N SREEKANTH

@gpcet.ac.in

PROFESSOR / ELECTRONICS AND COMMUNICATION ENGINEERING (ECE)
G.Pullaiah College of Engineering and Technology

N SREEKANTH


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https://researchid.co/sreekanth

RESEARCH INTERESTS

MICROWAVE ANTENNAS, COMMUNICATIONS, IMAGE & SIGNAL PROCESSING
14

Scopus Publications

Scopus Publications

  • Quantum AI-Enabled Privacy-Preserving Mechanisms for Building Resilient Digital Identity Security in Global Cyber-Physical Ecosystems
    N. Sreekanth, M. Nagoor Meeral, A. Ameer Rashed Khan, C. Preethi, Vishwajeet Swapnil Kulkarni
    Resilient Privacy Preserving Mechanisms for Digital Identity Management, 2026
    The increasing interconnectedness of cyber-physical ecosystems in industries across the world has led to a growing need for secure and resilient infrastructures for digital identities. While traditional privacy protocols are incredibly effective in isolated instances, they are susceptible to a growing list of data leak incidents, attacks on quantum-capable cryptographic systems, and adversarial tampering with the identity credential payload. As a construct of quantum and AI, the proposed framework becomes a way to support dynamic trust negotiations, context adaptive and tailored authentication, and self-learning resilience against classical and quantum-capable adversaries. From a methodological standpoint, this chapter outlines a hybrid architecture namely, unsupervised quantum-entropy driven key fabrication, with an AI-driven risk analytics phase, operated through a federated, and privacy-preserving ecosystem.
  • Next Generation Quantum-IoT Communication Framework for Intelligent and Sustainable Network Solutions
    N Sreekanth, M Vijayasanthi, Sayed Sayeed Ahmad, Shaik Taj Mahaboob, Gayathri K M, Thangadurai N
    Conference Proceedings 1st International Conference on Advancing Sustainable Solutions Through Technologies Icasst 2026, 2026
    The relationship between quantum computing and the Internet of Things (IoT) is becoming a powerful pathway to develop secure, intelligent and sustainable network systems – however, present-day IoT architectures experience scalability challenges, as well as energy and data vulnerability, due to classical models in cryptography. This paper presents a Next Generation Quantum-IoT Communication Framework (QIoT-CF) that addresses these issues with quantum-based encryption, entanglement-assisted routing, and dynamic sustainability optimization for communication in the framework. The architecture uses Quantum Key Distribution (QKD) to ensure quality data security that is unbreakable, and it uses reinforcement learning–based queuing to balance energy consumption and communication reliability with heterogeneous IoT nodes. The simulations suggest that latency decreased by 45%, energy consumption improved by 43%, and the quantum bit error rate (QBER) improved by 52% relative to current IoT systems. The sustainability controller will change transmission policies based on carbon-aware energy metrics providing an acceptable trade-off between quantum performance and sustainability at-scale. The framework represents a critically significant advancement toward eco-intelligent quantum communication security situated within the 6G vision and beyond.
  • Intelligent QoS-Driven Ad Hoc On-Demand Distance Vector Routing for 5G MANET Using Physically Recurrent Neural Network
    S. Sankar Ganesh, V. Kalpana, T. R. Vijaya Lakshmi, N. SreeKanth
    International Journal of Communication Systems, 2025
    Mobile ad hoc networks (MANET) continue to evolve within the 5G era, which has gained increased attention. Its primary characteristic is that nodes are constantly applied to heavy traffic loads and the QoS requirements are necessary. The routing protocols often struggle to maintain high QoS under dynamic and unpredictable network conditions. It is difficult to create a routing protocol that effectively adjust to node mobility, changing traffic and fluctuating network quality while maintaining crucial QoS metrics. To address this challenge, this manuscript proposes an intelligent QoS‐driven AODV routing for 5G‐MANET using physically recurrent neural network (AODV‐5G‐MANET‐PRNN). The PRNN is used to update the traffic loads on database and to improve QoS‐ensured route destinations. Then, the database fed to AODV‐PRNN detects the guaranteed QoS in routing. The proposed AODV‐5G‐MANET‐PRNN technique is implemented and analyzed using performance metrics like end‐to‐end delay, throughput, network lifetime, energy consumption, packet delivery ratio (PDR), signal‐to‐noise ratio (SNR), jitter, delay variance, and computational cost. The proposed approach attains 19.68%, 22.34%, and 30.22% higher throughput; 9.75%, 14.86%, and 10.42% lower Jitter; and 9.44%, 12.38%, and 7.29% lower delay variance compared with existing AODV‐EQOS‐5G‐MANET, RP‐QOS‐DDE‐5G, and QOS‐5G‐EEC models, respectively.
  • Ensemble deep learning approach with hybrid optimisation for enhanced underwater acoustic OFDM communication systems
