Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

AI Engineer | Florida International University | United States

Sachin Sravan Kumar Komati is an accomplished researcher in Artificial Intelligence and Machine Learning, specializing in biomedical applications, particularly in gastrointestinal disease diagnosis, cancer prognosis, and postoperative complication prediction. His research integrates deep learning, computer vision, and multimodal AI frameworks to develop intelligent healthcare solutions. He has contributed significantly to the fields of predictive analytics, medical imaging, and surgical AI, creating advanced models using LSTM, Vision Transformers, and Autoencoders for enhanced diagnostic precision. His works explore AI-driven insights in clinical and imaging datasets, focusing on improving real-time disease detection and patient-specific treatment strategies. Sachin’s scholarly contributions include numerous peer-reviewed publications in reputed international journals such as PLOS One, Gastroenterology, Gastrointestinal Endoscopy, Critical Care Medicine, and the Journal of Clinical Oncology. His research has earned global recognition through multiple conference acceptances, including at ACG, AASLD, and UEG Week. According to Google Scholar, he has received 2 citations, with an h-index of 1 and an i10-index of 0, reflecting his emerging influence in AI-driven healthcare research. His Scopus metrics also indicate growing visibility and scholarly impact. Sachin’s research continues to advance the integration of artificial intelligence into clinical decision-making and medical imaging, aiming to bridge the gap between AI innovation and patient-centered healthcare.

Profile

Google Scholar | ORCID

Featured Publications

Boppana, S. H., Tyagi, D., Komati, S. S. K., Boppana, S. L., Raj, R., & Mintz, C. D. (2025). AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients. PLOS One, 20(6), e0322032.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., Aakash, F., & Dang, A. K. (2025). Enhancing gastrointestinal bleeding detection in wireless capsule endoscopy using convolutional autoencoders. American College of Gastroenterology, 120(10S2).

Boppana, S. H., Chitturi, R. H., Komati, S. S. K., Raj, R., & Mintz, C. D. (2025). DiabCompSepsAI: Integrated AI model for early detection and prediction of postoperative complications in diabetic patients using a Random Forest Classifier. Journal of Clinical Medicine, 14(20), 7173.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Predictive modeling of GI disease: GastroEndo-Seq for progression and outcome forecasting. Gastroenterology, 120(10S2).

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Vision Transformer-based framework for risk stratification and prognostic assessment in gastrointestinal lesion management. Gastrointestinal Endoscopy, 120(10S2).

Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Mehtab Alam |Assistant Professor | Delhi University | India

Dr. Mehtab Alam is an accomplished IT professional and academic specializing in Artificial Intelligence (AI), Internet of Things (IoT), Cyber Forensics, and Information Security. His research primarily focuses on developing AI-based smart IoT frameworks for intelligent healthcare systems, with a strong emphasis on predictive modeling, machine learning integration, and cloud-based data analytics. His scholarly contributions demonstrate a multidisciplinary approach combining computer science, data-driven healthcare innovation, and digital transformation. He has explored diverse research areas including smart city technologies, blockchain applications in e-governance, cybersecurity frameworks, and the application of swarm intelligence in network optimization. Dr. Alam has published extensively in reputed international journals and conferences, contributing to advancements in AI-driven sustainable systems and smart healthcare solutions. His works reflect technical depth and practical applicability, addressing modern challenges in digital infrastructure, public health informatics, and secure communication systems. He has authored 15 Scopus-indexed publications, with 30 Scopus citations and an h-index of 4. On Google Scholar, his research has received 256 citations with an h-index of 10 and an i10-index of 11, showcasing his growing academic influence.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Alam, M., Khan, E. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). The DIABACARE CLOUD: Predicting diabetes using machine learning. Acta Scientiarum Technology, 46(1).

Alam, M., Khan, I. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). Smart healthcare: Making medicine intelligent. Journal of Propulsion Technology, 44(3).

Alam, M., Khan, R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). AI for sustainable smart city healthcare. China Petroleum Processing and Petrochemical Technology Catalyst Research, 23(2), 2245–2258.

