Nageswara Rao Eluri

@rvrjc.ac.in

Associate Professor, Department of CSE(IOT)
RVR&JC College of Engineering

Nageswara Rao Eluri


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https://researchid.co/eluri76

EDUCATION

Ph.D. CSE From Acharya Nagarjuna University

RESEARCH INTERESTS

Machine Learning, Deep Learning, Computer Security and Artificial Intelligence
7

Scopus Publications

255

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Quantum-Resilient Cloud Data Protection: A Novel Framework for Secure Encryption and Key Exchange
    Swetha Gadde, Ananda Kumar Kurilinga Sannalingappa, Hanumantharao Nadendla, Nageswararao Eluri, Gowrisankar Kalakoti, Venkata Srinivasu Veesam
    Concurrency and Computation Practice and Experience, 2025
    Nowadays, securing data in the cloud is a challenging task; hence, many studies are being conducted on the use of cryptographic techniques for authentication‐based cloud data security. Apart from traditional cryptographic techniques, data encryption can also utilize certain physical principles of quantum models, known as cryptography. In the existing method, the secure quantum key distribution for cloud data security (SQKD‐CDS) model has high computational complexity and low storage efficiency. However, the quantum cryptography‐based cloud security (QC‐CSM) model uses an optical fiber connection to prevent attackers from altering the secret key shared with the network. This paper presents a novel model called enhanced non‐Abelian NTRU (N‐th degree truncated polynomial ring encryption) (En‐naNTRU) with supersingular isogeny key exchange (SIKE). SIKE's function is to generate a secure key exchange mechanism; thus, it communicates with two parties using a shared session key. This session key acts as a symmetric key for encrypting messages. However, the En‐naNTRU uses this session key to encrypt and decrypt the message securely. The En‐naNTRU handles both encryption and decryption mechanisms. Thus, the integrated method combines and ensures robust security against both quantum and classical threats. The proposed model evaluates computation time, integrity score, encryption and decryption time, processing time, and memory utilization and compares the performance of integrity‐based encryption to attribute‐based encryption. Therefore, the proposed model reduces time complexity and effectively outperforms both classical and quantum security.
  • Deep Convolutional Neural Network with Artificial Bee Colony Optimization For Plant Disease Detection and Classification
    K. Anbazhagan, Nageswara Rao Eluri, Srilatha Toomula, T. V. Brindha
    Tqcebt 2024 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024, 2024
    Plant diseases pose severe risks to agricultural production and global food security. For prompt intervention and mitigation, early and precise disease detection is crucial. Due to the development of trustworthy computer vision and machine learning technologies, automated plant disease detection systems are currently considered a potential solution to this issue. In this study, we provide a unique Deep Convolutional Neural Networks (DCNN)-based method for diagnosing plant illnesses. Our DCNN model automatically extracts pertinent features from plant photos, making it