Dr. Lakshmi K

@reva.edu.in

Associate Professor, school of Computer Science and Applications
REVA University

Dr. Lakshmi K


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https://researchid.co/lakshmikes

EDUCATION

M.C.A, M.Phil, Ph.D

RESEARCH INTERESTS

Data Mining, Machine Learning
11

Scopus Publications

68

Scholar Citations

5

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Promises and Perils of AI: Understanding Its DUAL Capabilities
    K. Lakshmi, K. Deeba
    Mitigating the Risks of AI Deepfakes, 2026
  • Privacy-Aware Federated Graph Neural Networks for Adaptive and Explainable Cancer Drug Personalization
    Tripti Sharma, Lakshmi K, M. Misba, Jasgurpreet Singh Chohan, R. Aroul Canessane, et al.
    International Journal of Advanced Computer Science and Applications, 2025
  • Rose Leaves Disease Detection Using a Customized Convolutional Neural Network
    Lakshmi K, Sneha Bharti, Vani Harave
    International Conference on Intelligent Communication Networks and Computational Techniques Icicnct 2025, 2025
  • Cyber Alerter: Automated Vulnerability Monitoring System Integrating NVD and OEM APIs
    Chandan. L, Vani Harave, Sneha Bharti, Lakshmi. K
    International Conference on Intelligent Communication Networks and Computational Techniques Icicnct 2025, 2025
  • Efficient Signature Forgery Detection System using CLAHE-Enhanced VGG16 Model
    Sushmitha. K M, Sneha Bharti, Vani Harave, Lakshmi. K, Ch. Vanipriya
    5th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2025, 2025
  • Life Expectancy Prediction and Diet Recommendation System for Cardiovascular and Diabetes Disease Using Machine Learning
    K Lakshmi, K Deeba, Vani Harave, Sneha Bharti
    2024 International Conference on Knowledge Engineering and Communication Systems Ickecs 2024, 2024
    In the current era, people face many health issues and diseases due to inadequate and inappropriate food intake. People rely on medicine rather than having a proper dietary plan due to a lack of concise information on a proper diet. The diverse options in food components and people’s preferences with underlying health conditions make it difficult to perform real-time nutrition selection that fulfills a proper dietary plan. This problem is addressed through the implementation of a machine learning algorithm that effectively detects diseases and calculates life expectancy, enabling the formulation of suitable diet plans to mitigate their impact. The proposed work focuses on two common diseases: Diabetes and Cardiovascular Disease (CVD).A supervised classification algorithm has been used for predicting diseases and an unsupervised clustering algorithm has been used for diet recommendation. The objective is to facilitate convenient disease prediction at home and offer personalized, healthy diet recommendations. By motivating users to adopt a healthy lifestyle, the proposed work aims to prevent or reduce the influence of diseases.
  • PCA based Machine Learning techniques for Heart Disease Diagnosis System
    K. Vijayalakshmi, Lakshmi. K, Deeba. K, Ashika Devi R
    Proceedings of the 4th IEEE International Conference on Smart Technologies in Computing Electrical and Electronics Icstcee 2023, 2023
    Prominent and preliminary information mining has spread to a great extent globally with innovative real-time applications. It rapidly addresses the challenges in marketing, e-business, retail, education, health sectors, etc., in different areas and determination. Medical services are among those that need quick recommendation services and diagnosis through technological upgrades. In any situation, effective investigation apparatus is limited in identifying abandoned patterns in data. With the application of machine learning, this work aims to provide a model with better performance measured based on various classification metrics like precision and accuracy.Method: The proposed work applies data preprocessing techniques like label encoding, standardization scaling and principal component analysis on the heart diagnosis dataset. Further, the model is built using various classification algorithms like SVM, KNN, Random Forest, Logistic Regression and decision tree and evaluated on unseen observations.Results: The alignment of predictive machine learning techniques within the same dataset has been examined to detect heart disease based on two binary class labels: yes and no. The observed results show better performances for Decision Tree on unscaled data and PCA (both 80%), Logistic regression with PCA (86%), Random Forest on Scaled and PCA (both 88%), KNN and SVM on PCA with scaled data (86% & 87%). The Random Forest model surpasses all other models with more accuracy.
  • Efficiency Analysis of Firefly Optimization-Enhanced GAN-Driven Convolutional Model for Cost-Effective Melanoma Classification
    Lakshmi K, Sridevi Gadde, Murali Krishna Puttagunta, G. Dhanalakshmi, Yousef A. Baker El-Ebiary
    International Journal of Advanced Computer Science and Applications, 2023
    — Early identification is essential for successful treatment of melanoma, a potentially fatal type of skin cancer. This work takes a fresh approach to addressing the urgent need for an accurate and economical melanoma categorization system. Inaccuracy, efficiency, and resource usage are common problems with current techniques. A model that incorporates a number of innovative methods to get beyond these restrictions was used in this study. To improve data quality, first applied the pre-processing with a Gaussian filter and augment our dataset with Generative Adversarial Networks (GAN). To extract and classify features, this suggested model makes use of Convolutional Long Short-Term Memory (LSTM) networks. The model performs better and is substantially more accurate when Firefly Optimization is used. It analyses the model's ability to lower healthcare costs by doing a cost-effective analysis, especially when detecting melanoma, including situations involving bleeding lesions. The proposed FFO Enhanced Conv-LSTM's cost-effective analysis makes it possible to compare it favourably to deep convolutional neural networks (DCNN), showcasing its promise for melanoma classification accuracy and healthcare resource allocation optimization. For this study, Python software was used as the implementation tool. The suggested model achieves a 99.1% accuracy rate, which is better than current techniques. A comparative study with well-known models such as Res Net 50, Mobile Net, and Dense Net 169 highlights the notable enhancement provided by the proposed Firefly Optimization-enhanced Conv-LSTM method. This model offers a promising advancement in the precise and economical classification of melanoma due to its high accuracy and cost-effectiveness. In comparison to existing approaches like Res Net 50, Mobile Net, and Dense Net 169, the suggested Firefly Optimization-enhanced Convolutional LSTM (FFO Enhanced Conv-LSTM) method shows an average gain of roughly 5.6% in accuracy.
  • Compact in-memory representation of large graph databases for efficient mining of maximal frequent sub graphs
    K Lakshmi, T Meyyappan
    Concurrency and Computation Practice and Experience, 2021
    Complex networks have been used in many scientific disciplines like sociology, microbiology, and telecommunication to represent the interactions among them. Graphs are generally used for representing such complex networks. Mining significant frequent patterns from graph databases has been a challenging area of research. A number of sub graph mining algorithms have been proposed for finding frequent fragments in molecular databases. A very few algorithms have been proposed for mining frequent patterns from large communication networks. All these algorithms perform well on medium size networks and fail on very large graphs. The scalability of these algorithms has been an issue because of the enormous memory requirements and also due to the exponential number of frequent sub graphs possible. In this paper, we propose a compact way of representing graph databases and also use it in a maximal frequent sub graph mining algorithm. The algorithm is found to be efficient and scalable to very large graph databases.
  • Efficient frequent sub graph mining-M
    Journal of Advanced Research in Dynamical and Control Systems, 2019
  • Efficient mining of frequent sub graphs
    K. Lakshmi, T. Meyappan
    2017 IEEE International Conference on Current Trends in Advanced Computing Icctac 2017, 2017

