A Multi-Label Fake News Detection Framework Using Undersampling and Transformer-Based Deep Learning Models Pratyush Ranjan Mohapatra, Jayadeep Pakala, Nagagopiraju Vullam, Vunnava Dinesh Babu, Naladi Ram Babu, P. Chandra Sekhar Reddy Proceedings of 5th International Conference on Communication Computing and Electronics Systems Iccces 2026, 2026 The rapid spread of misleading and fabricated information across social media platforms has intensified the need for robust fake news detection systems. Traditional binary classification methods are insufficient for real-world scenarios where news items often receive multiple credibility judgments. To address this challenge, this paper proposes a multi-label fake news detection framework that integrates undersampling strategies with advanced transformer-based deep learning models. The dataset used in this study consists of tweets annotated with multiple credibility ratings, enabling multi-label classification through annotator-based label vectors. Two undersampling techniques—One-vs-Rest (OVR) and MultiLabel Random Undersampling (MLRU)—are employed to mitigate the effects of label imbalance and improve detection of minority credibility classes. Classical machine-learning algorithms, deep learning architectures, and transformer models including BERT and RoBERTa are evaluated on both undersampled datasets. Experimental results demonstrate that transformer models consistently outperform traditional approaches, with RoBERTa achieving the highest performance across all evaluation metrics. Furthermore, the application of MLRU significantly improves class-wise recall in minority categories. These findings highlight the effectiveness of combining undersampling with transformer architectures for multi-label fake news identification in highly imbalanced realworld settings.
Integrated quantum-classical hybrid architectures for robust lung lesion segmentation in volumetric CT video data samples Sai Babu Veesam, Lalitha Kumari Pappala, Aravapalli Rama Satish, Sravan Kumar Chirumamilla, Vunnava Dinesh Babu, Shonak Bansal, Krishna Prakash, Mohamad A. Alawad, Mohammad Tariqul Islam Engineering Science and Technology an International Journal, 2026 Segmentation of lung lesions in volumetric CT data is crucial for the clinical aspects of diagnosis, therapy planning, and monitoring disease progression. Currently, deep learning applications are unable to model spatiotemporal coherency alongside anatomical consistency and uncertainty-aware refinement across sequential slices. In this study, we propose a hybrid quantum–classical framework that would accommodate multiple innovative modules. The architecture features a Quantum Latent Entanglement Consistency validator to establish spatiotemporal coherence across slices by maximizing von Neumann entropy. A Quantum-Classical Interventional Gradient Alignment ensures the harmony of gradients between classical CNN encoders and quantum discriminators. Further, the Temporal Quantum Attention for Boundary Stabilization captures the temporal context in the boundary refinement using controlled quantum gates. Alongside these, a Quantum-Enhanced Structural Similarity Feedback mechanism is proposed that exploits anatomical priors for retrofitting spatial lesion structures, as well as a Hybrid Quantum Adversarial Ensemble Validation, which provides confidence-aware validity through disagreement modeling. Collection and experimental evaluations over LIDC IDRI, NSCLC-Radiomics, and MosMedData datasets depict that the entirety of the systems significantly increases the Dice Similarity Coefficient by 5–7%, holds Hausdorff Distance lower at 10–12%, narrows down the over-segmentation errors by 8–10%, while reducing overall false positives near lung boundaries by 15% or even less. This represents a significant advancement toward fusing quantum learning with clinical-grade imaging pipelines, demonstrating clear improvements in segmentation stability, precision, and trustworthiness in real-world settings.
