Dr. Premkumar Borugadda

@premkumar.borugadda@eratosthenes.org.cy

Post-Doctoral Research Fellow
ERATOSTHENES Centre of Excellence, Limassol, Cyprus

Dr. Premkumar Borugadda


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https://researchid.co/premkumar
Dr. Premkumar Borugadda is a Post-Doctoral Research Fellow in the Department of Environment and Climate at the ERATOSTHENES Centre of Excellence. He obtained his Ph.D. in Computer Science and Engineering (Artificial Intelligence & Machine Learning) from Pondicherry University, India, where his doctoral research focused on “A Novel Approach for Multi-Level Dimensional Reduction for Classification of Tomato Plant Leaf Diseases using Transfer Learning on the VGG16 Prior to joining ERATOSTHENES CoE, Dr. Borugadda served as a Post-Doctoral Fellow at SRM University-AP, India, and as an Assistant Professor at leading academic institutions including Ashoka School of Business, Hyderabad, and Bapatla Engineering College, Andhra Pradesh, India. His research interests encompass Machine Learning, Deep Learning, and Computer Vision. He has published numerous papers in Q1 and Q2 journals.

EDUCATION

M.Tech., Ph.D. (AI & ML)., Post-Doc.

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition, Agricultural and Biological Sciences, General Business, Management and Accounting
13

