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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences
Pinajala, Jayasree
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Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Artificial intelligence-based multi-key security for protected and transparent medical cloud storage Bagadi, Ravi Kiran; Koraganji, Neelima Santoshi; Venkata Seshukumari, Bandreddi; Karuturi, Kavya Ramya Sree; Abotula, Sireesha; Rajanna, Bodapati Venkata; Annavarapu, Mahalakshmi; Kolukula, Nitalaksheswara Rao; Pinajala, Jayasree; Meka, James Stephen
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1241-1250

Abstract

Ensuring the security and privacy for the patient medical records and medical reports data is a crucial challenge as cloud-based healthcare technologies become more prevalent. For cloud-hosted medical data, internet of things (IoT) and artificial intelligence (AI) technologies shows best solutions for the challenges in the medical domain. This study suggests a Secure and Transparent Multi-Key Authentication Framework that makes use of AI. Using Z-score normalization, the framework first preprocesses the data before clustering to create a multi-level multi-key security structure. The physics-informed triangulation aggregation neural network (PITANN) model in the study reduces computation costs by minimizing overhead, ensuring secure handling of location-based and medical data for enhanced data classification and encryption effectiveness. A multi-key derivation of an elliptic curve, the ElGamal cryptography scheme is presented, which allows for safe multi-key encryption with little increase in the length of the ciphertext. This method guarantees safe, confidential access to cloud-hosted encrypted health information. An envisioned amalgamation improves flexibility by enhancing performance metrics such as speed of computation while safeguarding patient information through enhanced security measures and ensuring precise medical record integrity within virtual healthcare systems.
Artificial intelligence framework for multi-stage lung disease detection with audio signals Venkata Seshukumari, Bandreddi; Tayi, Jyothirmayi; Bhuthkuri, Rajeshkhanna; Madireddy, Bhavani; Yellapu, Jhansi; Rajanna, Bodapati Venkata; Kolukula, Nitalaksheswara Rao; Kodali, Siva Sairam Prasad; Pinajala, Jayasree; Meka, James Stephen; Rami Reddy, Chilakala
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.
Co-Authors Abotula, Sireesha Annavarapu, Mahalakshmi Bagadi, Ravi Kiran Bhuthkuri, Rajeshkhanna Karuturi, Kavya Ramya Sree Kodali, Siva Sairam Prasad Kolukula, Nitalaksheswara Rao Koraganji, Neelima Santoshi Madireddy, Bhavani Meka, James Stephen Rajanna, Bodapati Venkata Rami Reddy, Chilakala Tayi, Jyothirmayi Venkata Seshukumari, Bandreddi Yellapu, Jhansi