Sentiment classification for telugu using transformed based approaches on a multi-domain dataset Kannaiah Chattu, K. Adi Narayana Reddy, Sai babu veesam, Pardha Saradhi Chirumamilla, Vunnava Dinesh Babu, Krishna Prakash, Shonak Bansal, Mohammad Rashed Iqbal Faruque, K. S. Al-mugren Scientific Reports, 2025 Sentiment analysis is an essential component of Natural Language Processing (NLP) in resource-abundant languages such as English. Nevertheless, poor-resource languages such as Telugu have experienced limited efforts owing to multiple considerations, such as a scarcity of corpora for training machine learning models and an absence of gold standard datasets for evaluation. The current surge of transformed based models in NLP enables the attainment of exceptional performance in many different tasks. Nevertheless, researchers are increasingly interested in exploring the potential of transformed based models that have been pre-trained in several languages for various natural language processing applications, particularly for languages with limited resources. This research examines the efficacy of four pre-trained transformed based models, specifically IndicBERT, RoBERTa, DeBERTa, and XLM-RoBERTa, for sentence-level sentiment analysis in the Telugu language. Evaluated the performance of all four models using our dataset, "Sentikanna," which consists of numerous domain datasets for the Telugu language. We compared the performance of these models with three different datasets and observed a promising outcome. XLM-RoBERTa achieves a good accuracy of 79.42% for a binary sentiment classification. This work can be considered a reliable standard for sentiment analysis in the Telugu language.
A Novel Multistage Approach for Medicinal Plant Classification with Deep Learning Techniques Narayana Rao K, Srinivas Kalime, Sujatha P, Dinesh Babu Vunnava, Sushma S, Tulasi Krishna Sajja International Research Journal of Multidisciplinary Technovation, 2025 Accurate classification of medicinal plant images into high-level categories and specific sub-groups is essential for various applications, including agriculture, plant research, and conservation. This paper proposes a multi-stage deep learning approach to enhance the precision of medicinal plant image classification. In the first stage, known as Broad Classification, CNN and pre-trained models such as VGG16, ResNet50 and EfficientNetB0 are utilized to categorize images into high-level groups, including "Medicinal Plants," "Fruit-Related Plants," and "Flower-Related Plants." The model is fine-tuned using data augmentation techniques to ensure robust learning and generalization. In the second stage, referred to as Detailed Classification, separate models are trained for each high-level group to classify images into specific sub-groups within that category. The architecture of these models is adjusted to accommodate the unique number of classes in each sub-group. Each model undergoes training with optimized hyperparameters and is evaluated based on precision, recall, F1-score, and accuracy. The proposed multi-stage method demonstrates the ability to handle both broad and fine-grained medicinal plant classifications effectively, showcasing an improvement in classification performance over traditional single-stage models. This approach highlights the potential for deep learning to contribute to more precise and practical medicinal plant image classification solutions.
Modeling and forecasting of TEC using subspace-based SSA-LRF-ANN model J.R.K. Kumar Dabbakuti, Mallika Yarrakula, Dinesh Babu Vunnava, Gopi Krishna Popuri Geodesy and Geodynamics, 2025 Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks (ANN) that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications (EOA). This paper examines the fundamentals of subspace-based methods and explores the most promising algorithm for forecasting ionospheric signal delays, which was designed explicitly regarding signal and noise subspaces. The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis (SSA) significantly influences the implementation of Linear Recurrent Formula (LRF) and ANN models. The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System (GPS)– Total Electron Content (TEC) forecasts based on SSA. The GPS-derived TEC at Bangalore (13.02°N and 77.57°E) location grid during sunspot cycle 25 (2020) is considered for analysis. The SSA–LRF–ANN model demonstrates superior accuracy compared with the SSA–LRF, Autoregressive Moving Average (ARMA), and Holt–Winter (HW) models, achieving a correlation of 0.99, a Mean Absolute Error (MAE) of 0.55 TECU, a Mean Absolute Percentage Error (MAPE) of 7.06%, and a Root Mean Square Error (RMSE) of 0.75 TECU. Furthermore, the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA–LRF–ANN and its application.
