Madhavi karumudi

@klh.edu.in

Assistant Professor
Koneru Lakshmaiah Educational Foundation, Bachupally

Madhavi karumudi


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https://researchid.co/madhavi.karumudi

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Engineering, Computer Science
9

Scopus Publications

35

Scholar Citations

3

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • An Intelligent Decision Support Architecture for Clinical Environment based on Artificial Intelligence
    Vijay Keerthika, Bhaskar Mekala, Madhavi Karumudi, Hana Saeed, Debasmita Sahu, Pothu Juhi Sree
    Proceedings of 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2025, 2025
    Liver disease remains a major cause of global morbidity, often diagnosed at advanced stages due to the limitations of traditional and manual diagnostic methods. This study presents CareAssist, an AI-powered clinical decision support system designed to detect and stage liver fibrosis from ultrasound images. The system integrates Convolutional Neural Networks (CNN) for deep feature extraction with a Random Forest classifier for accurate stage prediction (F0–F4). Experimental evaluation on a publicly available liver ultrasound dataset achieved an accuracy of 89.2%, outperforming conventional models such as SVM (85.6%) and Logistic Regression (79.4%). CareAssist not only predicts fibrosis stages but also provides evidence-based preventive recommendations, enhancing decision-making for healthcare professionals. By enabling early detection and reducing diagnostic variability, CareAssist has the potential to improve patient outcomes, reduce clinical workload, and contribute to the broader adoption of AI in medical diagnostics.
  • An Explainable AI Approach to Alzheimer's Prediction using Ensemble Methods on Imbalanced Data
    Kande Archana, Mahesh Karre, K. Bharath Kumar, K. Balamurugan, Madhavi Karumudi, Bazani Shaik
    2025 2nd Asia Pacific Conference on Innovation in Technology Apcit 2025, 2025
    Alzheimer’s disease (AD) is a progressive neurodegenerative condition that necessitates early detection to enable effective interventions and slow its progression. This research presents an explainable machine learning approach using ensemble classifiers to predict Alzheimer’s risk based on demographic, clinical, cognitive, and lifestyle factors. Mutual Information (MI) was employed for feature selection, and models using Extra Trees, XGBoost, and AdaBoost classifiers were developed. The findings of this study reveal that ensemble methods, particularly XGBoost, significantly outperform individual models in predicting Alzheimer’s disease risk. The dataset, sourced from Kaggle, includes 2,149 patient records with 33 input features. After comprehensive preprocessing, which involved managing outliers, scaling, and oversampling for minority classes, the data was split into training and testing sets in an 80:20 ratio. Performance was evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. Among the models, XGBoost achieved the highest performance, with a test accuracy of 92.1%, a recall of 90.0%, and an ROC-AUC score of 96.7%, with AdaBoost and Extra Trees closely following. The confusion matrix indicates that XGBoost had the most true positives (271) and the fewest false negatives (30), highlighting its superior reliability in identifying positive AD cases. To enhance model transparency, SHAP (SHapley Additive Explanations) was used to analyze feature importance. SHAP summary and bar plots identified the top predictors. Overall, this study demonstrates the effectiveness of ensemble classifiers, especially XGBoost, in predicting Alzheimer’s and emphasizes the importance of Explainable AI for clinical trust. The use of SHAP improves interpretability, making the proposed system a practical and transparent tool for healthcare professionals aiming for early detection of Alzheimer’s risk.
  • Edge Computing for Real-Time Analytics in Embedded IoT Systems
    Raveendranadh Bokka, Naveen Kumar Pola, Madhavi Karumudi, P. Chinnasamy, Shofia Priyadharshini.D, Kavitha K
    2025 Global Conference on Information Technology and Communication Networks Gitcon 2025, 2025
    The increasing number of IoT devices requires efficient data processing methods because embedded systems need real-time analysis under limited resources. The conventional cloud-based analytics system introduces delays and increased communication strain that prevents them from serving time-dependent IoT applications. The research introduces an edge computing system for speedy analytics execution in IoT embedded structures which seeks to boost operational speed while decreasing answer duration. The data processing carries out efficiently using lightweight machine learning methods adopted throughout distributed edge nodes. The experimental outcome reveals decreased latency together with enhanced real-time choice-making performance than cloud-based systems. According to the findings edge computing demonstrates its ability to improve real-time analytics that has positive effects on smart healthcare and industrial automation and intelligent transportation systems. Research moving forward will aim at improving energy efficiency along with deployment scalability in extensive systems.
