Tao Hu | Artificial Intelligence| Best Researcher Award

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Dr. Tao Hu | Artificial Intelligence | Best Researcher Award

The Affiliated Yuyao Yangming Hospital of Medical School of Ningbo University | China

Dr. Tao Hu is a highly accomplished medical professional and researcher from China, serving at The Affiliated Yuyao Yangming Hospital of the Medical School of Ningbo University, with specialization in thyroid surgery, breast surgery, and anorectal surgery. Having completed his doctoral education in health sciences, Dr. Hu has developed an expertise in combining surgical practice with advanced computational methods, particularly artificial intelligence and machine learning applications in clinical diagnostics and predictive modeling. His professional experience includes independently completing over surgical operations and contributing to multiple provincial-level scientific research projects, including support from the Zhejiang Health Information Association Research Program , which highlights his ability to bridge medical practice with innovative research applications. Dr. Hu’s research interests lie primarily in developing predictive tools that integrate clinical information data with artificial intelligence to forecast disease occurrence, progression, and postoperative risks, especially in thyroid carcinoma, where his recent work has introduced novel models for preoperative risk stratification and lymph node metastasis prediction. His research skills are demonstrated through proficiency in clinical data analysis, ultrasound imaging interpretation, radiomics, and the application of machine learning frameworks to enhance diagnostic accuracy and surgical decision-making. In recent years, Dr. Hu has published several impactful articles in high-quality, peer-reviewed journals such as Endocrine, Frontiers in Endocrinology, and the Journal of Clinical Ultrasound, marking him as a significant contributor to evidence-based surgical practices. While his awards and honors primarily reflect academic and clinical achievements, his recognition through this nomination underscores his growing international reputation as a leader in health sciences research. In conclusion, Dr. Hu’s blend of clinical excellence, innovative research in artificial intelligence applications, and dedication to improving surgical outcomes make him a highly deserving recipient of the Best Researcher Award, as his work holds great promise for advancing both scientific knowledge and patient care globally.

Profile:  Orcid

Featured Publications:

Hu, T., Cai, Y., Zhou, T., Zhang, Y., Huang, K., Huang, X., Qian, S., Wang, Q., & Luo, D. (2025). Machine learning‐based prediction of lymph node metastasis and volume using preoperative ultrasound features in papillary thyroid carcinoma. Journal of Clinical Ultrasound. Advance online publication.

Hu, T., Zhou, T., Zhang, Y., Zhou, L., Huang, X., Cai, Y., Qian, S., Huang, K., & Luo, D. (2024). The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule‐to‐muscle gray‐scale ratio. Journal of Clinical Ultrasound, 52(1).

Zhao, L., Hu, T., Cai, Y., Zhou, T., Zhang, W., Wu, F., Zhang, Y., & Luo, D. (2023). Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: Construction of a dynamic predictive model. Frontiers in Endocrinology, 14, 1254124.

Zhou, T., Xu, L., Shi, J., Zhang, Y., Lin, X., Wang, Y., Hu, T., Xu, R., Xie, L., & Sun, L., et al. (2023). US of thyroid nodules: Can AI-assisted diagnostic system compete with fine needle aspiration? European Radiology. Advance online publication.

Zhou, T., Hu, T., Ni, Z., Yao, C., Xie, Y., Jin, H., Luo, D., & Huang, H. (2023). Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules. Endocrine. Advance online publication.

Sheeba Rachel S | Machine Learning | Best Researcher Award

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Mrs. Sheeba Rachel S | Machine Learning| Best Researcher Award

