Dr. N. Deepa

@pondiuni.edu.in

Assistant Professor, Department of Computer Science
Pondicherry University, Karaikal Campus, Karaikal, Puducherry - 609 605

Dr. N. Deepa


                  Download Compact PDF  
https://researchid.co/deeparajesh
is presently working as Assistant Professor in the Department of Computer Science, School of Engineering and Technology, Pondicherry University, Karaikal Campus, Karaikal, Puducherry Union Territory, India. She completed her Ph.D in the area of Predictive Analytics in September 2018. She is having 18 years of teaching experience. She has consistently published more than 30 research articles in Scopus and SCI indexed journals with high impact factor. She is having more than 600 citations, h-index of 14 and i10-index of 20. She has published three patents in the year 2019. Her research area includes machine learning, artificial intelligence, operation research, predictive analytics and data mining.

RESEARCH INTERESTS

Machine learning, Artificial Intelligence, Predictive analytics, Blockchain, Industry 5.0
52

Scopus Publications

5441

Scholar Citations

23

Scholar h-index

35

Scholar i10-index

Scopus Publications

  • A review on recent advancements of ChatGPT and datafication in healthcare applications
    Senthil Kumar Jagatheesaperumal, Abinaya Pandiyarajan, Prabadevi Boopathy, N. Deepa, Artur Gomes Barreto, Victor Hugo C. de Albuquerque
    Computers in Biology and Medicine, 2025
  • Explainable Trust Mechanisms in Privacy-Aware Multi-Factor Decision Support on Cloud
    Gomathi V, N. Deepa
    2025 4th International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2025, 2025
    Cloud-based decision support systems (DSS) play a pivotal role in multi-factor decision-making by integrating performance, security, cost, and compliance. However, challenges arise in ensuring both privacy preservation and user trust. This paper presents an explainable trust framework for privacy-aware multi-factor DSS on cloud. The proposed system combines privacy-preserving techniques with explainable artificial intelligence (XAI) to justify recommendations. System architecture demonstrates layered modules for trust scoring, decision modeling, and transparency. Our study highlights that trustworthiness is enhanced when users understand not only the outcomes but also the reasoning behind decisions, particularly under strict privacy requirements. The proposed work uses Explainable AI(XAI) models such as Local Interpretable Model Agonistic Explainer (LIME) and SHAP (Shapely Additive Explainer) for extending the prediction of Machine Learning (ML) models. The Decision Tree model is used for the explanation of LIME and SHAP since the value of the accuracy and precision-recall is 0.99. The proposed framework identifies the key performance factors on the secondary cloud dataset to propose a performance mitigation strategy as per the demand of the cloud service.
  • Lightweight Privacy-Preserving Framework for Multi-Factor Cloud Decision Systems
    Gomathi V, N. Deepa
    2025 4th International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2025, 2025
    With the proliferation of digital services, cloud-based decision systems are increasingly leveraging multi-factor inputs from diverse sources such as IoT devices, health monitoring systems, and financial platforms. However, these systems inherently process sensitive data, posing privacy risks if adequate security mechanisms are not implemented. Especially for the Internet of Things (IoT) based cloud resource allocation this is really more complex. This work proposes a lightweight privacypreserving framework that ensures data confidentiality while maintaining computational efficiency. The proposed method integrates Federated Learning for resource allocation and provides increased performance of 16 % for accuracy, 17.7 % precision, 13.8 % recall and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15.75 \% \mathrm{f}$</tex> score compared with the proposed ML models such as Random Forest (RF), Decision Tree (DT), Ada Boost (AB), Gradient Boost (GB), and Logistic Regression (LR).
  • XAI for Industry 5.0—Concepts, Opportunities, Challenges, and Future Directions
    Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Prabadevi Boopathy, Natarajan Deepa, Rajeswari Chengoden, Nancy Victor, Wei Wang, Weizheng Wang, Yaodong Zhu, Kapal Dev
    IEEE Open Journal of the Communications Society, 2025
