Raguraman Purushothaman

@mits.ac.in

Assistant Professor, CSE(Artificial Intelligence)
Madanapalle Institute of Technology & Science

Raguraman Purushothaman


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https://researchid.co/raguram4u
Mr. Raguraman Purushothaman is currently working as an Assistant Professor in the Department of Artificial Intelligence and Data Science, Madanapalle Institute of Technology & Science, Angallu, Madanapalle, Andhra Pradesh. His area of interest includes Theory of Computation, Design and Analysis of Algorithms, Image processing and Data Science.

EDUCATION

M.Tech in CSE

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Multidisciplinary
7

Scopus Publications

60

Scholar Citations

3

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Comparative analysis of wind speed prediction: enhancing accuracy using PCA and linear regression vs. GPR, SVR, and RNN
    Somasundaram Deepa, Jayanthi Arumugam, Raguraman Purushothaman, D. Nageswari, L. Rajasekhara Babu
    International Journal of Power Electronics and Drive Systems, 2025
    For power systems with significant wind power integration to operate in an efficient and dependable manner, wind speed prediction accuracy is crucial. Factors such as temperature, humidity, air pressure, and wind intensity heavily influence wind speed, adding complexity to the prediction process. This paper introduces a method for wind speed forecasting that utilizes principal component analysis (PCA) to reduce dimensionality and linear regression for the prediction model. PCA is employed to identify key features from the extensive meteorological data, which are subsequently used as inputs for the Linear Regression model to estimate wind speed. The proposed approach is tested using publicly available meteorological data, focusing on variables such as temperature, air pressure, and humidity. Popular models like recurrent neural networks (RNN), support vector regression (SVR), and Gaussian process regression (GPR) are used to compare its performance. Evaluation metrics such as root mean square error (RMSE) and R² are used to measure effectiveness. Results show that the PCA combined with Linear Regression model yields more accurate predictions, with an RMSE of 94.11 and R² of 0.9755, surpassing the GPR, SVR, and RNN models.
  • Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network
    Yenework Alayu Melkamu, Raguraman Purushothaman, Madugula Sujatha, Komal Kumar Napa, Mareye Zeleke Mekonen, Tsehay Admassu Assegie, Ayodeji Olalekan Salau
    Bulletin of Electrical Engineering and Informatics, 2025
    Mobile ad hoc networks (MANETs) operate without fixed infrastructure, with mobile nodes acting as both hosts and routers. These networks face challenges due to node mobility and limited resources, causing frequent changes in topology and instability. Clustering is essential to manage this issue. Significant research has been devoted to optimal clustering algorithms to improve cluster-based routing protocols (CBRP), such as the weighted clustering algorithm (WCA), optimal stable clustering algorithm (OSCA), lowest ID (LID) clustering algorithm, and highest connectivity clustering (HCC) algorithm. However, these protocols suffer from high re-clustering frequency and do not adequately account for energy efficiency, leading to network instability and reduced longevity. This work aims to improve the CBRP to create a more stable and long-lasting network. During cluster head (CH) selection, nodes with high residual energy or degree centrality are chosen as CH and backup cluster head (BCH). This approach eliminates the need for re-clustering, as the BCH can seamlessly replace a failing CH, ensuring continuous cluster maintenance. The proposed modified cluster-based routing protocol (MCBRP) evaluated network simulator 2 (ns2) demonstrates that MCBRP is more energy-efficient, selecting optimal CH and balancing the load to enhance network stability and longevity.
  • Automated Malaria Detection and Severity Prediction using Deep Learning: A VGG19-based Approach
    Nandimedala Sridathu, Raguraman Purushothaman, Pacharla Sai Kumar, Haji Shameena, Gande Upendra
    Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2025, 2025
    This is a cause for life threatening disease of Plasmodium parasites, Malaria. Effective treatment and management require early detection and severity prediction. The main contribution of this paper is to present a deep learning based approach for automated detection and prediction of malaria severity from blood smear images. To classify images between infected and uninfected red blood cells, we use VGG19, a pre trained Convolutional Neural Network (CNN). Data augmentation techniques like rotation, zoom, brightness adjustments are used to augment the dataset and improve the model generalization in the proposed model. Moreover, the severity level of malaria is predicted by percentage of infected cell and is classified as mild, moderate or severe. To overcome the limitations of preceding methods in the context of mainly detecting without predicting severity, we propose an approach. This model is evaluated on a well annotated Kaggle dataset and performs well. The developed methodology may offer an efficient and scalable option for malaria diagnosis and assessment of severity that may be useful in resource limited settings.