Scopus Publications

271

Scholar Citations

9

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • A comprehensive analysis of artificial intelligence, machine learning, deep learning and computer vision in food science
    Premkumar Borugadda, Hemantha Kumar Kalluri
    Journal of Future Foods, 2026
  • Corporate social responsibility (CSR) and corporate financial performance (CFP): a panel data analysis of BSE 500 companies in India
    Shahin Sultana Mohammed, Musah Mohammed Saeed, Manisha Kumari, Premkumar Borugadda, Nafeesathul Basariya Mohamed Ismail
    Discover Sustainability, 2025
  • Developing a Logistic Regression Model for Predicting Chronic Kidney Disease
    Nageswara Rao Lavuri, Premkumar Borugadda, Punnanpu Rama Krishna, G Karthik Reddy
    Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025
    This study presents an optimized Logistic Regression-based predictive model for early detection of Chronic Kidney Disease (CKD) using clinical and demographic data. The framework integrates feature selection techniques and hyperparameter tuning to enhance classification accuracy and interpretability. Unlike traditional approaches, our model is evaluated against Decision Tree, Random Forest, and SVM, ensuring comparative robustness. The results show that the proposed approach achieves an accuracy of X %, outperforming existing models while maintaining low computational complexity. This research contributes to personalized healthcare by offering an early-warning system, improving decision-making for medical practitioners, and enabling timely interventions.
  • Obesity Health Risk Prediction using Random Forest and SVM Algorithms
    Nageswara Rao Lavuri, M Basha, Nagarjuna Nallametti, G Karthik Reddy, Voruganti Santhosh Kumar, et al.
    Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025
    Obesity-related health dangers have become a serious global issue that requires adequately pinpointing and quickly preventing health problems. One of the research studies in the article suggests a supervised learning-based framework that uses the Random Forest method for feature selection and the Support Vector Machine (SVM) technique for classification to predict obesity-induced health dangers. Aiming at increased accuracy in risk assessment, the framework involves the analysis of pertinent health and lifestyle parameters, including BMI, regular physical activity, and a proper diet. The solutions use machine learning as the main technical approach, which leads to real-time personal health recommendations and early medical interventions. Furthermore, the solution model ensures that it is scalable and adaptive, broadening the population to diverse patient demographics and thus contributing to proactive management of obesity conditions and achieving good patient outcomes.
  • A Novel Approach for Prediction of Liver Disease Using Voting Algorithm Based on Machine Learning Models
    Snehit Shaprapawad, Premkumar Borugadda
    Smart Innovation Systems and Technologies, 2024
  • Transfer Learning VGG16 Model for Classification of Tomato Plant Leaf Diseases: A Novel Approach for Multi-Level Dimensional Reduction
    Premkumar Borugadda, Ramasami Lakshmi, Satyasangram Sahoo
    Pertanika Journal of Science and Technology, 2023
    Tomato is the most popular and cultivated crop in the world. Nevertheless, the quality and quantity of tomato crops have been declining due to various diseases that afflict tomato crops. Hence, it becomes necessary to detect the disease early to prevent crop damage and increase the yield. The proposed model in this article predicts the infected tomato leaf images (9 classified diseases and also healthy class) obtained from the Plant Village dataset. In this model, Transfer learning was used to extract features from images by VGG16, yielding a high dimension of 25088 features. Overfitting is a commonly anticipated problem because of the higher dimensionality of data. To mitigate this problem, the authors have adopted a novel dimensional reduction-based technique: filter methods, feature extraction techniques like Principal Components Analysis (PCA), and the Boruta feature selection technique of wrapper methods. This adoption enables the proposed model to attain a significantly improved high accuracy of 95.68% and 95.79% in MLP and VGG16, respectively, by reducing its initial dimension on the tomato dataset containing 18160 images across 10 classes.
  • Car Price Prediction:An Application of Machine Learning
    Snehit Shaprapawad, Premkumar Borugadda, Nirmala Koshika
    6th International Conference on Inventive Computation Technologies Icict 2023 Proceedings, 2023
    In order to determine the worthiness of a car based on a variety of factors using machine learning models. In this study, the challenge is to prevent the model from becoming overfit and to generalize it. A combination of regularization techniques as well as hyperparameter tuning techniques was employed to overcome this challenge. Develop linear regression, lasso regression, ridge regression, elastic net regression, random forest, decision tree and Support Vector Machine models with hyperparameters. The objective of this article is to build a generalized model that can predict the price of used cars based on some factors, such as the car's mileage, the year it was made, the road tax, the type of fuel it uses, the size of its engine etc. Optimal model can help sellers, buyers, and car manufacturers. A relatively accurate prediction of price can be made based on information provided by users. Among the seven models, the support vector regressor is the optimal model based on the evaluation metrics such as R Squared (R^2) of 95.27%, Mean Absolute Error (MAE) of 0.142, Mean Squared Error (MSE) of 0.047, and Root Mean Squared Error (RMSE) of 0.218 at 90% of the train data and 10% of the validation data.
  • Prediction of Heart Disease Based on Machine Learning Algorithms
    Nirmala Koshiga, Premkumar Borugadda, Snehit Shaprapawad
    6th International Conference on Inventive Computation Technologies Icict 2023 Proceedings, 2023
    The objective of this study is to create efficient machine learning (ML) models for the Heart Disease Prediction System (HDPS). This study shows how classification techniques for machine learning can forecast heart illness. To forecast and alert patients about potential cardiac abnormalities, machine learning (ML) models including Logistic Regression (LR), Decision Tree Classifiers (DTC), Random Forest Classifiers (RFC), Support Vector Classifiers (SVC), and voting classifiers are employed. Few challenges were encountered while developing the models, such as underfitting the model without balancing the data with decision tree classifier. The voting ensemble technique overcame the challenges and allowed for a generalized model on balanced data with high accuracy. The purpose of this investigation is to see whether the technique for properly forecasting heart disease is based on health factors. A voting classifier is made up of LR and RFC. Among all models, this voting classifier had the highest accuracy of 98.36%.
  • A Machine Learning Model to Predict a Diagnosis of Brain Stroke
    Sairam Vasa, PremKumar Borugadda, Archana Koyyada
    6th International Conference on Inventive Computation Technologies Icict 2023 Proceedings, 2023
    A stroke is caused by a disturbance in blood flow to a specific location of the brain. This might occur due to an issue with the arteries. The objective of this research to develop the optimal model to predict brain stroke using Machine Learning Algorithms (MLA's), namely Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Support Vector Machine (SVC), Naive Bayes Classifier (NBC), KNN Classifier (KNN), and XGBoost Classifier (XGB).Apply the above algorithms with hyperparameter along with GridSearchCV (CV= 5) on the given dataset. The given dataset is imbalanced, while training the models, a few difficulties were met, including underfitting, a dataset with null values, and a model without balancing the data to boost performance of the models, need to balance the data by using a data sampling method such as SMOTE. Among the Seven models, XGB is the optimal model based on the accuracy of 96.34%.
  • Offline handwritten character classification of the same scriptural family languages by using transfer learning techniques
    Satyasangram Sahoo, Prem Kumar B., R. Lakshmi
    Proceedings of 3rd International Conference on Emerging Technologies in Computer Engineering Machine Learning and Internet of Things Icetce 2020, 2020
    Transfer Learning by using Convolutional Neural Network has shown its outstanding performance in large scale image classification. India is a multi-script and multilingual country. Out of all languages, Telugu and Kannada have shared almost similar structure characters. Offline character recognition of both handwriting characters is a challenging task. Different feature extraction models have been used in character recognition studies. Convolutional Neural Network is used as efficiently supervised feature vector extraction. Feature vectors from large scale pre-trained ImageNet or COCO were proved more efficient than other script datasets for better result. Fine-tuning model of transfer learning was used in the studies for comparison studies.
  • Performance evaluation of deep learning algorithms in biomedical document classification
    Bichitrananda Behera, G. Kumaravelan, Prem Kumar B.
    Proceedings of the 11th International Conference on Advanced Computing Icoac 2019, 2019
  • Mixed data through multiple input for price prediction with multilayer perception and mini VGG net
    Satyasangram Sahoo, Prem Kumar Borugadda, Dr R Lakshmi, and
    International Journal of Recent Technology and Engineering, 2019
  • A analysis of machine learning in wireless sensor network
    International Journal of Engineering and Technology Uae, 2018