    Industry 5.0 has become a reality now and it is a paradigm that integrates contemporary innovations and concepts. Artificial Intelligence (AI) is a key component and asset of the industrial transformation which allows intelligent devices to perform functionalities such as self-examination, assessment, and evaluation autonomously. AI-based methodologies using ML and deep learning assist manufacturers and industrialists in forecasting service requirements and minimizing downtime. Recent research has discovered a remarkable change in the processes, systems, applications, and products in industries. Also, there is a significant challenge with the explainability of the decisions provided by the models using deep learning algorithms and their inadequate ability to be coupled with each other. Therefore, Explainable artificial intelligence (XAI) is required without compromising the efficiency of the models developed using deep learning algorithms. XAI investigates and develops algorithms, techniques, and models that produce human-comprehensible explanations of AI-based systems and can increase transparency and performance. The explainability nature of XAI will help humans understand the model and the reason behind the predictions, thus improving the model’s transparency and the reliability of the outcomes. Furthermore, an Industry 5.0-enabled environment has a variety of data from varied sources, and this multi-source information must be fused to derive meaningful and optimal decisions. Therefore, all AI-integrated applications must derive actionable insights through information fusion. Hence, the adoption of XAI methodologies in Industry 5.0 can help humans make trustworthy decisions for critical applications requiring information fusion. In this paper, we present a state-of-the-art survey on adopting XAI in Industry 5.0. We discuss the adoption of XAI in various applications such as smart factories, smart Healthcare, E-Governance, smart transportation, Education 5.0, Agriculture 5.0, and Energy 5.0. Finally, some research issues and future directions of integrating XAI with Industry 5.0 are also discussed and highlighted to promote more study in the potential field.
  • The Metaverse for Industry 5.0 in NextG Communications: Potential Applications and Future Challenges
    Prabadevi Boopathy, Natarajan Deepa, Praveen Kumar Reddy Maddikunta, Nancy Victor, Thippa Reddy Gadekallu, Gokul Yenduri, Wei Wang, Quoc-Viet Pham, Thien Huynh-The, Madhusanka Liyanage
    IEEE Open Journal of the Computer Society, 2025
    With the advent of new technologies and endeavours for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. The metaverse enables people to communicate with each other by combining the physical world with the virtual world. However, realizing the Metaverse requires symmetric content delivery, low latency, dynamic network control, etc. Industry 5.0 is expected to reform the manufacturing processes through human-robot collaboration and effective utilization of technologies like Artificial intelligence for increased productivity and less maintenance. The metaverse with Industry 5.0 may have tremendous technological integration for a more immersive experience and enhanced productivity. In this review, we present an overview of the metaverse and Industry 5.0, focusing on key technologies that enable the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twins, blockchain, and 6G communication networks. The article then discusses the metaverse's diverse applications across various Industry 5.0 sectors, such as agriculture, supply chain management, healthcare, education, and transportation, illustrated through several research initiatives. Additionally, the article addresses the challenges of implementing the industrial metaverse, proposes potential solutions, and outlines directions for future research.
  • Blockchain for Edge Computing in Smart Environments: Use Cases, Issues, and Challenges
    B. Prabadevi, N. Deepa, S. Sudhagara Rajan, Gautam Srivastava
    Journal of Circuits Systems and Computers, 2024
    The Cenozoic era is the digital age where people, things, and any device with network capabilities can communicate with each other, and the Internet of Things (IoT) paves the way for it. Almost all domains are adopting IoT from smart home appliances, smart healthcare, smart transportation, Industrial IoT and many others. As the adoption of IoT increases, the accretion of data also grows. Furthermore, digital transformations have led to more security vulnerabilities, resulting in data breaches and cyber-attacks. One of the most prominent issues in smart environments is a delay in data processing while all IoT smart environments store their data in the cloud and retrieve them for every transaction. With increased data accumulations on the cloud, most smart applications face unprecedented delays. Thus, data security and low-latency response time are mandatory to deploy a robust IoT-based smart environment. Blockchain, a decentralized and immutable distributed ledger technology, is an essential candidate for ensuring secured data transactions, but it has a variety of challenges in accommodating resource-constrained IoT devices. Edge computing brings data storage and computation closer to the network’s edge and can be integrated with blockchain for low-latency response time in data processing. Integrating blockchain with edge computing will ensure faster and more secure data transactions, thus reducing the computational and communicational overhead concerning resource allocation, data transaction and decision-making. This paper discusses the seamless integration of blockchain and edge computing in IoT environments, various use cases, notable blockchain-enabled edge computing architectures in the literature, secured data transaction frameworks, opportunities, research challenges, and future directions.
