Computer Engineering, Multidisciplinary, Computer Networks and Communications, Artificial Intelligence
23
Scopus Publications
75
Scholar Citations
4
Scholar h-index
2
Scholar i10-index
Scopus Publications
REAL-TIME PATH REROUTING AND OBSTACLE AWARE NAVIGATION IN AUTONOMOUS VEHICLES: A SIMULATION AND DATA ANALYSIS APPROACH Narayana I Lakshmi, TMN Vamsi Journal of Engineering and Technology for Industrial Applications, 2026 Autonomous vehicle research has gained significant attention due to its potential in improving road safety, traffic efficiency, and intelligent mobility solutions. However, real-world testing remains costly and complex, making simulation-based models an effective approach for validating navigation and obstacle avoidance strategies. In this project, we present a simulation-driven autonomous vehicle framework capable of navigating between user-defined source and destination coordinates while ensuring real-time obstacle detection, dynamic rerouting, and journey visualization. The methodology integrates a virtual GPS for location tracking, an A*-based pathfinding algorithm enhanced with dynamic obstacle avoidance, and a simulation interface that allows users to input coordinates and visualize the entire navigation process. Camera-based or sensor-simulated modules are employed to detect obstacles in real time, triggering the rerouting logic to compute safe and collision-free alternative paths. Live data such as route progress, obstacle events, and estimated time of arrival are continuously displayed through the simulation dashboard. Following several iterations of testing, data logs were collected and analyzed using machine learning techniques to evaluate navigation efficiency, obstacle response time, and rerouting accuracy. Results demonstrate that the system successfully adapts to dynamic environments, offering a cost-effective and scalable platform for smart transportation research, algorithm benchmarking, and assistive mobility applications.
A LIGHTWEIGHT FEDERATED PREDICTION APPROACH FOR URBAN VRU MOVEMENT UNDERSTANDING IN AUTONOMOUS DRIVING Narayana I Lakshmi, T M N Vamsi Journal of Engineering and Technology for Industrial Applications, 2026 The growing use of autonomous vehicles in modern city transport systems shows that there is an urgent need for accurate short-term prediction of how pedestrians and cyclists will move, especially in mixed and crowded environments where movement intention, social interaction, and road layout keep changing, and this forms the main background and motivation of the study. Existing centralized learning techniques do not scale well because they face privacy rules, data ownership issues, and heavy communication requirements, which finally result in weak performance across different domains. To solve these difficulties, this research puts forward a new federated trajectory prediction approach that mixes onboard perception, lightweight tracking of detected objects, and a Social-LSTM prediction model that is improved using the FedProx algorithm, which becomes the main method contribution. The system first uses YOLO detection to find vulnerable road users, then uses SORT tracking to keep motion continuity, and trains the Social-LSTM locally while sharing only gradient updates for safe global aggregation without sharing raw sensing data. Experiments on ETH, UCY, SDD, and NuScenes datasets show reduced domain drift, better stability in different scenes, and improved ADE and FDE scores over centralized models, showing the results achieved. The concluding part states that this federated spatio-temporal learning system offers a scalable, privacy-safe, and ready-to-deploy solution for trajectory prediction, giving a new and practical step toward safer autonomous driving decisions.
INTEGRATED AUDIO–VISUAL EMERGENCY VEHICLE DETECTION FOR AUTONOMOUS VEHICLES WITH REAL-TIME RESPONSE T M N Vamsi, Lakshmi Narayana Journal of Engineering and Technology for Industrial Applications, 2025 In real traffic, change a lane safely mostly relies on the system’s ability to judge the distance to the car behind and in front of it. Applying rigid rules with specific limits frequently ends up being too restrictive and doesn’t help when making decisions about changing a lane. In this research, I propose a new system that learns from the context and improves it with reinforcement learning that makes it a more accurate and reliable system. To understand the lane change risks in the traffic at the moment I apply ResNet50 with transfer learning and enhance it with LSTM layers. To detect and track cars and also know what they might do I use Mask R-CNN with CNN and LSTM so that all these three things can be done by the model.Since the traffic conditions are always different I also apply an analysis of the weather, speed, acceleration, steering angle, and the road surface conditions as additional inputs to the system.To make the decisions safer I added a Double Deep Q-Network, which was found to be a steadier and faster to train than older reinforcement learning methods in heavy traffic conditions. From the simulation results, we can see that the check of the risks is clearer and more accurate, the decisions are better, and the change of a lane is smoother. So the system is more safe and reliable, and we move one step closer to the smarter transport systems.