  • Integrating Optical Characteristic Recognition with Conversational AI: A Multimodal Chatbot Featuring Speech and Poster Generation
    Chennuru Chetan Sai, Raguraman Purushothaman, Chinthaparthy Reddy Dhanush Reddy, Peddi Reddy Gangothri, V. Dhanush, Chintamanipeta Bhavana
    Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2025, 2025
    Artificial Intelligence (AI) has progressed so far in human computer interaction that it is much more natural and interesting. Optical Character Recognition (OCR) conjointly with Conversational AI is capable of processing visual alongside the textual input and generating intelligent and context aware responses, and therefore the work on a multimodal chatbot system is introduced in this paper. The proposed system extracts text from images, Natural Language Processing (NLP) processes user queries, and enhances the interaction by speech output through text to speech synthesis. In particular, this chatbot doesn’t accept speech as input modality but tries to translate text response to speech to make the interface more accessible for visually impaired users. Additionally, there is a poster generation module in the system for visual summarization of the conversations and the extracted content. The chatbot uses state of the art deep learning models and language frameworks to handle real time processing, grammatical accuracy in real time and also across different scenarios. From education, assistive technologies, customer support and all the possibilities in between, the applications take advantage of multimodal, voice enriched and visually enhanced communication to include users.
  • A Comprehensive Survey of AI-Driven Biomedical Image Processing for Intracerebral Hemorrhage Detection and Classification: Current Trends, Challenges, and Future Directions
    P. Raguraman, M. Kumaresan, S. Ramesh
    8th International Conference on Electronics Communication and Aerospace Technology Iceca 2024 Proceedings, 2024
    Amongst many life-threatening conditions, a fatal condition is the Intracerebral hemorrhage (ICH). It may result in death if not treated immediately and hence necessitates the need for accurate diagnosis for effective treatment. In recent times, concepts of Artificial intelligence (AI), have emerged as a transformative tool in medical image processing, offering faster and more reliable ICH detection and classification. This research study reviews the current advancements in AI-driven biomedical image processing for ICH, exploring various state-of-the-art models involving Convolutional Neural Networks (CNNs), variants of CNN with more efficient feature extraction procedures, Long Short-Term Memory (LSTM) networks, and optimization methods like Genetic Algorithms (GA), Ant Colony Model (ACO), rParticle Swarm Optimization (PSO). Additionally, challenges such as data scarcity, model interpretability, and generalization across diverse populations are discussed. The paper highlights future research directions, including the need for explainable AI, multimodal data integration, and real-time, low-cost AI solutions for resourceconstrained environments. By addressing these challenges, AI has the potential to revolutionize ICH detection and improve patient outcomes globally.
  • Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier
    Tsehay Admassu Assegie, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan, Napa Komal Kumar
    Journal of Robotics and Control Jrc, 2023
    Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.
  • PREDICTING THE REVIEWS OF THE RESTAURANT USING NATURAL LANGUAGE PROCESSING TECHNIQUE
    P Raguraman, Yemineni Siva Pravalika
    Ecs Transactions, 2022
    Recommendation programs are finally being used to provide customers with a customized selection of services. To put it another way, they're set up to produce recommendations (such as restaurants or tourist attractions) that cater to the needs of the customer and may be used to a variety of situations. In order to make recommendation processes more efficient and effective, and to deal with any problems that may arise, a variety of helpful methods to data management may be used. In this article, a machine learning method is proposed to address the issue of customizing restaurant preferences based on tripadvisor.com search data. The hotel's services are used, and client feedback is taken into account.

