A Secure Authentication and Task Offloading Model Using Blockchain-Assisted Hybrid Serial Learning in Multiaccess Edge Computing for Vehicular Ad Hoc Networks Sector Jafar A. Alzubi, Nageswara Rao Lavuri, Krishna Dharavath, Nagarjuna Nallameti, Sumanth Venugopal, Preethi Palanisamy International Journal of Communication Systems, 2026 The intelligent transportation system (ITS) is enabled by the vehicular ad hoc networks (VANETs), but the security threats, such as node impersonation, node tampering, and eavesdropping, are the greatest challenges and cause security concerns within the system. The large‐scale vehicular environment is not effectively handled by the previous static and centralized security approaches, which can greatly increase the latency and data integrity problems within the network. Thus, this research proposes a deep learning–assisted blockchain approach for enabling the decentralized, reliable, and secure communication in the VANET. The main contribution of the research is to perform secure authentication and task offloading to enable secure task offloading within the VANET and to guarantee communication performance with minimum energy consumption and delays. First, the data confidentiality, privacy of the task offloading, authentication, and integrity are achieved by introducing blockchain technology. Second, the node authentication is performed using adaptive and attention‐based hybrid serial learning (AAHSL), which is developed with the combination of a deep belief network (DBN) and temporal convolution network (TCN). After authenticating the data within the nodes, the adaptive deep reinforcement learning (ADRL)–based task offloading is proposed for reducing the task completion time within the network. In both models, the parameters are tuned using the pattern improvement parameter–based poor and rich optimization algorithm (PIP‐PROA). The experimental results demonstrate that the proposed approach achieves an FNR of about 3.46% during the authentication process, and the reward score achieved by the designed model during the task offloading process is 9.22. Thus, the effectiveness of the suggested model is confirmed by the experimental analysis.
Obesity Health Risk Prediction using Random Forest and SVM Algorithms Nageswara Rao Lavuri, M Basha, Nagarjuna Nallametti, G Karthik Reddy, Voruganti Santhosh Kumar, Premkumar Borugadda Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025 Obesity-related health dangers have become a serious global issue that requires adequately pinpointing and quickly preventing health problems. One of the research studies in the article suggests a supervised learning-based framework that uses the Random Forest method for feature selection and the Support Vector Machine (SVM) technique for classification to predict obesity-induced health dangers. Aiming at increased accuracy in risk assessment, the framework involves the analysis of pertinent health and lifestyle parameters, including BMI, regular physical activity, and a proper diet. The solutions use machine learning as the main technical approach, which leads to real-time personal health recommendations and early medical interventions. Furthermore, the solution model ensures that it is scalable and adaptive, broadening the population to diverse patient demographics and thus contributing to proactive management of obesity conditions and achieving good patient outcomes.
Crop Disease Detection using Edge IoT Devices and YOLOv5-based Classification Yeluripati Bala Tripura Sundari, Kohila R, Sowmiya M, Nagarjuna Nallametti, Jeethu Philip, Keerthana N V Proceedings 3rd International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2025, 2025 The prompt and precise identification of crop diseases can be said to be a quality far above the size that determines agricultural productivity and food security of a place. This proposed study depicts an immediate crop disease detection framework using Edge IoT devices and the YOLOv5 object detection algorithm to deal with real-time problems. The system enhances the functionality of edge devices to process the images of crops that have been taken in the field and make on-the-spot disease classification without the cloud-based server being a must. Fast and accurate YOLOv5 is a model that recognizes objects known for its speed and precision. Moreover, the model has been trained on diseased and healthy crop pictures; thus, it is possible to accurately identify and classify multiple crop diseases. The edge computing model confirms that the responses will be instant. What is more, the cost of data transmission will be low, and privacy will be improved. The experimental outcomes suggest that the proposed system proposed here is a good fit for disorder identification in resource-conditioned environments and that it is an efficient agricultural efficacy precision and smart farming applications solution. Experimental outcomes show a detection accuracy of 94.8% across three vital crop disorder datasets, with an average inference period to be 0.04 seconds per image on a Raspberry Pi-based controller in version 4. The proposed model outperforms other traditional edge-dependent approaches in both accuracy and speed.