Hybrid Lane Detection and Turn Prediction Framework using U-Net-based Lane Marking Visibility and Geometric Curve Analysis Lakshmi Narayana I, T. M. N Vamsi Proceedings of International Conference on Modern Sustainable Systems Cmss 2025, 2025 Autonomous driving systems rely heavily on accurate lane detection and turn prediction for safe and reliable navigation. However, faded, occluded, or inconsistent lane markings present significant challenges, especially under varying road conditions. To address these issues, this paper introduces a hybrid framework that integrates deep learning with classical vision techniques to enhance lane perception and directional understanding. U-Net, a convolutional neural network architecture, is employed to perform semantic segmentation for lane marking visibility, ensuring robust detection even in degraded scenarios. For straight lanes, a region of interest is extracted, and the Hough Transform is applied to identify solid and dashed lanes based on line continuity and slope filtering. Curved lane detection is achieved by generating a bird’s-eye view using homography, followed by binary and HSV thresholding to isolate white and yellow lanes. Polynomial fitting models the lane curvature, and the radius of curvature is computed accordingly. Turn direction is predicted by evaluating the sign of the highest-degree polynomial coefficient. Experimental validation demonstrates that the proposed framework improves both lane boundary detection accuracy and turn prediction reliability. This method provides a balanced trade-off between deep learning precision and classical algorithm efficiency, making it suitable for real-world autonomous vehicle applications.
Benchmarking India's Agricultural Performance: A Data Visualization Tool for High-Demand Crop Analysis T.M.N. Vamsi, Aditya Ganti, Lakshmojee Koduru Proceedings of 2025 6th International Conference on Communication Computing and Industry 6 0 C2i6 2025, 2025 India’s agricultural sector continues to face persistent challenges in meeting global production standards for several highdemand crops. This study aims to identify 25 such crops where India’s output significantly lags behind international benchmarks and to provide actionable insights through an interactive, datadriven visualization tool. Leveraging global datasets, such as data from United Nations Food and Agriculture Organization (UN FAO) spanning 1961 to 2023, the research employs advanced data wrangling and analysis techniques using Excel, Python, and SQL to perform a comprehensive gap analysis between India and leading agricultural nations. The methodology integrates climate, resource availability, and yield potential into a benchmarking framework to assess India’s relative performance. Power BI is used to visualize key findings in the form of dynamic dashboards, enabling stakeholders-such as policymakers, researchers, and agribusinesses-to make informed, evidence-based decisions. By highlighting regional inefficiencies and production deficits, the proposed tool not only fosters long-term agricultural planning but also supports India’s strategic goal of achieving agricultural self-sufficiency and competitiveness in the global market.
IoT-Enabled Unmanned Aerial Vehicle Monitoring System for Precision Agriculture: Integrating IEEE 802.15.4 based HyLaR-OF-M Routing Algorithm J.N.V.R. Swarup Kumar, Kuna Venkateswararao, T.M.N. Vamsi, Laxmi Sai Sriya Godavarthi, Veeramalla Gautam Sri Harsha Transportation Research Procedia, 2025 This research paper presents an approach to enhance efficiency in agriculture by combining advanced routing algorithms with Unmanned Aerial Vehicle (UAV) technology capabilities. By positioning the sensor nodes throughout the agricultural field, crucial factors like soil moisture, temperature, and nutrient levels are continuously monitored. The key innovation here lies in promoting a routing algorithm adapted from the Enhanced Latency Objective Function (OF) designed initially for Low Power and Lossy Networks (LLNs), cleverly repurposed for the specific needs of modern precision agriculture. The system considers factors like latency, connection quality, remaining energy, and congestion to optimize data routing and establish strong and dependable communication channels for tracking and alert purposes. The paper explains how this integrated system creates, implements, and evaluates the potential to enhance production methods and transform crop management practices. The practicality and effectiveness of this approach are supported by results that demonstrate its practicality and lead to reduced latency levels—a significant step forward in advancing farming techniques for today’s needs.
