Computer Science, Computer Science, Computer Networks and Communications, Multidisciplinary
18
Scopus Publications
161
Scholar Citations
6
Scholar h-index
5
Scholar i10-index
Scopus Publications
A Hybrid Energy and Proximity-Based Confined Clustering Scheme for Wireless Sensor Networks Mallikarjuna Swamy S, Sharmila N, Pavithra Goravi Sukumar, Chandana Chikkasiddaiah, Annegowda Manjunath Prasad Journal of Robotics and Control Jrc, 2026 Wireless Sensor Networks (WSNs) are essential for environmental monitoring, industrial automation, and smart infrastructure, but their performance is commonlylimited due to scarce node energy. Classic clustering algorithms, like LEACH (Low-Energy Adaptive Clustering Hierarchy) and HEED (Hybrid Energy-Efficient Distributed Clustering),exhibit biased cluster head (CH) selection, nonuniform energy distribution, and high communication overhead, which undermine network stability and lifetime in real-worlddeployments. In this research, we present the Energy- and Proximity-Constrained Confined Clustering Algorithm (EPCCCA), an dual-criteria CH selection protocol combiningresidual energy measures with proximity-based node aggregation to balance energy expenditure and distribute load throughout the network. In contrast to prior works, EPCCCAlimits clusters within optimized boundaries to further save intra-cluster communication energy, yet scales effectively. Experimental assessment on a 100-node, 100 m × 100 msimulation area reveals an average 0.24% boost to network lifetime and 0.18% decrease in average energy expenditure compared to LEACH and HEED, with statistical verificationestablishing confidence in observed benefits. The robustness of the algorithm is further determined through multiple simulation iterations with changing node densities and trafficloads, revealing consistent performance enhancements. Based on these results, EPCCCA stands as an efficacious, scalable, and energy-aware clustering solution for long-term WSNdeployments in energy-limited environments.
Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier H. N. Mahendra, V. Pushpalatha, V. Rekha, N. Sharmila, D. Mahesh Kumar, et al. Nature Environment and Pollution Technology, 2025 Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data.
An Assessment of Land Use Land Cover Using Machine Learning Technique V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, et al. Nature Environment and Pollution Technology, 2024 This research paper presents a comprehensive assessment of the built-up area in Mysuru City over the decade spanning from 2010 to 2020, employing advanced geospatial techniques. The study aims to analyze the spatiotemporal patterns of urban expansion, land-use dynamics, and associated factors influencing the city’s built environment. Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. The methodology involves the acquisition and preprocessing of multi-temporal satellite imagery to delineate and map the built-up areas at different time intervals. Land-use change detection techniques are employed to identify and quantify alterations in urban morphology over the specified period. Additionally, socio-economic and environmental variables are integrated into the analysis to discern the drivers of urban growth. The outcomes of this research contribute valuable insights into urbanization dynamics and land-use planning strategies, facilitating informed decision-making for sustainable urban development.
An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips Pavithra Goravi Sukumar, Modugu Krishnaiah, Rekha Velluri, Pooja Satish, Sharmila Nagaraju, et al. International Journal of Electrical and Computer Engineering, 2024 The deadlock-free and live lock-free routing at the same time is minimized in the network on chip (NoC) using the proposed adoptive reconfigurable routing protocol (ARRP). Congestion condition emergencies are avoided using the proposed algorithm. The input packet distribution process is improved among all its shortest paths of output points. The performance analysis has been initiated by considering different configuration (N*N) mesh networks, by sending various ranges of data packets to the network on chip. The average and maximum power dissipation of XY, odd-even, Dy-XY algorithm, and proposed algorithm are determined. In this paper, an analysis of gate utilization during data packet transfer in various mesh configurations is carried out. The number of cycles required for each message injection in different mesh configurations is analyzed. The proposed routing algorithm is implemented and compared with conventional algorithms. The simulation has been carried out using reconfigurable two-dimensional mesh for the NoC. The proposed algorithm has been implemented considering array size, the routing operating frequency, link width length, value of probability, and traffic types. The proposed ARRP algorithm reduces the average latency, avoids routing congestion, and is more feasible for NoC compared to conventional methods.
