Dr. Nagendra Panini Challa

Scopus Publications

Meta-CAMAF: A meta-learning enhanced context-aware multimodal alignment framework for explainable risk stratification in lung diseases
Subba Rao Dusari, Nagendra Panini Challa
Array, 2026
Dual-Branch Attention Fusion for Multimodal Sanskrit Script Classification
Basaraboyina Yohoshiva, Nagendra Panini Challa
Engineering Reports, 2026
Ancient Sanskrit manuscripts constitute an important source of historical and scientific knowledge, but their automated processing remains challenging due to script diversity, handwriting variability, image degradation, and limited annotated data. From an engineering perspective, these challenges demand robust and scalable solutions capable of operating under real‐world constraints. This paper presents a multimodal engineering framework for Sanskrit script classification that integrates complementary visual and textual information through an attention‐based fusion strategy. The proposed system adopts a dual‐branch architecture in which a transformer‐based visual module extracts hierarchical structural features from manuscript images, while a textual module captures linguistic representations derived from recognized script content. An attention‐driven fusion mechanism combines these heterogeneous feature streams into a unified representation, improving classification reliability across varying manuscript conditions. The framework is evaluated on a dataset of 2846 manuscript images encompassing three major Sanskrit scripts: Bengali, Devanagari, and Grantha. Experimental results demonstrate that the proposed approach consistently outperforms visual‐only, textual‐only, and conventional convolutional baselines in terms of accuracy, precision, recall, and F1‐score. The findings highlight the effectiveness of multimodal feature integration for engineering robust document analysis systems and support the development of automated tools for large‐scale manuscript digitization and archival applications.
HTCTA: A Hybrid Transformer Based Temporal Attention Mechanism for Early Diagnosis of Cardiovascular Abnormalities from PCG Signals
Bollapalli Althaph, Nagendra Panini Challa
International Research Journal of Multidisciplinary Technovation, 2026
Cardiovascular diseases are the leading cause of death across the world and responsible for about one third of all deaths. It is important to detect heart problems early and accurately, before serious damage can occur. Recording of the heart sounds, known as phonocardiograms (PCGs), is a non-invasive and inexpensive method for diagnosis. Nevertheless, the natural non-stationarity, noise and variability of PCG signals remain as a grand challenge for traditional DL methods. Although convolutional neural networks can effectively model the local features of spectrograms, it is difficult to model long-term dependencies in spectral feature maps. On the one hand, transformer based models may model temporal relationships but likely do not possess the capability to localize fine grain clinical patterns. To address the above problems, in this paper we introduce a Hybrid Architecture of Transformer-CNN with Temporal Attention (HTCTA). The model consists of CNNs in capturing localized time-frequency features, temporal attention to highlight diagnostically significant cardiac segments (e.g., systole or diastole), and Transformer encodings in pooling long-range dependencies over the heart cycle. Mel-spectrograms processed from heart sound recordings are forwarded through the hybrid model for classification. The proposed HTCTA model was tested over the two benchmark datasets, namely, PhysioNet CinC Challenge 2016 and CirCor DigiScope 2022. It reached a classification accuracy, precision, recall and F1-score of 94.70%, 94.20%, 95.15% and 94.67%, respectively, outperformed a number of state-of-the-art models, including Whisper-based as well as CRNN architectures. The model is moreover resistant to noise and variability between (auscultation) positions. Because of negligible difference between the reference and restored data, by virtue of being accurate, interpretable, efficient, HTCTA has potential in its real-time clinical diagnosis and medicine application. In the future, multimodal inputs will be incorporated and cross-patients validation will be performed to improve the generalization.
Recognition of Audio and Summarizing the Emotions
Challa Sanjan Shivadeep Reddy, Tanikonda Yasmitha, Tejeswara Reddy Annapureddy, Nagendra Panini Challa, A. Ajithram, et al.
Lecture Notes in Networks and Systems, 2026
