Dr R Praneetha Sree has been doing research in proposing approaches and solutions to some problems based on the research in the fields of Data Mining, Machine Learning, and Data Science. She has proposed an tree based approach for Sequential Pattern Mining during her MTech at NIT Agartala. She has worked on four related problems on trajectory data at Ph.D. level at NIT Warangal.
Currently, Dr R Praneetha Sree has been working at Indian Institute of Technology Design and Manufacturing Kurnool as Assistant Professor in Computer Science & Engineering department. She has been guiding BTech, MTech students and Two PhD students on problems related to Data Mining, Machine Learning. She is chief coordinator on a worklet given by Samsung to IIITDM Kurnool and successfully reaching its final stage of completion of the worklet. She has experience in handling various kinds of data and applying various methods and techniques. She has been guiding independent research and capable of proposing the
Smart agriculture 5.0: blockchain and reinforcement learning synergy for multicropping optimization and traceable IoT-Enabled supply chains R. N. V. Jagan Mohan, Pravallika Sree Rayanoothala, R. Praneetha Sree Frontiers in Blockchain, 2026 Agriculture faces multifaceted challenges including climate variability, soil degradation, and supply chain inefficiencies, particularly for smallholder farmers practicing multicropping. This study systematically integrates blockchain technology for secure, transparent transactions with reinforcement learning (RL)-optimized Neutrosophic multi-regression for precise crop loss prediction in multicropping systems. Using real-world data from six crops (rice, banana, turmeric, elephant foot yam, coconut, cocoa), Neutrosophic multi-regression estimated losses with RL hyperparameter tuning, achieving superior prediction accuracy. A blockchain framework was developed for farmer validation, transaction security, and smart contract execution using Ethereum/Ganache. Results demonstrate 25%–35% reduction in predicted crop losses and enhanced supply chain traceability. This Smart Agriculture 5.0 framework advances Agriculture 4.0 through human-AI symbiosis and uncertainty modeling, addressing single-point failures, data privacy, and trust deficits for scalable sustainable farming Through this multidimensional approach, the study endeavors to not only enhance the productivity and sustainability of agricultural practices but also to foster resilience in the face of evolving challenges.
RDGT-GAN: Robust Distribution Generalization of Transformers for COVID-19 Fake News Detection U. Shivani Sri Varshini, R. Praneetha Sree, M. Srinivas, R. B. V. Subramanyam IEEE Transactions on Computational Social Systems, 2024 Social media platforms have become a vital source of information during the outbreak of the pandemic (COVID-19). The phenomena of fake information or news spread through social media have become increasingly prevalent and a powerful tool for information proliferation. Detecting fake news is crucial for the betterment of society. Existing fake news detection models focus on increasing the performance which leads to overfitting and lag generalizability. Hence, these models require training for various datasets of the same domain with significant variations in the distribution. In our work, we have addressed this overfitting issue by designing a robust distribution generalization of transformers-based generative adversarial network (RDGT-GAN) architecture, which can generalize the model for COVID-19 fake news datasets with different distributions without retraining. Based on our experimental findings, it is evident that the proposed model outperforms the current state-of-the-art (SOTA) models in terms of performance.
Next-gen agriculture: integrating AI and XAI for precision crop yield predictions R. N. V. Jagan Mohan, Pravallika Sree Rayanoothala, R. Praneetha Sree Frontiers in Plant Science, 2024 Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors. Using Exploratory Data Analysis (EDA), the study identifies temperature as the most critical factor influencing crop yields, with notable interactions observed between rainfall patterns and macronutrient levels. Advanced regression models, including Decision Tree Regressor, Random Forest Regressor, and LightGBM Regressor, achieved exceptional predictive performance, with R² scores reaching 0.92, mean squared errors as low as 0.02, and mean absolute errors of 0.015. Additionally, XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) enhanced the interpretability of the predictions, offering actionable insights into the relative importance of key features. These insights inform strategies for agricultural decision-making and climate adaptation. By integrating AI-driven predictions with XAI-based interpretability, this research presents a robust and transparent framework for mitigating the adverse effects of climate change on agriculture, emphasizing its potential for scalable application in precision farming and policy development.
IoT and ML based Smart Pill Dispenser (SPD) Application for Monitoring Elderly People Vindhya Avvari, Sugandha Yarlagadda, Bethi Pardhasaradhi, Praneetha Sree R., Y V Srinivasa Murthy Proceedings 2023 IEEE International Symposium on Smart Electronic Systems Ises 2023, 2023 With the increase in health issues these days, a majority of the people are adhered to medication. There might be a chance that most of them may forget to take their medications as prescribed due to a variety of factors such as busy schedule, mental stress, health issues, and so on. As a result, it may take longer to recover from illness and origin for side effects as well, especially for elderly. Henceforth, it is necessary that the patient must take the relevant medications in the correct dosage and at the correct time. In this paper, an approach has been proposed using internet of things (loT) that guides the elderly people to take the proper medication on time. The proposed smart pill Dispenser (SPD) system is economical and effective. Also, a web application is integrated that collects the information about the diabetes and heart disease of each patient. Further, machine learning models are embedded in order to predict the chance of diabetes, heart stroke, and kidney disease for the patient. This would be economical and effective model to dispense the medicines on time to the elderly people.
