Yin Fei Xu | Deep Learning | Excellence in Research Award

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Assoc. Prof. Dr. Yin Fei Xu | Deep Learning | Excellence in Research Award 

Associate Professor | Southeast University  | China

Yinfei Xu is an Associate Researcher in the Department of Signal Processing, School of Information Science and Engineering at Southeast University, a master’s and doctoral supervisor and a Zhishan Young Scholar of Southeast University. He received his PhD in signal and information processing from Southeast University and carried out research as a research assistant and postdoctoral fellow at the Chinese University of Hong Kong and as a visiting PhD scholar at McMaster University in Canada. His research is deeply rooted in statistical signal processing, information theory, machine-learning-driven algorithmic design, optimization for real-world scenarios, statistical data analysis, and the development of artificial-intelligence models for image, speech, and multimodal applications. He has led or participated in more than ten national, provincial, industrial, and laboratory research projects and has published over seventy academic papers in high-impact international journals. As first author he has contributed to a number of influential publications including New Proofs of Gaussian Extremal Inequalities With Applications in IEEE Transactions on Information Theory, Information Embedding With Stegotext Reconstruction in IEEE Transactions on Information Forensics and Security, Secret Key Generation From Vector Gaussian Sources With Public and Private Communications in IEEE Transactions on Information Theory, Vector Gaussian Successive Refinement With Degraded Side Information in IEEE Transactions on Information Theory, Asymptotical Optimality of Change Point Detection With Unknown Discrete Post-Change Distributions in IEEE Signal Processing Letters, The Sum Rate of Vector Gaussian Multiple Description Coding with Tree-Structured Covariance Distortion Constraints in IEEE Transactions on Information Theory.

Profile: Orcid

Featured Publications:

Zhang, J., Xu, H., Zheng, A., Cao, D., Xu, Y., & Lin, C. (2025). Transmitting status updates on infinite capacity systems with eavesdropper: Freshness advantage of legitimate receiver. Entropy.

Zhang, J., & Xu, Y. (2022). Age analysis of status updating system with probabilistic packet preemption. Entropy.

Xu, Y., Zu, Y., & Zhang, H. (2021). Optimal inter-organization control of collaborative advertising with myopic and far-sighted behaviors. Entropy.

Zhang, J., & Xu, Y. Age analysis of status updating system with probabilistic packet preemption.

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:

Dr. Talent Diotrefe Banda | Artificial Neural Networks | Best Researcher Award

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Dr. Talent Diotrefe Banda | Artificial Neural Networks | Best Researcher Award

Dr. Talent Diotrefe Banda, ZAKUMI Consulting Engineers, South Africa

Talent Diotrefe Banda is a highly accomplished civil engineer with over 20 years of experience, specializing in water science, AI, ANNs, and WQIs. He holds numerous qualifications, including a PhD in Engineering from the University of KwaZulu-Natal, an MBA from the University of Cape Town, and multiple degrees from the Tshwane University of Technology and Bulawayo Polytechnic. As the CEO of ZAKUMI Consulting Engineers, Talent has a robust professional background with memberships in ECSA, SAICE, SABTACO, WISA, and ZIE. He has received multiple academic and business excellence awards and has published extensively in peer-reviewed journals.

Professional Profiles:

Google Scholar

QUALIFICATIONS 🎓

Master of Business Administration (MBA) – University of Cape Town, South Africa (2022)Doctor of Philosophy in Engineering (Civil) – University of KwaZulu Natal, South Africa (2020)Master of Technology Degree in Engineering: Civil – Tshwane University of Technology, South Africa (2015)Bachelor of Technology Degree in Engineering: Civil – Tshwane University of Technology, South Africa (2011)National Diploma in Civil Engineering – Bulawayo Polytechnic, Zimbabwe (2003)National Certificate in Civil Engineering – Bulawayo Polytechnic, Zimbabwe (2001)

Talent Diotrefe Banda: Chief Executive Officer (Civil Engineer) 🌟

Employer: ZAKUMI Consulting Engineers (Pty) LtdName of Staff: Talent Diotrefe BANDADate of Birth: 19th of October 1982National Identity Number: 821019 5989 188Nationality: Zimbabwean with South African Permanent Residence PermitExperience: Twenty (20) yearsArea of Specialisation: Civil Engineering, Water Scientist, Artificial Intelligence (AI), Artificial Neural Networks (ANNs), Water Quality Indices (WQIs), Research Scientist