    S. Pradeep, Subba Reddy Borra, C.V.P.R. Prasad, N. Sreekanth, Sudhakar Kallur
    International Journal of Ad Hoc and Ubiquitous Computing, 2024
    Underwater acoustic (UWA) communication involves transmitting information through sound waves in aquatic environments, which presents challenges due to signal attenuation, multi-path propagation, and background noise. This research presents a novel approach using ensemble deep learning (EDL) combined with hybrid optimisation for UWA-orthogonal-frequency-division-multiplexing (OFDM) systems. Contrary to the traditional receiver dependence on channel estimation and equalisation for symbol detection, the proposed EDL model directly retrieves transmitted symbols after sufficient training. It employs convolutional neural networks (CNNs), bi-directional long-short-term memory (bi-LSTM) networks, and recurrent neural networks (RNNs) to capture spatial, temporal, and sequence-based dependencies in the signal. To optimise the training process, a hybrid strategy, OppTalO, which integrates driving training-based optimisation (DTBO) and osprey optimisation algorithm (OOA), is utilised. The effectiveness of this EDL approach with hybrid optimisation is assessed across various system parameters: cyclic prefix length and pilot symbol count; and found to have less error rate than existing methods.
  • Evaluation of estimation in software development using deep learning-modified neural network
    N. Sreekanth, J. Rama Devi, Kumar A. Shukla, D. K. Mohanty, Amedapu Srinivas, G. Nageswara Rao, Afaque Alam, Ankur Gupta
    Applied Nanoscience Switzerland, 2023
  • Multipath Routing Based on Energy Centric Modified Shuffled Frog Leaping Algorithm with Inertia Weight to Improve Cross Layer Performance in MANETs
    Bura Vijay Kumar, Y. M. Mahaboob John, N Sreekanth, Myasar Mundher Adnan, A. H. A. Hussein
    IEEE 1st International Conference on Ambient Intelligence Knowledge Informatics and Industrial Electronics Aikiie 2023, 2023
    Developing energy-efficient control techniques is vital for mobile ad hoc networks (MANETs) longevity. Effective routing systems, considering variables like channel assumptions and traffic load, are crucial for addressing power-related issues throughout the stack. To address these issues, an Energy Centric Modified Shuffled Frog Leaping Algorithm (EC-MSFLA) is proposed to improve the cross layer routing in MANETS. Metrics such as Residual Energy (RE), Communication cost, Mobility, and Data Success Rate (DSR) are measured to estimate the fitness function of the EC-MSFLA. Further, an Adaptive Competition Window (ACW) is used to reduce the energy consumption. The performance of the proposed EC-MSFLA is compared with the existing methods such as an Energy Efficient Secure Cross-layer routing protocol with Particle Swarm Optimization (ESCL-PSO), and Energy Efficient Disjoint Multipath Routing Protocol using Simulated Annealing (EEDMRSA) by means of Packet Delivery Rate (PDR), End-to-End Delay (EED), routing overhead, and Energy Consumption. Results demonstrated that the energy consumption of EC-MSFLA is 2.92 Joules for 50 nodes when compared to ESCL-PSO and EEDMRSA.
  • A Cluster Based Routing Using Energy Efficiency-Based Multi-Objective Optimization in Wireless Sensor Networks
    N Sreekanth, G. Nagendra Babu, Gotte Ranjith Kumar, Madhukar G, Aboothar Mahmood Shakir
    IEEE 1st International Conference on Ambient Intelligence Knowledge Informatics and Industrial Electronics Aikiie 2023, 2023
    Wireless Sensor Network (WSN) is a growing technology used for monitoring the physical world. The WSN's sensors are utilised to monitor the surrounding environment, and the information gathered is communicated wirelessly. The primary objective of wireless sensor network design is to maximise network lifetime. Network lifetime can be extended and energy consumption can be properly balanced through the use of clustering and routing. However, the WSN's restricted battery supply means that energy efficiency is regarded as a difficult task in a network. In this paper, an Energy Efficiency-based Multi-Objective Optimization method (EE- MOOA) is used to construct a cluster-based routing method for achieving energy efficiency in WSN. The Improved Butterfly Optimization Algorithm (IBOA) is used in the development of the EE-MOOA. Improving energy efficiency is the main goal of the EE-MOOA approach, which aims to improve data delivery for WSNs agricultural applications. The energy efficiency of the EE-MOOA method is 99.12% for 100 nodes, which is a higher measure when compared to the energy efficiency value of existing methods.
  • Scaling AI-Driven Solutions for Semantic Search
    Anshul Srivastava, Mounika Nalluri, Tarun Lata, Geetha Ramadas, N Sreekanth, Hrishikesh Bhanudas Vanjari
    2023 International Conference on Power Energy Environment and Intelligent Control Peeic 2023, 2023
