Maridu Bhargavi

@vignan.ac.in

Assistant Professor
Vignan's Foundation for Science, Technology and Research

Maridu Bhargavi


               Download Compact PDF  
https://researchid.co/maridubhargavi

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Vision and Pattern Recognition
48

Scopus Publications

155

Scholar Citations

7

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Harnessing Machine Learning for Accurate Water Quality Monitoring and Prediction
    Madira Srilatha, Maridu Bhargavi, Sanagapati Akanksha, Manne Kirety Chowdary, Biladugu Ramanjamma
    Smart Innovation Systems and Technologies, 2026
  • Exploring Rank-Popularity Dynamics in Anime: Insights from Comprehensive Data Analysis
    Koralla Dasaanjaneya, Vamsi Krishna Kambala, Chaitanya Thakur, Maridu Bhargavi, Teja Swaroop Dharanikota
    Communications in Computer and Information Science, 2026
  • Hybrid approach for multi-class skin cancer classification with DCNN feature and ensemble techniques
    Maridu Bhargavi, Sivadi Balakrishna
    Engineering Research Express, 2025
    The classification of skin cancer is a critical factor in early diagnosis and treatment. This paper presents a hybrid multi-class skin cancer classification that uses a deep convolutional neural network as the feature extraction model and an ensemble of Histogram Gradient Boosting, Random Forest, and XGBoost classifiers with soft voting for the final prediction. The proposed model achieved a high classification accuracy of 92% on the ISIC dataset and 93% on the PAD-UFES-20 dataset, with corresponding specificity values of 98.92% and 98.75%, respectively. The overall false positive rates were 0.21% and 0.17%, respectively. The precision, recall, and F1-scores also exceeded 91% demonstrating high sensitivity and fairly balanced performance. The false negative rate was also kept relatively low at 1.90% for ISIC and 0.67% for PAD, while the Matthews correlation coefficients(MCC) were quite high at 97.60% for ISIC and 91.85% for PAD. The proposed model is also evaluated on an external dataset, HAM10000, yielding an accuracy of 66.67% confirming generalization capability under a domain shift. This DCNN-ensemble approach to automated skin cancer detection has shown itself to be a solid and scalable solution. Future studies should focus on feature fusion, expanding this approach to more diverse datasets that may encompass different patient populations and analysis, and integrating applications that enable real-time diagnostic support.
  • Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration
    Maridu Bhargavi, Sivadi Balakrishna
    Engineering Science and Technology an International Journal, 2025
    Skin cancer continues to be a major health problem worldwide, with excessive misdiagnosis of skin cancer among dermatologists resulting in delayed treatment and poor patient outcomes. To improve survival chances, skin cancer must be identified accurately and promptly. However, current diagnostic methods lack feature representation and model generalization. Among the primary challenges in automated skin cancer classification are addressing differences in lesion appearance, occlusions, and data class imbalance that impact model performance and reliability. To address these issues, this research proposes the DRMv2Net model, a feature fusion deep learning-based technique that integrates multiple pre-trained convolutional neural networks to enhance skin cancer diagnosis. The method applies a systematic pipeline that includes pre-processing, feature extraction, fusion, and classification. The pre-processing techniques such as adaptive thresholding for hair artifact removal, image inpainting to remove occlusions, and data augmentation for class balancing, were applied to enhance the quality of inputs. Using DenseNet201, ResNet101, and MobileNetV2, diverse features like edges, texture, and color change were extracted and concatenated to build a rich feature representation, followed by fully connected layers for classification. The two benchmark datasets, ISIC 2357 and PAD-UFES 20, are used extensively in testing the DRMv2Net model. A comparison with standalone CNN models such as DenseNet201, ResNet101, MobileNetV2, VGG19, and Xception shows that feature fusion had better accuracy results of 96.11 % on ISIC 2357 and 96.17 % on PAD-UFES 20 respectively when compared to values obtained by existing standalone models. These results demonstrate the strength of feature fusion and pre-processing in boosting how accurately skin cancer is identified and offer a robust and scalable automatic medical image classification solution.