Eco Harvesting Using 5G Technology Kuna Venkateswararao, Saaketh Choudarapu, Tejas M. Modi, TMN Vamsi, I Sundara Siva Rao, J. N.V.R. Swarup Kumar 2025 International Conference on Computing Technologies and Data Communication Icctdc 2025, 2025 Agriculture has always been in the bigger picture regarding human sustenance, yet traditional farming methods struggle to meet the increasing global demand for food production. With modern technologies, precision agriculture has emerged as a revolutionary approach to optimizing farming practices. Our project presents a cutting-edge system integrating 5G networks, drones, and deep learning techniques to enhance crop health monitoring and disease detection. By leveraging convolutional neural networks (CNNs) and the ResNet152v2 model, the system accurately identifies crop diseases from aerial imagery, enabling early intervention. Incorporating IoT sensors and realtime data transmission via 5 G ensures seamless communication between components, allowing for precise and targeted pesticide application. This intelligent framework minimizes chemical overuse and enhances sustainability by reducing environmental impact. Furthermore, the proposed system enables large-scale monitoring with unprecedented accuracy, addressing limitations in traditional methods that rely on manual observation. By utilizing high-resolution drone imagery and AI-driven analysis, farmers can receive instant insights into crop conditions, enabling proactive decision-making. The integration of GPS-based mapping further ensures that treatments are applied only where necessary, reducing costs and preventing unnecessary chemical exposure to healthy crops. This holistic approach to smart farming improves yield and resource efficiency and aligns with global efforts to promote sustainable agricultural practices. By combining machine learning with high-speed connectivity, our project paves the way for a more intelligent, efficient agrarian system that maximizes productivity while promoting eco-friendly farming practices.
Unsupervised Detection of Groundwater Quality Anomalies via Autoencoder-LOF Ensemble T.M.N. Vamsi, Saaketh Choudarapu, Pratibha Lanka, G. Kamal, DNVSLS Indira, J. N.V.R. Swarup Kumar Proceedings of the 4th International Conference on Intelligent Computing Information and Control Systems Icoiics 2025, 2025 An unsupervised pipeline is introduced for detecting groundwater quality anomalies by combining an autoencoder (AE) with Local Outlier Factor (LOF) in the AE latent space. The pipeline targets multi-parameter hydrochemistry and operates without labels. Robustness is improved through deterministic training and a lightweight ensemble that averages LOF percentile ranks across nearby neighborhood sizes with MC‒dropout latent draws; an optional consensus with AE reconstruction error further suppresses spurious flags. Evaluation is conducted on three consecutive post-monsoon datasets from Telangana State, India (2018‒2020), comprising village-level measurements of pH, EC, TDS, major ions, hardness, SAR, and geospatial attributes. In the absence of ground-truth labels, performance is assessed using policy-aligned proxies: Stability Index under bootstrap resampling, Regulatory Violation Enrichment against BIS/WHO limits (EC, TDS, F, NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>), and separation in latent space (silhouette). The AE‒LOF ensemble attains near-perfect violation enrichment (≈1.00) and the highest separation (silhouette ≈0.70), with competitive stability after ensembling. Visualizations (latent projections, parameter distributions, geospatial maps) and case studies confirm salinity-, nitrate-, and mixed salinity-hardness anomalies that are actionable for water management. The approach is scalable, tunable via a single percentile threshold, and readily implemented in Python/Colab. The resulting anomaly watchlist supports prioritization of inspections and targeted interventions and is applicable to other environmental sensing contexts.