Brain Tumour Identification using Deep Belief Networks: A Resilient Deep Learning Technique Nageswara Rao Lavuri, V Leelashyam, Bhukya Shankar, Voruganti Santhosh Kumar, Nagarjuna Nallametti, G Karthik Reddy Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, 2025 This research work is devoted to the researching of an effective framework for brain tumor identification with Deep Belief based Networks (DBNs), an excellent supervisor learning method. Brain tumors are a serious health issue, and early detection is essential for the best patient results. Traditional methods of diagnosing, like a manual MRI study, take a long time and can be wrong due to human errors. In a demographic dataset, DBN-based method automates the identification process by deep learning complex patterns, resulting in the improvement of accuracy and efficiency in tumor detection. The DBN model, that consists of stacked Restricted Boltzmann Machines (RBMs), reaches the precision of 96.4%, thus outstripping the average of conventional CNN-based models, which stands at 92.8%. Additionally, the model displays a precision of 94.7%, a result that impressively outperforms previously held traditional method which had been performing at 91.2%. In addition, the recall rate of 95.5% helps to minimize false negatives and, in this way, to reduce the unrecognized tumor risk compared to the previous 89.6% recall rate. Moreover, the F1-score of 95.1% underlines the model's good overall performance in classifying tasks. The research enhances a compelling case for using DBNs' generative capabilities in the optimization of the models for increased diagnosis accuracy. The findings will mostly shape the area of medical imaging and bring a properly automated brain tumor detection system that will be the fastest and most precise for diagnosing.
Enhancing Big Data Forecasting with Gradient Boosting Machine Learning Framework Sankara Rao, Seema M, VijayaDurga D, Nagarjuna Nallametti, Manasa S, Keerthana N V Proceedings of the 9th International Conference on Electronics Communication and Aerospace Technology Iceca 2025, 2025 Nowadays, rapid digital transformation and the capacity to precisely predict the future from enormous and intricate data sets are essential for many industries. The paper outlines a powerful method of improvement in big data prognosis by deploying Gradient Boosting Machine (GBM) learning algorithms, which are capable of outperforming and achieving the scalability required. The outlined system is a multi-stage data processing pipeline that integrates a distributed data ingestion framework initially to ensure efficient handling of both real-time and batch data streams. Data preprocessing techniques such as outlier filtering, normalization, and dimensionality reduction are utilized in order to increase the quality of data, and thus, the system can work with less computational load. Feature engineering is also enhanced through mutual information scores and recursive feature elimination to get rid of unnecessary variables from the dataset. The system's nucleus utilizes a Gradient Boosting Machine that consecutively constructs a set of weak learners— mostly decision trees—by successively reducing the error via iterations. Due to the size and complexity of the data, the system is set up in a distributed computing environment for parallel training and faster convergence of the model. The process of hyper-parameter tuning is done through Bayesian optimization, which intends to effectively manage the bias-variance trade-offs. Experimental results reveal that the suggested GBM-based architecture decisively trumps classical machine learning algorithms such as Random Forest, Support Vector Machines, and Logistic Regression in several basic metrics. The main points of this paper are in its employment of efficient preprocessing techniques, intelligent feature selection, distributed learning architecture, and sophisticated GBM optimization, which all combine to provide a scalable, high-accuracy prediction solution for big data scenarios.
Gradient Boosting-Based mechanism to Encryption Algorithm and Hash Function differentiation in Cryptanalysis Hemasree Koganti, Deiwakumari K, Ravi A T, Nagarjuna Nallametti, Anju V Abraham, Keerthana N V 2025 1st International Conference on Advancement in Futuristic Technologies Icaft 2025, 2025 The linear development of cryptographic mechanisms has suit to a growing issue for an effective classification mechanism that can support cryptanalysis and provide advanced security systems. This work gives a gradient-type boosting -supportive schema for differentiation of encryption schema and to employ hash-related manipulation, telling a data-driven solution in enhancement of the understanding of cryptographically defined behaviour activity. By employing the ensemble-based learning ability of Gradient type Boosting, the model systematically way of detecting any structural difference between several cryptographically defined related primitives to confirm a more accurate way of determination and its categorization process. The basis of this research relies on merging the Gradient type Boosting schema formed by preprocessing and in training phase, that the extraction of both relevant with distinct cryptographically defined patterns. This hybrid model not only improves classification efficacy but also gives a deeper investigation into the advantage of schema against potential external threat attacks. The study examines the effectiveness of the advancement of supervised learning mechanisms in cryptanalysis.