Augmented Reality Based on the Design and Development of an Interactive Virtual Museum S Keerthana, G Pavithra, Karthikeyan Jothikumar, Santhini K V, K S Kirthykaa 2024 1st International Conference on Data Computation and Communication Icdcc 2024, 2024 The Augmented Reality-Based Virtual Museum project aims to transform the traditional museum experience by utilizing AR technology to provide users with an immersive and interactive way to explore exhibits. Through AR-enabled devices like smartphones, users can view 3D models of artifacts in their real-world environment, allowing them to examine and interact with exhibits from any location. This project enhances learning by offering features such as rotating, zooming, and exploring objects in detail, along with media content like audio guides to provide deeper context. By making museums accessible remotely, the project opens up cultural and educational opportunities for users around the world. Developed using Unity and C#, with AR capabilities powered by ARCore, the Virtual Museum combines technology and education to create a dynamic, personalized museum experience, engaging users in new ways and offering a modern approach to learning about history, art, and science.
Intelligent Fault Detection and Prediction in Smart Grids Using Supervised Learning Model S M Usha, D Mahesh Kumar, M Kavitha, G S Pavithra, S Mallikarjunaswamy, et al. IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024 The integration of renewable energy sources and distributed power generation in smart grids has significantly increased the complexity of fault detection and prediction. Traditional methods like Supervisory Control and Data Acquisition (SCADA) and Rule-Based Detection (RBD) face challenges in processing the vast amounts of real-time data generated by modern grids, often leading to delayed responses and low fault detection accuracy. Furthermore, conventional techniques such as Fault Tree Analysis (FTA) are rule-dependent and lack adaptability to dynamic grid conditions, resulting in inefficient fault prediction and grid stability issues. To overcome these challenges, this paper introduces an Intelligent Fault Detection and Prediction Model (IFDPM), utilizing supervised learning techniques to enhance the detection and prediction of faults in smart grids. The IFDPM leverages machine learning algorithms to process real-time grid data, improving both the accuracy and speed of fault identification. By continuously adapting to the grid’s operational environment, the proposed model predicts faults before they occur, enabling timely preventive actions. Through extensive simulations and testing, the IFDPM has demonstrated a 0.25% improvement in fault detection accuracy and a 0.20% reduction in response time compared to conventional methods. This approach provides a more reliable and efficient framework for managing fault events in next-generation smart grids.
Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm H.S Kavitha, H Anu, M Komala, G S Pavithra, S Mallikarjunaswamy, et al. IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024 The integration of solar power into smart grids necessitates efficient solar tracking to maximize energy capture. Conventional methods like Single-Axis Trackers (SAT) and Dual-Axis Trackers (DAT) have limitations, including high maintenance costs, limited adaptability, and inefficiency under variable weather conditions. These drawbacks result in suboptimal energy generation, hindering the performance of smart grids. The proposed Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm (SGST-DRLA) addresses these issues by utilizing a Deep Reinforcement Learning (DRL) approach to dynamically optimize solar panel orientation. Unlike static control strategies of SAT and DAT, SGST-DRLA continuously learns from realtime data, adjusting the tracker’s position to maximize sunlight exposure and energy capture. This adaptive method enhances tracking precision and reduces operational costs. SGST-DRLA significantly improves performance metrics, achieving a $0.25 \\%$ increase in energy capture efficiency, a $\\mathbf{0. 2 0 \\%}$ reduction in operational costs, and a $\\mathbf{0. 3 0 \\%}$ improvement in adaptability to varying weather conditions compared to traditional methods. This approach not only optimizes solar tracking in smart grids but also supports sustainable and efficient renewable energy integration, making it a viable solution for overcoming the limitations of existing tracking systems.
