SATURI , Pursuing Post doctoral from university of south Florida ,USA , completed PhD and M.Tech in computer science & engineering from University college of engineering, Osmania University Hyderabad, B.Tech in computer science & engineering from KITS, Warangal, Diploma in computer science & engineering from VMR polytechnic Warangal. Had 15+ years of experience in teaching & research, published 6 textbooks,6 patents and 40 research papers in national & international Scopus indexed, SCI journals and conferences. Presently working as Associate professor in computer science & engineering , Vignana Bharathi Institute of Technology, Aushapur, ghatkesar, Medchal (Dist). Editorial member for various journals and Reviewer for various IEEE & Springer conferences.
EDUCATION
Ph.D from Osmania University
RESEARCH, TEACHING, or OTHER INTERESTS
Cancer Research, Computer Engineering, Computer Science, Artificial Intelligence
Customer Churn Prediction for Retention Analysis Rajesh Saturi, Siripothula Rahul, Zuha Siddiqui, Rachamalla Nikhitha Security Intelligence in the Age of AI Navigating Legal and Ethical Frameworks, 2025 This abstract provides a comprehensive overview of the research on Customer Churn Prediction for Retention Analysis. In today's corporate context, understanding and mitigating customer churn has become critical for long-term success. This study focusses on the building and testing of predictive churn models aimed at forecasting customer attrition behaviour. Using advanced deep learning methods such as artificial neural networks (ANNs), the study examines past customer data to uncover trends and indications linked with attrition. It also investigates the integration of diverse information including customer involvement, contentment and transactional history to improve forecast accuracy. The proposed approach comprehends the heterogeneity of client bases and employs customer segmentation using the K-means algorithm to personalise retention strategies to distinct customer groups, detecting and addressing varied requirements and preferences. The project's unique feature is the inclusion of duration prediction for churn, which allows organisations to prioritise retention efforts based on the projected duration of churn for individual customers. In essence, the project aims to enhance the field of customer churn prediction and retention analysis by combining cutting-edge methodologies to apply targeted and timely retention measures, eventually nurturing customer loyalty and increasing the lifetime value of their customer base.
Quantum-enhanced customer retention: Leveraging predictive analytics for optimized supply chain strategies Rajesh Saturi, Rahul Siripothula, Zuha Siddiqui, Rachamalla Nikhitha Quantum Computing and Artificial Intelligence in Logistics and Supply Chain Management, 2025 In the era of digital transformation, customer churn presents a critical challenge that not only impacts revenue but also destabilizes supply chain operations. This chapter explores a quantum-enhanced predictive analytics framework aimed at improving customer retention strategies and optimizing supply chain management. By analyzing past customer data to uncover trends and indicators linked with attrition, the study integrates diverse information such as customer involvement, contentment, and transactional history to enhance forecast accuracy. The proposed approach leverages advanced machine learning techniques, such as Artificial Neural Networks (ANNs), to model customer behavior, while employing the K-means algorithm for customer segmentation. This allows businesses to tailor retention strategies to distinct customer groups, addressing varied requirements and preferences. Moreover, by integrating quantum computing for faster data processing, the framework significantly enhances the accuracy and efficiency of churn prediction, enabling real-time adjustments in supply chain strategies. The quantum advantage becomes particularly evident in processing complex customer behavior patterns and large-scale historical data, reducing computational overhead while maintaining high prediction accuracy. This quantum-assisted model not only helps businesses reduce customer churn and increase retention but also strengthens supply chain stability by anticipating demand fluctuations driven by customer behavior. The framework's implementation has demonstrated a 25% improvement in churn prediction accuracy compared to traditional methods while reducing processing time by 40%. These improvements translate to more effective customer retention strategies and optimized supply chain operations. Ultimately, the framework nurtures long-term customer loyalty, improves the lifetime value of the customer base, and ensures sustainable supply chain operations in today's competitive business landscape.