Diabetes Prediction using Machine Learning and Deep Neural Models with Hybrid Resampling Techniques G.Kumari, UdayaLaxmi Aditya Teki, Budharaju VenkataVarma, Vunnava Dinesh Babu, Rangam Suresh, Naladi Ram Babu Proceedings of 5th International Conference on Soft Computing for Security Applications Icscsa 2025, 2025 Accurate diabetes prediction plays a vital role in early diagnosis and intervention, particularly in the context of growing global health concerns. However, medical datasets often suffer from class imbalance, where diabetic cases are significantly outnumbered by non-diabetic ones, limiting the effectiveness of traditional predictive models. This paper proposes a hybrid framework that integrates advanced resampling strategies with both machine learning and deep neural models to improve the detection of diabetes. The study explores three sampling methods SMOTE, ADASYN, and Borderline-SMOTE to address imbalance, and evaluates the performance of diverse classifiers including XGBoost, LightGBM, Naive Bayes, Random Forest, Gradient Boosting, 1D-CNN, and LSTM. The models were tested on original and balanced datasets, and assessed using accuracy, precision, recall, and F1-score. Results show that XGBoost consistently achieves the best predictive performance, attaining an F1-score of 85.0% and accuracy of 96.4% on the SMOTE-balanced dataset. Deep learning models, particularly 1D-CNN and LSTM, showed moderate but improved recall after resampling, confirming their applicability to structured medical data. Overall, the findings indicate that the proposed hybrid approach significantly enhances model sensitivity and offers a practical and scalable framework for early diabetes detection using imbalanced healthcare datasets.
An Efficient Spatio-Temporal Deep Learning Framework for Wildfire Detection Using Satellite Imagery A.V.S.Asha, G. Prasanthi, Nagagopiraju Vullam, Vunnava Dinesh Babu, A.Lakshmanarao, P Chandra Sekhar Reddy 2025 1st International Conference on Advancement in Futuristic Technologies Icaft 2025, 2025 Wildfires have emerged as a major environmental threat, causing severe ecological and economic damage across the globe. Timely detection is critical for preventing large-scale spread, yet traditional ground-based monitoring methods remain limited in coverage and response speed. This paper presents a spatio-temporal deep learning framework for wildfire detection using high-resolution satellite imagery. The proposed approach integrates spatial CNN models (CNN, VGG16, ResNet50, EfficientNet), temporal architectures (CNN–LSTM, ConvLSTM, 3D-CNN), and transformer-based models (ViT, Swin Transformer) to capture both localized fire signatures and long-range spatial dependencies. Experiments conducted on the Kaggle Wildfire Prediction Dataset, consisting of more than 40,000 labelled images, show that spatio-temporal networks outperform purely spatial models, with the ConvLSTM achieving an accuracy of 0.95. Transformer architectures deliver the best overall performance, with the Swin Transformer achieving 0.97 accuracy and demonstrating superior robustness across diverse wildfire scenes. These results confirm that combining spatial, temporal, and attention-based modeling provides a powerful and scalable solution for automated wildfire detection using satellite imagery.
Hybrid Autoencoder and Ensemble Deep Learning Model for Stroke Prediction Soujanya Thirunagari, A.V. Lalitha Devi, Pinninti Siva Deepthi, Vunnava Dinesh Babu, Vangapandu Venkata Kalyani, A. Lakshmanarao Proceedings of 6th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2025, 2025
Leveraging CNN and LSTM for Identifying Citrus Leaf Disorders Dasari Ashok, Neelam Mary Vijaya Nirmala, D Srilatha, K Venkateswara Rao, Vunnava Dinesh Babu, Shaik Johny Basha 2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
Stock market price prediction with efficient generative artificial intelligence with predictor network A Balaji, SS Babu, HK Deevi, VD Babu, VA Latha, P Srinivasarao Knowledge and Information Systems 68 (1), 158 , 2026 2026
News Article Classification Using Capsule Networks and Transformer-Based Contextual Embeddings S Jani, N Vullam, D Vikram, VD Babu, SB Vadde, ALA Lakshmanarao 2026 International Conference on Smart Futuristic Technology , 2026 2026
