Vidhushavarshini Suresh kumar

@ritchennai.edu.in

Associate Professor/CSE
Rajalakshmi Institute of Science and Technology

Vidhushavarshini Suresh kumar


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https://researchid.co/vidhusha

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Science Applications
22

Scopus Publications

227

Scholar Citations

8

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • Mechanical, moisture, and morphological evaluation of ZnO-modified vetiver root fiber epoxy bio composites for sustainable applications
    Ganesh Babu. L, Sureshkumar V, Pradeep G.M, Tamilselvi S, Shalom N, Girimurugan R
    Interactions, 2026
  • Equilibrium and kinetic data for adsorption of methylene blue onto nickel ferrite nanocomposites
    N. Saravanan, S. Balamurugan, V. Sureshkumar, R. Vivek, R. Mohan, S. Nanthakumar, A. Revathi, R. Girimurugan
    Aip Conference Proceedings, 2025
  • Multi-Label Fundus Image Classification Model Based on Deep Learning for Multiclass Imbalanced Ocular Disease Dataset
    Vinodhini Velleangiri, Sathiyabhama Balasubramaniam, Vidhushavarshini Suresh Kumar, Basker Nagarajan, Namitha Govindarajan, Pavitra Panneerselvam, Sakthy Elango Sathya
    Aip Conference Proceedings, 2025
  • Hybrid Cryptographic Technique for Secure Spatial Range Query Processing in Cloud Computing
    Vinodhini Velleangiri, Sathiyabhama Balasubramaniyam, Vidhushavarshini Suresh Kumar, Jayareka Subramanian, Fathima Shahul Hameed, Basker Nagarajan, Varshaa Athiyappan Sasikumar, Yashhwanthin Suresh Kumar, Shri Sowmiya Karthikeyan, Thrissa Priya Sathiya Ramesh
    Aip Conference Proceedings, 2025
  • Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique
    T. K. Revathi, B. Sathiyabhama, S Kaliraj, Vidhushavarshini Sureshkumar
    BMC Cardiovascular Disorders, 2025
    Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. Diabetic Retinopathy (DR) is a kind of eye disease was associated with increasing risk factors for all-causes of CVD events. The early diagnosis of DR plays a significant role in preventing CVD. However, there are many works have been carried out on classification of the disease but they focused less on feature selection and increasing the accuracy of the model. The proposed work introduces Improvised Deep Belief Network named I-DBN to resolve the above mentioned problems and mainly to concentrate on improving the entire performance of the model leading to the unbiased output. We used Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) algorithm for feature extraction and selection respectively. Five performance metrics have been used to assess the proposed model. The results of I-DBN outperform other state-of-the-art methods. The result validation ensures that I-DBN can deliver trustworthy recommendations to doctors to treat the patients by enhancing the accuracy of CVD prediction up to 98.95%.
  • Machine Learning-Based Analysis of Lifestyle Factors for Sleep Apnea Detection
    M Nandhini, C Suganthi Evangeline, M Mohan Kumar, V Sureshkumar
    2025 8th International Conference on Circuit Power and Computing Technologies Iccpct 2025, 2025
    Sleep apnea is a common sleep disorder that is linked to severe health complications, such as cognitive impairment and cardiovascular diseases. This study introduces a machine learning-based method for predicting the likelihood of sleep apnea by utilizing lifestyle and sleep-related factors. A dataset that included variables such as sleep quality, physical activity, stress levels, and daily step count was analyzed using MATLAB’s Regression Learner. The data was preprocessed and visualized to identify significant patterns and correlations that influenced the occurrence of sleep apnea. The results indicate that machine learning models are capable of accurately capturing the impact of lifestyle behaviors on sleep health, providing a promising foundation for early detection and continuous monitoring. Future improvements will concentrate on the integration of deep learning techniques and real-time implementation using embedded systems such as Arduino for personalized, AI-driven health monitoring.
