Dr. Rama Devi Burri

@iare.ac.in

Professor, Information Technology
Institute of Aeronautical Engineering

Dr. Rama Devi Burri


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https://researchid.co/dr.ramadevi

EDUCATION

Ph.D from Acharya Nagarjuna University

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science, Computer Science
17

Scopus Publications

80

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Effects of objects and image quality on melanoma classification using Spatio Temporal Joint graph Convolutional Network
    V. Suryanarayana, B. Prabhu Shankar, Rama Devi Burri, T. Priyanka, Ravi Kumar Saidala, et al.
    Biomedical Signal Processing and Control, 2025
  • Smart Card Fraud Detection using Ensemble Methods in Machine Learning
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Multi Fruit Classification and Grading Using Transfer Learning
    Korlapati Teja Arjun, Dindi Sai Swaroop, Gadamsetty Sai Puneeth, Rama Devi Burri, Ms. C S L Vijaya Durga
    2025 International Conference on Sustainability Innovation and Technology Icsit 2025, 2025
  • An NLP-Based Real-Time Cyberbullying Detection Model for Pre-Teens With Advanced Slang and Emoji Processing
    Ravi Kumar Saidala, Nagaraj S, Rama Devi Burri, K. Vijay Babu, Nisha Robin Rohit, et al.
    3rd International Conference on Emerging Computation and Information Technologies Icecit 2025 Book of Abstracts, 2025
  • Vague Strong Implicative Filters of Lattice Wajsberg Algebras
    Praveen Vardhan Kuppili
    Communications on Applied Nonlinear Analysis, 2024
    In this paper, we introduce the notation of a vague strong implicative filter of lattice wajsberg algebra. Also, we investigate some of its properties with illustrations. Further, we obtain the relation between vague implicative filter and anti vague strong implicative filter In lattice wajssberg algebra. Finally, we establish the equivalent condition of a vague strong implicative filter.
  • Enhancing Road Safety with Real-time Driver Drowsiness Detection Using Machine Learning
    Rama Devi Burri, Lalitha Aradhini Kusampudi, Shaik Mohammed Sharfuddin, Nandigam Venkata Siva Sai
    Proceedings of Inc4 2024 2024 IEEE International Conference on Contemporary Computing and Communications, 2024
    In these days there has been greater concern about the driver's drowsiness on road safety. According to the survey of the National Highway Traffic Safety Administration (NHTSA) a greater percentage of fatalities, injuries and even deaths every year is because of drowsy driving. So, there is an immediate necessity to implement a system which detects the drowsiness of the driver and alerts the driver. These systems, which rely on visual behaviour analysis, hold the potential to significantly decrease accidents by providing timely alerts when drivers exhibit signs of drowsiness. These systems make use of cameras and computer vision algorithms, such as the Haar cascade classifier and CNN. These systems scrutinize facial features, eye movements, and other indicators to assess levels of alertness and identify signs of drowsiness. The cameras that are integrated continuously capture facial expressions, enabling the evaluation of eyelid closure for the Eye Aspect Ratio (EAR) and Mouth aspect ratio (MAR) across frames. If predefined thresholds for EAR values are surpassed, an alert system triggers, notifying both the driver and passengers. The real-time detection of driver drowsiness, reliant on visual behaviour analysis, carries immense potential to save lives, curtail accidents, and enhance economic outcomes. By promptly alerting drivers to their drowsy state, these systems serve as crucial preventatives of accidents while promoting safer driving practices.
  • Supervised Deep Learning Methodology for Autonomous Vehicle Routing
    Ravi Kumar Saidala, Subrahmanya S Meduri, Anil Kumar Reddy Tetali, Rama Devi Burri, Umesharaddy Radder, et al.
    3rd International Conference on Advances in Computing Communication and Materials Icaccm 2024, 2024
    Automated driving is a revolutionary technology that is fundamentally changing worldwide transportation networks. An essential component of autonomous vehicle operation is path planning, which guarantees secure and effective navigation across intricate and ever-changing surroundings. This work presents an innovate neural network design that integrates deep Q-learning with policy gradients, therefore improving the current deep reinforcement learning paradigm. This methodology allows the self-driving vehicle to acquire knowledge and adjust to intricate situations using huge quantities of data, strict adherence to demanding global criteria. To verify the efficacy of our suggested approach, we performed simulations using both realistic and benchmark scenarios. The findings illustrate that our methodology greatly improves the vehicle's performance in difficult circumstances while upholding a commendable degree of safety. An analysis was conducted on important parameters like route completion time, energy usage, and adaptation to changing environmental circumstances. This approach demonstrates superior performance compared to both conventional and modern autonomous driving methods, therefore highlighting its capacity to propel the field of route planning in autonomous systems forward. The present study makes a valuable contribution to the advancement of autonomous driving technology by the introduction of an advanced deep learning framework that enhances path planning and decision-making capabilities.
