ASHISHA G R

@karunya.edu

Assistant Professor, Biomedical Engineering
Karunya Institute of Technology and Sciences

ASHISHA G R


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https://researchid.co/ashisha

RESEARCH INTERESTS

Real Time Embedded Systems, Biomedical Signal Processing, Wearable Devices
13

Scopus Publications

72

Scholar Citations

5

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Development of a real-time sweat sensor using functionalized graphene oxide as an efficient electrode nanomaterial: Towards point of care sweat sensor
    X. Anitha Mary, G.R. Ashisha, B. Jebasingh, P. Manimegalai, C. Karthik, Ayodeji Olalekan Salau
    Hybrid Advances, 2026
    Human sweat is an accessible biofluid that carries essential electrolytes and metabolites which reflects the physiological condition of body. Its use in routine diagnostics is limited due to the non-availability of sensing system. , In this paper, a cost effective sweat sensor employing functionalized Graphene Oxide (FGO) with tris(hydroxymethyl)amino- methane (GO-THAM) electrodes. The functionalization step increases the interlayer spacing and introduces multiple hydroxyl groups, improving the interaction of the nanosheets with sodium and potassium ions. The fabricated device consists of a microfluidic channel coupled to the functionalized electrode and an ATmega328 microcontroller for real-time monitoring of ion-induced conductivity changes. The sensor was calibrated with artificial sweat solutions and further evaluated using sweat collected from volunteers during walking and after exercise. The recorded voltage outputs showed strong agreement with reference flame photometry measurements, demonstrating the reliability of the system. Because of its low cost, straightforward fabrication, and ability to detect Na + and K + concentrations in real time, this platform has strong potential for athletic performance assessment and future point-of-care applications.
  • Automated Real-Time Intravenous Fluid Monitoring System and Blocking of Retrograde Fluid Flow
    S.Nishanthini, Niszha Krishnan, K.Kamalikadevi, Utpal Chandra De, Bibhuti Bhusan Dash, G. R. Ashisha, X. Anitha Mary, M. Lavanya, Sudhansu Shekhar Patra
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking Gcwcn 2025, 2025
    Technological innovations have ushered in remarkable improvements in medical care. For all hospitalized individuals, receiving cutting-edge therapeutic interventions and continuous surveillance has become essentially indispensable. Given intravenous fluid administration is amongst the swiftest and most prevalent modes of therapy, a nurse diligently regulates saline delivery to patients as it is furnished. Regrettably, busy workloads or fleeting lapses in focus can potentially result in medical staff neglecting to deactivate infusion mechanisms, risking overhydration. Meanwhile, automated monitors promise to avert such pitfalls, liberating professionals to dedicate full attention to patients' wellbeing. To begin, a brief overview of the risks posed by improper saline administration and the potential harms of reverse blood transfusion. Addressing this issue demands innovation to guarantee patient safety throughout lengthy treatments. The proposed design integrates a solenoid valve, load cell and microcontroller core to continually monitor intravenous fluid levels. This automated system prevents hazardous imbalances by shutting off the saline feed automatically once the bottle runs dry. Moreover, the microcontroller maintains a record of fluid administration over the full treatment period for physician review. Occasional longer sentences mix with shorter ones to better mimic human-style writing and increase complexity and variation in sentence structures.
  • Enhancing Breast Cancer Detection Using SVM and Explainable AI
    J. Anushree, GR. Ashisha, Utpal Chandra De, Bibhuti Bhusan Dash, X. Anitha Mary, C.Karthik, Matam Mohan Babu, P. Jyotheeswari, Sudhansu Shekhar Patra
    2025 5th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2025, 2025
    Currently, among all cancers, breast cancer is among the leading diagnosed cancers and ranks as a significant contributor to female mortality according to global statistics. Breast cancer may be Highly treatable when detected in the initial stages. Tumors associated with breast cancer are of two types: malignant and benign. Malignant tumor pose a greater threat compared to benign tumors. Therefore, early identification of tumor type is important for providing the patient with appropriate treatment. Machine learning (ML) has become a significance tool in medical diagnostics. In this paper we use Wisconsin Breast Cancer Dataset (WBCD) to create a Support Vector Machine (SVM) based classification model that differentiates between benign and malignant tumors is compared to give more accuracy the other classifiers. The SVM model achieves a 98.25 percentage accuracy rate, demonstrating its potential in predicting breast cancer. Additionally, we investigate strategies for hyperparameter tuning and feature selection for boosting the algorithm's capability. In order to tackle the challenges of interpretability in ML models, we employ Explainable AI (XAI) techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). The classification technique aims to identify breast cancer at an early stage.
  • Machine Learning Technology based Heart Disease Detection Model
    M. Subhashini, GR. Ashisha, X. Anitha Mary, Bibhuti Bhusan Dash, C. Karthik, Utpal Chandra De, Matam Mohan Babu, P. Jyotheeswari, Sudhansu Shekhar Patra
    2025 2nd Asia Pacific Conference on Innovation in Technology Apcit 2025, 2025
