Prof Dr. Amar Hassan Khamis | Machine Learning for Computer Vision | Best Researcher Award

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Prof Dr. Amar Hassan Khamis | Machine Learning for Computer Vision | Best Researcher Award

Prof Dr. Amar Hassan Khamis | Mohammed Bin Rashid University of Medicine and Health Sciences | United Arab Emirates

Dr. Amar Hassan Khamis holds a Ph.D. in Biostatistics & Genetic Epidemiology (2003) from the University of Méditerranée AIX Marseille and the University of Gazira under a sandwich program. He also earned a DEA in Biostatistics from the University of Paris XI (1994) and a certificate in Medical and Biological Studies with a focus on epidemiology and biostatistics (1991).

Professional Profiles

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🎓Academic  Qualifications 

Dr. Khamis boasts a robust academic background, having completed a PhD in Biostatistics & Genetic Epidemiology through a sandwich program between University of Méditerranée AIX Marseille, France, and University of Gazira, Sudan in 2003. His other qualifications include a DEA in Biostatistics from the University Paris XI, France, and a Certificate in Medical and Biological Studies with a focus on Epidemiology and Biostatistics. Additionally, he holds a B.Sc. in Statistics & Computer Science from the University of Khartoum, Sudan.

🏢Professional Career Highlights  

Dr. Amar Hassan Khamis is a distinguished Professor of Biostatistics, currently serving at the Hamdan Bin Mohammed College of Dental Medicine, part of the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) in Dubai since January 17, 2018. Renowned for his expertise, he has also contributed as an Adjunct Professor at Ajman University, teaching Biostatistics and Research Methods for postgraduate dentistry programs. Over his extensive career, Dr. Khamis has held key academic roles at esteemed institutions like the University of Dammam, KSA, University of Khartoum, Sudan, and the Ahfad University for Women, Sudan, demonstrating unwavering commitment to the field of Biostatistics and health sciences.

📚🧑‍🏫Teaching and Mentorship 

Dr. Khamis has a prolific teaching portfolio, having taught a variety of courses across undergraduate and postgraduate levels, including Mathematics, Biostatistics, Research Methodology, Clinical Trials, and Epidemiology. His professional workshops on Meta-Analysis, Advanced Biostatistics, and Respondent Driven Sampling are highly acclaimed. Moreover, he has supervised numerous higher diploma, MSc, and PhD theses, playing a pivotal role in advancing biostatistical research and application.

🌐🤝Global Collaboration and Leadership 

Dr. Khamis has played significant roles in global health initiatives, including consulting for WHO EMRO and conducting missions across the Eastern Mediterranean region. As a member of the Board of Research Committee of ALBASAR International Foundation and other international scientific associations, he has facilitated cross-border collaborations. His contributions to achieving the Millennium Development Goals (MDGs) in Africa highlight his dedication to improving public health outcomes.

🛠️💻Training and Skill Development 

An expert in statistical computing, Dr. Khamis is proficient in tools like SPSS, Stata, R-language, and Comprehensive Meta-Analysis (CMA). He has attended several advanced training programs worldwide, including courses on Meta-Analysis, Health Management, and Population Surveys at renowned institutions such as Johns Hopkins Bloomberg School of Public Health and Oxford University.

🏅🌟Recognition and Honors 

Dr. Khamis has been acknowledged as a pioneer in biostatistics, playing a transformative role in his academic and professional engagements. He has served as an external examiner for universities across Africa and the Middle East and as a member of research ethics committees in Sudan, Saudi Arabia, and the UAE.

Publications Top Noted 📝

Three-dimensional computed tomography analysis of airway volume in growing class II patients treated with Frankel II appliance

Authors: Ahmed, M.J.; Diar-Bakirly, S.; Deirs, N.; Hassan, A.; Ghoneima, A.

Journal: Head and Face Medicine

Year: 2024

Comparative Assessment of Pharyngeal Airway Dimensions in Skeletal Class I, II, and III Emirati Subjects: A Cone Beam Computed Tomography Study

Authors: AlAskar, S.; Jamal, M.; Khamis, A.H.; Ghoneima, A.