PROFESSIONAL MEMBERSHIP 🏆

Professional Engineering Technician (Pr. Techni Eng) – Engineering Council of South Africa (ECSA)Registration Number: 201030123 from 06th of July 2010Member (MSAICE) – South Africa Institution of Civil Engineering (SAICE)Registration Number: 207890 from 06th of December 2007 (Changed from Associate Member on the 14th of December 2015)Member (MSABTACO) – South African Black Technical & Allied Careers Organization (SABTACO)Registration Number: LIMP/0095I from 22nd of October 2007Member (MWISA) – Water Institute of Southern Africa (WISA)Registration Number: 25927 from 23rd of June 2014Graduate Technician (GradTZweIE) – Zimbabwe Institution of Engineers (ZIE)Registration Number: ZIE073967 from 30th of April 2007Competent Person (Competent Engineer) – National Home Builders Registration Council (NHBRC)Registration Number: 3000166150 from 01st of June 2016

PROFILE & KEY EXPERIENCE 📈

A multi-award-winning Civil Engineer with several university qualifications, including a Doctor of Philosophy in Engineering (Civil) from the University of KwaZulu-Natal, a Master of Technology in Engineering (Civil), and a Bachelor of Technology in Engineering (Civil) from the Tshwane University of Technology where he obtained an award for the Outstanding Research: Young Water Scientist of 2012 with WaterNet/WARFSA/GWP – SA for his paper entitled “Analysis of the gazetted pricing strategy for raw water use charges in South Africa”. Furthermore, Talent holds a National Diploma in Engineering (Civil) and a National Certificate in Engineering (Civil) from Bulawayo Polytechnic, where he received three academic awards. Dr. Banda is registered as a Professional Engineering Technician (Pr. Techni. Eng) with the Engineering Council of South Africa, where he is a serving member of the Registration Committee of Professional Engineering Technicians. He gained more than twenty (20) years of experience working on Civil Engineering Projects responsible for water and wastewater designs, roads and stormwater drainage, contract and tender documentation, project implementation, and management.Additionally, Talent has a Master of Business Administration (MBA) from the University of Cape Town, Graduate School of Business (GSB) in South Africa, where he obtained an academic award as the Best Student for the Economics for Business Module. Dr. Talent Banda has ten academic and business excellence awards from institutions of higher learning and corporate organizations in Zimbabwe and South Africa. Additionally, Talent has ten publications, including peer-reviewed journal articles, conference papers, master’s dissertations, and doctoral thesis. Dr. Banda serves as Peer Reviewer and Editor in three Academic Journals. Lastly, Talent holds key positions in various working groups, committees, and management boards. 

✍️Publications Top Note :

Development of Water Quality Indices (WQIs): A Review 🌊📊

Authors: TD Banda, MV Kumarasamy Journal: Polish Journal of Environmental Studies Volume: 29 (3) Citations: 74 Year: 2020

Application of Multivariate Statistical Analysis in the Development of a Surrogate Water Quality Index (WQI) for South African Watersheds 🏞️🔬

Authors: TD Banda, M Kumarasamy Journal: Water Volume: 12 (6), 1584 Citations: 62 Year: 2020

Development of a Universal Water Quality Index (UWQI) for South African River Catchments 🌍💧

Authors: TD Banda, M Kumarasamy Journal: Water Volume: 12 (6), 1534 Citations: 34 Year: 2020

A Review of the Existing Water Quality Indices (WQIs) 📚📈

Authors: TD Banda, M Kumarasamy Journal: Pollution Research Volume: 39 (2), 489-514 Citations: 23 Year: 2020

Developing an Equitable Raw Water Pricing Model: The Vaal Case Study 💰💦

Author: TD Banda Institution: Tshwane University of Technology Citations: 11 Year: 2015

Aggregation Techniques Applied in Water Quality Indices (WQIs) 🔄🌐

Authors: TD Banda, M Kumarasamy Journal: Pollution Research Volume: 39 (2), 400-412 Citations: 8 Year: 2020

Development of a Universal Water Quality Index and Water Quality Variability Model for South African River Catchments 🌊🧪

Author: TD Banda Year: 2020

Artificial Neural Network (ANN)-Based Water Quality Index (WQI) for Assessing Spatiotemporal Trends in Surface Water Quality—A Case Study of South African River Basins 🤖🌍

Authors: TD Banda, M Kumarasamy Journal: Water Volume: 16 (11), 1485 Year: 2024