Majdi Khalid | Machine learning | Best Researcher Award

Posted on by Academic achievements

Assoc Prof. Dr. Majdi Khalid | Machine learning | Best Researcher Award 

Associate Professor at Umm Al-Qura University

Assoc. Prof. Dr. Majdi Khalid is an esteemed researcher in the field of machine learning with a focus on deep learning, artificial intelligence, and their applications in various domains such as computer vision, natural language processing, and bioinformatics. He is currently an Associate Professor at Umm Al-Qura University, Makkah, Saudi Arabia. Dr. Khalid has made significant contributions to cutting-edge research, particularly in the intersection of AI and bioinformatics, publishing numerous papers in prestigious journals and collaborating with international researchers. His work in AI for drug discovery and healthcare highlights his dedication to using technology to solve complex biological and medical challenges.

Profile:

ORCID

Education:

Dr. Khalid holds a Ph.D. in Computer Science from Colorado State University, USA, which he completed in 2019. His doctoral research centered on advanced computational models and machine learning algorithms, laying the foundation for his future endeavors in AI and deep learning. Prior to his Ph.D., Dr. Khalid earned his Master of Computer Science (M.C.S.) from the same institution in 2013, and a Bachelor of Science (B.S.) in Computer Science from Umm Al-Qura University in 2006. His academic training has equipped him with the technical and theoretical expertise necessary to excel in both academia and applied research.

Experience:

Dr. Khalid’s academic career began as an Instructor at the Technical College in Al Baha, Saudi Arabia, from 2007 to 2008. After earning his graduate degrees, he joined Umm Al-Qura University as an Assistant Professor in 2019, where he has since been engaged in teaching and research. Throughout his academic journey, Dr. Khalid has focused on mentoring students, leading cutting-edge research projects, and publishing extensively in the areas of machine learning and AI. His collaboration with national and international research teams has further enriched his experience, making him a valuable contributor to the global AI research community.

Research Interests:

Dr. Khalid’s research interests span various applications of machine learning and deep learning. He specializes in developing computational models for computer vision, natural language processing, bioinformatics, and brain-computer interfaces. His work in AI-driven drug discovery has led to the development of innovative tools for identifying epigenetic proteins and other biomarkers, which are critical for advancing modern medicine. Dr. Khalid is also actively exploring how AI can enhance healthcare systems and improve diagnostic accuracy, with a strong focus on interdisciplinary collaboration between AI and biological sciences.

Awards:

Dr. Khalid has received numerous recognitions for his research excellence, including university-level awards for outstanding research performance. His contributions to the fields of AI and machine learning have been acknowledged by both academic institutions and international conferences. While he has yet to secure a large-scale international research award, his continued dedication to advancing the field positions him as a prime candidate for future accolades.

Publications:

  1. Ali, Farman, Abdullah Almuhaimeed, Majdi Khalid, et al. (2024). “DEEPEP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.” Methods.
    • Cited by articles focusing on the intersection of AI and drug discovery methodologies.
      Read the article here
  2. Khalid, Majdi, Farman Ali, et al. (2024). “An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.” Journal of Biomolecular Structure and Dynamics.
    • Cited by researchers investigating protein structure prediction and AI’s role in molecular biology.
      Read the article here
  3. Alsini, Raed, Abdullah Almuhaimeed, et al. (2024). “Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with 2D-CNN.” Journal of Biomolecular Structure and Dynamics.
  4. Alohali, Manal Abdullah, et al. (2024). “Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems.” Transactions on Emerging Telecommunications Technologies.
    • Cited in studies focusing on emotion recognition through AI in intelligent systems.
      Read the article here
  5. Majdi Khalid (2023). “Advanced Detection of COVID-19 through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis.” International Journal of Advanced Computer Science and Applications.
  1. Ali, Muhammad Umair, Majdi Khalid, et al. (2023). “Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.” Bioengineering, 10(12): 1430.
    • Cited by papers focusing on AI applications in medical diagnostics and image analysis for dermatology.
      Read the article here
  2. Alghushairy, Omar, Farman Ali, Wajdi Alghamdi, Majdi Khalid, et al. (2023). “Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.” Journal of Biomolecular Structure and Dynamics, 2023: 1-12.
    • Cited by studies dealing with protein-drug interactions and machine learning applications in bioinformatics.
      Read the article here
  3. Obayya, Marwa, Fahd N. Al-Wesabi, Rana Alabdan, Majdi Khalid, et al. (2023). “Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems.” IEEE Transactions on Consumer Electronics, 2023.
    • Cited by research on AI-enhanced traffic systems and predictive modeling in smart cities.
      Read the article here
  4. Alruwais, Nuha, Eatedal Alabdulkreem, Majdi Khalid, et al. (2023). “Modified Rat Swarm Optimization with Deep Learning Model for Robust Recycling Object Detection and Classification.” Sustainable Energy Technologies and Assessments, 59: 103397.
    • Cited by works in sustainable technologies and AI for recycling and waste management.
      Read the article here
  5. Adnan, Adnan, Wang Hongya, Farman Ali, Majdi Khalid, et al. (2023). “A Bi-Layer Model for Identification of piwiRNA using Deep Neural Learning.” Journal of Biomolecular Structure and Dynamics, 2023: 1-9.
  • Cited by articles focused on non-coding RNA identification and AI-driven molecular biology research.
    Read the article here

Conclusion

Assoc. Prof. Dr. Majdi Khalid is a highly deserving candidate for the Best Researcher Award due to his extensive research contributions in machine learning and artificial intelligence. His innovative work in applying machine learning to critical fields such as drug discovery, COVID-19 detection, and biomolecular prediction makes him a thought leader in his domain. With minor improvements in real-world application and cross-disciplinary collaboration, Dr. Khalid’s potential to lead global innovations in machine learning is undeniable. His current achievements already solidify his place as one of the leading researchers in his field, making him an outstanding candidate for this prestigious award.