    Heart disease is a group of illnesses that impact your cardiovascular system and can affect anyone regardless of age. It is also known as cardiac disease, is a concern for worldwide well-being, and is the most prominent cause of death nationwide. Approximately 27 percent of all deaths occurring are due to heart disease. It is happening with a prevalence rate of 7.5 percent. Common types of heart diseases include Heart Failure, Arrhythmias, Coronary Artery Disease, and many more. The primary goal of the model is, to find the presence of heart disease with machine learning representation by providing precise reading. Four models are compared such as Logistic Regression, Naïve Bayes, K-Nearest neighbours and decision tree. The result shows that Logistic Regression gives the highest accuracy with 86.89%.
  • Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms
    G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice
    International Journal of Computational Intelligence Systems, 2024
    Diabetes mellitus is considered one of the main causes of death worldwide. If diabetes fails to be treated and diagnosed earlier, it can cause several other health problems, such as kidney disease, nerve disease, vision problems, and brain issues. Early detection of diabetes reduces healthcare costs and minimizes the chance of serious complications. In this work, we propose an e-diagnostic model for diabetes classification via a machine learning algorithm that can be executed on the Internet of Medical Things (IoMT). The study uses and analyses two benchmarking datasets, the PIMA Indian Diabetes Dataset (PIDD) and the Behavioral Risk Factor Surveillance System (BRFSS) diabetes dataset, to classify diabetes. The proposed model consists of the random oversampling method to balance the range of classes, the interquartile range technique-based outlier detection to eliminate outlier data, and the Boruta algorithm for selecting the optimal features from the datasets. The proposed approach considers ML algorithms such as random forest, gradient boosting models, light gradient boosting classifiers, and decision trees, as they are widely used classification algorithms for diabetes prediction. We evaluated all four ML algorithms via performance indicators such as accuracy, F1 score, recall, precision, and AUC-ROC. Comparative analysis of this model suggests that the random forest algorithm outperforms all the remaining classifiers, with the greatest accuracy of 92% on the BRFSS diabetes dataset and 94% accuracy on the PIDD dataset, which is greater than the 3% accuracy reported in existing research. This research is helpful for assisting diabetologists in developing accurate treatment regimens for patients who are diabetic.
  • Early Detection of Diabetes Using ML Based Classification Algorithms
    G. R. Ashisha, X. Anitha Mary, Subrata Chowdhury, C. Karthik, Tanupriya Choudhury, Ketan Kotecha
    Communications in Computer and Information Science, 2024
  • Prediction of Blood Pressure and Diabetes with AI Techniques—A Review
    G. R. Ashisha, X. Anitha Mary
    Lecture Notes in Networks and Systems, 2023
  • Investigation of Machine Learning Techniques and Sensing Devices for Mental Stress Detection
    M Smirthy, M Dhanushree, G R Ashisha
    Icspc 2023 4th International Conference on Signal Processing and Communication, 2023
    Nowadays, stress has become a major cause of many diseases. According to the World Health organization, stress affects about 280 million people globally (i.e.) 3.8% of the population. Early identification of stress is important to prevent its negative impacts on people and is therefore an important step in the service of humanity and health care. These days, smart devices have become an important part of our lives and it achieved a huge amount of usage. This led to the query of whether smartphones and wearable sensors can identify and prevent stress. Although there are numerous works on mental stress detection that exist in controlled laboratory settings, the number of methods for stress detection in everyday life is inadequate. A variety of studies have been conducted to establish an association between stressful events and human responses using diverse psychological, physiological, physical, and behavioral measures. This paper conducts an integrated review of human stress detection and it provides an analysis of strategies and techniques that have been proposed for detecting human stress. This study also focuses on the prospective strategies, advantages, and challenges of mental stress detection using machine learning (ML), and wearable sensors.
  • Early Diabetes prediction with optimal feature selection using ML based Prediction Framework
    G. R. Ashisha, X. Anitha Mary, H. Mohamed Ashif, I. Karthikeyan, J. Roshan
    Icspc 2023 4th International Conference on Signal Processing and Communication, 2023
    The notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. The use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an International Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. This growing disease requires a lot of attention. Machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. The major goal of this work is to design a model that can more accurately predict patients’ diabetes. The research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. The result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes.
  • Analysis of Diabetes disease using Machine Learning Techniques: A Review
    Journal of Information Technology Management, 2023
  • Advances in photoplethysmogram and electrocardiogram signal analysis for wearable applications
    G. R. Ashisha, X. Anitha Mary
    Advances in Intelligent Systems and Computing, 2021
  • Design challenges for embedded based wireless postoperative bedside monitoring system
    G. R. Ashisha, X. Anitha Mary, Lina Rose
    Journal of Interdisciplinary Mathematics, 2020
  • IoT-Based Continuous Bedside Monitoring Systems
    G. R. Ashisha, X. Anitha Mary, K. Rajasekaran, R. Jegan
    Advances in Intelligent Systems and Computing, 2019

