Namrata Bhatt

@svvv.edu.in

ASSOCIATE PROFESSOR, INFORMATION SECURITY AND CLOUD COMPUTING DEPARTMENT
shri vaishnav institute of technology and science indore

Namrata Bhatt


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https://researchid.co/namratabhatt

EDUCATION

Ph.D. (INFORMATION TECHNOLOGY) | M. TECH. | B.TECH.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Sensory Systems, Artificial Intelligence, Computer Science
5

Scopus Publications

20

Scholar Citations

3

Scholar h-index

Scopus Publications

  • AI-Augmented IIoT: Using Edge-based Deep Learning to Improve Industrial Intelligence and Reduce Network Congestion
    R. Radhika, C. Pretty Diana Cyril, Venkata Ramanaiah Chintha, Namrata Bhatt, Preshni Shrivastava, R. Senthamil Selvan
    AI Modernisation Techniques for Contemporary Trends, 2026
    The Industrial Internet of Things (IIoT) is a common application of the Internet of Things (IoT). It enables the ubiquitous connection of all relevant IoT sensing and actuating devices, allowing for the control and monitoring of various industrial processes. The integration of deep learning into IIoT systems can enhance their intelligence and automation by providing an effective method for big data-driven modeling and analysis. Cloud servers are ideal for deploying deep learning because of the substantial amounts of computing power they require. Consequently, training necessitates the transmission of data from IoT devices to the cloud, which affects the performance of both the IoT network and the applications that depend on it. This contributes to network congestion. To shift the deep learning process from cloud servers to edge nodes, this study proposes a deep learning approach based on edge computing. This reduces network congestion and transmission demands in the IIoT network. They tackle this issue by utilizing the edge or fog computing paradigm. This approach optimizes deep learning methods for edge nodes, which have limited processing capabilities compared to servers. To evaluate this proposed solution, the researcher developed a Google Cloud testbed and deployed the suggested convolutional neural network (CNN) on a real-world IIoT dataset. With edge-based deep learning, this study introduces AI-Augmented IIoT (AAI), which reduces network congestion while preserving model performance. This research demonstrates that our solution lowers network traffic costs for IIoT while maintaining classification accuracy compared to baseline approaches.
  • Climate Change Data Forecasting using Deep Learning LSTM Model
    Namrata Bhatt, Jagriti Singh Thakur, Ankit Upadhyay, Mayank Bhatt, Unnati Mishra
    2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2024, 2024
    Time series forecasting is a method that leverages historical data to predict future information, holding immense importance across various applications. However, current techniques for time series forecasting still face challenges, particularly concerning accuracy, particularly when working with intricate multivariate and non-stationary temporal data. To overcome the shortcomings of current approaches, this study introduces the LSTM model as a technique for forecasting time series data. This model integrates two Long Short-Term Memory (LSTM) model; one serves as an encoder and the other as a decoder. The innovation lies in the introduction of an attention mechanism between these encoder and decoder components. This model boasts two key features. Firstly, the attention mechanism calculates the interrelationships within sequence data, thereby overcoming a drawback of traditional encoder-and-decoder models. This drawback relates to the decoder's challenge in handling longer input sequences effectively. Secondly, the model demonstrates its competence in predicting sequences with extended time steps. This adaptability enhances its utility for various time series forecasting scenarios. In essence, the LSTM model signifies a substantial advancement in time series forecasting. By amalgamating the robust LSTM architecture with an attention mechanism, it adeptly captures intricate relationships within temporal data. This model not only address current challenges but also presents a promising solution for achieving accurate and adaptable predictions in dynamic contexts.