  • AI for Clean Water: Predicting Potability with Machine Learning
    Karumuri Akhila, Maridu Bhargavi, Akash Ajay
    Proceedings of 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2025, 2025
    Access to safe drinking water is a huge global problem, with contamination being a major danger to public health. This study proposes a machine learning framework for predicting potability of water using its physicochemical properties, allowing rapid and scalable assessment of water quality attributes. Pre-processing of the Kaggle water potability dataset, made up of 3,276 samples with 9 inputs, was done through mean imputation and Min–Max normalization as a strategy to address missing values and standardized features. The five supervised models, consisting of Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Gradient Boosting (GB), were explored and evaluated. The experimental results showed that GB was the finest, attaining an accuracy of 86%, significantly more than other models, and better than previously claimed. This machine learning framework set forth will be beneficial for automated and data-driven water quality monitoring. However, the practical challenges of an imbalanced dataset and not using any microbiological characteristics were recognized and would be planned for in future work by conducting further experimentation on expanded dataset sample sizes and evolving to hybrid models with complex features.
  • AI-Driven Evaluation Techniques: Revolutionizing Student Practices
    S. K. Sajida Sultana, R. Renugadevi, Maridu Bhargavi, Shaik Abdul Afzal Biyabani
    Adopting Artificial Intelligence Tools in Higher Education Student Assessment, 2025
    The chapter delves into the transformative impact of artificial intelligence (AI) on educational assessments, highlighting how AI significantly enhances accuracy, efficiency, and personalization compared with traditional methods. By analyzing vast datasets, AI provides deeper insights into student performance, allowing for personalized learning experiences tailored to individual needs. One of its key advantages is delivering real-time feedback, which facilitates immediate corrections, reinforces learning, and ultimately improves student understanding and retention. AI also ensures consistent and unbiased evaluations by applying predefined criteria, thus minimizing human biases and promoting fairness. Its role is crucial in both formative and summative assessments. In formative assessments, AI tracks student progress over time, offering continuous feedback that helps educators adjust their teaching strategies as needed. However, the integration of AI into educational assessments comes with challenges, including concerns about data privacy, algorithm transparency, and potential biases within the systems. Addressing these issues is vital for the ethical and effective use of AI in education. Despite these challenges, AI-driven assessment techniques hold the potential to revolutionize the way student evaluations are conducted, making them more accurate, efficient, personalized, and fair.
  • Climate Change Analysis and Prediction
    Talupula Subhashini, Mulaka Durga Bhavani, Gadde Jeshmitha, Kakarala Pallavi Sai Sarvani, Maridu Bhargavi
    2025 IEEE International Conference on Emerging Trends in Computing and Communication Etcom 2025, 2025
    Climate change is one of the most critical global challenges in the present time due to the continuous damage it causes to the environment and living organisms as a result of rising temperatures and increasing levels of greenhouse gas emissions. This paper applies several machine learning techniques to analyze and predict variations in temperature, using a global dataset containing average temperature and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> emissions. The preprocessing of data included feature engineering, normalization, and classification of temperature levels into two classes. Several models were developed and compared: Decision Tree, Naïve Bayes, Random Forest, Support Vector Machine, and XGBoost. The paper also applied ensemble methods like Stacking and Voting Classifiers to improve the accuracy of the predictions. The comparison indicated that, overall, the performance of the ensemble models in classifying high and low variations of temperatures was better compared to individual classifiers. As a whole, this study focuses on the fact that machine learning can be a strong tool for understanding the variation in climate change and predicting it, thus being of great help for environmental planning, policy framing, and sustainability studies.
  • Enhanced Prediction of Food Wastage Using Ensemble Learning and Feature Selection
    Venkata Kamya Punugupati, Bhargavi Maridu, Vijayalakshmi Maddiboyina, Akshay Kanuri, Ruthvik Peddineni
    Lecture Notes in Networks and Systems, 2025
  • AI for Finance: Hybrid Learning Models for Stock Prediction
    Padusuri SaiBabu, Maridu Bhargavi, Vemula Jayasri, Ganpisetty Pravallika, Uzma Afzal
    2025 IEEE International Conference on Emerging Trends in Computing and Communication Etcom 2025, 2025
    Stock price prediction remains one of the most challenging problems in financial analytics due to market volatility, nonlinear dependencies, and sensitivity to external economic factors. Accurate stock forecasting is essential for investors and financial institutions to make informed decisions, reduce risk, and maximize returns. Traditional econometric approaches, such as ARIMA, struggle to model the nonlinear and dynamic behavior of stock data, leading to increased interest in machine learning (ML) and deep learning (DL) techniques. This paper presents a hybrid framework that integrates Support Vector Regression (SVR), Random Forest Regression (RFR), and Long Short-Term Memory (LSTM) networks for stock price prediction. The methodology includes rigorous data preprocessing, feature extraction using technical indicators such as Moving Averages, RSI, MACD, Bollinger Bands, and OBV, followed by normalization for model stability. SVR and RFR serve as classical regression baselines, while LSTM captures temporal dependencies. An ensemble layer combines their predictions through weighted averaging optimized using validation performance. Experiments conducted using five years of S&P 500 daily historical data show that the LSTM model achieved an RMSE of 2.15 and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> of 0.91, outperforming the standalone ML models. The hybrid ensemble achieved further improvements with an RMSE of 1.92 and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> of 0.93, proving that hybridization enhances both robustness and predictive accuracy. The results confirm that deep learning effectively captures sequential dependencies, while ensemble methods provide resilience and generalization. Future work will incorporate sentiment analysis, macroeconomic indicators, and explainable AI (XAI) to improve interpretability and real-world adoption.