Assistant Professor | Sri Sai Ram Engineering College | India

  S. Sheeba Rachel has contributed extensively to the fields of artificial intelligence, machine learning, deep learning, healthcare technologies, smart devices, image processing, cloud computing, and Internet of Things with publications including Cardiovascular Disease Prediction Using Machine Learning and Deep Learning, Heart Disease Prediction of an Individual Using SVM Algorithm, Automated Driving License Testing System, Real-Time Face Detection and Identification Using Machine Learning Algorithm for Improving the Security in Public Places Using Closed Circuit Television, LEARNAUT – Upgraded Learning Environment and Web Application for Autism Environment Using AR-VR, VATTEN – A Smart Water Monitoring System, Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model, EDSYS – A Smart Campus Management System, TRACKME – Smart Watch for Women, Women’s Safety with a Smart Foot Device, Mental Health Monitoring Using Sentimental Analysis, Facilitation of Multipurpose Gloves for Impaired People, Extending OVS with Deep Packet Inspection Functionalities, Courier Service Management and Tracking Using Android Application, Detecting the Abandoned Borewell Using Image Processing, Smart Hospitals E-Medico Management System, ADROIT LIMB – Brain Controlled Artificial Limb, Autonomous Movable Packrat for Habitual Chores, Postal Bag Tracking and Alerting System, Applying Social Network Aided Efficient Live Streaming System for Reducing Server Overhead, Image Fusion of MRI Images Using Discrete Wavelet Transform, Probabilistic Flooding Based File Search in Peer to Peer Network, Multi Stage for Informative Gene Selection, Mutual Information in Stages for Informative Gene Selection, Computation of Mutual Information in Stages for Gene Selection from Microarray Data, and several other impactful studies in international journals and conferences indexed in Scopus, IEEE, and UGC; she has further contributed to innovation through consultancy projects such as AI-based pre-examination dental software and non-invasive sugar detection using eye retina, authored books and chapters including Fundamentals of Machine Learning, Management Analytics and Software Engineering, Recent Trends in Engineering and Technology – Edge Computing, and secured patents like Artificial Intelligence Based Heart Rate Monitoring Device for Sports Training, IOT Based Washing Machine for Agricultural Crops, Human Identity Recognition System Using Cloud Machine Learning and Deep Learning Algorithms, Gesture Based Anti-Rape Device, while also holding active memberships with IEEE, ISTE, IEI, UACEE, IAENG, and IACSIT; her academic journey has been marked by mentorship of award-winning projects, reviewer and session chair responsibilities in international conferences, and recognition such as the Best Faculty Advisor Award demonstrating her influence in advancing technology-driven solutions for healthcare, safety, smart systems, and education through research, teaching, patents, and community engagement.

Profile:  Google Scholar

Featured Publications:

Xiang Zhang | Data Science and Deep Learning | Best Researcher Award

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Xiang Zhang | Data Science and Deep Learning | Best Researcher Award

Mr. Xiang Zhang, Hainan university, China

Xiang Zhang is a dedicated researcher specializing in resource utilization, plant protection, and ecological remote sensing. He holds a Master’s degree in Resource Utilization and Plant Protection and a Bachelor’s degree in Ecology from Hainan University. His expertise includes terrestrial ecosystem simulation, vegetation monitoring, and global change ecology. Xiang has contributed to mangrove carbon storage estimation, ecological restoration, and satellite image processing. He has worked with Hainan Silan Low Carbon Investment Co., Ltd. and Changguang Satellite Technology Co., Ltd.. A recipient of multiple scholarships, he actively researches carbon sequestration strategies for sustainable ecosystems.

Profile

orcid

Education 🎓

Xiang Zhang pursued his Master’s degree at the Ecological College, specializing in Resource Utilization and Plant Protection 🌱. With an impressive GPA and ranking within the top 5% 📊, he excelled in courses such as Agricultural Product Safety Production, Advanced Experimental Design & Biostatistics, and Ecological Restoration Technologies. His Bachelor’s degree in Ecology 🌿 further strengthened his expertise, where he ranked in the top 20% and gained knowledge in Forestry, Microbiology, GIS, and Ecological Economics. His academic journey reflects a strong foundation in environmental protection, sustainable agriculture, and ecological governance 🌍.

Experience 🧪

Xiang Zhang has actively contributed to mangrove conservation 🌿 through extensive field investigations in key areas of Hainan, including Dongfang, Sanya, Danzhou, Haikou, and Wanning. He conducted soil and plant sampling 🧪, measuring element content, dry weight, and length. Utilizing satellite remote sensing 🛰️, he analyzed data and estimated the carbon ecological value of mangroves in Xinying Port. His expertise includes real-time image collection, manual vegetation recognition, and data mapping using ArcGIS and ENVI. He also worked on cloud removal techniques ☁️ and point interpolation to enhance coastal habitat studies 🌍.

Research Focus 🔍

Xiang Zhang’s research primarily focuses on forest ecology 🌳, soil organic carbon dynamics 🌱, and the impacts of environmental disturbances on ecosystems 🌪️. His studies analyze spatial distribution changes of topsoil organic carbon across different forest types in Hainan Island, exploring key factors influencing carbon storage. Additionally, he investigates gross primary production (GPP) losses and recovery in subtropical mangrove forests affected by tropical cyclones, highlighting the resilience of these ecosystems. His work contributes to climate change adaptation 🌍, carbon sequestration strategies 📉, and forest conservation efforts 🌾, offering valuable insights for sustainable environmental management.

Publications📚

Spatial Distribution Changes and Factor Analysis of Topsoil Organic Carbon Across Different Forest Types on Hainan Island

Evaluating the Losses and Recovery of GPP in the Subtropical Mangrove Forest Directly Attacked by Tropical Cyclone: Case Study in Hainan Island