Recommending Music tracks based on Listener's Emotional State using various Architectures Rishitha G. M., Lakshmi Sahithi T., Vishnu V.R. K., Praneetha Sree R., Srinivasa Murthy Y. V. 2023 IEEE 20th India Council International Conference Indicon 2023, 2023 Everyone in the modern era is influenced by stress. Many health issues are developing as a result of stress. Most people are spending more money and undergoing treatment to lessen their stress. One of the best strategies for lowering stress is to listen to music. Therefore, it is essential to create a recommender system that could create personalized music collection using machine learning (ML) and deep learning (DL) algorithms based on the user’s current mood captured through a web camera. In the present popular artificial intelligence (AI) field, recognizing an individual’s emotions based on their facial expression is very much essential. The idea behind this paper is to recognise music and help the user by recognizing their emotions based on their facial expressions as music and emotion are strongly correlated. Music recommender systems (MRS) act as decision support systems that lessen information overload by only obtaining the content that is thought to be useful to listeners based on their predicted moods. The objective of this study is to perform a comparative study between five deep network architectures. The highest accuracy of 89.16% is achieved by Mobilenet architecture while the lowest accuracy of 85.81% is achieved by VGG16 architectures. Further, a music playlist is generated according to the emotion of the user using real-time detection using the most effective architecture.
MSMVAN: Multi Step Multi Variate Deep Attention Network for Renewable Energy Forecast U. Shivani Sri Varshini, R. Praneetha Sree, Murukessan Perumal, M. Srinivas, R. B. V. Subramanyam IEEE Transactions on Industry Applications, 2023 The importance of renewable energy in our everyday lives cannot be overstated, specifically highlighting solar and wind energies as two crucial sustainable power sources. Nevertheless, it is crucial to acknowledge that the amount of energy derived from renewable sources is subjected to variability, being influenced by environmental conditions. To ensure efficient management of energy load demands in diverse applications and enhance the performance of storage grids, precise energy production forecasting is vital. However, existing models doesn't recognise the important variables for forecasting and fails in the presence of noisy or missing data. To address these issues, we proposed a MultiStep Multi Variate Deep Attention Network (MSMVAN) model with masked attention and dilated Fourier convolutions. The proposed approach utilizes a sequential model to analyze non-linear relationships in time series data. Further, dilated fourier convolutions are incorporated to capture multistep spatial and cross correlations, while attention mechanisms are employed to identify significant variables. As a result, the model acquires improved vector representations, leading to enhanced energy forecasting power. We evaluated our model on three standard datasets and from the experimental results we conclude that our proposed model outperforms the state-of-the-art models in forecasting solar and wind energy.
DMCT-GAN: dynamic modeling of collision-free trajectory prediction using generative adversarial networks A Chennupati, PS Rayanoothala Knowledge and Information Systems 68 (1), 44 , 2026 2026
Smart Agriculture 5.0: Blockchain and Reinforcement Learning Synergy for Multicropping Optimization and Traceable IoT-Enabled Supply Chains DPSR RNV Jagan Mohan, Pravallika Sree Rayanoothala Frontiers in Blockchain - Sec. Blockchain in Industry 9 (https://doi.org/10 … , 2026 2026 Citations: 4
Systematic literature review of Blockchain-enhanced privacy in fog computing networks B Swathi, RP Sree Cluster Computing 28 (15), 943 , 2025 2025 Citations: 6
Next-gen agriculture: integrating AI and XAI for precision crop yield predictions RNV Mohan, PS Rayanoothala, RP Sree Frontiers in Plant Science 15, 1451607 , 2025 2025 Citations: 110
I-S FND: a novel interpretable self-ensembled semi-supervised model based on transformers for fake news detection S RBV Journal of Intelligent Information Systems 62 (2), 355-375 , 2024 2024 Citations: 7
