may help to increase the human life span. This study introduces a robust framework for automatic lung disease detection using respiratory sound signals. The methodology brings together a series of activities like preprocessing, feature extraction, selection, and classification to improve diagnostic accuracy. The adaptive empirical stockwell-transform (AEST) is used to enhance the quality of the signal, whereby extracting and refining features, mainly Mel-frequency cepstral coefficients (MFCC), and Mel-spectrograms, are used. The scalable convolutional geyser network (SCGN) helps to mitigate challenges posed by imbalanced datasets, redundant features, and overfitting, ensuring reliable classification of the features. The model is validated when using the International Conference on Biomedical and Health Informatics (ICBHI) dataset, which validates the performance indicators of the model (F1-score 0.94, accuracy 0.95, precision 0.93, recall 0.94). This is shown superior performance compared to other existing models and demonstrates the framework's ability to diagnose a serviceable and reliable medical diagnosis; which indicates the strengths of combining advances in signal processing and scalable deep learning (DL) in healthcare applications.
Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems Bodapati Venkata Rajanna; Aaluri Seenu; Kondragunta Rama Krishnaiah; Anantha Sravanthi Peddinti; Nelaturi Nanda Prakash; Bandreddi Venkata Seshukumari; Giriprasad Ambati; Shaik Hasane Ahammad; Chakrapani Srivardhan Kumar; Allamraju Shubhangi Rao
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp257-274

Abstract

In the evolving landscape of energy systems, batteries play a critical role in enabling hybrid and stand-alone renewable energy storage solutions. Precisely estimating battery life and remaining useful operational life will go a long way in enhancing the efficiency of the system with assured reliability in smart power storage devices. This report comprehensively surveys advanced approaches in the management of batteries through state-of-the-art artificial intelligence tools-support vector machines, relevance vector machines (RVM), long short-term memory (LSTM) models, and bayesian filters-that are being used with a view to enhancing remaining useful life (RUL) estimates and making real-time system health monitoring capabilities possible. Modeling approaches surveyed include state estimation, capacity, and thermal management, while discussing their applicability to lithium-ion batteries. The review also explores publicly available battery datasets, feature engineering strategies, and hybrid diagnostic frameworks. A technoeconomic perspective is provided to assess system performance in renewable-integrated power grids. This paper aims to consolidate current knowledge, provide comparative insights into the strengths and limitations of different approaches, and highlight open research challenges to guide future developments in smart AI-enabled battery systems that support sustainable and resilient energy infrastructure.
Co-Authors Aaluri Seenu Allamraju Shubhangi Rao Anantha Sravanthi Peddinti Bhavani Madireddy Bodapati Venkata Rajanna Chakrapani Srivardhan Kumar Chilakala Rami Reddy Giriprasad Ambati James Stephen Meka Jayasree Pinajala Jhansi Yellapu Jyothirmayi Tayi Kondragunta Rama Krishnaiah Nelaturi Nanda Prakash Nitalaksheswara Rao Kolukula Rajeshkhanna Bhuthkuri Shaik Hasane Ahammad Siva Sairam Prasad Kodali