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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Rajyalakshmi Uppada
Aditya University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Machine learning-enabled joint antenna selection and precoding Monica Nilesh Kalbande; Kanala Sai Madhuri; M. Venkateswara Rao; Saradha Rani Sabbavarapu; Rajyalakshmi Uppada; Lakshmi Durga Rajamahendravarapu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2369-2376

Abstract

Joint antenna selection (AS) and precoding design is essential for improving spectral efficiency and energy efficiency in multi-antenna wireless communication systems. However, conventional optimization-based solutions rely on exhaustive search and iterative processing, leading to high computational complexity that limits real-time applicability. This work proposes a machine learning-enabled framework that shifts the computational burden from online operation to offline training. Optimal AS and precoding decisions are first generated offline using model-based optimization under diverse channel conditions. A supervised machine learning model is then trained to learn the relationship between channel state information (CSI) and optimal transmission configurations. During online operation, the trained model enables fast and efficient AS with significantly reduced processing time. Numerical results demonstrate that the proposed approach achieves near-optimal system performance while substantially lowering computational complexity, making it well suited for real-time and next-generation wireless communication systems.
FireDetXplainer: an explainable artificial intelligence framework for wildfire detection Janjhyam Venkata Naga Ramesh; Bhargavi Peddi Reddy; Jillellamoodi Naga Madhuri; Rajyalakshmi Uppada; Gaddam Venu Gopal; Rajesh Tulasi
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp576-585

Abstract

Wildfires pose significant environmental, ecological, and socioeconomic threats, necessitating rapid and reliable detection systems for timely emergency response and disaster mitigation. Recent advances in deep learning have substantially improved wildfire detection accuracy; however, most existing models operate as black-box systems, limiting transparency and reducing user trust in safety-critical applications. This study proposes FireDetXplainer (FDX), an explainable artificial intelligence (XAI) framework designed to enhance the interpretability of deep learning-based wildfire detection while maintaining high predictive performance. The proposed framework integrates convolutional neural network (CNN)-based image classification with explainability techniques to identify the visual regions that contribute most to wildfire detection decisions. By generating intuitive visual explanations, FDX enables users to understand, validate, and trust the model's predictions, thereby supporting transparent and accountable decision-making. Experimental evaluation demonstrates that the proposed framework effectively distinguishes wildfire images from non-fire scenes while providing meaningful visual interpretations that improve model transparency without compromising detection performance. The findings highlight the potential of explainable AI to strengthen the reliability, usability, and practical deployment of intelligent wildfire monitoring systems for environmental surveillance, disaster management, and early warning applications.
Detection of stages in diabetic retinopathy using computer aided ensemble network Rayudu Prasanti; Rajyalakshmi Uppada; Leela Kumari Balivada
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10407

Abstract

Diabetic retinopathy (DR) is one of the progressive micro vascular disorders of diabetes and a major cause of preventable blindness in the world. Manual ophthalmologist evaluation is costly in terms of time and more likely to have inter-observer error, whereas current automated methods tend to fail to differentiate between intermediate stages of DR; yet, proper assessment of DR severity is critical to its successful intervention. This paper suggests a framework of hybrid ensemble deep learning (DL) model that incorporates VGG16, InceptionV3 and ResNet50 based on a weighted feature fusion model and a feature-scaled parametric activation (FSPA) model to maximize inter-classes separability. The Kaggle EyePACS dataset containing 35,126 retinal fundus images was used. Image normalization, contrast enhancement, and data augmentation improved robustness to class imbalance. The overall accuracy of the proposed ensemble was 95.0% and the sensitivity and specificity were 92.0 and 94.0 respectively and the quadratic weighted Kappa (QWK) was 0.91, with up to +5% improvements in accuracy over individual convolutional neural network (CNN) baselines. Although there are still limitations to differentiating moderate versus severe DR, the findings demonstrate that the proposed framework enables consistent and reliable DR grading for large-scale clinical screening.
Co-Authors Bhargavi Peddi Reddy Gaddam Venu Gopal Janjhyam Venkata Naga Ramesh Jillellamoodi Naga Madhuri Kanala Sai Madhuri Lakshmi Durga Rajamahendravarapu Leela Kumari Balivada M. Venkateswara Rao Monica Nilesh Kalbande Rajesh Tulasi Rayudu Prasanti Saradha Rani Sabbavarapu