RECENT SCHOLAR PUBLICATIONS

  • Effective Predictive model for diabetes classification using optimized machine learning on imbalanced dataset
    GR Ashisha, S Kiran
    Sustainable Global Societies Initiative 1 (4) , 2026
    2026
  • Advances in diabetes prediction: a systematic literature review of Artificial Intelligence based methods
    GR Ashisha, SK Oruganti
    Sustainable Global Societies Initiative 1 (2) , 2026
    2026
  • Development of a Real-Time Sweat Sensor Using Functionalized Graphene Oxide as an Efficient Electrode Nanomaterial: Towards Point of Care Sweat Sensor
    XA Mary, GR Ashisha, B Jebasingh, P Manimegalai, C Karthik, AO Salau
    Hybrid Advances, 100603 , 2026
    2026
  • Automated Real-Time Intravenous Fluid Monitoring System and Blocking of Retrograde Fluid Flow
    S Nishanthini, N Krishnan, K Kamalikadevi, UC De, BB Dash, GR Ashisha, ...
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • Machine Learning Technology based Heart Disease Detection Model
    M Subhashini, GR Ashisha, XA Mary, BB Dash, C Karthik, UC De, ...
    2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-6 , 2025
    2025
  • Enhancing Breast Cancer Detection Using SVM and Explainable AI
    J Anushree, GR Ashisha, UC De, BB Dash, XA Mary, C Karthik, MM Babu, ...
    2025 5th International Conference on Emerging Research in Electronics … , 2025
    2025
  • Random oversampling-based diabetes classification via machine learning algorithms
    GR Ashisha, XA Mary, EGM Kanaga, J Andrew, RJ Eunice
    International Journal of Computational Intelligence Systems 17 (1), 270 , 2024
    2024
    Citations: 18
  • Classification of diabetes using ensemble machine learning techniques
    GR Ashisha, M Raja
    Scalable Computing: Practice and Experience 25 (4), 3172-3180 , 2024
    2024
    Citations: 5
  • Comorbidies of Blood Pressure and Blood Glucose: Challenges and Future Trends
    GR Ashisha, C Karthik
    Preprints , 2024
    2024
  • Early Detection of Diabetes Using ML Based Classification Algorithms
    GR Ashisha, XA Mary, S Chowdhury, C Karthik, T Choudhury, K Kotecha
    International Advanced Computing Conference, 148-157 , 2023
    2023
    Citations: 1
  • Prediction of Blood Pressure and Diabetes with AI Techniques—A Review
    GR Ashisha, X Anitha Mary
    International Conference on Information, Communication and Computing … , 2023
    2023
    Citations: 2
  • Investigation of Machine Learning Techniques and Sensing Devices for Mental Stress Detection
    Smirthy M, Dhanushree M, Ashisha G R
    2023 4th International Conference on Signal Processing and Communication … , 2023
    2023
    Citations: 5
  • Early Diabetes prediction with optimal feature selection using ML based Prediction Framework
    GR Ashisha, XA Mary, HM Ashif, I Karthikeyan, J Roshan
    2023 4th International Conference on Signal Processing and Communication … , 2023
    2023
    Citations: 4
  • Analysis of Diabetes disease using Machine Learning Techniques: A Review
    S Ashisha G.R, Mary, Anitha X, George, Thomas S, Sagayam, Martin K ...
    Journal of Information Technology Management 15 (4), 139-159 , 2023
    2023
    Citations: 17
  • Advances in photoplethysmogram and electrocardiogram signal analysis for wearable applications
    GR Ashisha, X Anitha Mary
    Intelligence in Big Data Technologies—Beyond the Hype: Proceedings of … , 2020
    2020
    Citations: 9
  • Design challenges for embedded based wireless postoperative bedside monitoring system
    GR Ashisha, X Anitha Mary, L Rose
    Journal of Interdisciplinary Mathematics 23 (1), 285-292 , 2020
    2020
    Citations: 3
  • Automatic Skin wound Examining for the Early Diagnosis of Melanoma Skin Cancer Using Image Processing Technique
    ARSA Aruldhas, GR Ashisha
    2020
  • IoT-based continuous bedside monitoring systems
    GR Ashisha, X Anitha Mary, K Rajasekaran, R Jegan
    Advances in Big Data and Cloud Computing: Proceedings of ICBDCC18, 401-410 , 2018
    2018