  • Multimodal Skin Cancer Prediction: Integrating Dermoscopic Images and Clinical Metadata with Transfer Learning
    Ramya Panneerselvam, Sathiyabhama Balasubramaniam, Vidhushavarshini Sureshkumar, Vinayakumar Ravi, Siti Sarah Maidin
    Open Bioinformatics Journal, 2025
    Background Skin cancers exist as the most pervasive cancers in the world; to increase the survival rates, early prediction has become more predominant. Many conventional techniques frequently depend on visual review of clinical information and dermoscopic illustrations. In recent technological developments, the enthralling algorithms of combining modalities are used for increasing diagnosis accuracy in deep learning. Methods Our research proposes a multi-faceted approach for the prediction of skin cancer that incorporates clinical metadata with dermoscopic visuals. The pre-trained convolutional neural networks, like EfficientNetB3, were used for dermoscopic images along with transfer learning techniques to excavate some of the visual attributes in this study. Moreover, TabNet was used for processing the clinical metadata, including age, gender, and medical history. The features obtained from both fusion techniques were integrated to enhance the prediction accuracy. The benchmark datasets, like ISIC 2018, ISIC 2019, and HAM10000, were used to assess the model. Results The proposed multi-faceted system achieved 98.69% accuracy in the classification of skin cancer, surpassing the model that used dermoscopic snapshots with clinical data. The convergence of images with clinical metadata has substantially enhanced prediction resilience, demonstrating the importance of multimodal deep learning in skin lesion diagnosis. Conclusion This research focused mainly on the efficiency of integrating dermoscopic visuals and clinical information using transfer learning for skin cancer prediction. The proposed system offers a promising tool for improving diagnostic accuracy, and further research could explore its application in other medical fields requiring multimodal data integration.
  • Two-Stage FPGA and ML Integrated System for Biomedical Health Monitoring
    Nandhini M, Suganthi Evangeline C, Sureshkumar V, Saranyanandhini D, Emmanuel Joy, M. Karthiga
    Proceedings of the 7th International Conference on Innovative Data Communication Technologies and Application Icidca 2025, 2025
    This research presents a two-phase, real-time biomedical system architecture for the detection of QRS complexes and the classification of health states. An FPGA platform is used for its implementation. Using combinational logic, the system quickly detects R-peaks in ECG data in Phase 1 by utilizing the FPGA’s parallel processing. Real-time temporal feature extraction, including RR intervals, is then performed. Machine learning (ML) models, such as Support Vector Machines (SVMs), Decision Trees, and Neural Networks, employ the retrieved data in Phase 2 to categorize cardiac conditions like arrhythmias. The hybrid architecture offers a small, power-efficient solution appropriate for wearable and Internet of Things-enabled health care monitoring by fusing sophisticated ML-driven diagnosis with low-latency FPGA-based detection.
  • Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning
    Abirami Annadurai, Vidhushavarshini Sureshkumar, Dhayanithi Jaganathan, Seshathiri Dhanasekaran
    Fractal and Fractional, 2024
