ANIMONI NAGARAJU

@mrvv.edu.in

PROFESSOR, DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING ( ARTIFICIAL INTELLIGENCE & MACHINE LEARNING )
MALLA REDDY TECHNICAL CAMPUS, MALLA REDDY VISHWAVIDYAPET, HYDERABAD, TELANGANA

ANIMONI NAGARAJU


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https://researchid.co/animoni
25 YEARS OF TEACHING EXPERIENCE, 16 YEARS OF RESEARCH AND ADMINISTRATION,

EDUCATION

PH.D - COMPUTER SCIENCE AND ENGINEERING - OSMANIA UNIVERSITY, HYDERABAD, INDIA

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition
6

Scopus Publications

13

Scholar Citations

2

Scholar h-index

Scopus Publications

  • Gradient Boosting Algorithm For Chronic Obstructive Pulmonary Disease (COPD) Risk Prediction In Machine Learning
    Dr. Animoni Nagaraju, Dr. T. Shekar Reddy, Dr. T. Shyam Prasad
    International Journal of Drug Delivery Technology, 2026
    Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition that poses a significant global health challenge. Early detection and risk prediction are essential to enable timely clinical intervention and reduce long-term complications. This project presents an intelligent COPD Risk Prediction System powered by machine learning and an interactive web interface for efficient risk assessment. The system is designed with flexibility— utilizing a single, high-accuracy model trained on comprehensive medical data, including demographic, lifestyle, and clinical parameters. Multiple algorithms, such as Logistic Regression, Random Forest, XGBoost, CatBoost, and Gradient Boosting, were evaluated using accuracy, confusion matrix, and classification reports. The best-performing model is deployed through a real-time Streamlit interface, allowing users to input their details and instantly receive COPD risk classification as Low, Medium, or High. This approach demonstrates the potential of combining predictive analytics with an accessible, user-friendly platform to support early COPD detection—especially in rural health centers, telemedicine services, and public screening programs where advanced diagnostic tools may be limited.
  • SMS Spam Detection Using XGBoost Algorithm in Machine Learning
    Dr. T. Shekar Reddy, Dr. Animoni Nagaraju, Dr. T. Shyam Prasad
    International Journal of Drug Delivery Technology, 2026
    The exponential growth of mobile communication has led to a surge in unsolicited and fraudulent text messages, commonly referred to as spam. These messages not only inconvenience users but can also pose security threats such as phishing attacks. This project focuses on developing an efficient SMS spam detection system using machine learning techniques to automatically classify messages as "spam" or "ham" (non-spam). A dataset of labelled SMS messages is preprocessed through techniques such as tokenization, stopword removal, and vectorization (TF-IDF). Multiple algorithms, including Naïve Bayes, Support Vector Machine (SVM), Decision Tree, and XGBoost, are evaluated, with XGBoost achieving the highest accuracy. The system aims to provide a lightweight, fast, and reliable solution that can be integrated into messaging platforms to enhance user security and reduce spam intrusion
  • Optimizing Moving Object Tracking in Sensor Networks: A Novel Approach with SLAM and GES Sensor-Based Filter
    R. Kabilan, R. Ravi
    Technological Applications for Smart Sensors Intelligent Applications for Real Time Strategies, 2025
    Monitoring the quality of the water is now a crucial step in determining the chemical and physical characteristics of the local water supply. Since conventional water quality monitoring systems are cumbersome and difficult to use to obtain values of water parameters in real-time operation, they are unable to quickly adjust to rapid changes at a distance. A new system has been put in place to monitor the polluted water quality by implementing new technologies like the Internet of Things (IoTs) and communication standards. IoT has drawn attention due to its ability to link every connected object to a global network using industry-standard communication protocols in order to gather data about physical objects in all real-time applications. As a result, the system’s implementation raises awareness and reduces the significant risk associated with the longdistance, low-cost spread of industrial water pollution. Additionally, we may view its historical data graphically, which aids in obtaining a general understanding of water quality.
  • Prognosticate of Gestational Diabetes Mellitus (GDM) to Anticipate Preeclampsia
    Srinivasa Reddy Seelam, Bala Veeravatnam, Veena Nanda Jaya Krishna, Mantri Gayatri, M V Kamal, Animoni Nagaraju
    Icrteect 2025 2nd International Conference on Recent Trends in Electrical Electronics and Computing Technologies, 2025
    This paper introduces a new way to predict Gestational Diabetes Mellitus (GDM) using deep learning, specifically Convolutional Neural Networks (CNNs), achieving 92% accuracy in identifying at-risk pregnancies. GDM is a condition that can cause serious complications for both mothers and infants, such as preeclampsia. Early prediction is especially important in places where regular checkups are limited. By analysing clinical data, including patient demographics and medical history, this approach enhances the understanding of risk factors and allows for timely interventions like dietary changes and blood glucose monitoring. Overall, this method represents a significant improvement in maternal care, especially for vulnerable populations, by providing high predictive accuracy and enabling proactive health measures to improve outcomes for mothers and babies.
  • Deep Reinforcement Learning for Low-Cost Humanoid Robot Soccer Players: Dynamic Skills and Efficient Transfer
    Animoni Nagaraju, M. Guru Vimal Kumar, Y.Rama Devi, A Basi Reddy, Marrapu Aswini Kumar, Ajmeera Kiran
    Proceedings of the IEEE International Conference Image Information Processing, 2023
    This research focused on whether Deep Reinforcement Learning (Deep RL) can enable a cheap, small humanoid robot to develop complex and secure movement skills, which can then be integrated into intricate behavioral strategies in dynamic environments. The humanoid robot, equipped with 20 actuated joints, was trained using Deep RL to participate in a skewed one-versus-one (1v1) soccer match. Initially, we taught the robot to separate skills before combining them in a self-play scenario. The final policy demonstrated robust and dynamic movement characteristics such as rapid fall recovery, walking, turning, kicking, and more, exceeding the logical expectations for the robot. The robot seamlessly transitioned between these skills in a stable and effective manner. Moreover, the agents developed an understanding of the game’s strategy, including predicting ball trajectories and countering opponent moves. Surprisingly, a limited number of straightforward rewards led to a wide range of behaviors. The agents underwent simulated training before being transferred to real robots. Despite unmodeled effects and differences between robot instances, successful transfer was assisted by using a combination of high-voltage control, concentrated dynamics selection, and training with disturbances in simulation. By introducing small hardware modifications and applying basic behavior regularization during training, the robots achieved secure maneuvers while maintaining dynamic and agile performance. Notably, the agents in the experiments demonstrated significant improvements over a programmed baseline, walking 156% faster, standing up 63% faster, and kicking 24% quicker, all while effectively combining their skills to achieve long-term goals.
  • Multi-resource Task Scheduling for Minimum Task Completion Time in Cloud Computing Using Credit-Based Assignment Problem
    Animoni Nagaraju, Y. Rama Devi
    Smart Innovation Systems and Technologies, 2021