An Efficient Spatio-Temporal Deep Learning Framework for Wildfire Detection Using Satellite Imagery AVS Asha, G Prasanthi, N Vullam, VD Babu, A Lakshmanarao, ... 2025 1st International Conference on Advancement in Futuristic Technologies … , 2026 2026
A Multi-Label Fake News Detection Framework Using Undersampling and Transformer-Based Deep Learning Models PR Mohapatra, J Pakala, N Vullam, VD Babu, NR Babu, PCS Reddy 2026 5th International Conference on Communication, Computing and … , 2026 2026
A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic M K, VD Babu, M Ramkumar, M Ayyadurai, V K, HJ Aljawawdeh 2024 International Conference on Innovative Computing, Intelligent … , 2026 2026
A Climate-Aware Transformer Framework for Crop Yield Prediction using Satellite Imagery & Weather Data VD Babu 2026 International Conference on Electronics and Renewable Systems (ICEARS) , 2026 2026
CPS-Enabled Multimodal Health Monitoring Framework with AI-Driven Anomaly Interpretation for Intelligent Clinical Decision Support PKR Vunnava Dinesh Babu, Suresha D Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology 58 (1) , 2026 2026
Design of Flexible Polygon Shape Compact Patch Antenna with Slit for Biomedical Application VD Babu 2025 5th International Conference on Artificial Intelligence and Signal … , 2026 2026
A Robust Methodology Design for Removing Noise Content in Blurred and Deblurred Images Using Neural Optimization Principle VD Babu 2025 2nd International Conference on Artificial Intelligence and Knowledge … , 2026 2026
ENCS: A Novel Approach for Identifying Pneumonia Using Chest Radiographs Based on Enhanced Neural Classification Scheme VD Babu 2025 2nd International Conference on Artificial Intelligence and Knowledge … , 2026 2026
Efficient Cost Evaluation and Hybrid Optimization-Based Heterogeneous Resource Allocation in Cloud–Edge-IoT Environment VD Babu 2025 2nd International Conference on Artificial Intelligence and Knowledge … , 2026 2026
An Improved Cost-Effective Indoor Air Quality Prediction through Internet of Things Edge Network and Hybrid Model VD Babu 2025 2nd International Conference on Artificial Intelligence and Knowledge … , 2026 2026
An Intellectual Deep Resource Allocation with Task Scheduling for Semi-Synchronous Internet-Based Edge Computing Network VD Babu 2025 Tenth International Conference on Science Technology Engineering and … , 2026 2026
with Self-supervised Learning VS Desanamukula, P Sujatha, GP Kumar, VD Babu, K Polanki, AL Rao Computing and Machine Learning: Proceedings of CML 2025, Volume 1 1, 61 , 2026 2026
Integrated quantum-classical hybrid architectures for robust lung lesion segmentation in volumetric CT video data samples SB Veesam, LK Pappala, AR Satish, SK Chirumamilla, VD Babu, ... Engineering Science and Technology, an International Journal 73, 102272 , 2026 2026 Citations: 1
Deep Hybrid Attention Framework Combining CNN and Vision Transformers for Food Category Prediction VD Babu 2025 6th International Conference on IoT Based Control Networks and … , 2026 2026
Hybrid Autoencoder and Ensemble Deep Learning Model for Stroke Prediction VD Babu 2025 6th International Conference on IoT Based Control Networks and … , 2026 2026
Multi-Scale Transformer–CNN Fusion with Metric Learning for Medicinal Plant Identification N Vullam, J Pakala, D Vikram, VD Babu, NR Babu, PCS Reddy 2025 Seventh International Conference on Research in Computational … , 2025 2025
Hybrid Autoencoder and Ensemble Deep Learning Model for Stroke Prediction S Thirunagari, AVL Devi, PS Deepthi, VD Babu, VV Kalyani, ... 2025 6th International Conference on IoT Based Control Networks and … , 2025 2025
Deep Hybrid Attention Framework Combining CNN and Vision Transformers for Food Category Prediction P Karthi, J Pakala, N Vullam, VD Babu, NR Babu, PV Rao 2025 6th International Conference on IoT Based Control Networks and … , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
Three-stage multi-objective feature selection with distributed ensemble machine and deep learning for processing of complex and large datasets KM VUNNAVA DINESH BABU Measurement: Sensors 28, 6 , 2023 2023 Citations: 25