    In medical imaging, noise can significantly obscure critical details, complicating diagnosis and treatment. Traditional denoising techniques often struggle to maintain a balance between noise reduction and detail preservation. To address this challenge, we propose an “Efficient Transfer-Learning-Based Fractional Order Image Denoising Approach in Medical Image Analysis (ETLFOD)” method. Our approach uniquely integrates transfer learning with fractional order techniques, leveraging pre-trained models such as DenseNet121 to adapt to the specific needs of medical image denoising. This method enhances denoising performance while preserving essential image details. The ETLFOD model has demonstrated superior performance compared to state-of-the-art (SOTA) techniques. For instance, our DenseNet121 model achieved an accuracy of 98.01%, precision of 98%, and recall of 98%, significantly outperforming traditional denoising methods. Specific results include a 95% accuracy, 98% precision, 99% recall, and 96% F1-score for MRI brain datasets, and an 88% accuracy, 91% precision, 95% recall, and 88% F1-score for COVID-19 lung data. X-ray pneumonia results in the lung CT dataset showed a 92% accuracy, 97% precision, 98% recall, and 93% F1-score. It is important to note that while we report performance metrics in this paper, the primary evaluation of our approach is based on the comparison of original noisy images with the denoised outputs, ensuring a focus on image quality enhancement rather than classification performance.
  • Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine
    Vidhushavarshini Sureshkumar, Rubesh Sharma Navani Prasad, Sathiyabhama Balasubramaniam, Dhayanithi Jagannathan, Jayanthi Daniel, Seshathiri Dhanasekaran
    Journal of Personalized Medicine, 2024
    Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis.
  • Concatenated Modified LeNet Approach for Classifying Pneumonia Images
    Dhayanithi Jaganathan, Sathiyabhama Balsubramaniam, Vidhushavarshini Sureshkumar, Seshathiri Dhanasekaran
    Journal of Personalized Medicine, 2024
  • Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis
    Dhayanithi Jaganathan, Sathiyabhama Balasubramaniam, Vidhushavarshini Sureshkumar, Seshathiri Dhanasekaran
    Diagnostics, 2024
  • An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction
    T.K. Revathi, Sathiyabhama Balasubramaniam, Vidhushavarshini Sureshkumar, Seshathiri Dhanasekaran
    Diagnostics, 2024
  • A Comparative Study on Thyroid Nodule Classification Using Transfer Learning Methods
    Vidhushavarshini Sureshkumar, Dhayanithi Jaganathan, Vinayakumar Ravi, Vinodhini Velleangiri, Pradeep Ravi
    Open Bioinformatics Journal, 2024
  • Robust Error Resilience Network-On-Chip Router Architecture
    Veera Boopathy. E, Surya T, Sheik Arafat I, Rajeshwaran K, Sureshkumar V, Kiruba Shankar Gajendiran
    Cosmic 2024 IEEE International Conference on Computing Semiconductor Mechatronics Intelligent Systems and Communications Proceedings, 2024
  • Mobile tourism recommendation system for visually disabled
    Artificial Intelligence and Data Science in Recommendation System Current Trends Technologies and Applications, 2023
  • Smart Healthcare Monitoring System: Integrating IoT, Deep Learning, and XGBoost for Real-Time Patient Diagnosis
    Kruthika Paulraj, Nisha Soms, S. David Samuel Azariya, Sathya Priya S, Jeba Emilyn J, Vidhushavarshini Sureshkumar
    Ocit 2023 21st International Conference on Information Technology Proceedings, 2023
  • Optimization of process parameters on wire cut electrical discharge machining and surface integrity studies of AA6070/MgO composites
    S Vinoth, C Rajasekar, P Sathish, V Sureshkumar, A Yasminebegum, Sk Hasane Ahammad, R Girimurugan
    Journal of Physics Conference Series, 2023
  • A hybrid optimization algorithm-based feature selection for thyroid disease classifier with rough type-2 fuzzy support vector machine
    Vidhushavarshini Sureshkumar, Sathiyabhama Balasubramaniam, Vinayakumar Ravi, Ajay Arunachalam
    Expert Systems, 2022
  • A Deep Q Network Optimization Algorithm for DoS Attack in WSN
    A Puviarasu., P Jeyabharathi., K Lavanya., S. Vimalnath, V Sureshkumar., P Naveen.
    3rd International Conference on Smart Electronics and Communication Icosec 2022 Proceedings, 2022
  • A study of the cloud security attacks and threats
    V Sureshkumar, B Baranidharan
    Journal of Physics Conference Series, 2021
  • RETRACTION:Disease prediction based on micro array classification using deep learning techniques
    V. Chandrasekar, V. Sureshkumar, T. Satish Kumar, S. Shanmugapriya
    Microprocessors and Microsystems, 2020

