Rayanoothala Praneetha Sree
@iiitk.ac.in
Assistant Professor , Department of CSE
Assistant Professor
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
EDUCATION
BTech - CSE - JNTU Hyd
MTech - CSE - NIT Agartala
P.hD - CSE - NIT Warangal
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Computer Science, Computer Engineering, Decision Sciences
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
U. Shivani Sri Varshini, R. Praneetha Sree, M. Srinivas, and R. B. V. Subramanyam
Institute of Electrical and Electronics Engineers (IEEE)
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.
Vindhya Avvari, Sugandha Yarlagadda, Bethi Pardhasaradhi, Praneetha Sree R., and Y V Srinivasa Murthy
IEEE
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.
Rishitha G. M., Lakshmi Sahithi T., Vishnu V.R. K., Praneetha Sree R., and Srinivasa Murthy Y. V.
IEEE
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.
Shivani Sri Varshini U, Praneetha Sree R, Srinivas M, and Subramanyam R.B.V.
Springer Science and Business Media LLC
Murukessan Perumal, Akshay Nayak, R. Praneetha Sree, and M. Srinivas
Elsevier BV
Rayanoothala Praneetha Sree, D. V. L. N. Somayajulu, and S. Ravichandra
World Scientific Pub Co Pte Lt
Trajectory Data have been considered as a treasure for various hidden patterns which provide deeper understanding of the underlying moving objects. Several studies are focused to extract repetitive, frequent and group patterns. Conventional algorithms defined for Sequential Patterns Mining problems are not directly applicable for trajectory data. Space Partitioning strategies were proposed to capture space proximity first and then time proximity to discover the knowledge in the data. Our proposal addresses time proximity first by identifying trajectories which meet at a minimum of [Formula: see text] time stamps in sequence. A novel tree structure is proposed to ease the process. Our method investigates space proximity using Mahalanobis distance (MD). We have used the Manhattan distance to form prior knowledge that helps the supervised learning-based MD to derive the clusters of trajectories along the true spreads of the objects. With the help of minsup threshold, clusters of frequent trajectories are found and then in sequence they form [Formula: see text] length Sequential Patterns. Illustrative examples are provided to compare the MD metric with Euclidean distance metric, Synthetic dataset is generated and results are presented considering the various parameters such as number of objects, minsup, [Formula: see text] value, number of hops in any trajectory and computational time. Experiments are done on available real-time dataset, taxi dataset, too. Sequential Patterns are proved to be worthy of knowledge to understand dynamics of the moving objects and to recommend the movements in constrained networks.