  • Discovery of concealed patterns in skin cancer detection using deep learning techniques
    Ravi Kumar Saidala, Putta Srivani, Umesharaddy Radder, Rama Devi Burri, Prabhu Shankar B, et al.
    3rd International Conference on Advances in Computing Communication and Materials Icaccm 2024, 2024
    Cancer, known for its resilience, is the outcome of unregulated cellular proliferation in the human body, leading to the losses of numerous lives and causing significant concern within society. Among all types of cancer, the one that arises in the outermost layer of the skin, generally referred to as skin cancer, is most widespread. Machine learning has made significant contributions in the past, but these approaches have mostly depended on well-designed techniques. The introduction of the proposed model represents a significant advancement, since it effectively addresses the issue of feature extraction, either whole or partially, and significantly reduces the amount of effort required. The present study utilizes convolutional deep neural networks to analyse skin cancer using the publicly available ISIC dataset, emphasizing the criticality of early detection. Every individual machine learning model has inherent limitations, but the integration of these models leads to more reliable outcomes. Ensemble learning is the process of combining as many models as feasible, which leads to enhanced decision making and higher prediction accuracy. The objective of this work is to investigate the viability of combining the VGG, Caps Net, and Reset models for the purpose of cancer detection. As seen by the findings, the combined model outperforms individual models for each genre. The present work not only advances the technology used for the detection of skin cancer but also expands the potential for early diagnosis of other diseases.
  • Basic control system of gas turbine
    V. V. N. S. Tejaswi, V. B. V. N. Prasad, T. Rama Rao, Rama Devi Burri
    Aip Conference Proceedings, 2023
  • Green Marketing Analysis for significant Investment Utilizing NLP Process
    M Gopikrishna, Rama Devi Burri, Nellore Manoj Kumar, D. Mahesh, G. Siva Sankar, et al.
    2023 International Conference on New Frontiers in Communication Automation Management and Security Iccams 2023, 2023
    Machine learning techniques grounded on mathematical linguistics, such as Natural Language Processing, may detect shifts in public sentiment. This article explores the potential of using Latent Dirichlet Allocation (LDA) and natural language processing (NLP) methods to gauge the public’s rising concern for ecological and conservation issues. It will serve as the foundation upon which factories may base their eco-marketing choices. Massive expenditures are needed to refocus manufacturing on green commodities creation, market introduction of new ecological products, and promotion of energy-saving technology. Capturing the consistent demand from contractors and customers for decolonization is essential for generating a profit. The article contrasts the traditional data collection techniques (such as surveys) with the more modern machine learning techniques. Popularity, media coverage, and the variety of people actively engaged in the discussion are used to determine the importance of various data sources. The World Health Organization (WHO) has made the connection between pollution in the environment and deteriorating health. The author has identified foundational classes and terms that shape how people think about environmental marketing. This report uses NLP to gauge how people feel about ecological marketing concepts.
  • Recognition of Student Emotions in an Online Education System
    Sai Manvitha Enadula, Akshith Sriram Enadula, Rama Devi Burri
    2021 4th International Conference on Electrical Computer and Communication Technologies Icecct 2021, 2021
  • Some special characterstics of lattice ordered commutative loops
    V. B. V. N. Prasad, K. Prasad, M. Ramesh, R. D. Burri, T. Rama Rao
    Advances in Mathematics Scientific Journal, 2020
  • Research on sensation recognition using mobile phones
    International Journal of Engineering and Advanced Technology, 2019
  • Machine learning methods for software defect prediction a revisit.
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Snoezelen bubble tube – A therapy for the mentally challenged people
    International Journal of Recent Technology and Engineering, 2019
  • Insurance claim analysis using machine learning algorithms
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Classification model using genetic algorithm with correlated BPNN based artificial intelligent system
    Journal of Advanced Research in Dynamical and Control Systems, 2018