RECENT SCHOLAR PUBLICATIONS

  • An Intelligent AI-Powered Meeting Analytics Extension with Automated Calendar Synchronization, Productivity Scoring, Visual Insight Modeling, and Custom Task Export Framework
    VS Srimathi R , Monica K , Rithanya S
    International Journal of Scientific Research in Engineering and Management 1 … , 2026
    2026
  • Speech-Driven Smart Home Assistant for Elderly Health and Convenience
    PR M. Kiruthiga Devi, Joshi,Vidhushavarshini Sureshkumar
    2026 International Conference on Intelligent Computing (ICONIC) , 2026
    2026
  • ISNM-Net: A Deep Synergic CNN Architecture for Accurate ‎Grape Leaf Disease Classification Using Mobilenet and Res-‎Net
    International Journal of Basic and Applied Sciences 15 (1), 49-58 , 2026
    2026
  • ZeroHunger: A Digital Solution for Reducing Food Waste and Hunger Alleviation
    MB Vidhushavarshini Suresh¹, Anasuya Neharika Jonnalagadda², Dakshak Jagdheesh³
    International Conference on Intelligent Systems and Digital Transformation … , 2025
    2025
  • A Comprehensive Review of TriBERT-X: A Concatenated Transformer Approach for Explainable Cyberbullying Detection on Twitter
    TK Vidhushavarshini Suresh Kumar, Ranjini S , Sadhana B B
    International Conference on Intelligent Systems and Digital Transformation … , 2025
    2025
  • TriVerBERT-LLM: An Ensemble Multimodal Approach for Credibility Assessment of YouTube Video Transcripts via Logical Fallacy Detection and Claim Verification
    SS Vidhushavarshini Sureshkumar, Aaron Don Kattasserry, Nivediitha S
    International Conference on Intelligent Systems and Digital Transformation … , 2025
    2025
  • Hybrid cryptographic technique for secure spatial range query processing in cloud computing
    V Velleangiri, S Balasubramaniyam, VS Kumar, J Subramanian, ...
    AIP Conference Proceedings 3279 (1), 45-58 , 2025
    2025
  • Multi-label fundus image classification model based on deep learning for multiclass imbalanced ocular disease dataset
    SES Vinodhini Velleangiri, Sathiyabhama Balasubramaniam, Vidhushavarshini ...
    AIP Conference Proceedings 3279 (1), 15-22 , 2025
    2025
    Citations: 3
  • A hybrid approach for diabetic nephropathy prediction using advanced firefly optimization
    LM Vinodhini Velleangiri, Sathiyabhama Balasubramaniam, Vidhushavarshini ...
    AIP Conference Proceedings 3279 (1), 1-8 , 2025
    2025
  • Ensemble-based Heart Disease Diagnosis (EHDD) using Feature Selection and PCA Extraction Methods
    RS V. Vinodhini, B. Sathiyabhama, S. Vidhushavarshini, Vinayakumar Ravi
    The Open Bioinformatics Journal 18 (1), 1-18 , 2025
    2025
    Citations: 4
  • Multimodal Skin Cancer Prediction: Integrating Dermoscopic Images and Clinical Metadata with Transfer Learning
    SSM Ramya Panneerselvam, Sathiyabhama Balasubramaniam, Vidhushavarshini ...
    The Open Bioinformatics Journal 18, 1 -18 , 2025
    2025
    Citations: 5
  • Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique
    SKVS T. K. Revathi, B. Sathiyabhama
    BMC Cardiovascular Disorders 25 (30), 1-18 , 2025
    2025
    Citations: 4
  • Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning
    DJSD Abirami Annadurai,Vidhushavarshini Sureshkumar
    Fractals and Fractionals 8 (9), 511 , 2024
    2024
    Citations: 11
  • Breast cancer detection and analytics using hybrid CNN and extreme learning machine
    V Sureshkumar, RSN Prasad, S Balasubramaniam, D Jagannathan, ...
    Journal of Personalized Medicine 14 (8), 792 , 2024
    2024
    Citations: 45
  • A comparative study on thyroid nodule classification using transfer learning methods
    V Sureshkumar, D Jaganathan, V Ravi, V Velleangiri, P Ravi
    The Open Bioinformatics Journal 17 (1) , 2024
    2024
    Citations: 11
  • Concatenated modified LeNet approach for classifying pneumonia images
    D Jaganathan, S Balsubramaniam, V Sureshkumar, S Dhanasekaran
    Journal of Personalized Medicine 14 (3), 328 , 2024
    2024
    Citations: 21
  • Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis
    VSSD Dhayanithi Jaganathan, Sathiyabhama Balasubramaniam
    Diagnostics 14 (4), 422 , 2024
    2024
    Citations: 35
  • An improved long short-term memory algorithm for cardiovascular disease prediction
    TK Revathi, S Balasubramaniam, V Sureshkumar, S Dhanasekaran
    Diagnostics 14 (3), 239 , 2024
    2024
    Citations: 28
  • Smart healthcare monitoring system: Integrating iot, deep learning, and xgboost for real-time patient diagnosis
    K Paulraj, N Soms, SDS Azariya, V Sureshkumar
    2023 OITS International Conference on Information Technology (OCIT), 708-713 , 2023
    2023
    Citations: 12
  • Mobile Tourism Recommendation System for Visually Disabled
    P Selvarajan¹, P Selvan¹, V Sureshkumar¹, S Balasubramaniam¹
    Artificial Intelligence and Data Science in Recommendation System: Current … , 2023