  • Deep learning for intelligent demand response and smart grids: A comprehensive survey
    Prabadevi Boopathy, Madhusanka Liyanage, Natarajan Deepa, Mounik Velavali, Shivani Reddy, Praveen Kumar Reddy Maddikunta, Neelu Khare, Thippa Reddy Gadekallu, Won-Joo Hwang, Quoc-Viet Pham
    Computer Science Review, 2024
    Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in the transmission of electricity through the traditional grid, the concepts of smart grids and demand response have been developed. In such systems, a large amount of data is generated daily from various sources such as power generation (e.g., wind turbines), transmission and distribution (microgrids and fault detectors), load management (smart meters and smart electric appliances). Thanks to recent advancements in big data and computing technologies, Deep Learning (DL) can be leveraged to learn the patterns from the generated data and predict the demand for electricity and peak hours. Motivated by the advantages of deep learning in smart grids, this paper sets to provide a comprehensive survey on the application of DL for intelligent smart grids and demand response. Firstly, we present the fundamental of DL, smart grids, demand response, and the motivation behind the use of DL. Secondly, we review the state-of-the-art applications of DL in smart grids and demand response, including electric load forecasting, state estimation, energy theft detection, energy sharing and trading. Furthermore, we illustrate the practicality of DL via various use cases and projects. Finally, we highlight the challenges presented in existing research works and highlight important issues and potential directions in the use of DL for smart grids and demand response.
  • Moth–Flame Optimization based ensemble classification for intrusion detection in intelligent transport system for smart cities
    Thippa Reddy Gadekallu, Neeraj Kumar, Thar Baker, Deepa Natarajan, Prabadevi Boopathy, Praveen Kumar Reddy Maddikunta
    Microprocessors and Microsystems, 2023
  • An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm
    M Baritha Begum, N. Deepa, Mueen Uddin, Rajesh Kaluri, Maha Abdelhaq, Raed Alsaqour
    Heliyon, 2023
    Data stored on physical storage devices and transmitted over communication channels often have a lot of redundant information, which can be reduced through compression techniques to conserve space and reduce the time it takes to transmit the data. The need for adequate security measures, such as secret key control in specific techniques, raises concerns about data exposure to potential attacks. Encryption plays a vital role in safeguarding information and maintaining its confidentiality by utilizing a secret key to make the data unreadable and unalterable. The focus of this paper is to tackle the challenge of simultaneously compressing and encrypting data without affecting the efficacy of either process. The authors propose an efficient and secure compression method incorporating a secret key to accomplish this goal. Encoding input data involves scrambling it with a generated key and then transforming it through the Burrows-Wheeler Transform (BWT). Subsequently, the output from the BWT is compressed through both Move-To-Front Transform and Run-Length Encoding. This method blends the cryptographic principles of confusion and diffusion into the compression process, enhancing its performance. The proposed technique is geared towards providing robust encryption and sufficient compression. Experimentation results show that it outperforms other techniques in terms of compression ratio. A security analysis of the technique has determined that it is susceptible to the secret key and plaintext, as measured by the unicity distance. Additionally, the results of the proposed technique showed a significant improvement with a compression ratio close to 90% after passing all the test text files.
  • A Decision Model for Ranking Asian Higher Education Institutes Using an NLP-Based Text Analysis Approach
    B. Prabadevi, N. Deepa, K. Ganesan, Gautam Srivastava
    ACM Transactions on Asian and Low Resource Language Information Processing, 2023