RECENT SCHOLAR PUBLICATIONS

  • A Transparent and Reproducible Machine Learning Workflow for Chronic Kidney DiseasePrediction Using Clinical Features and SHAP-Based Interpretation
    R Purushothaman, D Sathyanarayanan, K Cornelius, A Jayanthi
    Vascular and Endovascular Review 8 (12s), 114-126 , 2025
    2025
  • Automated Malaria Detection and Severity Prediction using Deep Learning: A VGG19-based Approach
    N Sridathu, R Purushothaman, PS Kumar, H Shameena, G Upendra
    2025 Third International Conference on Augmented Intelligence and … , 2025
    2025
    Citations: 1
  • Integrating Optical Characteristic Recognition with Conversational AI: A Multimodal Chatbot Featuring Speech and Poster Generation
    CC Sai, R Purushothaman, CRD Reddy, PR Gangothri, V Dhanush, ...
    2025 Third International Conference on Augmented Intelligence and … , 2025
    2025
    Citations: 1
  • Comparative analysis of wind speed prediction: enhancing accuracy using PCA and linear regression vs. GPR, SVR, and RNN
    S Deepa, J Arumugam, R Purushothaman, D Nageswari, LR Babu
    International Journal of Power Electronics and Drive Systems (IJPEDS) 16 (1 … , 2025
    2025
    Citations: 1
  • Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network
    YA Melkamu, R Purushothaman, M Sujatha, KK Napa, MZ Mekonen, ...
    Bulletin of Electrical Engineering and Informatics 14 (1), 733-741 , 2025
    2025
    Citations: 4
  • Crop yield prediction and fertilizer recommendation using machine learning and remote sensing devices
    MS Naik, P Raguraman, P Ramisetty, KC Leha, MS Gayathri
    Challenges in information, communication and computing technology, 292-297 , 2024
    2024
    Citations: 2
  • A comprehensive survey of ai-driven biomedical image processing for intracerebral hemorrhage detection and classification: current trends, challenges, and future directions
    P Raguraman, M Kumaresan, S Ramesh
    2024 8th International Conference on Electronics, Communication and … , 2024
    2024
    Citations: 3
  • Early prediction of gestational diabetes with parameter-tuned K-Nearest Neighbor Classifier
    TA Assegie, T Suresh, R Purushothaman, S Ganesan, NK Kumar
    Journal of Robotics and Control (JRC) 4 (4), 452-457 , 2023
    2023
    Citations: 26
  • TRAFFIC SIGN AND LANE DETECTION USING SSLA
    PRR N. SURESH, U. CHANDRA SEKHAR VARMA, K. ROHITH, SK. RUHEE REHMAN
    Journal of Interdisciplinary Cycle Research 14 (6), 1378-1383 , 2022
    2022
  • DETECTING AND REMOVING WEB APPLICATION VULNERABILITIES WITH STATIC ANALYSIS AND DATA MINING
    PR K. VASANTHI, P. SUSHMA, P. LAKSHMI SOUMYA, N. SREEKANTH
    Journal of Interdisciplinary Cycle Research 14 (6), 1310-1318 , 2022
    2022
  • Predicting the Reviews of the Restaurant Using Natural Language Processing Technique
    YSP P Raguraman
    ECS Transactions, 17009 , 2022
    2022
    Citations: 2
  • College Connect App
    NCT P Raghu Raman, Purimetla Ganesh Yadav, Purimitla Bangaru Ganesh, Sk Ajay ...
    The International journal of analytical and experimental modal analysis 14 … , 2022
    2022
  • HAND GESTURE RECOGNITION AND VOICE CONVERSION FOR DEAF AND DUMB
    SS P.Raguraman, Ratala Radhika,Pulakandam Sruthi, Jampala Pavan, Naru ...