  • Unlocking Potential: Personalizing Learning and Assessment with Cutting-Edge Technologies
    R. Renugadevi, Maridu Bhargavi, G. Kalaiarasi, P. Ranjith Kumar, A. Arul Edwin Raj, B. Saritha
    Adopting Artificial Intelligence Tools in Higher Education Student Assessment, 2025
    This chapter investigates the growing significance of technology in education, specifically how it is transforming learning experiences and evaluation processes. The advancement of education through the use of cutting-edge technology such as artificial intelligence (AI), machine learning (ML), and big data analytics represents a radical move toward individualized learning and assessments. These modern technologies provide a personalized educational approach by delivering individualized support, curriculum, and assessments based on each learner’s specific needs. Real-time feedback and adaptive platforms enable instructors to address individual learning issues and improve teaching tactics using data-driven insights. This tailored method increases student engagement and improves learning results by tailoring to their interests and strengths. Despite difficulties like data privacy and digital equity, increasing technological improvements promise a more inclusive and effective learning environment. By embracing these breakthroughs and working toward solutions, we may build a future in which technology empowers educators and personalizes learning for all, enabling a dynamic and revolutionary educational environment. The future of education will increasingly rely on these innovations to create adaptive, responsive learning environments that support every student’s growth and success.
  • Integrating Multiple Machine Learning Models for Reliable Cardiovascular Risk Prediction
    Uppala Humesh Akhileswar, Maridu Bhargavi, Barnabas Gummadi, Naga Srinivasa Rao Kotapati, Chalicham Arjun, Vikas Immanni
    2025 1st International Conference on Advancement in Futuristic Technologies Icaft 2025, 2025
  • Hybrid intelligent diagnosis: Differentiating oligodendroglioma and astrocytoma through combined radiology and pathology using DL
    Bhargavi Maridu, Renugadevi R., Shareefunnisa Syed, Sajida Sultana S. K.
    Artificial Intelligence Transformations for Healthcare Applications Medical Diagnosis Treatment and Patient Care, 2024
  • Transfer learning based feature extraction with metaheuristic optimization algorithm for detecting gastric cancer using optoelectronic sensor in endoscope
    S. Famila, A. Arulmurugan, A. Mahendar, R. Kalaiyarasan, N. Supriya, Bhargavi Maridu
    Optical and Quantum Electronics, 2024
  • Probing Skin Cancer Awareness: Insights in to Classification Approaches
    Maridu Bhargavi, Sivadi Balakrishna
    Proceedings 3rd International Conference on Advances in Computing Communication and Applied Informatics Accai 2024, 2024
  • Classifying Skin Lesions: A Survey of Approaches in Skin Cancer Diagnosis
    Renugadevi R, Maridu Bhargavi, Sivadi Balakrishna
    Proceedings 2024 4th International Conference on Pervasive Computing and Social Networking Icpcsn 2024, 2024
  • Deep Learning Based Traffic Sign Recognition using CNN and TensorFlow
    P.Srinivasa Gowtham, Maridu Bhargavi, P. Kavyanjali, P.Naga Babu, K. Subhash
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Career Prediction Using Machine Learning
    Kundan Jha, D. Likhitha, M.Siri Chandana, M.Ram Prakash Reddy, Maridu Bhargavi
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • Stock Price Forecasting by Time Series Analysis
    Reshma Surekha, Divya Gupta, Adarsh Kumar Jha, Maridu Bhargavi, Poorna Sai
    Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024
  • Sentiment-Based Insights Into Amazon Musical Instrument Purchases