    Identification of the best institute for higher education has become one of the most challenging issues in the present education system. It has become more complicated as more institutes exist with extraordinary infrastructural facilities. Therefore, a decision model is required to identify the best institute for higher education based on multiple criteria. This article proposes a Natural Language Processing (NLP) -based decision model for the identification of the best higher education institute using MCDM methods. The existing decision models for the selection of the best higher education institutions consider a limited number of criteria for decision-making. In this proposed model, 17 criteria and 15 institute datasets have been identified for the development of the decision model through extensive research and experts opinion. The NLP-based text analysis approach is applied to extract the relevant information and convert it to a suitable format. As the relative importance of the criteria plays a crucial role in decision-making, CRITIC and Rank centroid methods are applied for the calculation of relative weights of criteria. TOPSIS method is used to generate the ranking grades of alternatives for each criterion. An objective function is defined to calculate the evaluation scores and select the best institute for higher education. It has been observed that the ranks obtained from the developed model match pretty well with the ranks obtained from other MCDM methods and the experts.
  • Bijective Soft Set-Based Decision Model for Classification Rule Generation
    N. Deepa, S. Bhuvaneswari, B. Prabadevi, J.Persis Jessintha
    2023 Innovations in Power and Advanced Computing Technologies I Pact 2023, 2023
  • A survey on blockchain for big data: Approaches, opportunities, and future directions
    N. Deepa, Quoc-Viet Pham, Dinh C. Nguyen, Sweta Bhattacharya, B. Prabadevi, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Fang Fang, Pubudu N. Pathirana
    Future Generation Computer Systems, 2022
  • Industry 5.0: A survey on enabling technologies and potential applications
    Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Prabadevi B, N Deepa, Kapal Dev, Thippa Reddy Gadekallu, Rukhsana Ruby, Madhusanka Liyanage
    Journal of Industrial Information Integration, 2022
  • Blockchain for Edge of Things: Applications, Opportunities, and Challenges
    Thippa Reddy Gadekallu, Quoc-Viet Pham, Dinh C. Nguyen, Praveen Kumar Reddy Maddikunta, N. Deepa, B. Prabadevi, Pubudu N. Pathirana, Jun Zhao, Won-Joo Hwang
    IEEE Internet of Things Journal, 2022
  • Detecting COVID-19-Related Fake News Using Feature Extraction
    Suleman Khan, Saqib Hakak, N. Deepa, B. Prabadevi, Kapal Dev, Silvia Trelova
    Frontiers in Public Health, 2022
  • Detecting heart ailments by investigating ECG with neural networks
    B. Prabadevi, N. Deepa, L.B. Krithika, Ravi Raj Gulati, R. Sivakumar
    International Journal of Medical Engineering and Informatics, 2022
  • Comparative analysis of machine learning algorithms for prediction of smart grid stability†
    Ali Kashif Bashir, Suleman Khan, B Prabadevi, N Deepa, Waleed S. Alnumay, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta
    International Transactions on Electrical Energy Systems, 2021
  • Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directions
    Prabadevi B, N Deepa, Quoc-Viet Pham, Dinh C. Nguyen, Praveen Kumar Reddy M, Thippa Reddy G, Pubudu N. Pathirana, Octavia Dobre
    IEEE Internet of Things Magazine, 2021
  • Integrated Ranking Algorithm for Efficient Decision Making
    N. Deepa, B. Prabadevi, Gautam Srivastava
    International Journal of Information Technology and Decision Making, 2021
  • An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier
    N. Deepa, B. Prabadevi, Praveen Kumar Maddikunta, Thippa Reddy Gadekallu, Thar Baker, M. Ajmal Khan, Usman Tariq
    Journal of Supercomputing, 2021
  • An ensemble model for intrusion detection in the Internet of Softwarized Things
    Gautam Srivastava, Thippa Reddy G, N. Deepa, B. Prabadevi, Praveen Kumar Reddy M
    ACM International Conference Proceeding Series, 2021
  • Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times
    P. M. Durai Raj Vincent, Nivedhitha Mahendran, Jamel Nebhen, N. Deepa, Kathiravan Srinivasan, Yuh-Chung Hu
    Computational Intelligence and Neuroscience, 2021
  • Expert system for stable power generation prediction in microbial fuel cell
    Kathiravan Srinivasan, Lalit Garg, Bor-Yann Chen, Abdulellah A. Alaboudi, N. Z. Jhanjhi, Chang-Tang Chang, B. Prabadevi, N. Deepa
    Intelligent Automation and Soft Computing, 2021
  • Predictive model for battery life in IoT networks
    Praveen Kumar Reddy Maddikunta, Gautam Srivastava, Thippa Reddy Gadekallu, Natarajan Deepa, Prabadevi Boopathy
    Iet Intelligent Transport Systems, 2020
  • Analysis of Machine Learning Algorithms on Cancer Dataset
    B. Prabadevi, N. Deepa, L.B. Krithika, Vani Vinod
    International Conference on Emerging Trends in Information Technology and Engineering Ic Etite 2020, 2020