Ansari, A. A., Narain, L., Prasad, S. N., & Alam, M. (2022). Behaviour of motion of infinitesimal variable mass oblate body in the generalized perturbed circular restricted three-body problem. Italian Journal of Pure and Applied Mathematics, 47, 221–239.

Alam, M., Parveen, S. (2021). Shipment delivery and COVID-19: An Indian context. International Journal of Advanced Engineering Research and Science, 8(8), 145–154.

Fan Fangfang | Artificial Intelligence Awards | Best Researcher Award

Dr. Fan Fangfang | Artificial Intelligence Awards | Best Researcher Award

Postdoctoral Researcher, Harvard University, United States

👩‍🔬 Dr. Fangfang Fan is a dedicated researcher currently serving as a Research Fellow at Harvard Medical School, Harvard University, Cambridge, MA, USA. She earned her Ph.D. in 2013 from Huazhong University of Science and Technology. Her work focuses on emotion regulation, mental health, and neural electrophysiology signal processing. With over a decade of experience in academic and research fields, Dr. Fan has made remarkable contributions to domains like domain adaptation, generative adversarial networks, and deep learning.

Publication Profile

Scopus

Education

🎓 Dr. Fangfang Fan completed her Ph.D. at Huazhong University of Science and Technology in 2013, focusing on advanced computational methods in neural and emotional studies.

Experience

💼 Currently, Dr. Fan is a Research Fellow at Harvard Medical School. Over the years, she has gained extensive expertise in cross-domain learning, audio-visual emotion recognition, and neural signal analysis, contributing significantly to innovative research and applications in these areas.

Awards and Honors

🏆 While specific awards are not mentioned, Dr. Fan’s impactful research, which includes 141 citations and an h-index of 6, highlights her esteemed recognition in the scientific community.

Research Focus

🔬 Dr. Fan’s research encompasses emotion regulation and mental health, neural electrophysiology signal processing, domain adaptation, and generative adversarial networks. Her innovative approaches extend to deep learning techniques, decision boundaries, and audio-visual data analysis, advancing fields like medical imaging, sleep classification, and emotion recognition.

Conclusion

🌟 Dr. Fangfang Fan’s impactful career as a researcher and her extensive publications contribute to diverse areas, from computational neuroscience to medical imaging. Her dedication to advancing knowledge in emotional health and neural systems continues to inspire innovation in the field.

Publications

A review of automatic sleep stage classification using machine learning algorithms based on heart rate variability
Published in: Sleep and Biological Rhythms, 2025.
Cited by: 0 articles.

Comparative Analysis of Single-Channel and Multi-Channel Classification of Sleep Stages Across Four Different Data Sets
Published in: Brain Sciences, 2024, Vol. 14(12), Article 1201.
Cited by: 0 articles.

A joint STFT-HOC detection method for FH data link signals
Published in: Measurement: Journal of the International Measurement Confederation, 2021, Vol. 177, Article 109225.
Cited by: 1 article.

Computer Vision for Brain Disorders Based Primarily on Ocular Responses
Published in: Frontiers in Neurology, 2021, Vol. 12, Article 584270.
Cited by: 6 articles.

Embedding semantic hierarchy in discrete optimal transport for risk minimization
Published in: ICASSP Proceedings, 2021.
Cited by: 6 articles.

Image2Audio: Facilitating semi-supervised audio emotion recognition with facial expression image
Published in: CVPR Workshops, 2020, pp. 3978–3983.
Cited by: 38 articles.

Classification-aware semi-supervised domain adaptation
Published in: CVPR Workshops, 2020, pp. 4147–4156.
Cited by: 38 articles.

Unimodal regularized neuron stick-breaking for ordinal classification
Published in: Neurocomputing, 2020, Vol. 388, pp. 34–44.
Cited by: 43 articles.

Two-Dimensional New Communication Technology for Networked Ammunition
Published in: IEEE Access, 2020, Vol. 8, pp. 133725–133733.
Cited by: 2 articles.

Research on recognition of medical image detection based on neural network
Published in: IEEE Access, 2020, Vol. 8, pp. 94947–94955.
Cited by: 0 articles.