Journal: Dentistry Journal

Year: 2024

High-fidelity simulation versus case-based tutorial sessions for teaching pharmacology: Convergent mixed methods research investigating undergraduate medical students’ performance and perception

Authors: Kaddoura, R.; Faraji, H.; Otaki, F.; Khamis, A.H.; Jan, R.K.

Journal: PLoS ONE

Year: 2024

Enamel demineralization around orthodontic brackets bonded with new bioactive composite (in-vitro study)

Authors: Ali, N.A.M.; Nissan, L.M.K.; Al-Taai, N.; Khamis, A.H.

Journal: Journal of Baghdad College of Dentistry

Year: 2024

Do Hall Technique Crowns Affect Intra-arch Dimensions? A Split-mouth Quasi-experimental Non-randomized Feasibility Pilot Study

Authors: Alramzi, B.; Alhalabi, M.; Kowash, M.; Ghoneima, A.; Hussein, I.

Journal: International Journal of Clinical Pediatric Dentistry

Year: 2024

Assoc Prof Dr. Peixian Zhuang | Image Processing | Best Researcher Award

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Assoc Prof Dr. Peixian Zhuang | Image Processing | Best Researcher Award

Peixian Zhuang at University of Science and Technology Beijing, China

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

CVANet: Cascaded visual attention network for single image super-resolution

  • Authors: Weidong Zhang, Wenyi Zhao, Jia Li, Peixian Zhuang, Haihan Sun, Yibo Xu, Chongyi Li
  • Journal: Neural Networks
  • Year: 2024

Underwater image enhancement via piecewise color correction and dual prior optimized contrast enhancement

  • Authors: Weidong Zhang, Songlin Jin, Peixian Zhuang, Zheng Liang, Chongyi Li
  • Journal: IEEE Signal Processing Letters
  • Year: 2023

Non-uniform illumination underwater image restoration via illumination channel sparsity prior

  • Authors: Guojia Hou, Nan Li, Peixian Zhuang, Kunqian Li, Haihan Sun, Chongyi Li
  • Journal: IEEE Transactions on Circuits and Systems for Video Technology
  • Year: 2023

Gacnet: Generate adversarial-driven cross-aware network for hyperspectral wheat variety identification

  • Authors: Weidong Zhang, Zexu Li, Guohou Li, Peixian Zhuang, Guojia Hou, Qiang Zhang, Chongyi Li
  • Journal: IEEE Transactions on Geoscience and Remote Sensing
  • Year: 2023

Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement

  • Authors: Weidong Zhang, Peixian Zhuang, Hai-Han Sun, Guohou Li, Sam Kwong, Chongyi Li
  • Journal: IEEE Transactions on Image Processing
  • Year: 2022

Prof. Zhi Gao | Image Processing | Best Researcher Award

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Prof. Zhi Gao, Image Processing, Best Researcher Award

Zhi Gao at Wuhan University, China

Professional Profile

Summary:

Zhi Gao is a highly accomplished Professor and Doctoral Supervisor at the School of Remote Sensing and Information Engineering, Wuhan University. He holds prestigious positions as the National Young Talent Program and Distinguished Professor of Hubei Province, China. With a solid background in engineering and extensive experience in academia and research, he has built strong collaborative networks with renowned universities and institutions worldwide.

👩‍🎓Education:

Zhi Gao received his Bachelor of Engineering (B.Eng.) and Doctor of Philosophy (Ph.D.) degrees from Wuhan University, China, in 2002 and 2007, respectively. His educational background provides him with a strong foundation in his field.

🧬 Work Experience:

Zhi Gao’s professional journey reflects a wealth of experience in both academia and industry. Highlights of his career include:

  • Research Fellow (A) and Project Manager at the Interactive and Digital Media Institute, National University of Singapore (NUS), Singapore, since 2008.
  • Research Scientist (A) at the Temasek Laboratories, NUS, contributing significantly to research endeavors.
  • Building strong collaborative relationships with prestigious institutions globally, including the Temasek Laboratory, National University of Singapore, Carnegie Mellon University Robotics Institute, Robert Gordon University, The Chinese University of Hong Kong, Beijing Normal University, and Beijing Institute of Technology.