Learning processes for the internet of autonomous vehicles and intelligent transportation systems T. Mohana Naga Vamsi, L. Pratibha Wireless Ad Hoc and Sensor Networks Architecture Protocols and Applications, 2024 This chapter explores the learning processes within the Internet of Vehicles (IoV), intelligent transportation systems (ITS), and autonomous vehicles (AVs), emphasizing the essential role of machine learning and autonomous system architecture in advancing these technologies. It begins by detailing the application of machine learning in ITS, IoV, and AVs, highlighting its forms—supervised, unsupervised, and reinforcement learning—and their impact on traffic management, route optimization, and autonomous navigation through real-world examples. The significance of sensor networks in data collection for decision-making and safety is discussed, along with the essential elements of autonomous system architecture that enable vehicle autonomy, such as sensor fusion and safety mechanisms, illustrating the synergy of machine learning in autonomous decision-making. Further, the chapter addresses machine learning models suited for IoV, ITS, and AV applications, from traffic forecasting to self-driving technologies, underscoring their adaptability and effectiveness. It provides model training with IoV data, emphasizing methods for validating and enhancing model robustness to ensure successful deployment. Additionally, the narrative explains the roles of deep learning in image recognition, reinforcement learning in improving driving experiences, and federated learning in maintaining privacy in decentralized training. It acknowledges the challenges faced by these technologies, including security, technological constraints, and regulatory issues, while also highlighting their collective evolution toward more intelligent, adaptable, and efficient transportation systems. Conclusively, the transformative potential of integrating machine learning and autonomous system architecture in future transportation technologies is underscored, illustrating a vision for innovation-led, improved transportation ecosystems.
An IoT Based Smart Water Contamination Monitoring System M N Vamsi Thalatam, Pratibha Lanka, J.N.V.R. Swarup Kumar Proceedings of the 2023 International Conference on Intelligent Systems for Communication Iot and Security Iciscois 2023, 2023
An IoT based Smart Home with Virtual Assistant T.M.N. Vamsi, B. Suchitra, Sai Kumar, K.V.V. Varma, K.N.S.Harshit Kumar 2021 6th International Conference for Convergence in Technology I2ct 2021, 2021
Real-Time Path Rerouting and Obstacle Aware Navigation in Autonomous Vehicles: A Simulation and Data Analysis Approach L Narayana, TMN Vamsi ITEGAM-JETIA 12 (58), 334-341 , 2026 2026
A Lightweight Federated Prediction Approach for Urban VRU Movement Understanding in Autonomous Driving L Narayana, TMN Vamsi ITEGAM-JETIA 12 (57), 787-797 , 2026 2026
Integrated Audio–Visual Emergency Vehicle Detection for Autonomous Vehicles with Real-Time Response L Narayana, TMN Vamsi ITEGAM-JETIA 11 (56), 148-156 , 2025 2025
Unsupervised Detection of Groundwater Quality Anomalies via Autoencoder-LOF Ensemble TMN Vamsi, S Choudarapu, P Lanka, G Kamal, D Indira, JS Kumar 2025 International Conference on Intelligent Computing, Information and … , 2025 2025
Dynamic Risk-Aware Lane Change Decision-Making for Autonomous Vehicles Using Deep Contextual Learning L Narayana, TMN Vamsi 2025
Eco Harvesting Using 5G Technology K Venkateswararao, S Choudarapu, TM Modi, TMN Vamsi, ISS Rao, ... 2025 International Conference on Computing Technologies & Data Communication … , 2025 2025