  • Classify-Imbalance Data Sets in IoT Framework of Agriculture Field with Multivariate Sensors Using Centroid-Based Oversampling Method
    Namrata Bhatt, Sunita Varma
    National Academy Science Letters, 2023
  • Recommendation System for Crops Integrating with Specific soil parameters by Machine Learning Techniques
    Namrata Bhatt, Sunita Varma
    2023 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2023, 2023
    The process of investigating a set of data to extract useful information from that set is known as “data mining.” Data mining has many applications, including in the areas of commerce, health, agriculture, and more. In order to improve the quality of production while reducing the usage of pollutants agriculture uses data mining to analyse the many different environmental factors. Agriculture is the primary contributor to India's economy and accounts for most of the country's workforce. The majority of Indian farmers have the same issue: they do not choose the most suitable crop for their land based on the needs of their environment. As a result, they will see a major decline in their total level of productivity. Precision agriculture has provided a solution to the problem that the farmers were experiencing. The term “precision agriculture” refers to a contemporary farming practice that uses collected research data on soil features and kinds, as well as crop yields, and then recommends to farmers the kind of crop that would yield the most in that specific area. This leads to a decrease in crop selection errors and an increase in yield. This paper proposes a recommendation system through an ensemble model with a majority voting technique that uses a Decision Tree, Naive Bayes, SVM, Logistic Regression, Random Forest (RF), and XGBoost as learners to recommend a crop for the site-specific parameters with high accuracy and efficiency. This problem is solved as a result of the implementation of this system. Results in terms of accuracy include Decision Tree 0.9, Naive Bayes 0.99, Support Vector Machine 0.97, Logistic Regression 0.95, Random Forest 0.99, and XGBoost 0.99.
  • An Enhanced Light GBM Model with Data Analytical Approach for Crop Recommendation
    Namrata Bhatt, Sunita Varma
    Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems Icears 2023, 2023
    The economy of our country depends on crop cultivation. In India, agriculture serves as the backbone of a growing financial system, so maintaining this financial development is crucial. It makes a significant contribution to the global economic and agricultural prosperity of all nations. Unpredictable weather conditions and soil parameters are frequently the cause of low crop productivity. The main objective is to suggest an ML (machine learning) based agriculture system that can assist farmers regarding crops that can be harvested with specific values of soil and environmental parameters. Through several benchmark tests, LightGBM demonstrates improved performance in terms of prediction accuracy, model stability, and computing efficiency. This paper also evaluates the elements necessary to guarantee the optimal functioning of the crop recommendation system.