Automated AI-Powered Drug Labels: Smart Packaging that Provides Real-Time Patientspecific Dosage Recommendations Soumyakant Dash, Nagarjuna Nallametti, Manisha Bhende, Niranjani V, V Sumalatha, Thella Preethi Priyanka 2025 IEEE 4th International Conference for Advancement in Technology Iconat 2025, 2025 Although the provision of personal medication remains a significant challenge due to static dose protocols and patient non-adherence, this article describes how a smart labeling system, powered by AI, can be integrated into drug packaging and provide real-time patient-specific dosage recommendations. The framework integrates decentralized multi-agent AI, continuous physiologic monitoring, and adaptive pharmacokinetics/pharmacodynamics to flexibly dose patients based on their data. Validation studies indicated substantial improvements against standard prescription protocols, smart devices that use rule-based approaches, and clinical decision support systems (CDSSs) in particular, for the following performance metrics: 95.1 % dose accuracy; 49.8 % improvements in adherence; and 55.2% reductions in adverse events. Additionally, the system demonstrated high reliability under varying network conditions. Although findings are reported from simulations, the results indicated a distinct potential for actual clinical use. Next steps focus on clinical trials, data privacy improvements, and ensuring integration with electronic health records (EHRs). This represents a promising step towards intelligent, precision-based drug delivery systems and improved patient safety/ therapeutic outcomes.
A Data-Driven Approach in predicting Heart Disease Detection in earlier stage Nagarjuna N, S. Kavitha, Lakshmi H. N Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024 Icnwc 2024, 2024 In the healthcare field accumulation of vast amounts of data, known as big data, that contains valuable insights important for decision-making is available. The Data-driven approaches consistently proves their effectiveness by highlighting the pivotal roles of data analysis in error detection, valuable information identification, healthcare variables assumption and validation, and correlation establishment among health-related System. The proposed system Heart-Risk predict (HRP) serves as a data analysis tool by using statistical modeling techniques. The proposed novelistic model focuses on prediction of heart disease and risk factors using the k-means grouping algorithm by employing data analytics and visualization tools, which is further tested on publicly available datasets. Some of basic health related attributes like patient age, chest pain category, blood pressure, glucose levels and heart rate aid in early-stage chest pain type of determination. Through preprocessing methods and classifier-based evaluations, the current study showcases heart disease prediction accuracy at a higher level. Furthermore, the proposed system integrates the Tabu search mechanism and the K-means algorithm in addressing traditional method limitations. This integration enhances efficacy in handling large datasets by reducing computational complexity and give improvement in prediction accuracy. Thorough experimental validation depict system's efficacy and superiority over existing methods by offering a promising implications for early heart disease detection and proactive management, thus improvising public health outcomes.
Classifying Network Abnormalities Into Faults and Attacks in Iot-Based Cyber-Physical Systems using Machine Learning P. Jagdish Kumar, Laith H. Jasim Alzubaidi, N. Nagarjuna, Ram Deshmukh, Madhura G K 2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024 Amidst the proliferation of Industry 4.0, the integration of artificial intelligence and smart techniques has emerged as a focal point in discussions surrounding industrial cyber-physical systems (CPS). It is still very difficult to detect anomalies in a way that protects security and productivity, especially when there isn't much labeled data available for cyber-physical security protection. A novel method called the Few-Shot Learning model with Siamese Neural Network (FSL-SNN) is presented in this paper with the goal of improving the accuracy of intelligent anomaly detection in industrial CPS and reducing the over-fitting problem. To calculate the distances between input samples using their optimum feature representations, a Siamese encoding network is developed. To bolster the efficiency of the training process, a robust cost function is designed, encompassing three specific losses. The culmination of these efforts results in the development of an intelligent anomaly detection algorithm. The results of experiments conducted on two datasets—one with sparse labels and the other fully labels shows the notable improvements that the proposed FSL-SNN achieves when it comes to lowering false alarm rate (FAR) of 0.041 and raising F1 score of 0.975 for intrusion signal identification in the context of industrial CPS security protection compared to the existing Siamese Convolutional Autoencoder.