Customer churn prediction for retention analysis Rajesh Saturi, Siripothula Rahul, Zuha Siddiqui, Rachamalla Nikhitha Artificial Intelligence Technologies for Engineering Applications, 2025 This chapter provides a comprehensive overview of the research on Customer Churn Prediction for Retention Analysis. In today’s corporate context, understanding and mitigating customer churn has become critical for long-term success. This study focuses on the building and testing of predictive churn models aimed at forecasting customer attrition behavior. Using advanced deep learning methods such as Artificial Neural Network (ANN), the study examines past customer data to uncover trends and indications linked with attrition. It also investigates the integration of diverse information, including customer involvement, contentment, and transactional history to improve forecast accuracy. The proposed approach comprehends the heterogeneity of client bases and employs customer segmentation using the K-means algorithm to personalize retention strategies to distinct customer groups, detecting and addressing varied requirements and preferences. The unique feature of this chapter is the inclusion of duration prediction for churn, which allows organizations to prioritize retention efforts based on the projected duration of churn for individual customers. In essence, the chapter aims to enhance the field of customer churn prediction and retention analysis by combining cutting-edge methodologies to apply targeted and timely retention measures, eventually nurturing customer loyalty and increasing the lifetime value of their customer base.
Robust Data-Driven System for cloud Burst using Machine Learning Techniques Sridhar Reddy Vulapula, A. Prashanthi, Nareddy Sudha Rani, Raju Yeligeti, Rajesh Saturi Proceedings of 5th International Conference on Soft Computing for Security Applications Icscsa 2025, 2025 Flash floods are caused by cloud bursts. In many parts of the world, they represent a serious threat to both human life and infrastructure. Timely and accurate prediction of these extreme weather events is crucial for minimizing their devastating impact. The proposed system aims to develop a cloud burst prediction model tailored to a specific geographic region. The primary objective is to create a robust, data-driven system that leverages historical weather data, topographical information, and advanced machine learning techniques to provide early warnings and forecasts for cloud bursts. The proposed system involves comprehensive data collection, pre-processing, and feature engineering to make the data suitable for analysis. Various machine learning and statistical models like Random forest, support vector machine, logistic regression, A user-friendly interface is developed to make the predictions accessible to meteorological experts and the general public. Collaboration with local authorities and emergency response agencies will ensure that the predictions can be effectively integrated into decision-making processes and public safety measures.
Early Detection and Diagnosis of Cardiac Disorders using Machine Learning Techniques G Sudhakar, Subhadra Perumalla, Rajesh Saturi, Sridhar Reddy Vulapula, Yellgeti Raju, Swapna Saturi 6th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2025 Proceedings, 2025 Heart disease (HD) is a wide spread health concern that impacts a significant number of people worldwide. Common symptoms of heart disease (HD) include swollen feet, weakness in the body, and shortness of breath. The recent advancements in ML has made significant role in transforming different diagnostic approaches Due to a variety of factors, current diagnostic methods are not very effective in early detection. To offer a practical approach for identifying cardiac disease, scientists are trying to increase precision and execution time. Heart disease diagnosis and treatment can be challenging in areas with limited access to modern medical professionals and equipment. With the right diagnosis and care, many lives can be saved. According to estimations from the European Society of Cardiology, HD affects 26 million individuals. Nowadays, a higher proportion of individuals globally have heart failure, a complex clinical condition.ECG is the primary tool used by hospitals and cardiac centres to evaluate and diagnose heart failure in its early stages. One may think of the ECG as a standard tool. Early identification of heart disease is a major priority for medical services. This paper mainly highlights improving cardiac care through early diagnostic contributing to better patient outcomes and reduced health risks.
Detecting Alzheimer Disease in ADNI MRI Data Using CNN Algorithm Rajesh Saturi, Panuganti Hanumantha Rao, Yenugandhula Vishnu, Yannam Raghuveer, Yalla Sravan 2023 2nd IEEE International Conference on Measurement Instrumentation Control and Automation Icmica 2023, 2024 In response to the World Health Organization's unexpected revelation of over 50 million Alzheimer's disease (AD) cases across the globe, this study highlights the significance for early recognition, which is critical to effective AD medical treatment. The research presents an original method to examining MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), using a customized six-layered Convolutional Neural Network (CNN). This improved model attempts to improve accuracy and efficiency in detecting early AD indications as compared to traditional methods. The CNN's complicated architecture allows for the extraction and learning of complex patterns in MRI data that are frequently undetectable to human interpretation. In contrast to standard approaches such as 3D RESNET, which are known for their effectiveness but potential limits in early-stage sensitivity, the tailored CNN expects to beat these models in identifying subtle neuroimaging markers indicative of early.