Enhancing Stockpile Management Through Deep Learning with a Focus on Demand Forecasting and Inventory Optimization S Sheela, A P Latha, S Jyothi, H J Vidyarani, M Komala, et al. IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024 Stockpile management is essential for industries to efficiently meet customer demands while minimizing costs. Traditional methods like the Economic Order Quantity (EOQ) model and ARIMA often struggle with adaptability and accuracy, leading to inefficiencies such as overstocking or stockouts. This study proposes using Long Short-Term Memory (LSTM) networks to improve demand forecasting in stockpile management. LSTMs are designed to handle sequential data, capturing complex patterns and long-term dependencies that conventional methods miss. The LSTM-based approach achieved up to a $\\mathbf{0. 3 0 \\%}$ improvement in forecasting accuracy, resulting in more precise inventory control and better alignment with actual demand. The proposed system, termed Deep Inventory Management (DIM), incorporates LSTM models that adapt to changes in demand by considering factors such as seasonality and trends. This adaptive approach allows for proactive inventory predictions, enhancing decision-making in procurement and stock levels. Key abbreviations include LSTM (Long Short-Term Memory), EOQ (Economic Order Quantity), and DIM (Deep Inventory Management). By aligning inventory more closely with demand, this deep learning approach enhances operational efficiency, reduces costs, and improves customer satisfaction, making it a valuable advancement over traditional inventory management methods.
Hybrid Edge-Cloud Approach for Renewable Energy Management Using Deep Learning with Predictive Analytics V Rekha, N Sharmila, M Komala, G S Pavithra, S Mallikarjunaswamy, et al. IEEE International Conference on Recent Advances in Science and Engineering Technology Icraset 2024, 2024 The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational costs.This paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a $0.25 \\%$ improvement in energy utilization efficiency, a $\\mathbf{0. 2 0 \\%}$ reduction in operational costs, and a $0.30 \\%$ increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids.
An Efficient Machine Learning-Based Power Management System for Smart Grids Using Renewable Energy Resources B M Kavya, S Mallikarjunaswamy, N Sharmila, M Shilpa, M Komala, et al. 2nd IEEE International Conference on Networks Multimedia and Information Technology Nmitcon 2024, 2024 In the dynamic landscape of energy systems, integrating renewable energy resources into smart grids presents substantial challenges, notably due to the inherent variability of wind and solar power. This study unveils an advanced power management system for smart grids that harnesses machine learning algorithms (ApmSGML) to optimize energy distribution and storage, ensuring stability, efficiency, and environmental sustainability. This approach is distinguished from conventional methods such as Fixed Threshold Control and Rule-Based Scheduling, which often struggle to adapt to the unpredictable nature of renewable energy sources. By leveraging predictive models, the proposed system anticipates energy production and consumption patterns, facilitating proactive adjustments to maintain supply and demand equilibrium. Additionally, A comparison of the ApmSGML’s performance against traditional power management techniques like Deterministic Load Forecasting and Static Energy Storage Management, underscoring the adaptive and continuous learning advantages offered by machine learning through techniques like deep learning and reinforcement learning. Our findings highlight significant advancements in energy efficiency and cost reduction, demonstrating the transformative potential of integrating machine learning into the energy sector.