RECENT SCHOLAR PUBLICATIONS

  • Discovery of concealed patterns in skin cancer detection using deep learning techniques
    RK Saidala, P Srivani, U Radder, RD Burri, P Shankar B, AKR Tetali
    2024 International Conference on Advances in Computing, Communication and … , 2025
    2025
  • Supervised Deep Learning Methodology for Autonomous Vehicle Routing
    RK Saidala, SS Meduri, AKR Tetali, RD Burri, U Radder, GV Londhe
    2024 International Conference on Advances in Computing, Communication and … , 2025
    2025
  • An IoT Based Real Time Traffic Monitoring System.
    RD Burri
    Explainable IoT Applications: A Demystification. Information Systems … , 2025
    2025
    Citations: 2
  • Enhancing Road Safety with Real-time Driver Drowsiness Detection Using Machine Learning
    DBR Devi
    2024 IEEE International Conference on Contemporary Computing and … , 2024
    2024
    Citations: 2
  • Vague Strong Implicative Filters of Lattice Wajsberg Algebra
    RDB Praveen kumar, VBVN prasad
    Communications on Applied Nonlinear Analysis 31 , 2024
    2024
  • Effects of objects and image quality on melanoma classification using Spatio Temporal Joint graph Convolutional Network
    JCP V. Suryanarayana, B. Prabhu Shankar , Rama Devi Burri , T. Priyanka ...
    Biomedical Signal Processing and Control 101 , 2024
    2024
  • Recognition of emotions in an education system using MTCNN
    Second International Conference on Recent innovations in computer science … , 2024
    2024
  • Integrating Facial Recognition and Emotional Support to Enhance User Experience
    DBR Devi
    12 th International Conference on Contemporary Engineering and Technology 2024 , 2024
    2024
  • Green Marketing Analysis for significant Investment Utilizing NLP Process
    DBR Devi
    IEEE International Conference on new frontiers in communication, Automation … , 2024
    2024
  • Basic Control System of Gas Turbine
    DBR Devi
    AIP Conference Proceedings 2707, 1-7 , 2023
    2023
  • Real-Time Driver Drowsiness Detection Using Visual Behaviour and MTCNN Algorithm
    PDS Dr. B. Rama Devi, Swarupa, Tanmayee
    Advances in Intelligent Systems and Computing book series 1415, 913–921 , 2022
    2022
    Citations: 1
  • VEHICLE COUNT PREDICTION APPROACH BY USING MACHINE LEARNING METHODOLOGY
    DBR Devi
    IJFANS International Journal of Food and Nutritional Sciences 11 (4), 982-988 , 2022
    2022
  • USEFUL DECISION MAKING FOR COMMON STOCK SELECTION BY USING REGRESSION TECHNIQUES
    DBR Devi
    IJFANS International Journal of Food and Nutritional Sciences 11 (4), 987-992 , 2022
    2022
  • PREDICTION OF HYPERTENSION BY USING ARTIFICIAL NEURAL NETWORK SYSTEMS
    DBR Devi
    IJFANS International Journal of Food and Nutritional Sciences 11 (4), 997-1006 , 2022
    2022
  • Recognition of Student Emotions in an Online Education System
    ASE Dr. B. Rama Devi, Sai manvitha Enadula
    2021 Fourth International Conference on Electrical, Computer and … , 2021
    2021
  • Prediction Of Missing Child Using Machine Learning
    BAMMA Dr. B. Rama Devi, Ch. S. Sudha Sri
    Vidyabharati International Interdisciplinary Research Journal, 388-394 , 2021
    2021
  • Research on Sensation Recognition Using Mobile phones
    BSR O. Rama Devi, B. Rama Devi
    International Journal of Engineering and Advanced Technology 8 (8) , 2020
    2020
  • A Multi Resolution Convolution Neural Network Based Face Recognition Analysis
    DNRS Rama Devi Burri, A.Madhuri
    Journal Of Critical Reviews 7 (18), 12-18 , 2020
    2020
    Citations: 3
  • A note on Stone Spaces of Advanced Distributive Lattices
    RDB K. Prasad, V.B.V.N. Prasad, T.S. Rao, G. Balaji Prakash
    International Journal of Science and Technology 29 (5) , 2020
    2020
    Citations: 1
  • Fraud Detection on Smart Cards Using Machine Learning Algorithms
    BNB Dr. B. Rama Devi,K.Sri harsha, Y.Himaja
    Test Engineering and Management 83 (May- June 2020), 2495-2501 , 2020
    2020