possible to classify healthy and unhealthy plants accurately. The dataset used in this study comprises a sizable collection of images with labels that show different plant species affected by various diseases. To improve its generalization and robustness to changing environmental circumstances and imaging artifacts, the proposed model is trained utilizing a mix of data augmentation via Haralick Texture. The suggested method uses a deep CNN to extract high-level information from photos of plants, allowing it to recognize subtle patterns suggestive of various diseases. The CNN architecture has been extensively crafted and adjusted to achieve the best accuracy and efficiency. The CNN's hyperparameters are optimized using the ABCO (Artificial Bee Colony Optimization) approach to increase classification accuracy further. ABCO mimics honeybee foraging to explore the hyperparameter space and find the best parameters efficiently. This improves the DCNN-ABCO performance. The findings demonstrate high accuracy, precision, and recall across various illness classes, demonstrating the model's dependability for practical applications and its capacity to identify previously unidentified disease patterns.
  • Handling Information Security Wisely Utilising the Aggressive Cuckoo Search Algorithm in Lock Systems
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach
    Nageswara Rao Eluri, Gangadhara Rao Kancharla, Suresh Dara, Venkatesulu Dondeti
    Data Technologies and Applications, 2022
    PurposeGene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.Design/methodology/approachThe proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.FindingsThe proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.Originality/valueThis paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.
  • Detection of heart disease by using reliable boolean machine learning algorithm
    Journal of Theoretical and Applied Information Technology, 2021
  • Implementation of Secrete Message Communication in Server/Client Environment Using Splines Based on PKCS
    Koduganti Venkata Rao, B. Prasanth Kumar, Ch. Viswanadh Sharma, Nageswara Rao Eluri, Beesetti Kiran Kumar
    Smart Innovation Systems and Technologies, 2020
  • Evolutionary Computation based Feature Selection: A Survey
    Suresh Dara, Mamidi Jagadeeshwara Reddy, Nageswara Rao Eluri
    Proceedings of the 2nd International Conference on Electronics Communication and Aerospace Technology Iceca 2018, 2018
    In previous years, different Lateral thinking optimization techniques have been developed based on evolutionary computation. Many of these methods are inspired by spill out behaviors in nature. In this Paper, a new optimization algorithm based on the law of gravity and mass interactions named as Gravitational Search Algorithm (GSA) is discussed for solving feature selection. In GSA, the searcher agents are a collection of masses which will interact with each other based on the law of motion and Newtonian gravity which gives the binary evolutionary optimized high performance. The detailed feature selection has been discussed in this paper and The GSA method has been compared with some well-known optimized search methods such as GA (Genetic Algorithm), PSO(Particle Swarm Optimization).