  • A TabNet-Based Deep Learning Approach for Cardiovascular Disease Prediction
    T. Aditya Sai Srinivas, B Sanjeev, Shaik Samsher, Madhavi Karumudi, Pilli Suneetha, S. Kaliappan
    Proceedings of International Conference on Sustainable Communication Networks and Application Icscn 2025, 2025
    Cardiovascular diseases (CVDs) remain the predominant cause of global mortality, responsible for around 17.9 million deaths per year. Accurate and prompt forecasting of cardiovascular disease risk is crucial for reducing mortality and enhancing patient treatment. This article presents a TabNet-Based deep learning system for predicting cardiovascular disease (CVD) utilizing advanced preprocessing techniques (Winsorization, Box-Cox, Normalization) and clinically pertinent feature engineering (BMI, MAP, PP). The model attained modest predictive efficacy (Accuracy 73%, ROC-AUC 0.73). Feature importance analysis identified cholesterol, systolic blood pressure, and age as the primary predictors. The paradigm, although interpretable, necessitates additional validation across several datasets to establish therapeutic usefulness. The proposed TabNet system efficiently amalgamates preprocessing, feature engineering, and explainable deep learning to enhance the early identification of cardiovascular diseases and promote transparent clinical decision-making. The use of domain-driven features improves predictive reliability and medical interpretability, establishing a robust basis for future integration into clinical decision support systems.
  • DYNAMIC PROGRAMMING-ENHANCED ENERGY-EFFICIENT TASK SCHEDULING IN EDGE-CLOUD ENVIRONMENTS
    Journal of Theoretical and Applied Information Technology, 2024
  • Achieving balance between innovation and security in the cloud with artificial intelligence of things: Semantic web control models
    Semantic Web Technologies and Applications in Artificial Intelligence of Things, 2024
  • MULTI-STRATEGY BASED FUZZY ENHANCED MONARCH BUTTERFLY OPTIMIZATION ALGORITHM (MS-FEMB0A) FOR TASK SCHEDULING AND LOAD BALANCING IN CLOUD ENVIRONMENTS
    Journal of Theoretical and Applied Information Technology, 2024
  • Efficient Workload Portability and Optimized Resource Utilization using Containerization in a Multi-Cloud Environment
    Saravanan M. S, Madhavi Karumudi
    Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics Icdici 2024, 2024
    Containerization has emerged as a pivotal technology in cloud computing, offering enhanced flexibility and efficiency in deploying applications across diverse cloud platforms. This paper explores the role of containerization in multi-cloud environments, focusing on its ability to facilitate workload portability and optimize resource utilization. This research work discusses various aspects such as the benefits of containerization, challenges encountered, and strategies for effectively leveraging containers in a multi-cloud setup. Through a comprehensive review of current practices and case studies, this study highlights how container orchestration tools like Kubernetes enable seamless deployment, scaling, and management of applications across different cloud providers. Additionally, examined the performance metrics and benchmarks used to evaluate the effectiveness of containerized solutions in achieving workload portability and resource efficiency. Finally, presented future research directions aimed at enhancing the integration of containerization technologies in multi-cloud environments.
  • IoT Applications in Smart Building Energy Management
    Thangjam Ravichandra, Madhavi Karumudi, M. Uma Maheswari, S. Leela, Thamizhkani. B, S.P. Kanniyappan
    Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024
    The technologies that are a part of the Internet of Things enable smart buildings to use energy more efficiently. The main focus of the research is on the use of internet-connected sensors and devices. Real-time monitoring, analysis, and management of energy consumption are the goals of this. Data is gathered from various sources, such as appliances, lighting, HVAC (heating, ventilation, and air conditioning), and other systems, employing networked systems. This offers the chance for a more effective distribution of energy. The goal of the project is to foresee energy patterns and automatically adjust settings by focussing on the integration of cloud computing, big data analytics, and machine learning algorithms. Furthermore, it highlights how the Internet of Things can help cut down on energy waste and improve sustainability by giving consumers advice and information on how to use less energy. Providing consumers with insights and recommendations helps achieve this. By promoting the adoption of more ecologically friendly energy practices, the implementation of these technologies not only reduces operating costs but also decreases the environmental impact that buildings have. The study's conclusions indicate that the field of smart buildings has advanced significantly, which may help to promote sustainability and higher energy efficiency.