Early Avalanche Detection using Ensemble Learning Techniques Atharva Bohini, Batta Sreeya, Chigurupati Pawan Sai, Rajesh Saturi, K. Priyabhashini 2024 2nd World Conference on Communication and Computing Wconf 2024, 2024 Avalanches are caused by changes in the balance of snowpack and present significant dangers to ecosystems and human communities. This study evaluates the efficiency of machine learning algorithms in predicting avalanches, with a focus on selecting features and evaluating models. Key elements such as snow depth, precipitation, temperature, and avalanche probabilities are integrated into the modeling process and further improved through correlation analysis. Performance indicators that includes accuracy, sensitivity, precision, etc, are utilized to assess the effectiveness of classifiers. Ensemble learners such as Random Forest (RF) and Gradient Boosting (GB), together with more recent methods like eXtreme Gradient Boosting (XGBoost)), can improve the ability of prediction. Feature engineering enhances model development by employing techniques such as class balancing and grid search to optimize performance. Evaluation entails comparing models like random forest, gradient boosting, and neural networks with variables such as slab, wet, and sum. Metrics including accuracy, recall, precision, Fl-score, and ROC curve are utilized for this purpose. The XGBoost technique enhances the performance of the gradient boosting model when compared to other factors. Future research may explore various techniques for different natural hazards, alternative hyperparameter optimization methods, and comparing probabilistic ML models. This study advances avalanche prediction understanding and highlights the potential of Machine Learning algorithms in disaster risk mitigation.
Blockchain Technology for Safeguarding Against Counterfeits Nareddy Sudha Rani, Anoosha Kaleru, Rajesh Saturi, V. Sridhar Reddy, M. Venkateswara Rao, Swapna Saturi 4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024 Blockchain technology has gained significant attention in recent years due to its transformative impact on financial transactions. However, its potential extends beyond the financial sector and can be applied to address various challenges, including counterfeiting. This study explores the application of blockchain technology in combating counterfeiting, examining existing anti-counterfeiting solutions and highlighting the key features of blockchain that make it well-suited for this purpose. The research findings emphasize that a comprehensive approach is necessary to effectively address counterfeiting, combining technological innovations with complementary strategies such as awareness campaigns, legal action, and robust alert systems. Blockchain technology offers several benefits, including improved product authentication, streamlined transactions, and increased stakeholder trust. Its transparency, immutability, and decentralized structure provide a strong foundation for combating counterfeiting and safeguarding consumer interests in the digital era. This study highlights the importance of combining technological advancements with complementary strategies to create a comprehensive and effective approach to combating counterfeiting. By leveraging blockchain technology, organizations can enhance product authenticity, streamline supply chains, and protect consumers from fake goods.