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • Breast cancer detection and analytics using hybrid CNN and extreme learning machine
    V Sureshkumar, RSN Prasad, S Balasubramaniam, D Jagannathan, ...
    Journal of Personalized Medicine 14 (8), 792 , 2024
    2024
    Citations: 45
  • A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine
    V Sureshkumar, S Balasubramaniam, V Ravi, A Arunachalam
    Expert Systems 39 (1), e12811 , 2022
    2022
    Citations: 36
  • Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis
    VSSD Dhayanithi Jaganathan, Sathiyabhama Balasubramaniam
    Diagnostics 14 (4), 422 , 2024
    2024
    Citations: 35
  • An improved long short-term memory algorithm for cardiovascular disease prediction
    TK Revathi, S Balasubramaniam, V Sureshkumar, S Dhanasekaran
    Diagnostics 14 (3), 239 , 2024
    2024
    Citations: 28
  • Concatenated modified LeNet approach for classifying pneumonia images
    D Jaganathan, S Balsubramaniam, V Sureshkumar, S Dhanasekaran
    Journal of Personalized Medicine 14 (3), 328 , 2024
    2024
    Citations: 21
  • Smart healthcare monitoring system: Integrating iot, deep learning, and xgboost for real-time patient diagnosis
    K Paulraj, N Soms, SDS Azariya, V Sureshkumar
    2023 OITS International Conference on Information Technology (OCIT), 708-713 , 2023
    2023
    Citations: 12
  • Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning
    DJSD Abirami Annadurai,Vidhushavarshini Sureshkumar
    Fractals and Fractionals 8 (9), 511 , 2024
    2024
    Citations: 11
  • A comparative study on thyroid nodule classification using transfer learning methods
    V Sureshkumar, D Jaganathan, V Ravi, V Velleangiri, P Ravi
    The Open Bioinformatics Journal 17 (1) , 2024
    2024
    Citations: 11
  • A comparison of classification techniques on thyroid detection using J48 and naive bayes classification techniques
    S Vidhushavarshini, B Sathiyabhama
    Proceedings of the International Conference on Intelligent Computing Systems … , 2017
    2017
    Citations: 6
  • Multimodal Skin Cancer Prediction: Integrating Dermoscopic Images and Clinical Metadata with Transfer Learning
    SSM Ramya Panneerselvam, Sathiyabhama Balasubramaniam, Vidhushavarshini ...
    The Open Bioinformatics Journal 18, 1 -18 , 2025
    2025
    Citations: 5
  • Ensemble-based Heart Disease Diagnosis (EHDD) using Feature Selection and PCA Extraction Methods
    RS V. Vinodhini, B. Sathiyabhama, S. Vidhushavarshini, Vinayakumar Ravi
    The Open Bioinformatics Journal 18 (1), 1-18 , 2025
    2025
    Citations: 4
  • Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique
    SKVS T. K. Revathi, B. Sathiyabhama
    BMC Cardiovascular Disorders 25 (30), 1-18 , 2025
    2025
    Citations: 4
  • Multi-label fundus image classification model based on deep learning for multiclass imbalanced ocular disease dataset
    SES Vinodhini Velleangiri, Sathiyabhama Balasubramaniam, Vidhushavarshini ...
    AIP Conference Proceedings 3279 (1), 15-22 , 2025
    2025
    Citations: 3
  • ABNORMAL PSYCHOLOGY AND MALADAPTIVE BEHAVIOUR EXISTS EVERYWHERE, DOES IT INFLUENCE SOCIETY?
    PKG M. Asok Rajkumar,.Vidhushavarshini SureshKumar
    International Journal of Advance Research In Science And Engineering 4 (01 … , 2015
    2015
    Citations: 3
  • Retrieving Information using Reversible Data Hiding
    P Lalitha, S Vidhushavarshini
    International Journal of scientific research and management (IJSRM) 2 (5 … , 2014
    2014
    Citations: 3
  • An Intelligent AI-Powered Meeting Analytics Extension with Automated Calendar Synchronization, Productivity Scoring, Visual Insight Modeling, and Custom Task Export Framework
    VS Srimathi R , Monica K , Rithanya S
    International Journal of Scientific Research in Engineering and Management 1 … , 2026
    2026
  • Speech-Driven Smart Home Assistant for Elderly Health and Convenience
    PR M. Kiruthiga Devi, Joshi,Vidhushavarshini Sureshkumar
    2026 International Conference on Intelligent Computing (ICONIC) , 2026
    2026
  • ISNM-Net: A Deep Synergic CNN Architecture for Accurate ‎Grape Leaf Disease Classification Using Mobilenet and Res-‎Net
    International Journal of Basic and Applied Sciences 15 (1), 49-58 , 2026
    2026
  • ZeroHunger: A Digital Solution for Reducing Food Waste and Hunger Alleviation
    MB Vidhushavarshini Suresh¹, Anasuya Neharika Jonnalagadda², Dakshak Jagdheesh³
    International Conference on Intelligent Systems and Digital Transformation … , 2025
    2025
  • A Comprehensive Review of TriBERT-X: A Concatenated Transformer Approach for Explainable Cyberbullying Detection on Twitter
    TK Vidhushavarshini Suresh Kumar, Ranjini S , Sadhana B B
    International Conference on Intelligent Systems and Digital Transformation … , 2025
    2025