Detecting Fraudulent Reviews: A Multi-algorithm Approach Using NLP and Machine Learning
Gavini Sreelatha, S. B. S. S. S. Vamsi Krishna, Gudditi Chetan, Kotha Lavanya, Nagendra Panini Challa, et al.
Lecture Notes in Networks and Systems, 2026
Explainable attention-based deep learning for classification and interpretation of heart murmurs using phonocardiograms
Bollapalli Althaph, Nagendra Panini Challa
Scientific Reports, 2025
BirdRecon: A free open source tool for image based bird species recognition
Hari Kishan Kondaveeti, Nabin Kumar Upadhaya, Dheeraj Sai Tukkugudam, Rahul Panigrahi, Sirivella Madhan Chandra Mouli, et al.
Ecological Informatics, 2025
Bilingual Handwritten Indian Language Translation
Boletim Da Sociedade Paranaense De Matematica, 2025
An Efficient CNN-Based Deep Learning Model with Ada-Belief Optimizer for Heart Disease Prediction
Boletim Da Sociedade Paranaense De Matematica, 2025
CAMAF: Context-Aware Multimodal Alignment Framework for explainable lung disease risk stratification
Subba Rao Dusari, Nagendra Panini Challa
Expert Systems with Applications, 2025
Multi Classification of ECG Signals based on Convolution and Residual Neural Networks for Effective Forecasting of Cardiovascular Disease
Kamepalli S L Prasanna, Nagendra Panini Challa, Nagaraju Jajam, Bollepalli Althaph, Venkata Sasi Deepthi Ch, et al.
International Research Journal of Multidisciplinary Technovation, 2025
Harnessing deep learning for the early detection of COVID-19 using chest X-rays
B. Narendra Kumar Rao, B. Bhaskar Kumar Rao, Nagendra Panini Challa
Big Data Analytics and Intelligent Applications for Smart and Secure Healthcare Services, 2025
Machine Learning Techniques Applied in Predictive Maintenance: A Review
P. Chenga Reddy, Karamala Naveen, Naveen Kilari, Nagendra Panini Challa
AI and Machine Learning for Mechanical and Electrical Engineering, 2025
Evaluating Optimization Algorithms for Enhanced Heart Disease Prediction Using Neural Networks
Beera Raju, Rajesh Kumar Kanji, Bollapalli Althaph, Nagendra Panini Challa
1st IEEE International Conference on Data Science and Intelligent Network Computing Icdsinc 2025, 2025
A Real-Time Automated System for Shrimp Quality Assessment Using YOLOv10
Sai Vennela Tipirneni, B. Nikhil Sri Harsha, Bollapalli Althaph, Beebi Naseeba, Nagendra Panini Challa
Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development Indiacom 2025, 2025
Pneumonia Disease Prediction UsingVGG19 Architecture
Govindgari Sai Srushik, Beebi Naseeba, Nagendra Panini Challa, Visalakshi Annepu, Chandana Gouri Tekkali
Lecture Notes in Networks and Systems, 2025
Metaverse for Indian palm leaf manuscripts
Basaraboyina Yohoshiva, Nagendra Panini Challa, Narendra Kumar Rao, Beebi Naseeba, Venkata Sasi Deepthi Ch
Engineering the Metaverse Enabling Technologies Platforms and Use Cases, 2025
Diabetic Prediction Using Deep Learning Techniques
B. Narendra Kumar Rao, Nagendra Panini Challa, S. Sreenivasa Chakravarthi, R. Ranjana, B. Bhaskar Kumar Rao
Lecture Notes in Networks and Systems, 2025
Next-Gen Quantum Computing—The Fusion of Atoms and Photonic Innovation
Quantum Computing the Past the Present and the Future, 2025
Gesture Assist: A Companion for Elderly with Gesture and Emotion Recognition
B. Narendra Kumar Rao, Nagendra Panini Challa, S. Sreenivasa Chakravarthi, J. Vellingiri, R. Ranjana, et al.
Lecture Notes in Networks and Systems, 2025
InsightBot: An Automated Text Summarization and Visualization Tool for Efficient Research Analysis
Beebi Naseeba, U. Sivaji, N. Purushotham, Ramisetti Umamaheswari, Nagendra Panini Challa, et al.
Lecture Notes in Networks and Systems, 2025
Enhancing Intent Classification in Airline Travel Information Systems Using NLP Models
A. Koteswara Rao, R. Subba Rao, Abbaraju Sai Sathwik, Beebi Naseeba, Nagendra Panini Challa, et al.
Lecture Notes in Networks and Systems, 2025
Metaverse for heart illness prediction and analysis
Bollapalli Althaph, Nagendra Panini Challa, Narendra Kumar Rao, Kamepalli SL Prasanna, Nagaraju Jajam, et al.
Engineering the Metaverse Enabling Technologies Platforms and Use Cases, 2025
Engineering the metaverse: Enabling technologies, platforms and use cases
Engineering the Metaverse Enabling Technologies Platforms and Use Cases, 2025
Improved Stochastic Gradient Descent-Decision Tree (ISGD-DT) Framework for Intelligent Heart Disease Prediction
Bollapalli Althaph, Nagendra Panini Challa
Ssrg International Journal of Electrical and Electronics Engineering, 2024

Dr. Nagendra Panini Challa

RESEARCH INTERESTS

Scopus Publications