Hongchuan Yu | Artificial Intelligence | Best Researcher Award

Posted on by Academic achievements

Dr. Hongchuan Yu | Artificial Intelligence | Best Researcher Award

Principal Academic | Bournemouth University | United Kingdom

Based on the provided information, Hongchuan Yu appears to be a strong candidate for the Best Researcher Award in the field of computer graphics and animation. Here’s an evaluation based on the strengths, areas for improvement, and conclusion:

Strengths for the Award:

  1. Publication Record: Hongchuan Yu has an impressive publication record with over 100 peer-reviewed papers in prestigious journals and conferences, demonstrating sustained scholarly output.
  2. Research Impact: His research spans diverse areas including computer vision, image processing, machine learning, and graphics, addressing both theoretical advancements and practical applications.
  3. Grant Success: Securing over £2+ million in research grants indicates strong support and recognition from funding bodies, showcasing the practical relevance and impact of his research.
  4. Professional Recognition: Membership in IEEE Computer Society, ACM Siggraph, and a Fellow of Higher Education Academy UK (FHEA) underscores his professional standing and recognition in the academic community.

Areas for Improvement:

  1. International Collaboration: While the research is prolific, increasing international collaboration could enhance the global impact of his work and foster broader research networks.
  2. Interdisciplinary Engagement: Further engagement with interdisciplinary fields could lead to novel applications of his research in areas beyond traditional computer science domains.
  3. Visibility and Outreach: Enhancing visibility through public engagement, science communication, and outreach efforts could broaden the impact of his research beyond academic circles.

Conclusion:

Hongchuan Yu is undoubtedly a leading researcher in computer graphics and animation, with a substantial body of work that has significantly contributed to the field. His publication record, grant success, and professional affiliations highlight his research excellence. To further consolidate his position, fostering international collaborations, engaging in interdisciplinary research, and enhancing visibility through outreach activities could strengthen his impact and leadership in the field. Overall, Hongchuan Yu is well-deserving of consideration for the Best Researcher Award based on his demonstrated achievements and contributions to the academic community.

Short Bio 🌐

Hongchuan Yu is a distinguished academic and researcher currently serving as a Principal Academic at the National Centre for Computer Animation (NCCA), Bournemouth University, UK. With a career spanning over two decades, Yu has made significant contributions to the fields of computer graphics, animation, and image processing. His research focuses on advanced computational techniques for visual computing, including geometric modeling, image analysis, and machine learning applications in computer vision. Yu has published extensively in renowned journals and conferences, shaping the landscape of computer graphics and animation through innovative research and scholarly contributions.

Profile

ORCID

Education 🎓

Hongchuan Yu earned his Ph.D. in Control Engineering from the Chinese Academy of Sciences in 2000. His doctoral research laid the foundation for his subsequent work in computer graphics and computational geometry. Throughout his academic journey, Yu has demonstrated a profound commitment to advancing knowledge in his field, integrating interdisciplinary perspectives to tackle complex challenges in digital media and visual computing.

Experience 💼

Yu’s academic journey includes pivotal roles at esteemed institutions worldwide. Before joining Bournemouth University, he served as a research fellow at the School of Computer Science & Software Engineering, The University of Western Australia, and the School of Electrical and Electronic Engineering, Nanyang Technological University (2000-2007). These positions enriched his research portfolio, allowing him to collaborate on diverse projects spanning computer vision, machine learning, and virtual reality.

Research Interests 🔍

Hongchuan Yu’s research interests are at the intersection of computer graphics, animation, and computational geometry. His work focuses on developing novel algorithms and methodologies to enhance the realism and interactivity of virtual environments. Key areas of interest include geometric modeling, image processing, 3D reconstruction, and machine learning applications in visual computing. Yu’s research has practical applications in industries such as entertainment, medicine, and engineering, contributing to advancements in digital media production and interactive simulations.

Awards and Recognition 🏆

Throughout his career, Hongchuan Yu has garnered recognition for his outstanding contributions to computer graphics and animation. He is a member of prestigious academic societies such as the IEEE Computer Society and ACM Siggraph. In 2010, he was honored as a Fellow of the Higher Education Academy UK (FHEA), acknowledging his dedication to excellence in teaching and research in higher education. Yu’s research has also been supported by multiple internal and external grants, totaling over £2 million, underscoring the impact and relevance of his work in the academic community.

Publications 📚

Hongchuan Yu has published over 100 peer-reviewed articles in leading journals and conferences in the fields of computer graphics and animation. His publications span a wide range of topics, including geometric modeling, image processing, virtual reality, and machine learning applications in visual computing. Some notable publications include:

A Single Photon Bone Densitometer, Nuclear Techniques Journal (Chinese), 1995.

An Improved LDI Algorithm for High-Quality Image Rendering, Computer Engineering and Applications Journal (Chinese), 2004.

GVF-based Anisotropic Diffusion Models, IEEE Transactions on Image Processing, 2006.

Complete Invariants for Robust Face Recognition, Pattern Recognition Journal, 2007.

Fast and Exact Discrete Geodesic Computation Based on Triangle-Oriented Wavefront Propagation, ACM Transactions on Graphics, 2016.