RECENT SCHOLAR PUBLICATIONS

  • Optimized Cloud Intrusion Detection Framework Leveraging Multi-Classifier Security Algorithms
    SS Kumar, K Indumathi, R Rakshitha, N Bhatt, C Anitha, ...
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025.0
  • A Data Driven Approach for Automated Brain Tumor Segmentation & Classification
    J Singh
    Vidhyayana-An International Multidisciplinary Peer-Reviewed E-Journal-ISSN … , 2024
    2024.0
  • Review of 3D Vessel Reconstruction and Quantification through OCTA Scans with Machine Learning and AI-Based Algorithm using GNN
    N Bhatt, U Mishra, N Agrawal
    Vidhyayana-An International Multidisciplinary Peer-Reviewed E-Journal-ISSN … , 2024
    2024.0
  • Climate Change Data Forecasting using Deep Learning LSTM Model
    N Bhatt, JS Thakur, A Upadhyay, M Bhatt, U Mishra
    2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical … , 2024
    2024.0
    Citations: 2
  • Big Data Analytics for Beginners
    MGJ Ms. Namrata Bhatt ,Ms.Nikita Verma, Ms.Jagriti Singh Thakur
    ISBN 978-81-976531-1-7 1, 323 , 2024
    2024.0
  • Classify-Imbalance Data Sets in IoT Framework of Agriculture Field with Multivariate Sensors Using Centroid-Based Oversampling Method: N. Bhatt, S. Varma
    N Bhatt, S Varma
    National Academy Science Letters 46 (6), 585-590 , 2023
    2023.0
    Citations: 3
  • An enhanced light GBM model with data analytical approach for crop recommendation
    N Bhatt, S Varma
    2023 Second international conference on electronics and renewable systems … , 2023
    2023.0
    Citations: 8
  • Recommendation system for crops integrating with specific soil parameters by machine learning techniques
    N Bhatt, S Varma
    2023 IEEE International Students' Conference on Electrical, Electronics and … , 2023
    2023.0
    Citations: 5
  • Technology (ICMMT2022)
    RK Dwivedi, PS Chauhan, E Jayamani, D Tiwari, V Gupta
    Journal of Physics: Conference Series 2484, 011001 , 2023
    2023.0
  • Commuication Challenges of Connected Vehicles with Integrations of Air Spare and Ground with IoT
    ADAM Vishal Bairagi, Namrata Bhatt
    International Conference on Material Science, Mechanics and Technology ISBN … , 2022
    2022.0
  • The COVID prediction model using LSTM: A novel technique
    G Jain, T Panse, N Bhatt
    Int. J. Artif. Intell. Intern. Things Cloud Comput. 1, 12-16 , 2022
    2022.0
    Citations: 1
  • Study of IOT Framework in Agriculture Field for Enhancing Production of Crop with Multivariate Sensors
    DSV Namrata Bhatt
    International Journal of Advanced Science and Technology 29, 4796 - 4803 , 2020
    2020.0
  • Face recognition using landmark estimation and convolution neural network
    N Patel, N Sharma
    Int. Res. J. Eng. Technol 6 (8), 1279-1284 , 2019
    2019.0
    Citations: 1
  • Forecasting of heart disease using hybrid algorithm
    SN Soni, N Bhatt
    Int. J. Sci. Res. Eng. Trends 5, 1234-1238 , 2019
    2019.0
  • Classifying Twitter Data in Multiple Classes Based On Sentiment Class Labels
    R Jain, N Sharma
    2018.0
  • An Improved Blood Splatters Analysis
    U Chirmade, MN Sharma, BS Patil
    Journal for Research| Volume 3 (07) , 2017
    2017.0
  • A survey of Analyzing Image Texture Using LBP with k Mean Clustering
    NS Akanxa Mishra
    International Journal of Computer Sciences and Engineering 4 (5), 173-175 , 2016
    2016.0
  • Generates UML Diagrams From Java Code Through Reverse Engineering
    MN Sharma, MGS Chandel
    IJECCE 3 (3), 445-450 , 2012
    2012.0
  • Reverse Engineering-An Intermediate Step towards Re-engineering
    SM Namrata Sharma
    International Journal of Computer Applications NTSACT 1 (1), 26-30 , 2011
    2011.0
  • AI-Augmented IIoT: Using Edge-based Deep Learning to Improve Industrial Intelligence and Reduce Network CongestionAI-Augmented IIoT: Using Edge-based Deep Learning to Improve …
    R Radhika, CPD Cyril, VR Chintha, N Bhatt, P Shrivastava, RS Selvan
    AI Modernisation Techniques for Contemporary Trends, 1-15 , 0