  • Integration of E-Commerce System with Various ERP Tools
    L.B Krithika, B. Prabadevi, N. Deepa, Shruthy Bhavanasi
    International Conference on Emerging Trends in Information Technology and Engineering Ic Etite 2020, 2020
  • An analysis on Version Control Systems
    N.Deepa, B.Prabadevi, Krithika L.B, B.Deepa
    International Conference on Emerging Trends in Information Technology and Engineering Ic Etite 2020, 2020
  • Efficient Process Scheduling Algorithm using RR and SRTF
    Preeti Sinha, B. Prabadevi, Sonia Dutta, N Deepa, Neha Kumari
    International Conference on Emerging Trends in Information Technology and Engineering Ic Etite 2020, 2020
  • Advanced Machine Learning for Enterprise IoT Modeling
    N. Deepa, B. Prabadevi
    Eai Springer Innovations in Communication and Computing, 2020
  • Realizing the resolution enhancement of tube-to-tube plate friction welding microstructure images via hybrid sparsity model for improved weld interface defects diagnosis
    Journal of Internet Technology, 2020
  • Quality assessment of tire shearography images via ensemble hybrid faster region-based convnets
    Chuan-Yu Chang, Kathiravan Srinivasan, Wei-Chun Wang, Ganapathy Pattukandan Ganapathy, Durai Raj Vincent, N Deepa
    Electronics Switzerland, 2020
  • Multiclass model for agriculture development using multivariate statistical method
    N. Deepa, Mohammad Zubair Khan, B. Prabadevi, Durai Raj Vincent P.M., Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu
    IEEE Access, 2020
  • Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection
    N. Deepa, K. Ganesan
    Soft Computing, 2019
  • Realizing sustainable development via modified integrated weighting MCDM model for Ranking Agrarian Dataset
    Deepa, Ganesan, Srinivasan, Chang
    Sustainability Switzerland, 2019
  • Predictive mathematical model for solving multi-criteria decision-making problems
    N. Deepa, K. Ganesan, Balaji Sethuramasamyraja
    Neural Computing and Applications, 2019
  • Sensors driven ai-based agriculture recommendation model for assessing land suitability
    Durai Raj Vincent, N Deepa, Dhivya Elavarasan, Kathiravan Srinivasan, Sajjad Hussain Chauhdary, Celestine Iwendi
    Sensors Switzerland, 2019
  • An efficient ensemble VTOPES multi-criteria decision-making model for sustainable sugarcane farms
    N Deepa, Durai Raj Vincent P M, Senthil Kumar N, Kathiravan Srinivasan, Chuan-Yu Chang, Ali Kashif Bashir
    Sustainability Switzerland, 2019
  • A Study on Virtual and Augmented Reality in Real-Time Surgery
    K Rahul, Vincent PM Durai Raj, Kathiravan Srinivasan, N Deepa, N Senthil Kumar
    2019 IEEE International Conference on Consumer Electronics Taiwan Icce TW 2019, 2019
  • Decision-making tool for crop selection for agriculture development
    N. Deepa, K. Ganesan
    Neural Computing and Applications, 2019
  • Multi-class classification using hybrid soft decision model for agriculture crop selection
    N. Deepa, K. Ganesan
    Neural Computing and Applications, 2018
  • Clustering of wireless sensor network data
    K. Nallakaruppan, P. Ilango, N. Deepa, Anand Muthukumarappan
    Research Journal of Pharmacy and Technology, 2017
  • Mahalanobis Taguchi system based criteria selection tool for agriculture crops
    N DEEPA, K GANESAN
    Sadhana Academy Proceedings in Engineering Sciences, 2016
  • A brief survey of decision making methods and its applications in various domains
    N Deepa, K Ganesan
    Research Journal of Pharmacy and Technology, 2016
  • Dimension reduction using principal component analysis for pharmaceutical domain
    N Deepa, Chandrasekar Ravi
    Research Journal of Pharmacy and Technology, 2016
  • Aqua site classification using neural network models
    N. Deepa, K. Ganesan
    Agris on Line Papers in Economics and Informatics, 2016
  • A generalized multi criteria decision making method based on extention of ANP by enhancing PAIR WISE comparison techniques
    Arpita Barve, N. Deepa, Chandrasekar Ravi
    Cybernetics and Information Technologies, 2015
  • An improved method for tracing IP packet's source
    Indian Journal of Science and Technology, 2014
  • NP-FARM: Negative and positive fuzzy association rule mining in transaction dataset
    Indian Journal of Science and Technology, 2014
  • Dimension reduction using multivariate statistical model
    International Journal of Applied Engineering Research, 2014
  • PARM: A novel positive association rule mining algorithm for discovering malevolent applications in windows operating systems
    International Journal of Engineering and Technology, 2013
  • Image based DLP security for risk professionals - A high impact strategy
    International Review on Computers and Software, 2012
  • Adaptive hypermedia using link nnotation technology and recommender model (AHLARM)
    Journal of Theoretical and Applied Information Technology, 2012

