Angeliki Antoniou | AI | Best Researcher Award

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Assoc. Prof. Dr. Angeliki Antoniou | AI | Best Researcher Award

Assoc. Prof. Dr. Angeliki Antoniou | AI | Associate Professor at University of West Attica | Greece

Assoc. Prof. Dr. Angeliki Antoniou is a distinguished scholar in the field of Human-Computer Interaction (HCI), Educational Technologies, and Digital Cultural Heritage, currently serving at the University of West Attica, Department of Archival, Library and Information Studies, Greece. She earned her Doctor of Informatics (Ph.D.) from the University of Peloponnese, focusing on adaptive educational technologies for museums, and holds an MSc in Human-Computer Interaction with Ergonomics from University College London (UCL). Additionally, she possesses undergraduate degrees in Psychology from the University of Kent and Early Childhood Education from the National and Kapodistrian University of Athens, illustrating her interdisciplinary foundation that bridges education, psychology, and informatics. Professionally, Assoc. Prof. Dr. Angeliki Antoniou has accumulated extensive teaching and research experience across institutions such as the University of Peloponnese and the University of West Attica, where she has led courses in cognitive psychology, human-computer interaction, and digital learning environments. Her research interests include user-centered design, cognitive modeling, serious games, digital storytelling, and technology-enhanced museum learning. She has successfully contributed to and coordinated several international and national projects on cultural heritage technologies, and her work is well-cited in high-impact academic journals indexed in Scopus and IEEE. Assoc. Prof. Dr. Angeliki Antoniou’s research skills encompass experimental design, usability evaluation, qualitative and quantitative analysis, and the development of adaptive systems for education and culture. She has received academic recognition for her leadership in interdisciplinary research, along with honors for her contributions to digital culture and innovation in educational informatics. In conclusion, Assoc. Prof. Dr. Angeliki Antoniou exemplifies academic excellence, innovative vision, and global impact through her scholarly research, educational leadership, and enduring contributions to the advancement of digital cultural heritage and human-computer interaction.

Profile: Google Scholar

Featured Publications 

  1. Lykourentzou, I., Antoniou, A., Naudet, Y., & Dow, S. P. (2016). Personality matters: Balancing for personality types leads to better outcomes for crowd teams. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. Citations: 158

  2. Theodoropoulos, A., & Antoniou, A. (2022). VR games in cultural heritage: A systematic review of the emerging fields of virtual reality and culture games. Applied Sciences, 12(17), 8476. Citations: 108

  3. Antoniou, A., & Lepouras, G. (2010). Modeling visitors’ profiles: A study to investigate adaptation aspects for museum learning technologies. Journal on Computing and Cultural Heritage (JOCCH), 3(2), 1–19. Citations: 84

  4. Lykourentzou, I., Claude, X., Naudet, Y., Tobias, E., Antoniou, A., & Lepouras, G. (2013). Improving museum visitors’ quality of experience through intelligent recommendations: A visiting style-based approach. Workshop Proceedings of the 9th International Conference on Intelligent Environments. Citations: 76

  5. Antoniou, A., Lepouras, G., Bampatzia, S., & Almpanoudi, H. (2013). An approach for serious game development for cultural heritage: Case study for an archaeological site and museum. Journal on Computing and Cultural Heritage (JOCCH), 6(4), 1–19. Citations: 69

  6. Katifori, A., Perry, S., Vayanou, M., Antoniou, A., Ioannidis, I. P., & McKinney, S. (2020). “Let them talk!” Exploring guided group interaction in digital storytelling experiences. Journal on Computing and Cultural Heritage (JOCCH), 13(3), 1–30. Citations: 67

  7. Antoniou, A., Katifori, A., Roussou, M., Vayanou, M., Karvounis, M., & Kyriakidi, M. (2016). Capturing the visitor profile for a personalized mobile museum experience: An indirect approach. Proceedings of the Digital Heritage International Congress. Citations: 60

 

Majdi Khalid | Machine learning | Best Researcher Award

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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.