RECENT SCHOLAR PUBLICATIONS

  • AI-Powered Assistive Technologies: Promoting Human Wellbeing through Smart and Autonomous Systems
    P Meenakshi, TAS Srinivas, AHS Magdalene, C Kate, M Saba, ...
    National Journal of Antennas and Propagation, 95-106 , 2026
    2026
  • A TabNet-Based Deep Learning Approach for Cardiovascular Disease Prediction
    TAS Srinivas, B Sanjeev, S Samsher, M Karumudi, P Suneetha, ...
    2025 International Conference on Sustainable Communication Networks and … , 2025
    2025
  • An Explainable AI Approach to Alzheimer’s Prediction using Ensemble Methods on Imbalanced Data
    K Archana, M Karre, KB Kumar, K Balamurugan, M Karumudi, B Shaik
    2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-8 , 2025
    2025
  • An Intelligent Decision Support Architecture for Clinical Environment based on Artificial Intelligence
    V Keerthika, B Mekala, M Karumudi, H Saeed, D Sahu, PJ Sree
    2025 3rd International Conference on Intelligent Cyber Physical Systems and … , 2025
    2025
  • Edge Computing for Real-Time Analytics in Embedded IoT Systems
    R Bokka, NK Pola, M Karumudi, P Chinnasamy
    2025 Global Conference on Information Technology and Communication Networks … , 2025
    2025
    Citations: 1
  • DYNAMIC PROGRAMMING-ENHANCED ENERGY-EFFICIENT TASK SCHEDULING IN EDGE-CLOUD ENVIRONMENTS
    DRS MS, MRSM KARUMUDI
    Journal of Theoretical and Applied Information Technology 102 (23) , 2024
    2024
  • IoT Applications in Smart Building Energy Management
    T Ravichandra, M Karumudi, MU Maheswari, S Leela, SP Kanniyappan
    2024 International Conference on Innovative Computing, Intelligent … , 2024
    2024
    Citations: 1
  • Stratification of Breast Cancer Images By Utilizing Spatial Information and Deep Learning Model
    DMSDAS RATHLAVATH KALAVATHI, DR SARAVANAN, MRS MS, ...
    Journal of Theoretical and Applied Information Technology 102 (7) , 2024
    2024
    Citations: 1
  • Efficient Workload Portability and Optimized Resource Utilization using Containerization in a Multi-Cloud Environment
    M Karumudi
    2024 5th International Conference on Data Intelligence and Cognitive … , 2024
    2024
    Citations: 3
  • Semantic Web Technologies and Applications in Artificial Intelligence of Things
    F Ortiz-Rodriguez, A Leyva-Mederos, S Tiwari, AR Hernandez-Quintana, ...
    IGI Global , 2024
    2024
    Citations: 3
  • MULTI-STRATEGY BASED FUZZY ENHANCED MONARCH BUTTERFLY OPTIMIZATION ALGORITHM (MS-FEMB0A) FOR TASK SCHEDULING AND LOAD BALANCING IN CLOUD ENVIRONMENTS
    DSMS Madhavi Karumudi
    Journal of Theoretical and Applied Information Technology 102 (5) , 2024
    2024
  • Achieving balance between innovation and security in the cloud with artificial intelligence of things: Semantic web control models
    R Sundar, PB Srikaanth, DA Naik, VP Murugan, M Karumudi, S Boopathi
    Semantic Web Technologies and Applications in Artificial Intelligence of … , 2024
    2024
    Citations: 16
  • Systematic Survey Of Scheduling And Load Balancing Methods In Cloud Computing
    DSMSM Karumudi
    UGC Care Group I Listed Journal 13 (17), 33-43 , 2023
    2023
  • Energy efficiency routing protocols in wireless sensor networks—A comparative study
    V Bhavana, J Rathi, KR Reddy, K Madhavi
    International Journal of Pure and Applied Mathematics 118 (9), 585-591 , 2018
    2018
    Citations: 10
  • VIDEO-BASED ABNORMAL DRIVING BEHAVIOUR DETECTION VIA DEEP LEARNING FUSIONS
    K SHALINI, B DHARANI, B VISHWES, M KARUMUDI
    2008