RECENT SCHOLAR PUBLICATIONS

  • Quantum-Resilient Cloud Data Protection: A Novel Framework for Secure Encryption and Key Exchange
    VSV Swetha Gadde, Ananda Kumar Kurilinga Sannalingappa, Hanumantharao ...
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 37 (18-20), 1-11 , 2025
    2025
    Citations: 1
  • Integrating Machine Learning With Nanotechnology For Enhanced Cancer Detection And Treatment
    MMVF Sunny Arora1 , Divya Nimma2 , Neelima Kalidindi3 , S. Mary Rexcy Asha4 ...
    Integrating Machine Learning With Nanotechnology For Enhanced Cancer … , 2024
    2024
    Citations: 8
  • Optimizing Customer Support for Small Businesses Using Machine Learning Algorithms
    SSM G. Nagaraj, Divya Nimma, Prasad H K, Nageswara Rao Eluri, Janardhana C
    Nanotechnology Perceptions 20 (s(14)), 1410-1417 , 2024
    2024
  • PerformanceAnalysisofaParameterSelection Model on a Big Data Set
    PS Adusumalli Balaji, Ch. Indira Priyadarsini, Eluri Nageswara Rao, Kalluri ...
    International journal of Intelligent Systems and Applications in Engineering … , 2024
    2024
  • Deep Convolutional Neural Network with Artificial Bee Colony Optimization For Plant Disease Detection and Classification
    K Anbazhagan, NR Eluri, S Toomula, TV Brindha
    2024 International Conference on Trends in Quantum Computing and Emerging … , 2024
    2024
    Citations: 3
  • Intelligent Systems and Applications in Engineering
    M Al-Hadi, GH Al-Gaphari
    stress 15, 17 , 2024
    2024
    Citations: 2
  • A ANN Based Machine Learning Predictive Techniques to Classify the Level of Cancer Diseases using Recurrent Neural Networks
    NM Nageswara Rao Eluri, Ram Mohan Reddy Dondeti
    International Journal of Research Studies in Computer Science and … , 2023
    2023
  • IoT Devices for Agricultural to Improve Food and Farming Technology
    KDV Prasad, G Poornima, Y Perwej, EN Rao, HB Patel, DN Sahu
    Journal of Survey in Fisheries Sciences 10 (1S), 4054-4069 , 2023
    2023
    Citations: 19
  • Picture Of Data Warehouse Portrayed With Thought For Challenges, Modernization, And Improvement Of A Conventional Data Warehouse, As Well As Its Possible Future Perspectives
    GD Saxena, DNRE Parmod, DS David, ST Kumbhar, N Premkumar
    Journal of Pharmaceutical Negative Results, 8244-8254 , 2022
    2022
  • Predictive Analysis of Students Performance Evaluation in Higher Education: A Machine Learning Approach
    RS E. Nageswara Rao , Pankajini Sahu , Dillip Narayan Sahu , S. Balaji
    International Journal of Advanced Research in Computer and Communication … , 2022
    2022
  • Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach
    NR Eluri, GR Kancharla, S Dara, V Dondeti
    Data Technologies and Applications 56 (2), 247-282 , 2022
    2022
    Citations: 20
  • Advanced cyber crime rate prediction using deep learning & ai based algorithm
    M Bheemalingaiah, EN Rao, AS Kanaka
    Journal Of Harbin Institute of Technology 54 (3), 2022 , 2022
    2022
    Citations: 5
  • SECURE INFORMATION TEAM SHARING AND CIRCULATION WITH CHARACTERISTICS AND TIME CONDITIONS IN PUBLIC CLOUD
    MP E.NAGESWARARAO, V.SUDHAKAR, Y.SARASWATHI
    http://ijte.uk/archives-2021-i4.html 13 (4), 92-102 , 2021
    2021
  • Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach
    VD Nageswara Rao Eluri, K.Gangadhara Rao, Suresh Dara
    Data Technologies and Applications , 2021
    2021
  • Detection of heart disease by using reliable boolean machine learning algorithm
    M Bheemalingaiah, GR Swamy, P Vishvapathi, PV Babu, EN Rao, ...
    J. Theor. Appl. Inf. Technol 99 (15), 3856-3880 , 2021
    2021
    Citations: 12
  • Feature Extraction In Gene Expression Dataset Using Multilayer Perceptron
    NR Eluri
    Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12 (2 … , 2021
    2021
    Citations: 1
  • Feature Extraction In Gene Expression Dataset Using Multilayer Perceptron
    SD Nageswara Rao Eluri, Gangadhara Rao Kancharla
    Turkish Journal of Computer and Mathematics Education 12 (2), 3069-3076 , 2021
    2021
  • A scalable tree boosting system: XG boost
    M Nalluri, M Pentela, NR Eluri
    Int. J. Res. Stud. Sci. Eng. Technol 7 (12), 36-51 , 2020
    2020
    Citations: 133
  • Implementation of Secrete Message Communication in Server/Client Environment Using Splines Based on PKCS
    KV Rao, B Prasanth Kumar, C Viswanadh Sharma, NR Eluri, BK Kumar
    Smart Intelligent Computing and Applications: Proceedings of the Third … , 2019
    2019
  • An efficient algorithm for feature selection problem in gene expression data: A spider monkey optimization approach
    GR Kancharla, NR Eluri, S Dara, N Ansari
    Proceedings of 2nd International Conference on Advanced Computing and … , 2019
    2019
    Citations: 9