    JOURNAL OF CRITICAL REVIEWS 9 (4), 195-202 , 2022
    2022
  • ENHANCING THE SECURITY OF THE SENSING DEVICES BY OUTSOURCING DATA AGGREGATION TASK TO A THIRD-PARTY SERVICE PROVIDER TO OVERCOME THE LIMITATION OF ENERGY AND BANDWIDTH
    AVD P.Raguraman, M.Himavarsha, M.Balavardhan
    Journal of Engineering Sciences 13 (12), 824-833 , 2022
    2022
  • Android App for House Rent Management System
    BST P Raghuraman, Dumpa Keerthi, Mamillapalli Harika
    The International journal of analytical and experimental modal analysis 13 … , 2021
    2021
  • Attribute-Based Cloud Data Integrity Auditing for Secure Outsourced Storage
    B P Raguraman, Swathi T, Beebi D, Raviteja N, Amrtha T
    JAC : A Journal Of Composition Theory 14 (7), 118-125 , 2021
    2021
  • Color Detection of RGB Images Using Python and OpenCv
    SI P. Raguraman, A. Meghana, Y. Navya, Sk. Karishma
    International Journal Science Research Computer Science Engineering … , 2021
    2021
    Citations: 20
  • Mobile Search Engine based on user’s priority
    JH P.Raguraman #1, P.Harshavardhan #2, P.Mounika #3, T.Naveen Kumar #4, O ...
    Science, Technology and Development 10 (2), 189-196 , 2021
    2021
  • Routing in Multi hop Networks
    P Raguraman, N Naveen, J Venkatesh, KS Krishna, KT Krishna
    JAC : A Journal Of Composition Theory 14 (2), 27-33 , 2021
    2021
  • EFFECTIVE HEART DISEASE PREDICTION USING HYBRID MACHINE LEARNING TECHNIQUES
    BSC P. Raguraman, Ch.V. Lavanya, K. R. Issac Samuel, M.V. Narayana
    Levant Journal 20 (7), 78-82 , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Early prediction of gestational diabetes with parameter-tuned K-Nearest Neighbor Classifier
    TA Assegie, T Suresh, R Purushothaman, S Ganesan, NK Kumar
    Journal of Robotics and Control (JRC) 4 (4), 452-457 , 2023
    2023
    Citations: 26
  • Color Detection of RGB Images Using Python and OpenCv
    SI P. Raguraman, A. Meghana, Y. Navya, Sk. Karishma
    International Journal Science Research Computer Science Engineering … , 2021
    2021
    Citations: 20
  • Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network
    YA Melkamu, R Purushothaman, M Sujatha, KK Napa, MZ Mekonen, ...
    Bulletin of Electrical Engineering and Informatics 14 (1), 733-741 , 2025
    2025
    Citations: 4
  • A comprehensive survey of ai-driven biomedical image processing for intracerebral hemorrhage detection and classification: current trends, challenges, and future directions
    P Raguraman, M Kumaresan, S Ramesh
    2024 8th International Conference on Electronics, Communication and … , 2024
    2024
    Citations: 3
  • Crop yield prediction and fertilizer recommendation using machine learning and remote sensing devices
    MS Naik, P Raguraman, P Ramisetty, KC Leha, MS Gayathri
    Challenges in information, communication and computing technology, 292-297 , 2024
    2024
    Citations: 2
  • Predicting the Reviews of the Restaurant Using Natural Language Processing Technique
    YSP P Raguraman
    ECS Transactions, 17009 , 2022
    2022
    Citations: 2
  • Automated Malaria Detection and Severity Prediction using Deep Learning: A VGG19-based Approach