Research Interests:

  • Computer Vision
  • Machine Learning
  • Remote Sensing
  • UAV-based Surveillance Research and Applications

Publications Top Noted:

Paper Title: Exploring the relationship between land use change patterns and variation in environmental factors within urban agglomeration
  • Authors: Xiao, R., Yin, H., Liu, R., Liu, L., Jia, T.
  • Journal: Sustainable Cities and Society
  • Volume: 108
  • Pages: 105447
  • Year: 2024
  • Citations: 0
Paper Title: Tracking by Detection: Robust Indoor RGB-D Odometry Leveraging Key Local Manhattan World
  • Authors: Zhou, Z., Gao, Z., Xu, J.
  • Journal: IEEE Robotics and Automation Letters
  • Volume: 9
  • Issue: 6
  • Pages: 4990–4997
  • Year: 2024
Paper Title: How Challenging is a Challenge? CEMS: a Challenge Evaluation Module for SLAM Visual Perception
  • Authors: Zhao, X., Gao, Z., Li, H., Fang, H., Chen, B.M.
  • Journal: Journal of Intelligent and Robotic Systems: Theory and Applications
  • Volume: 110
  • Issue: 1
  • Pages: 42
  • Year: 2024
  • Citations: 0
Paper Title: TJ-FlyingFish: An Unmanned Morphable Aerial–Aquatic Vehicle System
  • Authors: Liu, X., Dou, M., Yan, R., Chen, J., Chen, B.M.
  • Journal: Unmanned Systems
  • Volume: 12
  • Issue: 2
  • Pages: 409–428
  • Year: 2024
  • Citations: 1
Paper Title: WaterFormer: A Global-Local Transformer for Underwater Image Enhancement With Environment Adaptor
  • Authors: Wen, J., Cui, J., Yang, G., Dou, L., Chen, B.M.
  • Journal: IEEE Robotics and Automation Magazine
  • Volume: 31
  • Issue: 1
  • Pages: 29–40
  • Year: 2024
  • Citations: 1

 

Image Processing and Enhancement

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Introduction Image Processing and Enhancement:

Image Processing and Enhancement is a pivotal domain within the realm of computer vision and digital imaging. This field is dedicated to the development of algorithms and techniques that improve the quality, clarity, and interpretability of digital images. Whether it's enhancing the visibility of medical scans, restoring historical photographs, or improving image quality in satellite imagery, this research area has widespread applications across various industries.

Subtopics in Image Processing and Enhancement:

  1. Image Denoising and Restoration: Research in this subfield focuses on developing algorithms to remove noise and artifacts from images, making them clearer and more suitable for analysis or presentation.
  2. Image Super-Resolution: This subtopic explores methods to enhance the resolution of images, enabling the generation of high-resolution images from lower-resolution sources. It has applications in medical imaging, surveillance, and entertainment.
  3. Colorization of Black and White Images: Techniques for adding color to black and white images, often used for restoring historical photos and improving the visual appeal of visual content.
  4. Image Enhancement for Medical Imaging: Research in this area is dedicated to developing specialized image processing techniques for improving the quality and diagnostic value of medical images such as X-rays, MRIs, and CT scans.
  5. HDR Imaging (High Dynamic Range): HDR techniques aim to capture and display a wider range of brightness levels in images, improving the visualization of scenes with varying lighting conditions, such as landscapes or architectural photography.
  6. Image Enhancement for Satellite and Remote Sensing: Specialized techniques are developed to enhance satellite and remote sensing imagery for applications in agriculture, environmental monitoring, and disaster management.
  7. Image Compression and Transmission: Research focuses on efficient methods for compressing and transmitting images without significant loss of quality, crucial for applications like video conferencing and image sharing on the internet.
  8. Image Deblurring: Techniques to remove blurriness caused by factors such as camera shake or motion, improving the sharpness and clarity of images.
  9. Image Segmentation and Object Recognition: These techniques involve separating objects from the background in images and recognizing individual objects or regions, vital for various computer vision applications.
  10. Deep Learning-Based Image Enhancement: Utilizing deep learning models for image enhancement tasks, such as generative adversarial networks (GANs) for realistic image synthesis and enhancement.

Image Processing and Enhancement research continues to advance, driven by the increasing demand for high-quality images in diverse fields such as healthcare, entertainment, agriculture, and more. Researchers in this area are constantly developing innovative solutions to enhance the visual content that surrounds us, ultimately improving our ability to interpret and utilize digital imagery in a variety of applications.

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