    Alla Ammulu, Maridu Bhargavi, Ande Mokshagna, Bollimuntha Manasa, Parasa Ganesh
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Predictive Analysis for Retail: Sales Forecasting at Walmart
    Nemalikanti Naga Alekhyasri, Gunturu Bhanu Prasad, Tarigopula Pardhasaradhi, Arumalla Purna Vignesh Reddy, Maridu Bhargavi
    Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
  • Predicting App Ratings on Google Play Store: An Ensemble Learning Approach
    Ravirala Vinay Naga Gopi, Chaganti Siri Vigna, Jasti Phani, Kayala Vishnu Kanth, Maridu Bhargavi
    2024 IEEE International Conference on Information Technology Electronics and Intelligent Communication Systems Iciteics 2024, 2024
  • Network Intrusion Detection System using Machine Learning Algorithms
    Shivam Kumar, Boyapati Bhavana, Safuwan Shiblee, Kanchinadham Sri Pranathi, Maridu Bhargavi
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • Predicting Employee Attrition with Deep Learning and Ensemble Techniques for Optimized Workforce Management
    Chandana Mellachervu, Bhargavi Maridu, Renuka Sanikommu, Pavan Sai Kakumanu, Shatakshi Bajpai
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Fraud detection: Comparison of traditional methods, hybrid methods, monarch butterfly optimization, and Temporal Convolutional Networks
    P N L Gayatri Samanvita, Abhinay Balivada, Subham Kumar Satapathy, B Mani Ratna Kumar, Maridu Bhargavi
    Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
  • Credit Card Fraud Detection
    Alok Kumar, Marella Venkata Poojitha, Turlapati Anuhya, Katuri Srinivas, Maridu Bhargavi
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • Product Price Prediction Using Ensemble Learning Techniques
    V.Bhanu Prakash, Sahil, Harika Rayi, P.Narendra Reddy, Bhargavi Maridu
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • Detecting Data Manipulation in Electric Vehicle Charging Stations Using Machine Learning Algorithm
    Jannavarapu Vani Akhila, Maridu Bhargavi, Mondem Manikanta, Srigakolapu Sai Lakshmi, Nagulapati Phanindra Raja Mithra
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Navigating Car Price Predictions: Unveiling Machine Learning Insights
    Ch. Himaja, N. Jayasri, S. Pratheek, Sk. Sameer, M. Bhargavi
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • Leveraging Smote and Random Forest for Improved Credit Card Fraud Detection
    Maddala Ruchita, Maridu Bhargavi, Maddala Rakshita, Bellamkonda Chaitanya Nandini, Irfan Aziz, Jujjuri Gopi
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Stacking Models for Employee Attrition Prediction: Leveraging Logistic Regression and Random Forest
    Sravanthi Polisetti, Maridu Bhargavi, Sindhu Chitneni, Sushma Eluri, Neeharika Kattamuri, Renugadevi R
    8th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2024 Proceedings, 2024
  • Deep Learning for Skin Cancer Classification: Leveraging Feature Extraction and Transfer Learning Strategies
    Maridu Bhargavi, Syed Shareefunnisa, Sk Sajida Sultana, R Renugadevi, Talari Niteesh Varshan, Kamisetty Ramanjaneyulu
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • Leveraging XGBoost and Clinical Attributes for Heart Disease Prediction
    Kota Susmitha, Maridu Bhargavi, Achyuta Mohitha Sai Sri, Bogala Devi Prasaad Reddy, Paladugu Siva Satyanarayana, K V Ranga Rao
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Hybrid Model Fusion: Enhancing Water Quality Prediction using Ensemble Modelling
    Venkatesh Mannepati, Sri Lakshmi Abburi, Dasaradha Rami Reddy Dudla, Ramya Chukkapalli, Venkatesh Addagadda, Maridu Bhargavi
    Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
  • Deep Learning - based Banknote Classification: Harnessing Artificial Neural Networks