RECENT SCHOLAR PUBLICATIONS

  • Large Language Model Driven Named Entity Recognition for Soil and Land Information Extraction in the EMMENA Region
    P Borugadda, I Varvaris, D Hadjimitsis
    RSCy2026 – 12th International Conference on Remote Sensing and … , 2026
    2026
  • Enhancing churn prediction in telecommunications via machine learning and oversampling techniques
    MA Sivalanka, P Borugadda, SS Mohammed
    AIP Conference Proceedings 3348 (1), 030012 , 2026
    2026
  • An IoT-assisted Alzheimer’s disease patient monitoring system using adaptive deep learning models with recommendation about patient abnormality
    R Mohan, P Borugadda, N Lavuri, S Venugopal
    Journal on Advances in Signal Processing 2026, 1-44 , 2026
    2026
  • Advancements in Smart Farming: Using Internet of Things and Artificial Intelligence, Machine Learning, Deep Learning
    L Rupa, P Borugadda, K Lavanya, V Nadella
    Harnessing AI to Reshape the Future of Agriculture, 123-137 , 2026
    2026
    Citations: 2
  • Automated Papaya Fruit Classification Using CNN Models
    R Lalam, P Borugadda, K Lavanya, V Nadella
    International Conference on Innovations and Advances in Cognitive Systems … , 2025
    2025
  • Developing a Logistic Regression Model for Predicting Chronic Kidney Disease
    NR Lavuri, P Borugadda, PR Krishna, GK Reddy
    International Conference on Trends in Electronics and Informatics, 1077-1082 , 2025
    2025
  • Obesity Health Risk Prediction using Random Forest and SVM Algorithms
    NR Lavuri, M Basha, N Nallametti, GK Reddy, VS Kumar, P Borugadda
    2025 8th International Conference on Trends in Electronics and Informatics … , 2025
    2025
  • Corporate social responsibility (CSR) and corporate financial performance (CFP): a panel data analysis of BSE 500 companies in India
    MMS Shahin Sultana Mohammed, M Kumari, P Borugadda, NBM Ismail
    Discover Sustainability 6 , 2025
    2025
    Citations: 20
  • A comprehensive analysis of artificial intelligence, machine learning, deep learning and computer vision in food science
    P Borugadda, HK Kalluri
    Journal of Future Foods, 971-987 , 2025
    2025
    Citations: 11
  • A Comprehensive Analysis of Artificial Intelligence
    P Borugadda, HK Kalluri
    Machine Learning, Deep Learning and Computer Vision in Food Science. J … , 2025
    2025
    Citations: 6
  • A Novel Approach for Prediction of Liver Disease Using Voting Algorithm Based on Machine Learning Models
    S Shaprapawad, P Borugadda
    International Conference on Advanced Communications and Machine Intelligence , 2024
    2024
  • EXPLORING THE PARADIGM SHIFT: A COMPREHENSIVE SURVEY ON THE EVOLUTION OF DATA ANALYSIS
    N Siddartha, VR Manneni, P Borugadda
    Futuristic Trends in Artificial Intelligence 3, 178-184 , 2024
    2024
  • AN EVALUATION OF MACHINE LEARNING APPROACHES FOR PREDICTING CROP YIELD
    N Vinoda, PK Borugadda
    Futuristic Trends in Artificial Intelligence 3, 185-195 , 2024
    2024
  • Salary Prediction using Machine Learning Regression Algorithms
    N Siddartha, KH Prasad, S Reddy, P Borugadda
    Education and Society 48 (1), 88-94 , 2024
    2024
  • A Machine Learning Model to Predict a Diagnosis of Brain Stroke
    S Vasa, P Borugadda, A Koyyada
    6th International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 8
  • Car price prediction: an application of machine learning
    S Shaprapawad, P Borugadda, N Koshika
    2023 International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 20
  • Prediction of heart disease based on machine learning algorithms
    N Koshiga, P Borugadda, S Shaprapawad
    2023 International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 9
  • Transfer Learning VGG16 Model for Classification of Tomato Plant Leaf Diseases: A Novel Approach for Multi-Level Dimensional Reduction
    P Borugadda, R Lakshmi, S Sahoo
    Pertanika Journal of Science & Technology , 2023
    2023
    Citations: 31
  • In silico molecular docking and molecular dynamic simulation of agarwood compounds with molecular targets of Alzheimer’s disease
    P Alugoju, VV Bhandare, VS Patil, KSV K. D, PK Borugadda
    https://doi.org/10.12688/f1000research.130618.1 , 2023
    2023
    Citations: 8
  • Protocol 1: Protein-ligand docking protocol v1
    P Alugoju, VV Bhandare, VS Patil, KS VKD, PK Borugadda, T Tencomnao
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • Performance Evaluation of Deep Learning Algorithms in Biomedical Document Classification