YouTube Video Analyzer Using Sentiment Analysis International Journal of Intelligent Systems and Applications in Engineering, 2024
Robust Data-Driven System for cloud Burst using Machine Learning Techniques SR Vulapula, A Prashanthi, NS Rani, R Yeligeti, R Saturi 2025 5th International Conference on Soft Computing for Security … , 2025 2025
Quantum-enhanced customer retention: Leveraging predictive analytics for optimized supply chain strategies R Saturi, R Siripothula, Z Siddiqui, R Nikhitha Quantum Computing and Artificial Intelligence in Logistics and Supply Chain … , 2025 2025 Citations: 2
Customer churn prediction for retention analysis R Saturi, S Rahul, Z Siddiqui, R Nikhitha 2025 Citations: 1
Early Detection of Alzheimer’s Disease Using Cognitive Features R Saturi, SG Sree Journal of Sensors, IoT & Health Sciences (JSIHS, ISSN: 2584-2560) 3 (1) , 2025 2025
Early detection and diagnosis of cardiac disorders using machine learning techniques G Sudhakar, S Perumalla, R Saturi, SR Vulapula, Y Raju, S Saturi 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025 Citations: 4
Blockchain Technology for Safeguarding Against Counterfeits NS Rani, A Kaleru, R Saturi, VS Reddy, MV Rao, S Saturi 2024 4th International Conference on Sustainable Expert Systems (ICSES), 376-381 , 2024 2024 Citations: 1
Machine Learning Techniques for Predicting Usage of Crack Cocaine Drug N Seluguri, SG Sree, R Saturi, MV Rao, VS Reddy, N Arjun 2024 8th International Conference on I-SMAC (IoT in Social, Mobile … , 2024 2024
Checking the Robustness of Code Using Mutation Testing R Saturi, R Kandakatla, CM Kumar, M Vinay International Conference on Trends in Sustainable Computing and Machine … , 2024 2024
Innovative Approaches for Early Alzheimer’s Disease Detection through Novel Analysis of Brain MRI Images R Gangula, A Manjula, R Bhukya, R Saturi, N Gayatri, R Rajesh International Journal of Maritime Engineering 1 (1), 761-772 , 2024 2024 Citations: 1
Early avalanche detection using ensemble learning techniques A Bohini, B Sreeya, CP Sai, R Saturi, K Priyabhashini 2024 2nd World Conference on Communication & Computing (WCONF), 1-12 , 2024 2024 Citations: 3
Detecting alzheimer disease in adni mri data using cnn algorithm R Saturi, PH Rao, Y Vishnu, Y Raghuveer, Y Sravan 2023 Second IEEE International Conference on Measurement, Instrumentation … , 2024 2024 Citations: 2
Road Traffic Condition Monitoring Using Deep Learning K Jelli, P Suma Poojitha, N Sai Puneeth Rao, R Saturi International Conference on Communications and Cyber Physical Engineering … , 2024 2024
YouTube video analyzer using sentiment analysis G Sushma, V Raju, R Kandakatla, NS Prabha, R Saturi, L Mohan Original Research Paper International Journal of Intelligent Systems and … , 2024 2024 Citations: 4
Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer’s disease using electroencephalogram JC Sekhar, C Rajyalakshmi, S Nagaraj, S Sankar, R Saturi, ... Journal of King Saud University-Computer and Information Sciences 35 (10 … , 2023 2023 Citations: 24
A Framework to Authenticate Signature using Machine Learning Technique R Saturi, DS Goud, GS Reddy, G Swamy, PVS Srinivas 2023 7th International Conference on I-SMAC (IoT in Social, Mobile … , 2023 2023
Histopathology Breast Cancer Classification Using CNN M Venkateshwara Rao, R Saturi, D Srinivas Goud, G Srikanth Reddy, ... International Conference on Image Processing and Capsule Networks, 539-550 , 2023 2023 Citations: 1
Multiple choice question generation using BERT XL net SA Lakshmi, R Saturi, A Bharti, M Avvari, B Bhavana EasyChair Preprints, 10299 , 2023 2023 Citations: 3
Segmentation of brain tumor images using morphological reconstruction R Saturi, A Alavala, B Baddam, K Chatlaparthy, P Hanumanth Rao International Conference on Information and Communication Technology for … , 2023 2023 Citations: 1
Histopathology breast cancer detection and classification using optimized superpixel clustering algorithm and support vector machine R Saturi, P Chand Parvataneni Journal of The Institution of Engineers (India): Series B 103 (5), 1589-1603 , 2022 2022 Citations: 11
