Hsiu Hsia Lin | Machine learning | Best Researcher Award

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Prof. Hsiu Hsia Lin | Machine learning | Best Researcher Award

Research Fellow, Chang Gung Memorial Hospital, Taiwan

Dr. Hsiu-Hsia Lin is a dedicated Research Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital, Taiwan, and an Adjunct Assistant Professor at the Graduate Institute of Dental and Craniofacial Science, Chang Gung University. With a strong foundation in AI and 3D craniofacial image processing, her research contributes significantly to advancements in orthognathic surgery. Dr. Lin’s expertise in surgical navigation and CAD/CAM-assisted surgery is pivotal in improving craniofacial surgical outcomes. 🌟

Publication Profile

Education:

Dr. Lin earned her Ph.D. in Computer Science and Engineering from National Chung Hsing University, Taiwan, following a Master’s in Computer Science from Tunghai University. Her academic journey is deeply rooted in computer science, blending AI with craniofacial research. 🎓📚

Experience:

Dr. Lin has held key research positions, including Assistant Research Fellow and Postdoctoral Fellow at the Craniofacial Research Center, Chang Gung Memorial Hospital. Her postdoctoral work also extended to the Department of Computer Science and Engineering at National Chung Hsing University. Her extensive experience has helped bridge the gap between AI technology and clinical applications. 💼🔬

Research Focus:

Dr. Lin’s research revolves around Pattern Recognition, Artificial Intelligence, and 3D Craniofacial Image Processing. She specializes in computer-aided surgical simulation for orthognathic surgery, surgical navigation, and CAD/CAM-assisted procedures, aiming to optimize outcomes in facial surgery. 🧠💻

Awards and Honors:

Dr. Lin has received multiple recognitions for her contributions to craniofacial research and AI in surgery. Her work continues to shape modern surgical approaches, particularly in orthognathic surgery, enhancing patient outcomes. 🏆👏

Publication Top Notes:

Dr. Lin’s publications focus on integrating AI with medical applications, particularly in 3D craniofacial analysis and orthognathic surgery. Her studies offer novel methods for surgical planning, facial attractiveness assessment, and facial symmetry evaluation.

Quantification of facial symmetry in orthognathic surgery (Dec. 2024) in Comput Biol Med., cited by 5 articles. DOI

Average 3D virtual sk

eletofacial model for surgery planning (Feb. 2024) in Plast Reconstr Surg., cited by 3 articles. DOI

Facial attractiveness assessment using transfer learning (Jan. 2024) in Pattern Recognit., cited by 4 articles. DOI

Optimizing Orthognathic Surgery (Nov. 2023) in J. Clin. Med., cited by 6 articles. DOI

Single-Splint, 2-Jaw Orthognathic Surgery (Nov. 2023) in J Craniofac Surg., cited by 2 articles. DOI

Applications of 3D imaging in craniomaxillofacial surgery (Aug. 2023) in Biomed J., cited by 7 articles. DOI

Facial Beauty Assessment using Attention Mechanism (Mar. 2023) in Diagnostics, cited by 8 articles. DOI

 

Syed Ijaz Ul Haq | Machine Learning | Best Researcher Award

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Dr. Syed Ijaz Ul Haq | Machine Learning | Best Researcher Award 

Research associate, Shandong University of Technology, China

Syed Ijaz Ul Haq is a dedicated Research Assistant in Agronomy at Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan, since September 2021. Currently pursuing a Ph.D. in Agriculture Engineering and Food Science at Shandong University of Technology, China, he is passionate about advancing research in remote sensing, artificial intelligence, and deep learning. With a commitment to excellence and professional development, Syed aims to explore innovative solutions in agriculture. 🌱📚

Publication Profile

ORCID

Strengths for the Award

  1. Specialized Research Interests: Syed has a clear focus on Remote Sensing, AI, and Deep Learning, which are critical areas in modern agricultural research. His work on machine learning techniques for pest detection and weed analysis demonstrates innovative applications of technology in agriculture.
  2. Academic Background: Currently pursuing a Ph.D. in Agricultural Engineering and Food Science at Shandong University of Technology, Syed is in an excellent position to contribute cutting-edge research to the field.
  3. Professional Experience: His role as a Research Assistant at Pir Mehr Ali Shah Arid Agriculture University allows him to gain practical experience and engage with ongoing research projects, enhancing his research skills.
  4. Publication Record: With multiple publications in reputable journals, including articles on trace elements’ effects on crop growth and the use of AI for weed detection, he demonstrates the ability to conduct and disseminate impactful research.
  5. Peer Review Engagement: His involvement as a reviewer for the American Society of Plant Biologists reflects recognition by peers and contributes to his professional development.