    Sk Sajida Sultana, Maridu Bhargavi, Renuga Devi, Bethapudi Niharika, Chandu Rishitha, Allamneni Seswitha
    Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
  • Determining People's Opinion about Amazon Alexa using Machine Learning (ML) based Classification
    Harshitha Sugasani, Subhashini Jetti, Archana Nelluri, P. V. S. S. Neeraj, Lahari Manuru, Maridu Bhargavi
    Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
  • An Efficient Skin Cancer Classification System Using Deep CNN
    Maridu Bhargavi, Sivadi Balakrishna
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • A Comparative Sentiment Analysis on ChatGPT Reviews using Machine Learning Models
    Alokam Ujwala Bharati, Motamarri Bhargavi, K.V.N.D. Sai Harshith, Srinivasa Reddy K
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • Potato Leaf Disease Detection Using Deep Learning
    Renugadevi R, Maridu Bhargavi, Shaik Bibi Reshma, Chegu Manasa, Jarugula Hari Chandana
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • Ensemble Learning for Skin Lesion Classification: A Robust Approach for Improved Diagnostic Accuracy (ELSLC)
    Maridu Bhargavi, R. Renugadevi, S. Sivabalan, Pamulapati Phani, Janga Ganesh, Konda Bhanu
    3rd International Conference on Innovative Mechanisms for Industry Applications Icimia 2023 Proceedings, 2023
  • Double OptconNet architecture based facial expression recognition in video processing
    Melam Nagaraju, Adilakshmi Yannam, Siva Satya Sreedhar P, Maridu Bhargavi
    Imaging Science Journal, 2022
  • Effective utilization and optimization of waste plastic oil with ethanol additive in diesel engine using full factorial design
    Maridu Bhargavi, T. Vinod Kumar, Reddypalli Ali Azmath Shaik, S. Kishore Kanna, S. Padmanabhan
    Materials Today Proceedings, 2022
  • Data mining process with iAgents
    International Journal of Advanced Science and Technology, 2019
  • A framework for secure cloud data provenance in healthcare applications
    Journal of Advanced Research in Dynamical and Control Systems, 2019
  • Integrated density based clustering algorithm (Idbscan) for high dimensional datasets
    Journal of Advanced Research in Dynamical and Control Systems, 2019
  • Smart Camera Network Supervision for Competent Exploitation of Energy Recourses in vision Task
    M Bhargavi, Syed Shareefunnisa
    Proceedings of the 4th International Conference on Electrical Energy Systems Icees 2018, 2018
  • Fabrication of carbon nanospheres using natural resources and their voltametric studies of dopamine
    S. Yallappa, S.R. Kiran Kumar, K.L. Nagashree, Uma, M. Bhargavi, C.N. Rakshitha, D. Aneetta, Abhinethri, P. Mrinasha, Gurumurthy Hegde
    Materials Today Proceedings, 2018
  • Machining characteristics of fine grained AZ91 Mg alloy processed by friction stir processing
    G.V.V. SURYA KIRAN, K. HARI KRISHNA, Sk. SAMEER, M. BHARGAVI, B. SANTOSH KUMAR, G. MOHANA RAO, Y. NAIDUBABU, RAVIKUMAR DUMPALA, B. RATNA SUNIL
    Transactions of Nonferrous Metals Society of China English Edition, 2017

RECENT SCHOLAR PUBLICATIONS

  • ENDNet: A lightweight attention-enhanced feature fusion framework for multi-class skin cancer classification
    M Bhargavi, S Balakrishna
    Alexandria Engineering Journal 144, 92-116 , 2026
    2026
  • Enhancing speech emotion recognition with BiLSTM and self-attention using diverse pretrained models: WavLM, HuBERT, Whisper, wav2vec 2.0, XLSR-53, and Data2Vec audio
    S Shareefunnisa, M Bhargavi, N Pujitha, LK Praneetha
    Artificial Intelligence and Sustainable Innovation, 478-482 , 2026
    2026
  • Integrating Multiple Machine Learning Models for Reliable Cardiovascular Risk Prediction
    UH Akhileswar, M Bhargavi, B Gummadi, NSR Kotapati, C Arjun, ...