    B Behera, G Kumaravelan, B Premkumar
    2019 11th International Conference on Advanced Computing (ICoAC), 220-224 , 2019
    2019
    Citations: 75
  • Transfer Learning VGG16 Model for Classification of Tomato Plant Leaf Diseases: A Novel Approach for Multi-Level Dimensional Reduction
    P Borugadda, R Lakshmi, S Sahoo
    Pertanika Journal of Science & Technology , 2023
    2023
    Citations: 31
  • Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models
    P Borugadda, R Lakshmi, S Govindu
    Current Journal of Applied Science and Technology 40 (38), 29-37 , 2021
    2021
    Citations: 21
  • Corporate social responsibility (CSR) and corporate financial performance (CFP): a panel data analysis of BSE 500 companies in India
    MMS Shahin Sultana Mohammed, M Kumari, P Borugadda, NBM Ismail
    Discover Sustainability 6 , 2025
    2025
    Citations: 20
  • Car price prediction: an application of machine learning
    S Shaprapawad, P Borugadda, N Koshika
    2023 International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 20
  • Predicting the Success of Bank Telemarketing for Selling Long-term Deposits: An Application of Machine Learning Algorithms
    P Borugadda, P Nandru, C Madhavaia
    St. Theresa Journal of Humanities and Social Sciences 7 (1), 91-108 , 2021
    2021
    Citations: 20
  • A comprehensive analysis of artificial intelligence, machine learning, deep learning and computer vision in food science
    P Borugadda, HK Kalluri
    Journal of Future Foods, 971-987 , 2025
    2025
    Citations: 11
  • Offline Handwritten Character Classification of the Same Scriptural Family Languages by Using Transfer Learning Techniques
    S Satyasangram, B Premkumar, R Lakshmi
    3rd International Conference on Emerging Technologies in Computer … , 2020
    2020
    Citations: 10
  • Prediction of heart disease based on machine learning algorithms
    N Koshiga, P Borugadda, S Shaprapawad
    2023 International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 9
  • Dimensionality reduction-based approach to classify the cotton leaf images using transfer learning on VGG16
    N Vinoda, B Premkumar, V Beera, R Babu M
    The Pharma Innovation Journal 11 (7), 1361-1366 , 2022
    2022
    Citations: 9
  • A Analysis of Machine Learning in Wireless Sensor Network
    chander Bhanu, B Premkumar, Kumaravelan
    International Journal of Engineering & Technology 7, 185-192 , 2018
    2018
    Citations: 9
  • A Machine Learning Model to Predict a Diagnosis of Brain Stroke
    S Vasa, P Borugadda, A Koyyada
    6th International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 8
  • In silico molecular docking and molecular dynamic simulation of agarwood compounds with molecular targets of Alzheimer’s disease
    P Alugoju, VV Bhandare, VS Patil, KSV K. D, PK Borugadda
    https://doi.org/10.12688/f1000research.130618.1 , 2023
    2023
    Citations: 8
  • A Survey of Positioning Algorithms on Mobile Devices in Location Based Services
    B PremKumar, MS Ashok
    International Journal of Advanced Research in Computer Science and Software … , 2013
    2013
    Citations: 7
  • A Comprehensive Analysis of Artificial Intelligence
    P Borugadda, HK Kalluri
    Machine Learning, Deep Learning and Computer Vision in Food Science. J … , 2025
    2025
    Citations: 6
  • A Survey of Positioning Algorithms on Mobile Devices in Location Based Services
    PKBMS Ashok
    International Journal 3 (6) , 2013
    2013
    Citations: 4
  • Advancements in Smart Farming: Using Internet of Things and Artificial Intelligence, Machine Learning, Deep Learning
    L Rupa, P Borugadda, K Lavanya, V Nadella
    Harnessing AI to Reshape the Future of Agriculture, 123-137 , 2026
    2026
    Citations: 2
  • Recognition of Sudoku with Deep Belief Network and Solving with Serialisation of Parallel Rule-Based Methods and Ant Colony Optimisation
    SatyasangramSahoo, B PremKumar, R Lakshmi
    Data Science Theory, Algorithms, and Applications, 169-184 , 2021
    2021
    Citations: 1
  • Large Language Model Driven Named Entity Recognition for Soil and Land Information Extraction in the EMMENA Region
    P Borugadda, I Varvaris, D Hadjimitsis
    RSCy2026 – 12th International Conference on Remote Sensing and … , 2026
    2026
  • Enhancing churn prediction in telecommunications via machine learning and oversampling techniques
    MA Sivalanka, P Borugadda, SS Mohammed
    AIP Conference Proceedings 3348 (1), 030012 , 2026
    2026