Intelligent Rainfall Prediction and Flood Alert System using ML Nagarjuna Nallametti, C. Malarvizhi, Sheik Jamil Ahmed, R. Sathya, K. Harish Vishnu, J. N. Naveen Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024
Efficient Graph Convolutional Networks with Attention Mechanisms for Precise Prediction of Cardiovascular Diseases N Nallametti, S Kavitha 2026 IEEE International Conference for Convergence in Computing Technology … , 2026 2026
A Secure Authentication and Task Offloading Model Using Blockchain-assisted Hybrid Serial Learning in Multi-Access Edge Computing for Vehicular Ad Hoc Networks Sector PP Jafar A. Alzubi,Nageshwar Rao Lavuri,Krishna Dharavath, Nagarjuna ... International Journal of Communication Systems 39 (e70382), no. 3 , 2026 2026 Citations: 1
Gradient Boosting-Based mechanism to Encryption Algorithm and Hash Function differentiation in Cryptanalysis H Koganti, K Deiwakumari, AT Ravi, N Nallametti, AV Abraham, ... 2025 1st International Conference on Advancement in Futuristic Technologies … , 2025 2025
Enhancing Big Data Forecasting with Gradient Boosting Machine Learning Framework S Rao, M Seema, D VijayaDurga, N Nallametti, S Manasa, NV Keerthana 2025 9th International Conference on Electronics, Communication and … , 2025 2025
Automated AI-Powered Drug Labels: Smart Packaging that Provides Real-Time Patientspecific Dosage Recommendations S Dash, N Nallametti, M Bhende, V Sumalatha, TP Priyanka 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT … , 2025 2025
Crop Disease Detection using Edge IoT Devices and YOLOv5-based Classification YBT Sundari, R Kohila, M Sowmiya, N Nallametti, J Philip, NV Keerthana 2025 3rd International Conference on Self Sustainable Artificial … , 2025 2025 Citations: 1
Obesity Health Risk Prediction using Random Forest and SVM Algorithms NR Lavuri, M Basha, N Nallametti, GK Reddy, VS Kumar, P Borugadda 2025 8th International Conference on Trends in Electronics and Informatics … , 2025 2025
Brain Tumour Identification using Deep Belief Networks: A Resilient Deep Learning Technique NR Lavuri, V Leelashyam, B Shankar, VS Kumar, N Nallametti, GK Reddy 2025 7th International Conference on Intelligent Sustainable Systems (ICISS … , 2025 2025
Intelligent Rainfall Prediction and Flood Alert System using ML Nagarjuna Nallametti, C Malarvizhi, SJ Ahmed, R Sathya, KH Vishnu, ... 2024 5th International Conference on Smart Electronics and Communication … , 2024 2024 Citations: 3
NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection through Optimized Feature Reduction and Machine Learning SR Sagili, S Chidambaranathan, N Nallametti, HM Bodele, L Raja, ... 2024 Third International Conference on Electrical, Electronics, Information … , 2024 2024 Citations: 53
Machine Learning Analysis of Equity Mutual Funds’ Portfolio Characteristics and Investment Decisions E Uma Reddy, N Nagarjuna, K Prakash, CV Satyamurthy, M Lalitha International Conference on Intelligent Computing and Communication, 91-100 , 2024 2024
Predictive modeling of diabetes mellitus utilizing machine learning techniques N Nagarjuna, HN Lakshmi CVR Journal of Science and Technology 26 (1), 112-117 , 2024 2024 Citations: 4
Classifying Network Abnormalities Into Faults and Attacks in Iot-Based Cyber-Physical Systems using Machine Learning GK Kumar, P.J. , Alzubaidi, L.H.J. , Nagarjuna, N. , Deshmukh, R. , Madhura 2nd International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 3
PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm S Habelalmateen, M.I. , Archana Reddy, R. , Reddy, C. , Nagarjuna, N ... 2nd International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 1
Enhancing Wireless Sensor Network Routing Strategies with Machine Learning Protocols S Veeranjaneyulu, K. , Lakshmi, M. , Venkateswara Swamy, S. , ... Nagarjuna ... Proceedings of the 2nd IEEE International Conference on Networking and … , 2024 2024
A Data-Driven Approach in predicting Heart Disease Detection in earlier stage Nagarjuna N. , Kavitha S. , Lakshmi H.N. Proceedings of the 2nd IEEE International Conference on Networking and … , 2024 2024