RECENT SCHOLAR PUBLICATIONS

  • Rose Leaves Disease Detection Using a Customized Convolutional Neural Network
    K Lakshmi, S Bharti, V Harave
    2025 International Conference on Intelligent Communication Networks and … , 2025
    2025.0
  • Life expectancy prediction and diet recommendation system for cardiovascular and diabetes disease using machine learning
    K Lakshmi, K Deeba, V Harave, S Bharti
    2024 International Conference on Knowledge Engineering and Communication … , 2024
    2024.0
    Citations: 4
  • PCA based Machine Learning techniques for Heart Disease Diagnosis System
    K Vijayalakshmi, A Devi
    2023 Fourth International Conference on Smart Technologies in Computing … , 2023
    2023.0
  • Efficiency Analysis of Firefly Optimization-Enhanced GAN-Driven Convolutional Model for Cost-Effective Melanoma Classification.
    S Gadde, MK Puttagunta, G Dhanalakshmi, YAB El-Ebiary
    International Journal of Advanced Computer Science & Applications 14 (11) , 2023
    2023.0
    Citations: 8
  • Protecting Shopping preference with differential Privacy
    LK Channabasav Awati
    International Research journal of Modernization in Engineering and … , 2022
    2022.0
  • Sell or Rent Property- web Services
    Amit Kumar, Lakshmi K
    International Research and Development Journal in Engineering and Science 1 … , 2022
    2022.0
  • Detecting Bogus accounts in Social Media
    DLK Chaitra S
    International Journal of Modernization in Engineering Technology and Science … , 2022
    2022.0
  • Agricultural Food supply chain Traceability using Block Chain
    International Research and Development Journal in Engineering and Science , 2022
    2022.0
  • Neural Machine Translation for Indian Languages
    Anjali Jha, Dr. Lakshmi K
    International Journal of Modernization in Engineering Technology and Science … , 2022
    2022.0
  • Efficient Frequent Sub Graph Mining – M
    TM K. Lakshmi
    Journal of Advanced Research in Dynamical and Control Systems (JARDCS) 11 (04) , 2019
    2019.0
  • Compact in‐memory representation of large graph databases for efficient mining of maximal frequent sub graphs
    K Lakshmi, T Meyyappan
    Concurrency and Computation: Practice and Experience, e5243 , 2019
    2019.0
    Citations: 6
  • Spanning Tree- Properties, Algorithms and Applications
    TM K. Lakshmi
    International Journal of Computer Science and Engineering 5 (10), 54-58 , 2018
    2018.0
    Citations: 2
  • Identification of Significant nodes in a Communication network using efficient maximal frequent subgraph mining
    KL T Meyyappan
    JARDCS 10 (9), 342-349 , 2018
    2018.0
  • Efficient mining of frequent sub graphs
    K Lakshmi, T Meyappan
    2017 IEEE International Conference on Current Trends in Advanced Computing … , 2017
    2017.0
    Citations: 1
  • Efficient algorithm for mining frequent subgraphs (Static and Dynamic) based on gSpan
    K Lakshmi, T Meyyappan
    International Journal of Computer Applications 63 (19) , 2013
    2013.0
    Citations: 6
  • A comparative study of frequent subgraph mining algorithms
    K Lakshmi, T Meyyappan
    International Journal of Information Technology Convergence and Services 2 … , 2012
    2012.0
    Citations: 17
  • Frequent subgraph mining algorithms-a survey and framework for classification
    K Lakshmi, T Meyyappan
    CS & IT Conference Proceedings 2 (1) , 2012
    2012.0
    Citations: 24
  • FREQUENT SUBGRAPH MINING ALGORITHMS
    K Lakshmi, T Meyyappan
    2012.0
  • MINING ALGORITHMS
    K Lakshmi, T Meyyappan