Leveraging Federated Learning for Real-Time Disaster Response Optimization in Smart Cities Using Multi-modal Sensor Data L Koduru, SRC Polisetty, MNV Thalatam, Shubneet, AR Yadav, ... International Conference on Data Analytics & Management, 408-418 , 2025 2025
Hybrid Lane Detection and Turn Prediction Framework using U-Net-based Lane Marking Visibility and Geometric Curve Analysis I Lakshmi Narayana, TMN Vamsi 2025 International Conference on Modern Sustainable Systems (CMSS), 1194-1200 , 2025 2025
IoT-Enabled Unmanned Aerial Vehicle Monitoring System for Precision Agriculture: Integrating IEEE 802.15. 4 based HyLaR-OF-M Routing Algorithm JS Kumar, K Venkateswararao, TMN Vamsi, LSS Godavarthi, ... Transportation Research Procedia 84, 177-184 , 2025 2025 Citations: 1
Improving Text-Driven Image Synthesis: Diffusion Models for Photorealistic Outcomes TMN Vamsi International Journal of Computing 23 (4), 673-680 , 2024 2024
Learning processes for the internet of autonomous vehicles and intelligent transportation systems TMN Vamsi, L Pratibha Wireless Ad-hoc and Sensor Networks, 87-111 , 2024 2024 Citations: 3
Deep Learning–Based Surrounding Descriptor for the Visually Challenged MPBC T. Mohana Naga Vamsi, K. Ravi Shankar, G. Karthik Springer Proceedings in Mathematics & Statistics -ICDSAI 2023, 155–163 , 2024 2024 Citations: 1
IMPROVING REALISM IN face-swapping USING DEEP LEARNING AND K-MEANS CLUSTERING ISS Rao, JS Kumar, TMN Vamsi, TR Kumar, KR Prasad, KV Kumar Proceedings on Engineering 6 (4), 1751-1756 , 2024 2024 Citations: 1
Hybrid Movie Recommendation System Based on User Preferences and Item Similarity SSLDTB Sundara Siva Rao Ivaturi, Mohana Naga Vamsi Thalatam, Aravind Siva ... Springer Proceedings in Mathematics & Statistics 421, 671–681 , 2024 2024
A real-time v2i-based emergency vehicle traffic signals management system (evtms) JS Kumar, MNV Thalatam, ISS Rao, PK Ram, M Vignesh, P Manoj 2023 14th International Conference on Computing Communication and Networking … , 2023 2023 Citations: 4
Wireless Control System for Custom-Designed Application using IoT-Enabled Stepper Motor JS Kumar, BM Babu, P Manoj, M Vignesh, TMN Vamsi, R Alekhya 2023 International Conference on Sustainable Computing and Smart Systems … , 2023 2023
Hybrid movie recommendation system based on user preferences and item similarity SSR Ivaturi, MNV Thalatam, ASK Bugatha, SS Ladi, DT Botta XVIII International Conference on Data Science and Intelligent Analysis of … , 2023 2023 Citations: 2
A Mobile App for Age and Gender Identification Using Deep Learning Technique JS Kumar, BM Babu, M Vignesh, P Manoi, TMN Vamsi, G Kamal 2023 International Conference on Intelligent Systems for Communication, IoT … , 2023 2023 Citations: 1
An IoT based smart water contamination monitoring system MNV Thalatam, P Lanka, JS Kumar 2023 International Conference on Intelligent Systems for Communication, IoT … , 2023 2023 Citations: 17
A review on experimental analysis on hybrid vapour compression refrigeration system T Vamsi, SN Rasool, VR Ramesh, K Hemanth, JN Kumar, SV Rao Int J Res Appl Sci Eng Technol 10 (9), 572-576 , 2022 2022 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
An IoT based smart garbage monitoring and disposal support system TMN Vamsi, GK Chakravarthi, P Lanka, B Divakar 2021 5th International Conference on Computing Methodologies and … , 2021 2021.0 Citations: 23
An IoT based smart water contamination monitoring system MNV Thalatam, P Lanka, JS Kumar 2023 International Conference on Intelligent Systems for Communication, IoT … , 2023 2023.0 Citations: 17