A Novel Variant-Optimized Search Algorithm for Nuclei Detection in Histopathogy Breast Cancer Images R Saturi, P Prem Chand Smart Trends in Computing and Communications: Proceedings of SmartCom 2021 … , 2021 2021 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Data as a service (Daas) in cloud computing [data-as-a-service in the age of data] S Rajesh, S Swapna, PS Reddy Global Journal of Computer Science and Technology Cloud & Distributed 12 (11 … , 2012 2012 Citations: 37
Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer’s disease using electroencephalogram JC Sekhar, C Rajyalakshmi, S Nagaraj, S Sankar, R Saturi, ... Journal of King Saud University-Computer and Information Sciences 35 (10 … , 2023 2023 Citations: 24
Multi-Objective Feature Selection Method by Using ACO with PSO Algorithm for Breast Cancer Detection. R Saturi, P Premchand International Journal of Intelligent Engineering & Systems 14 (5) , 2021 2021 Citations: 18
Histopathology breast cancer detection and classification using optimized superpixel clustering algorithm and support vector machine R Saturi, P Chand Parvataneni Journal of The Institution of Engineers (India): Series B 103 (5), 1589-1603 , 2022 2022 Citations: 11
Data as a Service (Daas) in Cloud Computing [Data-As-A-Service in the Age of Data] Data as a Service Daas in Cloud Computing R Saturi Global Journal of Computer Science and Technology Cloud & Distributed 12 (11) , 2012 2012 Citations: 7
Early detection and diagnosis of cardiac disorders using machine learning techniques G Sudhakar, S Perumalla, R Saturi, SR Vulapula, Y Raju, S Saturi 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025 Citations: 4
YouTube video analyzer using sentiment analysis G Sushma, V Raju, R Kandakatla, NS Prabha, R Saturi, L Mohan Original Research Paper International Journal of Intelligent Systems and … , 2024 2024 Citations: 4
Implementation of efficient segmentation method for histopathological images R Saturi, PP Chand 2020 International conference on inventive computation technologies (ICICT … , 2020 2020 Citations: 4
Extracting Subset of Relevant Features for Breast Cancer to Improve Accuracy of Classifier R Saturi, R Dara, PP Chand IJITEE 8 (11) , 2019 2019 Citations: 4
Early avalanche detection using ensemble learning techniques A Bohini, B Sreeya, CP Sai, R Saturi, K Priyabhashini 2024 2nd World Conference on Communication & Computing (WCONF), 1-12 , 2024 2024 Citations: 3
Multiple choice question generation using BERT XL net SA Lakshmi, R Saturi, A Bharti, M Avvari, B Bhavana EasyChair Preprints, 10299 , 2023 2023 Citations: 3
A frame work to detect breast cancer using KNN and SVM,(in en) R Saturi, KVS Phani, PPP Chand, P PREM Eur J Mol Clinic Med 8 (3), 1432-1438 , 2021 2021 Citations: 3
Quantum-enhanced customer retention: Leveraging predictive analytics for optimized supply chain strategies R Saturi, R Siripothula, Z Siddiqui, R Nikhitha Quantum Computing and Artificial Intelligence in Logistics and Supply Chain … , 2025 2025 Citations: 2
Detecting alzheimer disease in adni mri data using cnn algorithm R Saturi, PH Rao, Y Vishnu, Y Raghuveer, Y Sravan 2023 Second IEEE International Conference on Measurement, Instrumentation … , 2024 2024 Citations: 2
A Novel Variant-Optimized Search Algorithm for Nuclei Detection in Histopathogy Breast Cancer Images R Saturi, P Prem Chand Smart Trends in Computing and Communications: Proceedings of SmartCom 2021 … , 2021 2021 Citations: 2
Recognizing the languages in WebPages—A framework for NLP S Rajesh, L Vandana, CA Carie, B Marapelli 2013 IEEE International Conference on Computational Intelligence and … , 2013 2013 Citations: 2
Customer churn prediction for retention analysis R Saturi, S Rahul, Z Siddiqui, R Nikhitha 2025 Citations: 1
Blockchain Technology for Safeguarding Against Counterfeits NS Rani, A Kaleru, R Saturi, VS Reddy, MV Rao, S Saturi 2024 4th International Conference on Sustainable Expert Systems (ICSES), 376-381 , 2024 2024 Citations: 1
Innovative Approaches for Early Alzheimer’s Disease Detection through Novel Analysis of Brain MRI Images R Gangula, A Manjula, R Bhukya, R Saturi, N Gayatri, R Rajesh International Journal of Maritime Engineering 1 (1), 761-772 , 2024 2024 Citations: 1
Histopathology Breast Cancer Classification Using CNN M Venkateshwara Rao, R Saturi, D Srinivas Goud, G Srikanth Reddy, ... International Conference on Image Processing and Capsule Networks, 539-550 , 2023 2023 Citations: 1