MOST CITED SCHOLAR PUBLICATIONS

  • Frequent subgraph mining algorithms-a survey and framework for classification
    K Lakshmi, T Meyyappan
    CS & IT Conference Proceedings 2 (1) , 2012
    2012.0
    Citations: 24
  • A comparative study of frequent subgraph mining algorithms
    K Lakshmi, T Meyyappan
    International Journal of Information Technology Convergence and Services 2 … , 2012
    2012.0
    Citations: 17
  • Efficiency Analysis of Firefly Optimization-Enhanced GAN-Driven Convolutional Model for Cost-Effective Melanoma Classification.
    S Gadde, MK Puttagunta, G Dhanalakshmi, YAB El-Ebiary
    International Journal of Advanced Computer Science & Applications 14 (11) , 2023
    2023.0
    Citations: 8
  • Compact in‐memory representation of large graph databases for efficient mining of maximal frequent sub graphs
    K Lakshmi, T Meyyappan
    Concurrency and Computation: Practice and Experience, e5243 , 2019
    2019.0
    Citations: 6
  • Efficient algorithm for mining frequent subgraphs (Static and Dynamic) based on gSpan
    K Lakshmi, T Meyyappan
    International Journal of Computer Applications 63 (19) , 2013
    2013.0
    Citations: 6
  • Life expectancy prediction and diet recommendation system for cardiovascular and diabetes disease using machine learning
    K Lakshmi, K Deeba, V Harave, S Bharti
    2024 International Conference on Knowledge Engineering and Communication … , 2024
    2024.0
    Citations: 4
  • Spanning Tree- Properties, Algorithms and Applications
    TM K. Lakshmi
    International Journal of Computer Science and Engineering 5 (10), 54-58 , 2018
    2018.0
    Citations: 2
  • Efficient mining of frequent sub graphs
    K Lakshmi, T Meyappan
    2017 IEEE International Conference on Current Trends in Advanced Computing … , 2017
    2017.0
    Citations: 1
  • Rose Leaves Disease Detection Using a Customized Convolutional Neural Network
    K Lakshmi, S Bharti, V Harave
    2025 International Conference on Intelligent Communication Networks and … , 2025
    2025.0
  • PCA based Machine Learning techniques for Heart Disease Diagnosis System
    K Vijayalakshmi, A Devi
    2023 Fourth International Conference on Smart Technologies in Computing … , 2023
    2023.0
  • Protecting Shopping preference with differential Privacy
    LK Channabasav Awati
    International Research journal of Modernization in Engineering and … , 2022
    2022.0
  • Sell or Rent Property- web Services
    Amit Kumar, Lakshmi K
    International Research and Development Journal in Engineering and Science 1 … , 2022
    2022.0
  • Detecting Bogus accounts in Social Media
    DLK Chaitra S
    International Journal of Modernization in Engineering Technology and Science … , 2022
    2022.0
  • Agricultural Food supply chain Traceability using Block Chain
    International Research and Development Journal in Engineering and Science , 2022
    2022.0
  • Neural Machine Translation for Indian Languages
    Anjali Jha, Dr. Lakshmi K
    International Journal of Modernization in Engineering Technology and Science … , 2022
    2022.0
  • Efficient Frequent Sub Graph Mining – M
    TM K. Lakshmi
    Journal of Advanced Research in Dynamical and Control Systems (JARDCS) 11 (04) , 2019
    2019.0
  • Identification of Significant nodes in a Communication network using efficient maximal frequent subgraph mining
    KL T Meyyappan
    JARDCS 10 (9), 342-349 , 2018
    2018.0
  • FREQUENT SUBGRAPH MINING ALGORITHMS
    K Lakshmi, T Meyyappan
    2012.0
  • MINING ALGORITHMS
    K Lakshmi, T Meyyappan