Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management Satheeskumar R Jcrpe Journal of Clinical Research in Pediatric Endocrinology, 2025 Objective: The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes. Methods: Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring data, glucose management indicator (GMI) reports, hemoglobin A1c (HbA1c) values, and patient medical history, were used to train ML models including long short-term memory (LSTM) networks, transformer models, random forest, and gradient boosting machines (GBMs). These were designed to analyze glucose trends and identify the honeymoon phase in T1D patients. Results: The transformer model achieved the highest accuracy at 91%, followed by GBMs at 89%, LSTM at 88%, and random forest at 87%. Key features, such as glucose variability, insulin adjustments, GMI values, and HbA1c levels were critical to model performance. Accurate identification of the honeymoon phase enabled optimized insulin adjustments, enhancing glucose control and reducing hypoglycemia risk. Conclusion: The ML-driven approach provides a robust method for detecting the honeymoon phase in T1D patients, demonstrating potential for improved personalized insulin management. The findings suggest significant benefits in patient outcomes, with future research focused on further validation and clinical integration.
AI-driven biomarker discovery for early diagnosis and prognosis in oral oncology Suresh Munnangi, Satheeskumar R Oral Oncology Reports, 2025 This study presents an AI-powered multi-omics framework for early detection and prognosis of oral squamous cell carcinoma (OSCC), integrating genomic, transcriptomic, and proteomic data through advanced deep learning architectures. Analysing 1,527 OSCC samples from TCGA and GEO databases, we developed a novel multimodal pipeline combining: (1) graph neural networks for heterogeneous data fusion, (2) LASSO regression for robust feature selection, and (3) explainable AI (SHAP, attention mechanisms) for clinical transparency. Our CNN-based diagnostic model demonstrated exceptional performance (accuracy: 93.2%, 95% CI: 91.4-94.7; sensitivity: 91.5% for Stage I tumors; AUC: 0.96), significantly surpassing conventional histopathology (p<0.001). Three clinically validated biomarker panels were established: (i) a diagnostic panel (TP53/CDKN2A/EGFR, 94.1% specificity), (ii) an HPV-associated prognostic panel (P16/RB1/E2F1), and (iii) a metastasis prediction panel (TWIST1/VIM/CDH1, C-index=0.82). Prospective validation in 412 patients showed 43% reduction in false negatives (15.2% to 8.7%) with 82% pathologist concordance. The modular platform addresses critical clinical needs: high-risk screening, therapeutic decision support, and intraoperative margin assessment. IRB-approved implementation confirms real-world viability, positioning this framework as a transformative tool for OSCC precision oncology.
AI-Driven Alignment of Educational Programs with Industry Needs and Emerging Skillsets , Satheeskumar R., Ch. V. Satyanarayana, Talatoti Ratna Kumar, Koteswara Rao M., Suresh M. International Journal of Modern Education and Computer Science, 2025 This research investigates the transformative potential of Artificial Intelligence (AI) in aligning educational programs with industry requirements and emerging skill sets.Developed and preliminarily tested an AI-driven framework designed to personalize learning paths, recommend pertinent educational content, and improve student engagement.The AI models achieved a peak classification accuracy of 90% in identifying educational materials relevant to industry needs, with an optimized average recommendation response time of 0.4 seconds.These results were derived from pilot testing involving 300 students (150 in the control group and 150 in the experimental group), with statistical significance confirmed using t-tests and chi-square tests.In pilot studies, students using AI-recommended materials experienced an average improvement of 15% in assessment scores compared to those using traditional methods.To validate these improvements, used both t-tests and chi-square tests to confirm statistical significance, with a control group of 150 students following traditional educational methods.Additionally, educators reported a 75% engagement rate with AI-driven learning paths, indicating strong acceptance and effective integration of AI tools within educational environments.The control group comparison showed that students using traditional methods had a significantly lower engagement rate of 60%, confirming the higher efficacy of the AI system.However, these results should be interpreted cautiously as further detailed statistical analysis and control mechanisms are necessary to fully validate the effectiveness of the AI framework.The study highlights the importance of addressing ethical considerations such as data privacy, algorithmic bias, and transparency to ensure responsible AI deployment.The results underscore AI's potential to enhance educational outcomes, adapt curricula dynamically, and better prepare students for future career demands, contributing to a more relevant and industry-aligned educational system.
Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning R. Satheeskumar Intelligent Pharmacy, 2025 Accurately predicting pharmacokinetic (PK) parameters such as absorption, distribution, metabolism, and excretion (ADME) is essential for optimizing drug efficacy, safety, and development timelines. Traditional experimental methods are often slow and expensive, driving the need for advanced AI-based approaches in PK modeling. This study compares cutting-edge machine learning models, including Graph Neural Networks (GNNs), Transformers, and Stacking Ensembles, against traditional models like Random Forest and XGBoost, using a dataset of over 10,000 bioactive compounds from the ChEMBL database. The Stacking Ensemble model achieved the highest accuracy ( R 2 of 0.92, MAE of 0.062), outperforming GNNs ( R 2 of 0.90) and Transformers ( R 2 of 0.89). These AI models excelled in capturing complex molecular interactions and long-range dependencies, significantly improving PK predictions. The high accuracy achieved ( R 2 = 0.92) by the Stacking Ensemble method indicates that AI models can streamline the drug discovery process by reducing costly in vivo experiments, enabling faster go/no-go decisions during preclinical evaluations, and ultimately accelerating the development of new therapeutics. This reduction in time and cost could facilitate broader industry adoption of AI-driven PK modeling. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters, further enhancing the performance and robustness of these predictive models.