RECENT SCHOLAR PUBLICATIONS

  • Intelligent Fake News Screening Using Hybrid Deep Learning for Regional and Global Languages
    N Deepa, JN Rani
    2025 International Conference On Emerging Computation and Information … , 2025
    2025
  • Transformer-Based Intelligent Tutoring System for Communication Skill Development
    S Venkatalakshmi, A Valarmathi, CS Angelin, M Swetha, ES Abishek, ...
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025
  • A review on recent advancements of ChatGPT and datafication in healthcare applications
    SK Jagatheesaperumal, A Pandiyarajan, P Boopathy, N Deepa, ...
    Computers in Biology and Medicine 197, 110885 , 2025
    2025
    Citations: 4
  • Prospective study on Platelet Count Indices as Predictive Biomarkers for Development of Complications in patients with Type 2 Diabetes Mellitus
    P Geetha, N Deepa, MI Jebastine, S Revetha
    Asian Journal of Pharmacy and Technology 15 (1), 13-16 , 2025
    2025
  • Blockchain for Edge Computing in Smart Environments: Use Cases, Issues, and Challenges
    B Prabadevi, N Deepa, SS Rajan, G Srivastava
    Journal of Circuits, Systems and Computers 33 (17), 2430009 , 2024
    2024
    Citations: 3
  • The Metaverse for industry 5.0 in NextG communications: potential applications and future challenges
    P Boopathy, N Deepa, PKR Maddikunta, N Victor, TR Gadekallu, ...
    IEEE Open Journal of the Computer Society 6, 4-24 , 2024
    2024
    Citations: 21
  • XAI for industry 5.0—Concepts, Opportunities, Challenges, and future directions
    TR Gadekallu, PKR Maddikunta, P Boopathy, N Deepa, R Chengoden, ...
    IEEE Open Journal of the Communications Society 6, 2706-2729 , 2024
    2024
    Citations: 63
  • Deep learning for intelligent demand response and smart grids: A comprehensive survey
    P Boopathy, M Liyanage, N Deepa, M Velavali, S Reddy, ...
    Computer science review 51, 100617 , 2024
    2024
    Citations: 135
  • Bijective Soft Set-Based Decision Model for Classification Rule Generation
    N Deepa, S Bhuvaneswari, B Prabadevi, JP Jessintha
    2023 Innovations in Power and Advanced Computing Technologies (i-PACT), 1-6 , 2023
    2023
  • Metaverse for industry 5.0 in NextG communications: Potential applications and future challenges
    B Prabadevi, N Deepa, N Victor, TR Gadekallu, PKR Maddikunta, ...
    arXiv preprint arXiv:2308.02677 , 2023
    2023
    Citations: 20
  • An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm
    MB Begum, N Deepa, M Uddin, R Kaluri, M Abdelhaq, R Alsaqour
    Heliyon 9 (6) , 2023
    2023
    Citations: 42
  • A decision model for ranking Asian Higher Education Institutes using an NLP-based text analysis approach
    B Prabadevi, N Deepa, K Ganesan, G Srivastava
    ACM Transactions on Asian and Low-Resource Language Information Processing … , 2023
    2023
    Citations: 13
  • Children Specifically Language Impairment Severity Level Prediction using Improved Conditional Random Fields and Comparison with Traditional Models
    N Deepa
    2023 3rd International Conference on Innovative Practices in Technology and … , 2023