Applying machine learning for portfolio switching decisions EU Reddy, N Nagarjuna International Conference on Multi-disciplinary Trends in Artificial … , 2023 2023 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection through Optimized Feature Reduction and Machine Learning SR Sagili, S Chidambaranathan, N Nallametti, HM Bodele, L Raja, ... 2024 Third International Conference on Electrical, Electronics, Information … , 2024 2024 Citations: 53
Predictive modeling of diabetes mellitus utilizing machine learning techniques N Nagarjuna, HN Lakshmi CVR Journal of Science and Technology 26 (1), 112-117 , 2024 2024 Citations: 4
Intelligent Rainfall Prediction and Flood Alert System using ML Nagarjuna Nallametti, C Malarvizhi, SJ Ahmed, R Sathya, KH Vishnu, ... 2024 5th International Conference on Smart Electronics and Communication … , 2024 2024 Citations: 3
Classifying Network Abnormalities Into Faults and Attacks in Iot-Based Cyber-Physical Systems using Machine Learning GK Kumar, P.J. , Alzubaidi, L.H.J. , Nagarjuna, N. , Deshmukh, R. , Madhura 2nd International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 3
Applying machine learning for portfolio switching decisions EU Reddy, N Nagarjuna International Conference on Multi-disciplinary Trends in Artificial … , 2023 2023 Citations: 3
A Secure Authentication and Task Offloading Model Using Blockchain-assisted Hybrid Serial Learning in Multi-Access Edge Computing for Vehicular Ad Hoc Networks Sector PP Jafar A. Alzubi,Nageshwar Rao Lavuri,Krishna Dharavath, Nagarjuna ... International Journal of Communication Systems 39 (e70382), no. 3 , 2026 2026 Citations: 1
Crop Disease Detection using Edge IoT Devices and YOLOv5-based Classification YBT Sundari, R Kohila, M Sowmiya, N Nallametti, J Philip, NV Keerthana 2025 3rd International Conference on Self Sustainable Artificial … , 2025 2025 Citations: 1
PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm S Habelalmateen, M.I. , Archana Reddy, R. , Reddy, C. , Nagarjuna, N ... 2nd International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 1
Efficient Graph Convolutional Networks with Attention Mechanisms for Precise Prediction of Cardiovascular Diseases N Nallametti, S Kavitha 2026 IEEE International Conference for Convergence in Computing Technology … , 2026 2026
Gradient Boosting-Based mechanism to Encryption Algorithm and Hash Function differentiation in Cryptanalysis H Koganti, K Deiwakumari, AT Ravi, N Nallametti, AV Abraham, ... 2025 1st International Conference on Advancement in Futuristic Technologies … , 2025 2025
Enhancing Big Data Forecasting with Gradient Boosting Machine Learning Framework S Rao, M Seema, D VijayaDurga, N Nallametti, S Manasa, NV Keerthana 2025 9th International Conference on Electronics, Communication and … , 2025 2025
Automated AI-Powered Drug Labels: Smart Packaging that Provides Real-Time Patientspecific Dosage Recommendations S Dash, N Nallametti, M Bhende, V Sumalatha, TP Priyanka 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT … , 2025 2025
Obesity Health Risk Prediction using Random Forest and SVM Algorithms NR Lavuri, M Basha, N Nallametti, GK Reddy, VS Kumar, P Borugadda 2025 8th International Conference on Trends in Electronics and Informatics … , 2025 2025
Brain Tumour Identification using Deep Belief Networks: A Resilient Deep Learning Technique NR Lavuri, V Leelashyam, B Shankar, VS Kumar, N Nallametti, GK Reddy 2025 7th International Conference on Intelligent Sustainable Systems (ICISS … , 2025 2025
Machine Learning Analysis of Equity Mutual Funds’ Portfolio Characteristics and Investment Decisions E Uma Reddy, N Nagarjuna, K Prakash, CV Satyamurthy, M Lalitha International Conference on Intelligent Computing and Communication, 91-100 , 2024 2024
Enhancing Wireless Sensor Network Routing Strategies with Machine Learning Protocols S Veeranjaneyulu, K. , Lakshmi, M. , Venkateswara Swamy, S. , ... Nagarjuna ... Proceedings of the 2nd IEEE International Conference on Networking and … , 2024 2024
A Data-Driven Approach in predicting Heart Disease Detection in earlier stage Nagarjuna N. , Kavitha S. , Lakshmi H.N. Proceedings of the 2nd IEEE International Conference on Networking and … , 2024 2024