Dynamic deep learning algorithm (DDLA) for processing of complex and large datasets VD Babu, K Malathi 2022 Second International Conference on Artificial Intelligence and Smart … , 2022 2022 Citations: 23
A novel trust assessment system for online social networking environment using learning assisted classification model S Nithya, D Deepa, VD Babu, H Fawareh, RD Kayalvizhy 2024 International Conference on Innovative Computing, Intelligent … , 2024 2024 Citations: 22
Smart Telemedicine Using IoT by Integrating 5G and Block-Chain Techniques SL Choudhary, RS Dixit, D Das, KR Singh, VD Babu 2023 6th International Conference on Contemporary Computing and Informatics … , 2023 2023 Citations: 15
Large dataset partitioning using ensemble partition-based clustering with majority voting technique karunakaran malathi vunnava dinesh babu indonesian journal of electrical engineering and computer science 29 (2), 8 , 2023 2023 Citations: 9
Three-stage multi-objective feature selection for distributed systems KM vunnava dinesh babu SOFT COMPUTING 27 (3), 16 , 2023 2023 Citations: 9
Accurate classification of forest fires in aerial images using ensemble model CR Madhuri, SS Jandhyala, DM Ravuri, VD Babu Bulletin of Electrical Engineering and Informatics 13 (4), 2650-2658 , 2024 2024 Citations: 5
A Hybrid Multimodal Biometric Recognition System (HMBRS) based on Fusion of Iris, Face, and Finger Vein Traits VD Babu 2024 5th International Conference on Smart Electronics and Communication … , 2024 2024 Citations: 4
A Novel Approach to Farm Weather Prediction with Hybrid CNN, LSTM, and Attention Mechanisms vunnava dinesh babu 2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025 2025 Citations: 3
A Hybrid Model for Heart Disease Prediction using K-Means Clustering and Semi supervised Label Propagation VD Babu 2024 3rd International Conference for Advancement in Technology (ICONAT) , 2024 2024 Citations: 3
Integrated CNN and Recurrent Neural Network Model for Phishing Website Detection VD BABU 2024 3rd International Conference for Advancement in Technology (ICONAT) , 2024 2024 Citations: 3
An Automated Epilepsy Seizure Detection System (AESD) Using Deep Learning Models DB Vunnava, RB Popuri, RK Daruvuri, A B ieee xplore, 8 , 2023 2023 Citations: 3
An Efficient Model for Brain Tumor Classification Through Transfer Learning Approaches Y Chapala, R Sridivya, N Vullam, VD Babu, A Lakshmanarao, ... 2024 International Conference on Innovative Computing, Intelligent … , 2024 2024 Citations: 2
Integrated quantum-classical hybrid architectures for robust lung lesion segmentation in volumetric CT video data samples SB Veesam, LK Pappala, AR Satish, SK Chirumamilla, VD Babu, ... Engineering Science and Technology, an International Journal 73, 102272 , 2026 2026 Citations: 1
Food Classification Using a Hybrid Framework with Transfer Learning and Machine Learning Models VD Babu 2025 International Conference on Advances in Modern Age Technologies for … , 2025 2025 Citations: 1
A Novel Multistage Approach for Medicinal Plant Classification with Deep Learning Techniques VD Babu Int. Res. J. multidiscip. Technovation 7 (4), 16 , 2025 2025 Citations: 1
Integrated Transfer Learning and Traditional ML Model approach for Enhanced Medicinal Plant Recognition SR Yarakaraju, PS Chirumamilla, CM Pothula, VD Babu, BS Penmetsa, ... 2025 International Conference on Knowledge Engineering and Communication … , 2025 2025 Citations: 1
Designing Neuro-Inspired Architectures for Efficient Signal Processing P Baxi, S Asha, AR Nawadkar, VD Babu, N Raj, J Praveena 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 1
Implementation of 5G cloud based technique development using radio access type of networks M Almusawi, M Balakrishnan, VD Babu, SS Naveena, A Sharma, ... 2024 4th International Conference on Advance Computing and Innovative … , 2024 2024 Citations: 1
A Study on Mobile Banking Services with Special Reference to Ponmalai Area at Tricky R Buvaneswari, B Bharathi, P Babu, M Venkatesh, V Babu IOSR Journal of Business and Management 16 (4), 66-74 , 2016 2016 Citations: 1