    2025 1st International Conference on Advancement in Futuristic Technologies … , 2025
    2025
  • AI for Finance: Hybrid Learning Models for Stock Prediction
    P SaiBabu, M Bhargavi, V Jayasri, G Pravallika, U Afzal
    2025 IEEE International Conference on Emerging Trends in Computing and … , 2025
    2025
  • Climate Change Analysis and Prediction
    T Subhashini, MD Bhavani, G Jeshmitha, KPS Sarvani, M Bhargavi
    2025 IEEE International Conference on Emerging Trends in Computing and … , 2025
    2025
  • Harnessing Machine Learning for Accurate Water Quality Monitoring
    M Srilatha, M Bhargavi, S Akanksha, MK Chowdary, B Ramanjamma
    Congress on Smart Computing Technologies: Proceedings of CSCT 2024, Volume 2 … , 2025
    2025
  • Hybrid approach for multi-class skin cancer classification with DCNN feature and ensemble techniques
    M Bhargavi, S Balakrishna
    Engineering Research Express 7 (3), 035260 , 2025
    2025
    Citations: 2
  • AI for Clean Water: Predicting Potability with Machine Learning
    K Akhila, M Bhargavi, A Ajay
    2025 3rd International Conference on Intelligent Cyber Physical Systems and … , 2025
    2025
  • Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration
    M Bhargavi, S Balakrishna
    Engineering Science and Technology, an International Journal 69, 102135 , 2025
    2025
    Citations: 7
  • Pedagogical revelations and emerging trends
    CS Joice, M Selvi
    CRC Press , 2025
    2025
    Citations: 6
  • A comparative analysis for air quality prediction by AQI calculation using different machine learning algorithms
    R Kumar, VK Likitha, M Harshida, S Afreen, G Manideep, M Bhargavi
    Pedagogical Revelations and Emerging Trends, 235-238 , 2025
    2025
  • Leveraging machine learning for paragraph-based answer generation
    M Bhargavi, C Sowndaryavathi, K Kumari, AK Prabhat, M Kumar
    Pedagogical Revelations and Emerging Trends, 124-127 , 2025
    2025
  • Enhancing employee turnover prediction with ensemble blending: A fusion of SVM and CatBoost
    NN Ambati, SS Gottipati, VR Polimera, T Malla, M Bhargavi
    Pedagogical Revelations and Emerging Trends, 222-224 , 2025
    2025
    Citations: 1
  • Student placement prediction
    M Bhargavi, K Kumar, GS Vijay, CS Teja, N Dharmasai
    Pedagogical Revelations and Emerging Trends, 141-143 , 2025
    2025
  • Unlocking Potential: Personalizing Learning and Assessment with Cutting-Edge Technologies
    R Renugadevi, M Bhargavi, G Kalaiarasi, PR Kumar, AAE Raj, B Saritha
    Adopting Artificial Intelligence Tools in Higher Education, 200-217 , 2025
    2025
    Citations: 1
  • AI-Driven Evaluation Techniques: Revolutionizing Student Practices
    SKS Sultana, R Renugadevi, M Bhargavi, SAA Biyabani
    Adopting Artificial Intelligence Tools in Higher Education, 1-22 , 2025
    2025
    Citations: 3
  • Assessing Skin Cancer Awareness: A Survey on Detection Methods
    B Vaishnavi, P Nithya, S Haseena, SS Sk
    ITM Web of Conferences 74, 01001 , 2025
    2025
  • Enhancing Renewable Energy Planning: Machine Learning-Based Solar Radiation Prediction
    JP Vemula, Bhargavi, UC Ramisetty, SPY Batchu, MA Shaik
    International Conference on Computational Intelligence, 241-252 , 2024
    2024
  • Predictive Analytics in Financial Transactions: A Comparative Study for Customer Risk Assessment and Revenue Prediction
    V Seggam, B Marida, BSM Vanka, S Shaik, B Nidubrolu
    International Conference on Computational Intelligence, 231-240 , 2024
    2024
  • Exploring Rank-Popularity Dynamics in Anime: Insights from Comprehensive Data Analysis
    K Dasaanjaneya, VK Kambala, C Thakur, M Bhargavi, TS Dharanikota
    International Conference on Machine Learning, Image Processing, Network … , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Effective utilization and optimization of waste plastic oil with ethanol additive in diesel engine using full factorial design
    M Bhargavi, TV Kumar, RAA Shaik, SK Kanna, S Padmanabhan
    Materials Today: Proceedings 52, 930-936 , 2022
    2022
    Citations: 46
  • Ensemble learning for skin lesion classification: A robust approach for improved diagnostic accuracy (elslc)
    M Bhargavi, R Renugadevi, S Sivabalan, P Phani, J Ganesh, K Bhanu