MSMVAN: Multi step multi variate deep attention network for renewable energy forecast USS Varshini, RP Sree, M Perumal, M Srinivas, RBV Subramanyam IEEE Transactions on Industry Applications 60 (2), 2462-2470 , 2023 2023 Citations: 10
IoT and ML based Smart Pill Dispenser (SPD) Application for Monitoring Elderly People Vindhya, Sugandha, Pardhasaradhi, P Sree R, YVS Murthy. 2023 IEEE International Symposium on Smart Electronic Systems (iSES), 421-424 , 2023 2023 Citations: 3
Recommending Music tracks based on Listener’s Emotional State using various Architectures GM Rishitha, VRK Vishnu, R Praneetha Sree, YV Srinivasa Murthy 2023 IEEE 20th India Council International Conference (INDICON), 1287-1292 , 2023 2023 Citations: 2
Human Trajectory Forecasting Through Safety-Driven Negative Sampling P Alupula, R Praneetha Sree, K Prasanna, R Srivastava International Conference on Information and Management Engineering, 251-261 , 2023 2023
Rdgt-gan: Robust distribution generalization of transformers for covid-19 fake news detection USS Varshini, RP Sree, M Srinivas, RBV Subramanyam IEEE Transactions on Computational Social Systems 11 (2), 2418-2432 , 2023 2023 Citations: 29
INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network M Perumal, A Nayak, RP Sree, M Srinivas ISA transactions 124, 82-89 , 2022 2022 Citations: 30
A Novel Approach for Mining Time and Space Proximity-based Frequent Sequential Patterns from Trajectory Data R Praneetha Sree, DVLN Somayajulu, S Ravichandra Journal of Information & Knowledge Management 19 (04), 2050040 , 2020 2020 Citations: 3
A Study on Sequential Patterns - Survey and Research Issues R PraneethaSree National Conference on Recent Trends in Data Mining Warehousing-IEEE Vizag … , 2015 2015
MOST CITED SCHOLAR PUBLICATIONS
Next-gen agriculture: integrating AI and XAI for precision crop yield predictions RNV Mohan, PS Rayanoothala, RP Sree Frontiers in Plant Science 15, 1451607 , 2025 2025 Citations: 110
INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network M Perumal, A Nayak, RP Sree, M Srinivas ISA transactions 124, 82-89 , 2022 2022 Citations: 30
Rdgt-gan: Robust distribution generalization of transformers for covid-19 fake news detection USS Varshini, RP Sree, M Srinivas, RBV Subramanyam IEEE Transactions on Computational Social Systems 11 (2), 2418-2432 , 2023 2023 Citations: 29
MSMVAN: Multi step multi variate deep attention network for renewable energy forecast USS Varshini, RP Sree, M Perumal, M Srinivas, RBV Subramanyam IEEE Transactions on Industry Applications 60 (2), 2462-2470 , 2023 2023 Citations: 10
I-S FND: a novel interpretable self-ensembled semi-supervised model based on transformers for fake news detection S RBV Journal of Intelligent Information Systems 62 (2), 355-375 , 2024 2024 Citations: 7
Systematic literature review of Blockchain-enhanced privacy in fog computing networks B Swathi, RP Sree Cluster Computing 28 (15), 943 , 2025 2025 Citations: 6
Smart Agriculture 5.0: Blockchain and Reinforcement Learning Synergy for Multicropping Optimization and Traceable IoT-Enabled Supply Chains DPSR RNV Jagan Mohan, Pravallika Sree Rayanoothala Frontiers in Blockchain - Sec. Blockchain in Industry 9 (https://doi.org/10 … , 2026 2026 Citations: 4
IoT and ML based Smart Pill Dispenser (SPD) Application for Monitoring Elderly People Vindhya, Sugandha, Pardhasaradhi, P Sree R, YVS Murthy. 2023 IEEE International Symposium on Smart Electronic Systems (iSES), 421-424 , 2023 2023 Citations: 3
A Novel Approach for Mining Time and Space Proximity-based Frequent Sequential Patterns from Trajectory Data R Praneetha Sree, DVLN Somayajulu, S Ravichandra Journal of Information & Knowledge Management 19 (04), 2050040 , 2020 2020 Citations: 3
Recommending Music tracks based on Listener’s Emotional State using various Architectures GM Rishitha, VRK Vishnu, R Praneetha Sree, YV Srinivasa Murthy 2023 IEEE 20th India Council International Conference (INDICON), 1287-1292 , 2023 2023 Citations: 2
DMCT-GAN: dynamic modeling of collision-free trajectory prediction using generative adversarial networks A Chennupati, PS Rayanoothala Knowledge and Information Systems 68 (1), 44 , 2026 2026
Human Trajectory Forecasting Through Safety-Driven Negative Sampling P Alupula, R Praneetha Sree, K Prasanna, R Srivastava International Conference on Information and Management Engineering, 251-261 , 2023 2023
A Study on Sequential Patterns - Survey and Research Issues R PraneethaSree National Conference on Recent Trends in Data Mining Warehousing-IEEE Vizag … , 2015 2015