An IoT based smart home with virtual assistant TMN Vamsi, B Suchitra, S Kumar, KVV Varma, KNSH Kumar 2021 6th International Conference for Convergence in Technology (I2CT), 1-4 , 2021 2021.0 Citations: 5
An embedded system design for guiding visually impaired personnel TMN Vamsi, GK Chakravarthi, T Pratibha 2019 International Conference on Recent Advances in Energy-efficient … , 2019 2019.0 Citations: 5
A real-time v2i-based emergency vehicle traffic signals management system (evtms) JS Kumar, MNV Thalatam, ISS Rao, PK Ram, M Vignesh, P Manoj 2023 14th International Conference on Computing Communication and Networking … , 2023 2023.0 Citations: 4
Study of Microsatellites Role in BRCA2 Gene Causing Pancreatic Cancer and Breast Cancer RA Appa, RG Sridhar, TMN Vamsi, RSN Ram BabuSS J Proteomics Bioinform S 1, S038-S040 , 2008 2008.0 Citations: 4
Learning processes for the internet of autonomous vehicles and intelligent transportation systems TMN Vamsi, L Pratibha Wireless Ad-hoc and Sensor Networks, 87-111 , 2024 2024.0 Citations: 3
Prediction of protein secondary structure using artificial neural network MNV Thalatam, PV Rao, K Varma, NVR Murty, A Apparao International Journal on Computer Science and Engineering 2 (5), 1615-1621 , 2010 2010.0 Citations: 3
Hybrid movie recommendation system based on user preferences and item similarity SSR Ivaturi, MNV Thalatam, ASK Bugatha, SS Ladi, DT Botta XVIII International Conference on Data Science and Intelligent Analysis of … , 2023 2023.0 Citations: 2
A review on experimental analysis on hybrid vapour compression refrigeration system T Vamsi, SN Rasool, VR Ramesh, K Hemanth, JN Kumar, SV Rao Int J Res Appl Sci Eng Technol 10 (9), 572-576 , 2022 2022.0 Citations: 2
A Machine Learning Approach for Estimating Crop Damage based on Leaf Disease Detection et.al M.N.Vamsi Thalatam International Journal of Research in Advent Technology 7 (4), 20-24 , 2019 2019.0 Citations: 2
IoT-Enabled Unmanned Aerial Vehicle Monitoring System for Precision Agriculture: Integrating IEEE 802.15. 4 based HyLaR-OF-M Routing Algorithm JS Kumar, K Venkateswararao, TMN Vamsi, LSS Godavarthi, ... Transportation Research Procedia 84, 177-184 , 2025 2025.0 Citations: 1
Deep Learning–Based Surrounding Descriptor for the Visually Challenged MPBC T. Mohana Naga Vamsi, K. Ravi Shankar, G. Karthik Springer Proceedings in Mathematics & Statistics -ICDSAI 2023, 155–163 , 2024 2024.0 Citations: 1
IMPROVING REALISM IN face-swapping USING DEEP LEARNING AND K-MEANS CLUSTERING ISS Rao, JS Kumar, TMN Vamsi, TR Kumar, KR Prasad, KV Kumar Proceedings on Engineering 6 (4), 1751-1756 , 2024 2024.0 Citations: 1
A Mobile App for Age and Gender Identification Using Deep Learning Technique JS Kumar, BM Babu, M Vignesh, P Manoi, TMN Vamsi, G Kamal 2023 International Conference on Intelligent Systems for Communication, IoT … , 2023 2023.0 Citations: 1
Softcomputing method for diagnosing diabetes patients SL Pratibha, SYSV Lakshmi, MNV Thalatam ijrpb. com , 0 Citations: 1
Real-Time Path Rerouting and Obstacle Aware Navigation in Autonomous Vehicles: A Simulation and Data Analysis Approach L Narayana, TMN Vamsi ITEGAM-JETIA 12 (58), 334-341 , 2026 2026.0
A Lightweight Federated Prediction Approach for Urban VRU Movement Understanding in Autonomous Driving L Narayana, TMN Vamsi ITEGAM-JETIA 12 (57), 787-797 , 2026 2026.0
Integrated Audio–Visual Emergency Vehicle Detection for Autonomous Vehicles with Real-Time Response L Narayana, TMN Vamsi ITEGAM-JETIA 11 (56), 148-156 , 2025 2025.0
Unsupervised Detection of Groundwater Quality Anomalies via Autoencoder-LOF Ensemble TMN Vamsi, S Choudarapu, P Lanka, G Kamal, D Indira, JS Kumar 2025 International Conference on Intelligent Computing, Information and … , 2025 2025.0