    N Sridathu, R Purushothaman, PS Kumar, H Shameena, G Upendra
    2025 Third International Conference on Augmented Intelligence and … , 2025
    2025
    Citations: 1
  • Integrating Optical Characteristic Recognition with Conversational AI: A Multimodal Chatbot Featuring Speech and Poster Generation
    CC Sai, R Purushothaman, CRD Reddy, PR Gangothri, V Dhanush, ...
    2025 Third International Conference on Augmented Intelligence and … , 2025
    2025
    Citations: 1
  • Comparative analysis of wind speed prediction: enhancing accuracy using PCA and linear regression vs. GPR, SVR, and RNN
    S Deepa, J Arumugam, R Purushothaman, D Nageswari, LR Babu
    International Journal of Power Electronics and Drive Systems (IJPEDS) 16 (1 … , 2025
    2025
    Citations: 1
  • A Transparent and Reproducible Machine Learning Workflow for Chronic Kidney DiseasePrediction Using Clinical Features and SHAP-Based Interpretation
    R Purushothaman, D Sathyanarayanan, K Cornelius, A Jayanthi
    Vascular and Endovascular Review 8 (12s), 114-126 , 2025
    2025
  • TRAFFIC SIGN AND LANE DETECTION USING SSLA
    PRR N. SURESH, U. CHANDRA SEKHAR VARMA, K. ROHITH, SK. RUHEE REHMAN
    Journal of Interdisciplinary Cycle Research 14 (6), 1378-1383 , 2022
    2022
  • DETECTING AND REMOVING WEB APPLICATION VULNERABILITIES WITH STATIC ANALYSIS AND DATA MINING
    PR K. VASANTHI, P. SUSHMA, P. LAKSHMI SOUMYA, N. SREEKANTH
    Journal of Interdisciplinary Cycle Research 14 (6), 1310-1318 , 2022
    2022
  • College Connect App
    NCT P Raghu Raman, Purimetla Ganesh Yadav, Purimitla Bangaru Ganesh, Sk Ajay ...
    The International journal of analytical and experimental modal analysis 14 … , 2022
    2022
  • HAND GESTURE RECOGNITION AND VOICE CONVERSION FOR DEAF AND DUMB
    SS P.Raguraman, Ratala Radhika,Pulakandam Sruthi, Jampala Pavan, Naru ...
    JOURNAL OF CRITICAL REVIEWS 9 (4), 195-202 , 2022
    2022
  • ENHANCING THE SECURITY OF THE SENSING DEVICES BY OUTSOURCING DATA AGGREGATION TASK TO A THIRD-PARTY SERVICE PROVIDER TO OVERCOME THE LIMITATION OF ENERGY AND BANDWIDTH
    AVD P.Raguraman, M.Himavarsha, M.Balavardhan
    Journal of Engineering Sciences 13 (12), 824-833 , 2022
    2022
  • Android App for House Rent Management System
    BST P Raghuraman, Dumpa Keerthi, Mamillapalli Harika
    The International journal of analytical and experimental modal analysis 13 … , 2021
    2021
  • Attribute-Based Cloud Data Integrity Auditing for Secure Outsourced Storage
    B P Raguraman, Swathi T, Beebi D, Raviteja N, Amrtha T
    JAC : A Journal Of Composition Theory 14 (7), 118-125 , 2021
    2021
  • Mobile Search Engine based on user’s priority
    JH P.Raguraman #1, P.Harshavardhan #2, P.Mounika #3, T.Naveen Kumar #4, O ...
    Science, Technology and Development 10 (2), 189-196 , 2021
    2021
  • Routing in Multi hop Networks
    P Raguraman, N Naveen, J Venkatesh, KS Krishna, KT Krishna
    JAC : A Journal Of Composition Theory 14 (2), 27-33 , 2021
    2021
  • EFFECTIVE HEART DISEASE PREDICTION USING HYBRID MACHINE LEARNING TECHNIQUES
    BSC P. Raguraman, Ch.V. Lavanya, K. R. Issac Samuel, M.V. Narayana
    Levant Journal 20 (7), 78-82 , 2021
    2021