Development of smart data acquisition and monitoring system for maternal health care Test Engineering and Management, 2019
RECENT SCHOLAR PUBLICATIONS
A Hybrid Energy and Proximity-Based Confined Clustering Scheme for Wireless Sensor Networks N Sharmila, PG Sukumar, C Chikkasiddaiah, AM Prasad Journal of Robotics and Control (JRC) 7 (1), 3259-3270 , 2026 2026
An Intelligent Genetic Ant Colony Optimization Approach for Better Lifetime and Good Sensor Agents MS Jayashree, M Usha, GS Pavithra, K Aditya, IM Ramya, ... 2025 International Conference On Emerging Computation and Information … , 2025 2025
LULC classification for change detection analysis of remotely sensed data using machine learning-based random forest classifier HN MAHENDRA, V Pushpalatha, V Rekha, N Sharmila, DM Kumar, ... 2025 Citations: 1
An assessment of land use land cover using machine learning technique V Pushpalatha, HN Mahendra, AM Prasad, N Sharmila, DM Kumar, ... Nature Environment and Pollution Technology 23 (4), 2211-2219 , 2024 2024 Citations: 6
Smart grid solar tracking optimization using deep reinforcement learning algorithm HS Kavitha, H Anu, M Komala, GS Pavithra, S Mallikarjunaswamy, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 5
Hybrid edge-cloud approach for renewable energy management using deep learning with predictive analytics V Rekha, N Sharmila, M Komala, GS Pavithra, S Mallikarjunaswamy, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 6
Intelligent fault detection and prediction in smart grids using supervised learning model SM Usha, DM Kumar, M Kavitha, GS Pavithra, S Mallikarjunaswamy, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 4
Enhancing stockpile management through deep learning with a focus on demand forecasting and inventory optimization S Sheela, AP Latha, S Jyothi, HJ Vidyarani, M Komala, N Sharmila, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 8
Optimized crop prediction and monitoring using ensemble machine learning algorithms HS Kavitha, SM Usha, SN Sheela, H Anu, N Sharmila, ... 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 6
Enhancing crop yield and growth prediction using IoT-based smart irrigation systems and machine learning algorithms M Shilpa, P Ravi, N Sharmila, S Mallikarjunaswamy, BL Deepak, ... 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 20
An efficient machine learning-based power management system for smart grids using renewable energy resources BM Kavya, S Mallikarjunaswamy, N Sharmila, M Shilpa, M Komala, ... 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 16
An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips. PG Sukumar, M Krishnaiah, R Velluri, P Satish, S Nagaraju, ... International Journal of Electrical & Computer Engineering (2088-8708) 14 (1) , 2024 2024 Citations: 27
Improving Learning and Engagement in programming language classes with Design Thinking Framework PGS Chethana R Murthy 7th International Conference on Emerging Research Paradigms in Business … , 2024 2024
Land Use/Land cover (LULC) change classification for change detection analysis of remotely sensed data using machine learning-based random forest classifier HN Mahendra, V Pushpalatha, V Rekha, N Sharmila, DM Kumar, ... Nature Environment and Pollution Technology 24 (2), B4238-. , 2024 2024 Citations: 2
Comprehensive analysis on vehicle-to-vehicle communication using intelligent transportation system GS Pavithra, S Pooja, V Rekha, HN Mahendra, N Sharmila, ... International conference on soft computing for security applications, 893-906 , 2023 2023 Citations: 32
Personality Detection using Social Media Posts (Twitter) PGS Settara Pramod, Udeshya Giri, C Santhosh, Rao National Conference on “Recent Advancements in Computer Engineering” , 2022 2022
A Cluster-Based Routing Protocol for WSN Based on Mahalanobis Distance and AODV Protocol GS Pavithra, NV Babu International Journal of e-Collaboration (IJeC) 18 (1), 1-19 , 2022 2022 Citations: 2
An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area GS Pavithra, NV Babu Indian Journal of Science And Technology 14 (22), 1813-1828 , 2021 2021 Citations: 2
An Energy Efficient Optimal Model Using Social Spider Optimization Algorithm DBNV Pavithra G S Journal of System and Management Sciences 11 (3), 163-184 , 2021 2021 Citations: 1
Energy Efficient Hierarchical Clustering using HACOPSO in Wireless Sensor Networks BNV Pavithra G.S. International Journal of Innovative Technology and Exploring Engineering … , 2019 2019 Citations: 23