MOST CITED SCHOLAR PUBLICATIONS

  • Achieving balance between innovation and security in the cloud with artificial intelligence of things: Semantic web control models
    R Sundar, PB Srikaanth, DA Naik, VP Murugan, M Karumudi, S Boopathi
    Semantic Web Technologies and Applications in Artificial Intelligence of … , 2024
    2024
    Citations: 16
  • Energy efficiency routing protocols in wireless sensor networks—A comparative study
    V Bhavana, J Rathi, KR Reddy, K Madhavi
    International Journal of Pure and Applied Mathematics 118 (9), 585-591 , 2018
    2018
    Citations: 10
  • Efficient Workload Portability and Optimized Resource Utilization using Containerization in a Multi-Cloud Environment
    M Karumudi
    2024 5th International Conference on Data Intelligence and Cognitive … , 2024
    2024
    Citations: 3
  • Semantic Web Technologies and Applications in Artificial Intelligence of Things
    F Ortiz-Rodriguez, A Leyva-Mederos, S Tiwari, AR Hernandez-Quintana, ...
    IGI Global , 2024
    2024
    Citations: 3
  • Edge Computing for Real-Time Analytics in Embedded IoT Systems
    R Bokka, NK Pola, M Karumudi, P Chinnasamy
    2025 Global Conference on Information Technology and Communication Networks … , 2025
    2025
    Citations: 1
  • IoT Applications in Smart Building Energy Management
    T Ravichandra, M Karumudi, MU Maheswari, S Leela, SP Kanniyappan
    2024 International Conference on Innovative Computing, Intelligent … , 2024
    2024
    Citations: 1
  • Stratification of Breast Cancer Images By Utilizing Spatial Information and Deep Learning Model
    DMSDAS RATHLAVATH KALAVATHI, DR SARAVANAN, MRS MS, ...
    Journal of Theoretical and Applied Information Technology 102 (7) , 2024
    2024
    Citations: 1
  • AI-Powered Assistive Technologies: Promoting Human Wellbeing through Smart and Autonomous Systems
    P Meenakshi, TAS Srinivas, AHS Magdalene, C Kate, M Saba, ...
    National Journal of Antennas and Propagation, 95-106 , 2026
    2026
  • A TabNet-Based Deep Learning Approach for Cardiovascular Disease Prediction
    TAS Srinivas, B Sanjeev, S Samsher, M Karumudi, P Suneetha, ...
    2025 International Conference on Sustainable Communication Networks and … , 2025
    2025
  • An Explainable AI Approach to Alzheimer’s Prediction using Ensemble Methods on Imbalanced Data
    K Archana, M Karre, KB Kumar, K Balamurugan, M Karumudi, B Shaik
    2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-8 , 2025
    2025
  • An Intelligent Decision Support Architecture for Clinical Environment based on Artificial Intelligence
    V Keerthika, B Mekala, M Karumudi, H Saeed, D Sahu, PJ Sree
    2025 3rd International Conference on Intelligent Cyber Physical Systems and … , 2025
    2025
  • DYNAMIC PROGRAMMING-ENHANCED ENERGY-EFFICIENT TASK SCHEDULING IN EDGE-CLOUD ENVIRONMENTS
    DRS MS, MRSM KARUMUDI
    Journal of Theoretical and Applied Information Technology 102 (23) , 2024
    2024
  • MULTI-STRATEGY BASED FUZZY ENHANCED MONARCH BUTTERFLY OPTIMIZATION ALGORITHM (MS-FEMB0A) FOR TASK SCHEDULING AND LOAD BALANCING IN CLOUD ENVIRONMENTS
    DSMS Madhavi Karumudi
    Journal of Theoretical and Applied Information Technology 102 (5) , 2024
    2024
  • Systematic Survey Of Scheduling And Load Balancing Methods In Cloud Computing
    DSMSM Karumudi
    UGC Care Group I Listed Journal 13 (17), 33-43 , 2023
    2023
  • VIDEO-BASED ABNORMAL DRIVING BEHAVIOUR DETECTION VIA DEEP LEARNING FUSIONS
    K SHALINI, B DHARANI, B VISHWES, M KARUMUDI
    2008