RECENT SCHOLAR PUBLICATIONS

  • Optimizing Moving Object Tracking in Sensor Networks: A Novel Approach with SLAM and GES Sensor-Based Filter
    A Nagaraju, KH Krishna, S Pal, S Chamoli, A Bhange
    Technological Applications for Smart Sensors, 387-407 , 2025
    2025
  • Deep reinforcement learning for low-cost humanoid robot soccer players: Dynamic skills and efficient transfer
    A Nagaraju, MGV Kumar, YR Devi, AB Reddy, MA Kumar, A Kiran
    2023 Seventh International Conference on Image Information Processing (ICIIP … , 2023
    2023
    Citations: 6
  • Multi-resource Task Scheduling for Minimum Task Completion Time in Cloud Computing Using Credit-Based Assignment Problem
    A Nagaraju, Y Rama Devi
    Smart Computing Techniques and Applications: Proceedings of the Fourth … , 2021
    2021
  • Task Scheduling in Cloud Computing using Credit Based Cluster Travelling Salesman problem
    Animoni Nagaraju1,Dr.Y.Ramadevi2
    IOSR Journal of Engineering (IOSRJEN),ISSN (e): 2250-3021, ISSN (p): 2278 … , 2018
    2018
  • Unique ID Card Design for Personal Data Transaction
    A Lavanya, S Naveen, A Nagaraju
    2015
    Citations: 1
  • Two stage mutant cell growth through stochastic modeling
    PT Rao, K Madhavi, P Kalpana
    Global Journal of Mathematical Sciences: Theory and Practical 3 (2), 101-110 , 2011
    2011
    Citations: 5
  • TIME MINIMIZATION ASSIGNMENT PROBLEM WITH VARIANT OBJECTIVES-A LEXI SEARCH APPROACH
    A Nagaraju, VV Gopal, SN Pandit
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 6 (1), 87-98 , 2010
    2010
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Deep reinforcement learning for low-cost humanoid robot soccer players: Dynamic skills and efficient transfer
    A Nagaraju, MGV Kumar, YR Devi, AB Reddy, MA Kumar, A Kiran
    2023 Seventh International Conference on Image Information Processing (ICIIP … , 2023
    2023
    Citations: 6
  • Two stage mutant cell growth through stochastic modeling
    PT Rao, K Madhavi, P Kalpana
    Global Journal of Mathematical Sciences: Theory and Practical 3 (2), 101-110 , 2011
    2011
    Citations: 5
  • Unique ID Card Design for Personal Data Transaction
    A Lavanya, S Naveen, A Nagaraju
    2015
    Citations: 1
  • TIME MINIMIZATION ASSIGNMENT PROBLEM WITH VARIANT OBJECTIVES-A LEXI SEARCH APPROACH
    A Nagaraju, VV Gopal, SN Pandit
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 6 (1), 87-98 , 2010
    2010
    Citations: 1
  • Optimizing Moving Object Tracking in Sensor Networks: A Novel Approach with SLAM and GES Sensor-Based Filter
    A Nagaraju, KH Krishna, S Pal, S Chamoli, A Bhange
    Technological Applications for Smart Sensors, 387-407 , 2025
    2025
  • Multi-resource Task Scheduling for Minimum Task Completion Time in Cloud Computing Using Credit-Based Assignment Problem
    A Nagaraju, Y Rama Devi
    Smart Computing Techniques and Applications: Proceedings of the Fourth … , 2021
    2021
  • Task Scheduling in Cloud Computing using Credit Based Cluster Travelling Salesman problem
    Animoni Nagaraju1,Dr.Y.Ramadevi2
    IOSR Journal of Engineering (IOSRJEN),ISSN (e): 2250-3021, ISSN (p): 2278 … , 2018
    2018

Publications

55 PAPERS PUBLISHED

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

15 PATENTS

STARTUP

BEST IIC RESEARCH INNOVATOR AWARD 2025 RECIVED