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • Random oversampling-based diabetes classification via machine learning algorithms
    GR Ashisha, XA Mary, EGM Kanaga, J Andrew, RJ Eunice
    International Journal of Computational Intelligence Systems 17 (1), 270 , 2024
    2024
    Citations: 18
  • Analysis of Diabetes disease using Machine Learning Techniques: A Review
    S Ashisha G.R, Mary, Anitha X, George, Thomas S, Sagayam, Martin K ...
    Journal of Information Technology Management 15 (4), 139-159 , 2023
    2023
    Citations: 17
  • Advances in photoplethysmogram and electrocardiogram signal analysis for wearable applications
    GR Ashisha, X Anitha Mary
    Intelligence in Big Data Technologies—Beyond the Hype: Proceedings of … , 2020
    2020
    Citations: 9
  • IoT-based continuous bedside monitoring systems
    GR Ashisha, X Anitha Mary, K Rajasekaran, R Jegan
    Advances in Big Data and Cloud Computing: Proceedings of ICBDCC18, 401-410 , 2018
    2018
    Citations: 8
  • Classification of diabetes using ensemble machine learning techniques
    GR Ashisha, M Raja
    Scalable Computing: Practice and Experience 25 (4), 3172-3180 , 2024
    2024
    Citations: 5
  • Investigation of Machine Learning Techniques and Sensing Devices for Mental Stress Detection
    Smirthy M, Dhanushree M, Ashisha G R
    2023 4th International Conference on Signal Processing and Communication … , 2023
    2023
    Citations: 5
  • Early Diabetes prediction with optimal feature selection using ML based Prediction Framework
    GR Ashisha, XA Mary, HM Ashif, I Karthikeyan, J Roshan
    2023 4th International Conference on Signal Processing and Communication … , 2023
    2023
    Citations: 4
  • Design challenges for embedded based wireless postoperative bedside monitoring system
    GR Ashisha, X Anitha Mary, L Rose
    Journal of Interdisciplinary Mathematics 23 (1), 285-292 , 2020
    2020
    Citations: 3
  • Prediction of Blood Pressure and Diabetes with AI Techniques—A Review
    GR Ashisha, X Anitha Mary
    International Conference on Information, Communication and Computing … , 2023
    2023
    Citations: 2
  • Early Detection of Diabetes Using ML Based Classification Algorithms
    GR Ashisha, XA Mary, S Chowdhury, C Karthik, T Choudhury, K Kotecha
    International Advanced Computing Conference, 148-157 , 2023
    2023
    Citations: 1
  • Effective Predictive model for diabetes classification using optimized machine learning on imbalanced dataset
    GR Ashisha, S Kiran
    Sustainable Global Societies Initiative 1 (4) , 2026
    2026
  • Advances in diabetes prediction: a systematic literature review of Artificial Intelligence based methods
    GR Ashisha, SK Oruganti
    Sustainable Global Societies Initiative 1 (2) , 2026
    2026
  • Development of a Real-Time Sweat Sensor Using Functionalized Graphene Oxide as an Efficient Electrode Nanomaterial: Towards Point of Care Sweat Sensor
    XA Mary, GR Ashisha, B Jebasingh, P Manimegalai, C Karthik, AO Salau
    Hybrid Advances, 100603 , 2026
    2026
  • Automated Real-Time Intravenous Fluid Monitoring System and Blocking of Retrograde Fluid Flow
    S Nishanthini, N Krishnan, K Kamalikadevi, UC De, BB Dash, GR Ashisha, ...
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN … , 2025
    2025
  • Machine Learning Technology based Heart Disease Detection Model
    M Subhashini, GR Ashisha, XA Mary, BB Dash, C Karthik, UC De, ...
    2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-6 , 2025
    2025
  • Enhancing Breast Cancer Detection Using SVM and Explainable AI
    J Anushree, GR Ashisha, UC De, BB Dash, XA Mary, C Karthik, MM Babu, ...
    2025 5th International Conference on Emerging Research in Electronics … , 2025
    2025
  • Comorbidies of Blood Pressure and Blood Glucose: Challenges and Future Trends
    GR Ashisha, C Karthik
    Preprints , 2024
    2024
  • Automatic Skin wound Examining for the Early Diagnosis of Melanoma Skin Cancer Using Image Processing Technique
    ARSA Aruldhas, GR Ashisha
    2020