    2023 3rd International Conference on Innovative Mechanisms for Industry … , 2023
    2023
    Citations: 13
  • Career prediction using machine learning
    K Jha, D Likhitha, MS Chandana, MRP Reddy, M Bhargavi
    2024 8th International Conference on Inventive Systems and Control (ICISC … , 2024
    2024
    Citations: 8
  • An efficient skin cancer classification system using deep CNN
    M Bhargavi, S Balakrishna
    2023 9th International Conference on Smart Structures and Systems (ICSSS), 1-5 , 2023
    2023
    Citations: 8
  • Transfer learning based hybrid feature learning framework for enhanced skin cancer diagnosis using deep feature integration
    M Bhargavi, S Balakrishna
    Engineering Science and Technology, an International Journal 69, 102135 , 2025
    2025
    Citations: 7
  • Credit card fraud detection
    A Kumar, MV Poojitha, T Anuhya, K Srinivas, M Bhargavi
    2024 8th International Conference on Inventive Systems and Control (ICISC … , 2024
    2024
    Citations: 7
  • Heart stroke prediction using machine learning
    S Shareefunnisa, SL Malluvalasa, TR Rajesh, M Bhargavi
    Journal of Pharmaceutical Negative Results 13 , 2022
    2022
    Citations: 7
  • Pedagogical revelations and emerging trends
    CS Joice, M Selvi
    CRC Press , 2025
    2025
    Citations: 6
  • Double OptconNet architecture based facial expression recognition in video processing
    M Nagaraju, A Yannam, SS Sreedhar P, M Bhargavi
    The Imaging Science Journal 70 (1), 46-60 , 2022
    2022
    Citations: 5
  • Network intrusion detection system using machine learning algorithms
    S Kumar, B Bhavana, S Shiblee, KS Pranathi, M Bhargavi
    2024 8th International Conference on Inventive Systems and Control (ICISC … , 2024
    2024
    Citations: 4
  • Predictive analysis for retail: sales forecasting at Walmart
    NN Alekhyasri, GB Prasad, T Pardhasaradhi, APV Reddy, M Bhargavi
    2024 3rd International Conference on Applied Artificial Intelligence and … , 2024
    2024
    Citations: 4
  • Probing Skin Cancer Awareness: Insights in to Classification Approaches
    M Bhargavi, S Balakrishna
    2024 International Conference on Advances in Computing, Communication and … , 2024
    2024
    Citations: 4
  • AI-Driven Evaluation Techniques: Revolutionizing Student Practices
    SKS Sultana, R Renugadevi, M Bhargavi, SAA Biyabani
    Adopting Artificial Intelligence Tools in Higher Education, 1-22 , 2025
    2025
    Citations: 3
  • Stacking Models for Employee Attrition Prediction: Leveraging Logistic Regression and Random Forest
    S Polisetti, M Bhargavi, S Chitneni, S Eluri, N Kattamuri
    2024 8th International Conference on I-SMAC (IoT in Social, Mobile … , 2024
    2024
    Citations: 3
  • Greenguard: CNN-based system for Intelligent Plant Disease Classification
    SS Sultana, S Shareefunnisa, VS Spandana, AS Bhargavi, G Sailaja, ...
    2024 8th International Conference on Inventive Systems and Control (ICISC … , 2024
    2024
    Citations: 3
  • Hybrid model fusion: enhancing water quality prediction using ensemble modelling
    V Mannepati, SL Abburi, DRR Dudla, R Chukkapalli, V Addagadda, ...
    2024 3rd International Conference on Applied Artificial Intelligence and … , 2024
    2024
    Citations: 3
  • Potato leaf disease detection using deep learning
    R Renugadevi, M Bhargavi, SB Reshma, C Manasa, JH Chandana
    2023 9th International Conference on Smart Structures and Systems (ICSSS), 1-6 , 2023
    2023
    Citations: 3
  • Hybrid approach for multi-class skin cancer classification with DCNN feature and ensemble techniques
    M Bhargavi, S Balakrishna
    Engineering Research Express 7 (3), 035260 , 2025
    2025
    Citations: 2
  • Leveraging XGBoost and clinical attributes for heart disease prediction
    K Susmitha, M Bhargavi, AMS Sri, BDP Reddy, PS Satyanarayana, ...
    2024 International Conference on Sustainable Communication Networks and … , 2024
    2024
    Citations: 2
  • Predicting App Ratings on Google Play Store: An Ensemble Learning Approach
    RVN Gopi, CS Vigna, J Phani, KV Kanth, M Bhargavi
    2024 IEEE International Conference on Information Technology, Electronics … , 2024
    2024
    Citations: 2