Yulin Yang | Algorithm optimization | Best Researcher Award

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Mr. Yulin Yang | Algorithm optimization | Best Researcher Award

Shenyang University, China

Yulin Yang is a dedicated graduate student at Shenyang University, specializing in logistics engineering and management. His research interests lie in swarm intelligence algorithm optimization and path planning, with a focus on improving computational efficiency and solving complex optimization problems. Passionate about advancing artificial intelligence techniques, he has contributed to algorithmic enhancements that improve convergence speed and search accuracy.

Publication Profile

ORCID

🎓 Education:

Yulin Yang is currently pursuing a master’s degree in logistics engineering and management at Shenyang University. His academic journey is centered around algorithm optimization, particularly in swarm intelligence applications for logistics and transportation systems.

💼 Experience:

As a researcher, Yulin Yang has actively explored novel computational techniques to enhance optimization algorithms. His recent work focuses on developing hybrid whale optimization algorithms to address challenges in search precision and problem-solving capabilities. His expertise extends to route optimization and intelligent decision-making models in logistics.

🏆 Awards and Honors:

While early in his academic career, Yulin Yang’s innovative research contributions have gained recognition, leading to the publication of his work in reputed international journals. His advancements in algorithmic optimization showcase his potential as a rising researcher in the field.

🔬 Research Focus:

Yulin Yang specializes in swarm intelligence algorithm optimization, particularly in improving the performance of metaheuristic techniques. His research emphasizes solving real-world computational problems in logistics through intelligent algorithmic design, enhancing efficiency in route planning and decision-making. His notable contribution includes a multi-strategy hybrid whale optimization algorithm aimed at overcoming limitations in search accuracy and convergence speed.

🔚 Conclusion:

With a strong foundation in optimization algorithms and artificial intelligence applications in logistics, Yulin Yang is poised to make significant contributions to computational research. His commitment to innovation and problem-solving drives his ongoing research, paving the way for impactful advancements in AI-driven optimization.

📄 Publication:

Multi-Strategy Hybrid Whale Optimization Algorithm Improvement. Applied Sciences, 15(4), 2224. DOI: 10.3390/app15042224. This study presents an advanced hybrid optimization approach to address challenges in convergence speed and search efficiency.

Carolina Magalhães | Machine Learning | Best Researcher Award

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Dr. Carolina Magalhães | Machine Learning | Best Researcher Award

Investigadora, INEGI – Instituto de Ciência e Inovação em Engenharia Mecânica e Industrial, Portugal

👩‍🔬 Carolina Magalhães is a dedicated biomedical engineer and PhD candidate with expertise in applying AI and imaging technologies to healthcare challenges. Based in Porto, Portugal, she combines her passion for modern technology with a problem-solving mindset to develop innovative solutions in skin cancer diagnostics. Carolina has worked collaboratively with clinical experts to bridge research and practical applications, contributing significantly to advancing imaging-based decision support systems.

Publication Profile

ORCID

Education

🎓 Carolina holds a PhD in Biomedical Engineering from the Faculdade de Engenharia da Universidade do Porto (2020–2024). She also completed her MSc in Biomedical Engineering at the same institution (2016–2018) and earned her Bachelor’s in Bioengineering – Biomedical Engineering from Universidade Católica Portuguesa (2013–2016).

Experience

💼 Carolina has a rich research background, currently serving as a Graduate Research Fellow at INEGI, focusing on skin lesion diagnosis using multispectral imaging. Her work spans from leveraging machine learning models for skin cancer classification to thermal and UV imaging techniques. Previously, she contributed to projects on hyperhidrosis diagnosis, prosthetic device design, and thermal image analysis for musculoskeletal disorders, collaborating with leading hospitals and research centers in Portugal.

Research Interests

🔬 Carolina is passionate about exploring artificial intelligence, machine learning, and advanced imaging technologies for healthcare applications. Her interests include developing multispectral imaging systems, improving diagnostic tools for skin cancer, and advancing infrared thermography for clinical support systems.

Awards

🏆 Carolina’s innovative work has been recognized with prestigious research grants from the Foundation for Science and Technology (SFRH/BD/144906/2019) and other funding organizations. These awards have supported her impactful contributions to biomedical engineering and healthcare innovation.

Publications

“Systematic Review of Deep Learning Techniques in Skin Cancer Detection”
BioMedInformatics, 11/2024
Read here

“Skin Cancer Image Classification with Artificial Intelligence Strategies: A Systematic Review”
Journal of Imaging, 10/2024
Read here

“Use of Infrared Thermography for Abdominoplasty Procedures in Patients with Extensive Subcostal Scars: A Preliminary Analysis”
Plast Reconstr Surg Glob Open, 06/2023
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“Classic Versus Scarpa-Sparing Abdominoplasty: An Infrared Thermographic Comparative Analysis”
J Plast Reconstr Aesthet Surg, 06/2023
Read here

“Towards an Effective Imaging-Based Decision Support System for Skin Cancer”
Handbook of Research on Applied Intelligence for Health and Clinical Informatics, 10/2022
Read here