MOST CITED SCHOLAR PUBLICATIONS

  • Insurance claim analysis using machine learning algorithms
    RD Burri, R Burri, RR Bojja, SR Buruga
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 55
  • Detection of Burr type XII Reliable Software Using SPRT on Interval Domain Data
    DGS Dr. R. Satya Prasad, , B. Ramadevi
    R. Satya Prasad et al, / (IJCSIT) International Journal of Computer Science … , 2015
    2015
    Citations: 5
  • Machine learning Methods for Software Defect Prediction a Revisit.
    R devi Burri Y.V.Raghava Rao V.B.V.N. Prasad
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 4
  • A Multi Resolution Convolution Neural Network Based Face Recognition Analysis
    DNRS Rama Devi Burri, A.Madhuri
    Journal Of Critical Reviews 7 (18), 12-18 , 2020
    2020
    Citations: 3
  • Assessing Burr Type XII software reliability for interval domain data using SPC
    BRDGS R.Satya Prasad
    Elixir International Journal 79, 30335-30340 , 2015
    2015
    Citations: 3
  • An IoT Based Real Time Traffic Monitoring System.
    RD Burri
    Explainable IoT Applications: A Demystification. Information Systems … , 2025
    2025
    Citations: 2
  • Enhancing Road Safety with Real-time Driver Drowsiness Detection Using Machine Learning
    DBR Devi
    2024 IEEE International Conference on Contemporary Computing and … , 2024
    2024
    Citations: 2
  • Some Special Characteristics of Atoms in Lattice Ordered Loops
    BRD R. Sunil Kumar,V. B .V .N. Prasad
    Test Engineering and Management 83 (March-April 2020), 5872 -5877 , 2020
    2020
    Citations: 2
  • Considering Residual Faults of Burr Type XII Software Reliability Growth Model
    DGS B.Rama Devi, Dr. R. Satya Prasad
    International Journal of Electronics and Computer Science Engineering 4 (2 … , 2015
    2015
    Citations: 2
  • Real-Time Driver Drowsiness Detection Using Visual Behaviour and MTCNN Algorithm
    PDS Dr. B. Rama Devi, Swarupa, Tanmayee
    Advances in Intelligent Systems and Computing book series 1415, 913–921 , 2022
    2022
    Citations: 1
  • A note on Stone Spaces of Advanced Distributive Lattices
    RDB K. Prasad, V.B.V.N. Prasad, T.S. Rao, G. Balaji Prakash
    International Journal of Science and Technology 29 (5) , 2020
    2020
    Citations: 1
  • Discovery of concealed patterns in skin cancer detection using deep learning techniques
    RK Saidala, P Srivani, U Radder, RD Burri, P Shankar B, AKR Tetali
    2024 International Conference on Advances in Computing, Communication and … , 2025
    2025
  • Supervised Deep Learning Methodology for Autonomous Vehicle Routing
    RK Saidala, SS Meduri, AKR Tetali, RD Burri, U Radder, GV Londhe
    2024 International Conference on Advances in Computing, Communication and … , 2025
    2025
  • Vague Strong Implicative Filters of Lattice Wajsberg Algebra
    RDB Praveen kumar, VBVN prasad
    Communications on Applied Nonlinear Analysis 31 , 2024
    2024
  • Effects of objects and image quality on melanoma classification using Spatio Temporal Joint graph Convolutional Network
    JCP V. Suryanarayana, B. Prabhu Shankar , Rama Devi Burri , T. Priyanka ...
    Biomedical Signal Processing and Control 101 , 2024
    2024
  • Recognition of emotions in an education system using MTCNN
    Second International Conference on Recent innovations in computer science … , 2024
    2024
  • Integrating Facial Recognition and Emotional Support to Enhance User Experience
    DBR Devi
    12 th International Conference on Contemporary Engineering and Technology 2024 , 2024
    2024
  • Green Marketing Analysis for significant Investment Utilizing NLP Process
    DBR Devi
    IEEE International Conference on new frontiers in communication, Automation … , 2024
    2024
  • Basic Control System of Gas Turbine
    DBR Devi
    AIP Conference Proceedings 2707, 1-7 , 2023
    2023
  • VEHICLE COUNT PREDICTION APPROACH BY USING MACHINE LEARNING METHODOLOGY
    DBR Devi
    IJFANS International Journal of Food and Nutritional Sciences 11 (4), 982-988 , 2022
    2022