MOST CITED SCHOLAR PUBLICATIONS
Comprehensive analysis on vehicle-to-vehicle communication using intelligent transportation system GS Pavithra, S Pooja, V Rekha, HN Mahendra, N Sharmila, ... International conference on soft computing for security applications, 893-906 , 2023 2023 Citations: 32
An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips. PG Sukumar, M Krishnaiah, R Velluri, P Satish, S Nagaraju, ... International Journal of Electrical & Computer Engineering (2088-8708) 14 (1) , 2024 2024 Citations: 27
Energy Efficient Hierarchical Clustering using HACOPSO in Wireless Sensor Networks BNV Pavithra G.S. International Journal of Innovative Technology and Exploring Engineering … , 2019 2019 Citations: 23
Enhancing crop yield and growth prediction using IoT-based smart irrigation systems and machine learning algorithms M Shilpa, P Ravi, N Sharmila, S Mallikarjunaswamy, BL Deepak, ... 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 20
An efficient machine learning-based power management system for smart grids using renewable energy resources BM Kavya, S Mallikarjunaswamy, N Sharmila, M Shilpa, M Komala, ... 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 16
Enhancing stockpile management through deep learning with a focus on demand forecasting and inventory optimization S Sheela, AP Latha, S Jyothi, HJ Vidyarani, M Komala, N Sharmila, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 8
An assessment of land use land cover using machine learning technique V Pushpalatha, HN Mahendra, AM Prasad, N Sharmila, DM Kumar, ... Nature Environment and Pollution Technology 23 (4), 2211-2219 , 2024 2024 Citations: 6
Hybrid edge-cloud approach for renewable energy management using deep learning with predictive analytics V Rekha, N Sharmila, M Komala, GS Pavithra, S Mallikarjunaswamy, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 6
Optimized crop prediction and monitoring using ensemble machine learning algorithms HS Kavitha, SM Usha, SN Sheela, H Anu, N Sharmila, ... 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 6
Smart grid solar tracking optimization using deep reinforcement learning algorithm HS Kavitha, H Anu, M Komala, GS Pavithra, S Mallikarjunaswamy, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 5
Intelligent fault detection and prediction in smart grids using supervised learning model SM Usha, DM Kumar, M Kavitha, GS Pavithra, S Mallikarjunaswamy, ... 2024 International Conference on Recent Advances in Science and Engineering … , 2024 2024 Citations: 4
Land Use/Land cover (LULC) change classification for change detection analysis of remotely sensed data using machine learning-based random forest classifier HN Mahendra, V Pushpalatha, V Rekha, N Sharmila, DM Kumar, ... Nature Environment and Pollution Technology 24 (2), B4238-. , 2024 2024 Citations: 2
A Cluster-Based Routing Protocol for WSN Based on Mahalanobis Distance and AODV Protocol GS Pavithra, NV Babu International Journal of e-Collaboration (IJeC) 18 (1), 1-19 , 2022 2022 Citations: 2
An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area GS Pavithra, NV Babu Indian Journal of Science And Technology 14 (22), 1813-1828 , 2021 2021 Citations: 2
LULC classification for change detection analysis of remotely sensed data using machine learning-based random forest classifier HN MAHENDRA, V Pushpalatha, V Rekha, N Sharmila, DM Kumar, ... 2025 Citations: 1
An Energy Efficient Optimal Model Using Social Spider Optimization Algorithm DBNV Pavithra G S Journal of System and Management Sciences 11 (3), 163-184 , 2021 2021 Citations: 1
A Hybrid Energy and Proximity-Based Confined Clustering Scheme for Wireless Sensor Networks N Sharmila, PG Sukumar, C Chikkasiddaiah, AM Prasad Journal of Robotics and Control (JRC) 7 (1), 3259-3270 , 2026 2026
An Intelligent Genetic Ant Colony Optimization Approach for Better Lifetime and Good Sensor Agents MS Jayashree, M Usha, GS Pavithra, K Aditya, IM Ramya, ... 2025 International Conference On Emerging Computation and Information … , 2025 2025
Improving Learning and Engagement in programming language classes with Design Thinking Framework PGS Chethana R Murthy 7th International Conference on Emerging Research Paradigms in Business … , 2024 2024
Personality Detection using Social Media Posts (Twitter) PGS Settara Pramod, Udeshya Giri, C Santhosh, Rao National Conference on “Recent Advancements in Computer Engineering” , 2022 2022