Areas for Improvement

  1. Broader Research Impact: While Syed has several publications, expanding his research to include interdisciplinary collaborations or more diverse agricultural challenges could enhance his visibility and impact in the field.
  2. Networking and Collaboration: Actively seeking collaborations with other researchers or institutions could provide Syed with additional insights and resources, fostering a more extensive research network.
  3. Professional Development: Attending more international conferences and workshops could enhance his skills and provide opportunities for exposure to global trends in agricultural research and technology.
  4. Outreach and Application of Research: Engaging with local communities or agricultural practitioners to apply his findings could bridge the gap between research and real-world application, leading to significant societal impacts.

Education

Syed is currently enrolled in a Ph.D. program in Agriculture Engineering and Food Science at Shandong University of Technology, Zibo, Shandong, China, where he has been studying since July 2022. His academic focus revolves around integrating advanced technologies to enhance agricultural practices. 🎓🌾

Experience

Since September 2021, Syed has served as a Research Assistant in Agronomy at Pir Mehr Ali Shah Arid Agriculture University, where he contributes to various agricultural research projects, gaining valuable experience and insights into the field. His role involves collaborating with researchers to explore sustainable agricultural practices and technologies. 🧑‍🔬🌍

Research Focus

Syed’s research primarily focuses on the application of remote sensing, AI, and deep learning techniques in agriculture. His work aims to improve crop yield, pest detection, and weed management, making significant contributions to sustainable farming practices. 🤖🌿

Awards and Honours

Syed has been recognized for his contributions to agricultural research, including serving as a Reviewer for the American Society of Plant Biologists since 2021. His academic excellence is reflected in his ongoing Ph.D. studies, showcasing his dedication to advancing the field. 🏆📜

Publications

Influence of Trace Elements (Co, Ni, Se) on Growth, Nodulation and Yield of Lentil
Published in Polish Journal of Environmental Studies, 2024
Cited by: Crossref

Identification of Pest Attack on Corn Crops Using Machine Learning Techniques
Published in 2023
Cited by: Crossref

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
Published in Applied Sciences, 2023
Cited by: Crossref

Conclusion

Syed Ijaz Ul Haq shows strong potential as a candidate for the Research for Best Researcher Award due to his focused research interests, current academic pursuits, publication record, and peer engagement. To further enhance his candidacy, he should consider broadening his research scope, expanding his professional network, and increasing the real-world applicability of his research findings. If he continues on this trajectory, he has the potential to make substantial contributions to agricultural research, making him a deserving recipient of this award.

 

Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

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Mr. Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

PhD student, Queensland University of Technology, Australia

Researcher specializing in drone-based remote sensing solutions for environmental and surveillance needs, focusing on precision agriculture and biosecurity. Expertise includes UAV remote sensing, artificial intelligence, and multispectral/hyperspectral image processing. Currently pursuing a PhD in Precision Agriculture at Queensland University of Technology.

Profile

Google Scholar

 

🎓 Education

PhD: Precision Agriculture, Queensland University of Technology (2021 – Present). MSc: Agricultural Engineering, Eastern University, Sri Lanka (2016 – 2018). BSc: Agriculture, Agricultural Engineering, Eastern University, Sri Lanka (2010 – 2015). BIT: Software Engineering, University of Colombo School of Computing, Sri Lanka (2011 – 2015)

🔍 Experience

Research Assistant at Charles Sturt University and Sunshine Coast Council on projects integrating AI and drone technology for environmental monitoring and invasive species detection.

🏆 Awards

QUT/Accelerate Higher Education Development Expansion and Development (AHEAD) World Bank Project Scholarship. Vice Chancellor’s Award for Early Career Researcher, Faculty of Technology, 2022.

🌍 Research Interests

Precision Agriculture, UAV-based Remote Sensing, Multispectral Image Processing, AI, Biosystems Engineering, Environmental Management.

📚 Publications

Co-authored numerous peer-reviewed articles in Q1 and non-Q1 ranking journals on topics related to UAV-based remote sensing and AI applications in agriculture and environmental management.

A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops
Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imageryDetection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models
N Amarasingam, F Gonzalez, ASA Salgadoe, J Sandino, K Powell
E-agricultural concepts for improving productivity: A review