    2023
    Citations: 3
  • A survey on blockchain for big data: Approaches, opportunities, and future directions
    N Deepa, QV Pham, DC Nguyen, S Bhattacharya, B Prabadevi, ...
    Future Generation Computer Systems 131, 209-226 , 2022
    2022
    Citations: 826
  • Industry 5.0: A survey on enabling technologies and potential applications
    PKR Maddikunta, QV Pham, N Deepa, K Dev, TR Gadekallu, R Ruby, ...
    Journal of industrial information integration 26, 100257 , 2022
    2022
    Citations: 2496
  • Detecting COVID-19-related fake news using feature extraction
    S Khan, S Hakak, N Deepa, B Prabadevi, K Dev, S Trelova
    Frontiers in Public Health 9, 788074 , 2022
    2022
    Citations: 73
  • Detecting heart ailments by investigating ECG with neural networks
    B Prabadevi, N Deepa, LB Krithika, RR Gulati, R Sivakumar
    International Journal of Medical Engineering and Informatics 14 (5), 414-423 , 2022
    2022
  • Expert System for Stable Power Generation Prediction in Microbial Fuel Cell.
    K Srinivasan, L Garg, AA Alaboudi, NZ Jhanjhi, B Prabadevi, N Deepa
    Intelligent Automation & Soft Computing 30 (1) , 2021
    2021
    Citations: 11
  • Blockchain for edge of things: Applications, opportunities, and challenges
    TR Gadekallu, QV Pham, DC Nguyen, PKR Maddikunta, N Deepa, ...
    IEEE Internet of Things Journal 9 (2), 964-988 , 2021
    2021
    Citations: 268
  • Blockchain for Edge of Things: Applications, Opportunities, and Challenges
    T Reddy Gadekallu, QV Pham, DC Nguyen, PK Reddy Maddikunta, ...
    arXiv e-prints, arXiv: 2110.05022 , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Industry 5.0: A survey on enabling technologies and potential applications
    PKR Maddikunta, QV Pham, N Deepa, K Dev, TR Gadekallu, R Ruby, ...
    Journal of industrial information integration 26, 100257 , 2022
    2022
    Citations: 2496
  • A survey on blockchain for big data: Approaches, opportunities, and future directions
    N Deepa, QV Pham, DC Nguyen, S Bhattacharya, B Prabadevi, ...
    Future Generation Computer Systems 131, 209-226 , 2022
    2022
    Citations: 826
  • Sensors driven AI-based agriculture recommendation model for assessing land suitability
    DR Vincent, N Deepa, D Elavarasan, K Srinivasan, SH Chauhdary, ...
    Sensors 19 (17), 3667 , 2019
    2019
    Citations: 290
  • Blockchain for edge of things: Applications, opportunities, and challenges
    TR Gadekallu, QV Pham, DC Nguyen, PKR Maddikunta, N Deepa, ...
    IEEE Internet of Things Journal 9 (2), 964-988 , 2021
    2021
    Citations: 268
  • An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier: N. Deepa et al.
    N Deepa, B Prabadevi, PK Maddikunta, TR Gadekallu, T Baker, MA Khan, ...
    The Journal of Supercomputing 77 (2), 1998-2017 , 2021
    2021
    Citations: 183
  • Comparative analysis of machine learning algorithms for prediction of smart grid stability †
    AK Bashir, S Khan, B Prabadevi, N Deepa, WS Alnumay, TR Gadekallu, ...
    International Transactions on Electrical Energy Systems 31 (9), e12706 , 2021