MOST CITED SCHOLAR PUBLICATIONS

  • A scalable tree boosting system: XG boost
    M Nalluri, M Pentela, NR Eluri
    Int. J. Res. Stud. Sci. Eng. Technol 7 (12), 36-51 , 2020
    2020
    Citations: 133
  • Feature Extraction in Medical Images by using Deep Learning Approach
    GRK Suresh dara, Priyanka Tumma, Nageswara Rao Eluri
    International Journal of Pure and Applied Mathematics 120 (Special ISSUE … , 2018
    2018
    Citations: 34
  • Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach
    NR Eluri, GR Kancharla, S Dara, V Dondeti
    Data Technologies and Applications 56 (2), 247-282 , 2022
    2022
    Citations: 20
  • IoT Devices for Agricultural to Improve Food and Farming Technology
    KDV Prasad, G Poornima, Y Perwej, EN Rao, HB Patel, DN Sahu
    Journal of Survey in Fisheries Sciences 10 (1S), 4054-4069 , 2023
    2023
    Citations: 19
  • Detection of heart disease by using reliable boolean machine learning algorithm
    M Bheemalingaiah, GR Swamy, P Vishvapathi, PV Babu, EN Rao, ...
    J. Theor. Appl. Inf. Technol 99 (15), 3856-3880 , 2021
    2021
    Citations: 12
  • An efficient algorithm for feature selection problem in gene expression data: A spider monkey optimization approach
    GR Kancharla, NR Eluri, S Dara, N Ansari
    Proceedings of 2nd International Conference on Advanced Computing and … , 2019
    2019
    Citations: 9
  • Integrating Machine Learning With Nanotechnology For Enhanced Cancer Detection And Treatment
    MMVF Sunny Arora1 , Divya Nimma2 , Neelima Kalidindi3 , S. Mary Rexcy Asha4 ...
    Integrating Machine Learning With Nanotechnology For Enhanced Cancer … , 2024
    2024
    Citations: 8
  • Evolutionary computation based feature selection: a survey
    S Dara, MJ Reddy, NR Eluri
    2018 Second International Conference on Electronics, Communication and … , 2018
    2018
    Citations: 7
  • Advanced cyber crime rate prediction using deep learning & ai based algorithm
    M Bheemalingaiah, EN Rao, AS Kanaka
    Journal Of Harbin Institute of Technology 54 (3), 2022 , 2022
    2022
    Citations: 5
  • Deep Convolutional Neural Network with Artificial Bee Colony Optimization For Plant Disease Detection and Classification
    K Anbazhagan, NR Eluri, S Toomula, TV Brindha
    2024 International Conference on Trends in Quantum Computing and Emerging … , 2024
    2024
    Citations: 3
  • Intelligent Systems and Applications in Engineering
    M Al-Hadi, GH Al-Gaphari
    stress 15, 17 , 2024
    2024
    Citations: 2
  • Quantum-Resilient Cloud Data Protection: A Novel Framework for Secure Encryption and Key Exchange
    VSV Swetha Gadde, Ananda Kumar Kurilinga Sannalingappa, Hanumantharao ...
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 37 (18-20), 1-11 , 2025
    2025
    Citations: 1
  • Feature Extraction In Gene Expression Dataset Using Multilayer Perceptron
    NR Eluri
    Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12 (2 … , 2021
    2021
    Citations: 1
  • Multilayer coding mechanisms for broadcasting over mimo networks
    M Thejovathi, EN Rao
    International Journal of Computer Science & Communication Networks 3 (2), 84 , 2013
    2013
    Citations: 1
  • Optimizing Customer Support for Small Businesses Using Machine Learning Algorithms
    SSM G. Nagaraj, Divya Nimma, Prasad H K, Nageswara Rao Eluri, Janardhana C
    Nanotechnology Perceptions 20 (s(14)), 1410-1417 , 2024
    2024
  • PerformanceAnalysisofaParameterSelection Model on a Big Data Set
    PS Adusumalli Balaji, Ch. Indira Priyadarsini, Eluri Nageswara Rao, Kalluri ...
    International journal of Intelligent Systems and Applications in Engineering … , 2024
    2024
  • A ANN Based Machine Learning Predictive Techniques to Classify the Level of Cancer Diseases using Recurrent Neural Networks
    NM Nageswara Rao Eluri, Ram Mohan Reddy Dondeti
    International Journal of Research Studies in Computer Science and … , 2023
    2023
  • Picture Of Data Warehouse Portrayed With Thought For Challenges, Modernization, And Improvement Of A Conventional Data Warehouse, As Well As Its Possible Future Perspectives
    GD Saxena, DNRE Parmod, DS David, ST Kumbhar, N Premkumar
    Journal of Pharmaceutical Negative Results, 8244-8254 , 2022
    2022
  • Predictive Analysis of Students Performance Evaluation in Higher Education: A Machine Learning Approach
    RS E. Nageswara Rao , Pankajini Sahu , Dillip Narayan Sahu , S. Balaji
    International Journal of Advanced Research in Computer and Communication … , 2022
    2022
  • SECURE INFORMATION TEAM SHARING AND CIRCULATION WITH CHARACTERISTICS AND TIME CONDITIONS IN PUBLIC CLOUD
    MP E.NAGESWARARAO, V.SUDHAKAR, Y.SARASWATHI
    http://ijte.uk/archives-2021-i4.html 13 (4), 92-102 , 2021
    2021