Machine Learning Models for Predictive Marketing Analytics R. Satheeskumar, Er. Vandana Dutt, Ch. Srinivasa Reddy, Zatin Gupta, Sundarapandiyan Natarajan, Muralidhar L B 2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences Ic3tes 2024, 2024
Mitigation Methods of Reducing DDOS Attack Frequency on Blockchain R. Sathees Kumar, Phanikanth Chintamaneni, Madarapu Naresh Kumar, Ankita Joshi, Bajila Swathi, Joshuva Arockia Dhanraj 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2023, 2023
RECENT SCHOLAR PUBLICATIONS
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Electrohydrodynamic instabilities of Walters-B fluids under dynamic electric fields in marine engineering systems R Satheeskumar Ocean Engineering 342, 122930 , 2025 2025 Citations: 1
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AI-Driven alignment of educational programs with industry needs and emerging skillsets [J] R Satheeskumar, CV Satyanarayana, TR Kumar, M Suresh International Journal of Modern Education and Computer Science 17 (3), 15-28 , 2025 2025 Citations: 8
Efficient Monkeypox Diagnosis Via Attention-Based MobileNetV2 Architecture M Suneetha, S Khaja Muneer, S Srikanth, S Vamsi, R Sathees Kumar, ... Congress on Smart Computing Technologies, 253-265 , 2024 2024 Citations: 1
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MOST CITED SCHOLAR PUBLICATIONS
Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning R Satheeskumar Intelligent Pharmacy 3 (2), 127-140 , 2025 2025 Citations: 43
AI-driven diagnostics and personalized treatment planning in oral oncology: Innovations and future directions R Satheeskumar Oral Oncology Reports 13, 100704 , 2025 2025 Citations: 34
AI-driven biomarker discovery for early diagnosis and prognosis in oral oncology S Munnangi, R Satheeskumar Oral Oncology Reports 14, 100749 , 2025 2025 Citations: 11
AI-Driven alignment of educational programs with industry needs and emerging skillsets [J] R Satheeskumar, CV Satyanarayana, TR Kumar, M Suresh International Journal of Modern Education and Computer Science 17 (3), 15-28 , 2025 2025 Citations: 8
Advancing Education: An In-depth Analysis of Artificial Intelligence in Children's Learning DR Satheeskumar International Journal of Scientific Research in Computer Science and … , 2024 2024 Citations: 3
Mitigation methods of reducing DDOS attack frequency on blockchain RS Kumar, P Chintamaneni, MN Kumar, A Joshi, B Swathi, JA Dhanraj 2023 3rd International Conference on Advance Computing and Innovative … , 2023 2023 Citations: 3
Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management R Satheeskumar Journal of Clinical Research in Pediatric Endocrinology 17 (3), 278 , 2025 2025 Citations: 2
Advancing Decision Review System (DRS) in Cricket: Harnessing Ai for Enhanced Decision Making R Satheeskumar International Journal of Education and Management Engineering (IJEME) 13 (6 … , 2023 2023 Citations: 2
Electrohydrodynamic instabilities of Walters-B fluids under dynamic electric fields in marine engineering systems R Satheeskumar Ocean Engineering 342, 122930 , 2025 2025 Citations: 1
Advanced AI and IoT-Enabled Statistical Modelling for Road Traffic Surveillance and RealTime Monitoring R Satheeskumar AI-based statistical modeling for road traffic surveillance and monitoring … , 2025 2025 Citations: 1
Next-generation ant-inspired cryptography for secure and resilient decentralized IoT ecosystems R Satheeskumar, V Premalatha, RK Talatoti, S Vecha, M Koteswara Rao Iran Journal of Computer Science, 1-16 , 2025 2025 Citations: 1
Efficient Monkeypox Diagnosis Via Attention-Based MobileNetV2 Architecture M Suneetha, S Khaja Muneer, S Srikanth, S Vamsi, R Sathees Kumar, ... Congress on Smart Computing Technologies, 253-265 , 2024 2024 Citations: 1
Machine Learning Models for Predictive Marketing Analytics R Satheeskumar, EV Dutt, CS Reddy, Z Gupta, S Natarajan, M LB 2024 Second International Conference Computational and Characterization … , 2024 2024 Citations: 1
Face Detection using L* A* B using Color Space R Satheeskumar 2022 Citations: 1
Integrative Strategies to Enhance Enzyme-Protein Interactions for Drug Discovery and Biocatalysis R Satheeskumar, V Premalatha, MN Krishnan, CH Satyanarayana, ... Applied Biochemistry and Biotechnology 198 (1), 591-618 , 2026 2026
Enhanced Hybrid Deep Learning Model for Cybersecurity Threat Detection Using CNN-Bi-LSTM and Feature Optimization R Satheeskumar, N Teja, KSH Kumar, J Gopi, V Chetan, YJN Kumar, ... 2025 IEEE International Conference on Blockchain and Distributed Systems … , 2025 2025
Adaptive AI framework for pharmacokinetics using GATs, transformers, and AutoML R Satheeskumar, P Devabalan, CHV Satyanarayana, D Attavar, ... Molecular Diversity, 1-18 , 2025 2025
Deep Learning in Adolescent Obesity Prevention: A Path to Health G Saranya, S Amireddy, C Indravati, R Satheeskumar, S Chandana International Conference on Computing and Machine Learning, 637-649 , 2025 2025
Comparative Analysis of Object Detection and Recognition in Video Streams Using Faster R-CNN, Mask R-CNN, and EfficientDet R Satheeskumar International Conference on Communication and Intelligent Systems, 135-148 , 2024 2024
Immutable Secure Online Electoral Voting System Using Blockchain Technology R Satheeskumar 2023