MOST CITED SCHOLAR PUBLICATIONS

  • An enhanced light GBM model with data analytical approach for crop recommendation
    N Bhatt, S Varma
    2023 Second international conference on electronics and renewable systems … , 2023
    2023.0
    Citations: 8
  • Recommendation system for crops integrating with specific soil parameters by machine learning techniques
    N Bhatt, S Varma
    2023 IEEE International Students' Conference on Electrical, Electronics and … , 2023
    2023.0
    Citations: 5
  • Classify-Imbalance Data Sets in IoT Framework of Agriculture Field with Multivariate Sensors Using Centroid-Based Oversampling Method: N. Bhatt, S. Varma
    N Bhatt, S Varma
    National Academy Science Letters 46 (6), 585-590 , 2023
    2023.0
    Citations: 3
  • Climate Change Data Forecasting using Deep Learning LSTM Model
    N Bhatt, JS Thakur, A Upadhyay, M Bhatt, U Mishra
    2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical … , 2024
    2024.0
    Citations: 2
  • The COVID prediction model using LSTM: A novel technique
    G Jain, T Panse, N Bhatt
    Int. J. Artif. Intell. Intern. Things Cloud Comput. 1, 12-16 , 2022
    2022.0
    Citations: 1
  • Face recognition using landmark estimation and convolution neural network
    N Patel, N Sharma
    Int. Res. J. Eng. Technol 6 (8), 1279-1284 , 2019
    2019.0
    Citations: 1
  • Optimized Cloud Intrusion Detection Framework Leveraging Multi-Classifier Security Algorithms
    SS Kumar, K Indumathi, R Rakshitha, N Bhatt, C Anitha, ...
    2025 IEEE 5th International Conference on ICT in Business Industry … , 2025
    2025.0
  • A Data Driven Approach for Automated Brain Tumor Segmentation & Classification
    J Singh
    Vidhyayana-An International Multidisciplinary Peer-Reviewed E-Journal-ISSN … , 2024
    2024.0
  • Review of 3D Vessel Reconstruction and Quantification through OCTA Scans with Machine Learning and AI-Based Algorithm using GNN
    N Bhatt, U Mishra, N Agrawal
    Vidhyayana-An International Multidisciplinary Peer-Reviewed E-Journal-ISSN … , 2024
    2024.0
  • Big Data Analytics for Beginners
    MGJ Ms. Namrata Bhatt ,Ms.Nikita Verma, Ms.Jagriti Singh Thakur
    ISBN 978-81-976531-1-7 1, 323 , 2024
    2024.0
  • Technology (ICMMT2022)
    RK Dwivedi, PS Chauhan, E Jayamani, D Tiwari, V Gupta
    Journal of Physics: Conference Series 2484, 011001 , 2023
    2023.0
  • Commuication Challenges of Connected Vehicles with Integrations of Air Spare and Ground with IoT
    ADAM Vishal Bairagi, Namrata Bhatt
    International Conference on Material Science, Mechanics and Technology ISBN … , 2022
    2022.0
  • Study of IOT Framework in Agriculture Field for Enhancing Production of Crop with Multivariate Sensors
    DSV Namrata Bhatt
    International Journal of Advanced Science and Technology 29, 4796 - 4803 , 2020
    2020.0
  • Forecasting of heart disease using hybrid algorithm
    SN Soni, N Bhatt
    Int. J. Sci. Res. Eng. Trends 5, 1234-1238 , 2019
    2019.0
  • Classifying Twitter Data in Multiple Classes Based On Sentiment Class Labels
    R Jain, N Sharma
    2018.0
  • An Improved Blood Splatters Analysis
    U Chirmade, MN Sharma, BS Patil
    Journal for Research| Volume 3 (07) , 2017
    2017.0
  • A survey of Analyzing Image Texture Using LBP with k Mean Clustering
    NS Akanxa Mishra
    International Journal of Computer Sciences and Engineering 4 (5), 173-175 , 2016
    2016.0
  • Generates UML Diagrams From Java Code Through Reverse Engineering
    MN Sharma, MGS Chandel
    IJECCE 3 (3), 445-450 , 2012
    2012.0
  • Reverse Engineering-An Intermediate Step towards Re-engineering
    SM Namrata Sharma
    International Journal of Computer Applications NTSACT 1 (1), 26-30 , 2011
    2011.0
  • AI-Augmented IIoT: Using Edge-based Deep Learning to Improve Industrial Intelligence and Reduce Network CongestionAI-Augmented IIoT: Using Edge-based Deep Learning to Improve …
    R Radhika, CPD Cyril, VR Chintha, N Bhatt, P Shrivastava, RS Selvan
    AI Modernisation Techniques for Contemporary Trends, 1-15 , 0