    Scaling AI-driven Sematic search solutions usually use a combination of technologies to enable a sturdy seek revel in. numerous tactics include: 1. natural Language Processing (NLP): Used to method the input question and convert it into seek phrases that can be used within the seek engine.2. Device gaining knowledge of (ML): Used to index the documents and locate relevant effects for the quest question. System studying algorithms which include unsupervised mastering, reinforcement getting to know, supervised studying, or even deep studying strategies can be used.3. Know-how Graphs: As a set of records factors linked to every different and representing the relationships between them, know-how graphs are used to capture the context of a selected domain in search engines like google.4. Ontologies: a set of vocabulary, regulations, and definitions which may be used to represent the area in a based manner.5. Textual content Analytics: tools used to analyze and extract insights from dependent and unstructured textual content assets.6. Indexing and search engine optimization (see): Used to create an index of content which can be scanned through search engines quickly. See also enables to optimize the internet page to make it easier for search engines like google to find applicable content.
  • Identification of Texts Using ML Algorithm for The Accurate Recognition
    M Jahir Pasha, K. Kajendran, Shrikala Deshmukh, G. Kumar, N Sreekanth, Sachin Pund
    2023 International Conference on Power Energy Environment and Intelligent Control Peeic 2023, 2023
    A Comparative evaluation of device mastering Algorithms for textual content classification textual content category is an vital assignment in herbal language processing, where the aim is to routinely assign a label or category to a given text file. With the increasing quantity of text facts available in diverse domain names, the need for efficient and accurate text class algorithms has turn out to be extra vital. In this paper, we gift a comparative analysis of machine mastering algorithms for text category. We experiment with one of a kind algorithms, such as aid Vector Machines, Naive Bayes, Random Forests, and Multilayer Perceptron, on numerous benchmark datasets. Our results reveal that the choice of set of rules considerably affects the overall performance of textual content classification, and there may be no unmarried high-quality algorithm that works well for all datasets. We also provide insights into the strengths and weaknesses of each set of rules and talk their applicability in specific eventualities. Our findings can guide researchers and practitioners in selecting the maximum suitable system gaining knowledge of algorithm for their text category duties. Textual content classification, device learning, assist vector machines, naive bays, random forests, multilayer perceptron, and comparative analysis.
  • Recognition of Tomato Leaf Disease Using 10-Layered DCNN
    N. VinaySeshu, A.G.K. SriHarsha, D. ShivaReddy, K. Swaraja, N. Sreekanth, C.N. Sujatha
    2023 IEEE 8th International Conference for Convergence in Technology I2ct 2023, 2023
    The primary causes of the detrimental effects on crops and plant life are majorly plant disease and leaf disease. For the agricultural unit, this is the main risk. Food scarcity is causing agony for millions of people. Farmers' ability to make a living is severely impacted by crop damage caused by damaged leaves. Crops are not receiving a good diagnosis, which has an impact on plant growth, due to ignorance about the type of illness and pesticide usage. Food security is seriously threatened by crop diseases. It might be difficult to diagnose a disease at an early stage in many places of the world. Early recognition and diagnosis of the disease is the solution to improve the overall health of the crop and thus reduce the scarcity of the food. To help farmers, a smart agricultural framework is designed by using CNN. In this paper a 10- DCNN is implemented for the identification and diagnosis of tomato leaf disease. The proposed framework attained 95.4% of training accuracy and 93.01% of testing accuracy.
  • Machine Learning Enabled Techniques for Protecting Wireless Sensor Networks by Estimating Attack Prevalence and Device Deployment Strategy for 5G Networks
    Parmod Kumar, Anupam Baliyan, K. Ramalingeswara Prasad, N. Sreekanth, Parag Jawarkar, Vandana Roy, Enoch Tetteh Amoatey
    Wireless Communications and Mobile Computing, 2022
  • An enhanced low rank approximation SVD-based method for image denoising
    N. Sreekanth
    International Journal of Engineering and Advanced Technology, 2019
  • X-RAY CT reconstruction by using spatially non homogeneous ICD optimization
    Professor, Dept. of ECE, HKBKCE, Bangalore., J. Surendiran, Dr.U. Satheeshwaran, N. Sreekanth
    International Journal of Engineering and Advanced Technology, 2019
  • Signal to interference ratio (SIR) analysis and interference cancellation in mimo OFDM systems with carrier frequency and timing offsets
    N. Sreekanth, M.N. GiriPrasad
    Iet Conference Publications, 2012