    2021
    Citations: 157
  • Deep learning for intelligent demand response and smart grids: A comprehensive survey
    P Boopathy, M Liyanage, N Deepa, M Velavali, S Reddy, ...
    Computer science review 51, 100617 , 2024
    2024
    Citations: 135
  • Predictive model for battery life in IoT networks
    PK Reddy Maddikunta, G Srivastava, T Reddy Gadekallu, N Deepa, ...
    IET Intelligent Transport Systems 14 (11), 1388-1395 , 2020
    2020
    Citations: 124
  • Toward blockchain for edge-of-things: a new paradigm, opportunities, and future directions
    B Prabadevi, N Deepa, QV Pham, DC Nguyen, T Reddy, PN Pathirana, ...
    IEEE Internet of Things Magazine 4 (2), 102-108 , 2021
    2021
    Citations: 81
  • Detecting COVID-19-related fake news using feature extraction
    S Khan, S Hakak, N Deepa, B Prabadevi, K Dev, S Trelova
    Frontiers in Public Health 9, 788074 , 2022
    2022
    Citations: 73
  • Decision-making tool for crop selection for agriculture development
    N Deepa, K Ganesan
    Neural Computing and Applications 31 (4), 1215-1225 , 2019
    2019
    Citations: 69
  • XAI for industry 5.0—Concepts, Opportunities, Challenges, and future directions
    TR Gadekallu, PKR Maddikunta, P Boopathy, N Deepa, R Chengoden, ...
    IEEE Open Journal of the Communications Society 6, 2706-2729 , 2024
    2024
    Citations: 63
  • Realizing sustainable development via modified integrated weighting MCDM model for ranking agrarian dataset
    N Deepa, K Ganesan, K Srinivasan, CY Chang
    Sustainability 11 (21), 6060 , 2019
    2019
    Citations: 57
  • Multiclass model for agriculture development using multivariate statistical method
    N Deepa, MZ Khan, B Prabadevi, DRV PM, PKR Maddikunta, ...
    IEEE Access 8, 183749-183758 , 2020
    2020
    Citations: 56
  • Multi-class classification using hybrid soft decision model for agriculture crop selection
    N Deepa, K Ganesan
    Neural Computing and Applications 30 (4), 1025-1038 , 2018
    2018
    Citations: 48
  • Deep learning for intelligent demand response and smart grids: A comprehensive survey
    QV Pham, M Liyanage, N Deepa, M Vvss, S Reddy, PKR Maddikunta, ...
    arXiv preprint arXiv:2101.08013 , 2021
    2021
    Citations: 45
  • An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm
    MB Begum, N Deepa, M Uddin, R Kaluri, M Abdelhaq, R Alsaqour
    Heliyon 9 (6) , 2023
    2023
    Citations: 42
  • Quality assessment of tire shearography images via ensemble hybrid faster region-based ConvNets
    CY Chang, K Srinivasan, WC Wang, GP Ganapathy, DR Vincent, ...
    Electronics 9 (1), 45 , 2019
    2019
    Citations: 41
  • Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection: N. Deepa, K. Ganesan
    N Deepa, K Ganesan
    Soft computing 23 (21), 10793-10809 , 2019
    2019
    Citations: 34
  • An ensemble model for intrusion detection in the internet of softwarized things
    G Srivastava, TR G, N Deepa, B Prabadevi, PK Reddy M
    Adjunct proceedings of the 2021 international conference on distributed … , 2021
    2021
    Citations: 32