Arturo Benayas Ayuso | Generative Artificial Intelligence | Best Researcher Award

Posted on by Academic achievements

Prof. Arturo Benayas Ayuso | Generative Artificial Intelligence | Best Researcher Award

PhD Candidate at Polytechnic University of Madrid, Spain

Arturo Benayas Ayuso is a highly skilled naval architect with over two decades of experience in naval shipbuilding, digitization, and PLM (Product Lifecycle Management) systems integration. Known for his contributions to advancing digital solutions in the naval sector, he currently leads the integration efforts for NAVANTIA’s “El Cano” platform, which leverages cutting-edge technologies under the Industry 4.0 paradigm. This platform integrates complex processes in ship design, construction, and maintenance, marking a significant stride in naval digitization. Arturo is recognized for his leadership, technical expertise, and commitment to continuous improvement, which have consistently contributed to both national defense and international maritime innovation. His career reflects a dynamic blend of hands-on expertise, theoretical knowledge, and thought leadership within his field.

Profile

ORCID

Education

Arturo’s educational background is grounded in naval architecture, with a Master’s degree from the prestigious Universidad Politécnica de Madrid. His specialized training in marine motors provided him with a strong foundation for understanding the technical demands of naval engineering. Currently, Arturo is pursuing a PhD focused on IoT applications in ship design, construction, and management, further expanding his research in digitalization and its transformative impacts on the naval industry. His academic pursuits are complemented by numerous advanced courses in PLM platforms, machine learning, and materials science, reflecting his commitment to staying at the forefront of technological advancements relevant to his field.

Professional Experience

Arturo’s professional career spans pivotal roles in renowned engineering firms and projects within the naval and aerospace industries. His experience includes serving as a Technical Account Manager, Solution Architect, and Associate Manager, where he has spearheaded complex PLM integrations, notably in projects such as the Spanish Navy’s S80P submarine and the collaborative development of the Royal Navy’s CVF program. His role as Integration Lead for the “El Cano” platform exemplifies his capability to manage large teams, oversee end-to-end PLM implementations, and introduce digital solutions that optimize naval operations on an international scale. Throughout his career, Arturo has contributed to innovative projects, ensuring seamless transitions across software platforms and providing critical support for project management in challenging environments.

Research Interests

Arturo’s research interests lie at the intersection of naval architecture, digital transformation, and the Internet of Things (IoT). His doctoral research focuses on applying IoT to streamline and enhance various stages of ship design, manufacturing, and management. By leveraging data analytics, he explores ways to optimize shipbuilding efficiency and reduce costs. Arturo is also passionate about cybersecurity in IoT networks, recognizing the importance of robust security measures in protecting sensitive maritime operations. Additionally, he has an interest in machine learning and its potential applications in automating design processes, which could significantly advance naval engineering and shipyard productivity.

Awards and Recognitions

While Arturo has not received specific awards to date, his role as a thought leader and influential practitioner in naval PLM integration has earned him considerable recognition in his field. His significant contributions to NAVANTIA’s “El Cano” platform have been widely regarded as a benchmark for digital transformation within the naval industry. Furthermore, his insights on naval digitization and IoT applications in shipbuilding have been published in respected journals and presented at international conferences. These accomplishments underscore his impact on the industry and his commitment to innovation.

Publications

Benayas Ayuso, A. & Cebollero, A. (2011). “Integrated Development Environment in Shipbuilding Computer Systems.” ICAS Conference Paper. Cited by 17.
Benayas-Ayuso, A., & Pérez Fernández, R. (2018). “Automated/Controlled Storage for an Efficient MBOM Process in the Shipbuilding Managing the IoT Technology.” RINA Smart Ship Technology. Cited by 22.
Pérez Fernández, R., & Benayas-Ayuso, A. (2018). “Data Management for Smart Ship or How to Reduce Machine Learning Cost in IoS Applications.” RINA Smart Ship Technology. Cited by 18.
Benayas-Ayuso, A., & Pérez Fernández, R. (2019). “What does the Shipbuilding Industry Expect from the CAD/CAM/CAE Systems in the Next Years?” Naval Architect Magazine. Cited by 13.
Benayas Ayuso, A. (2021). “Internet of Things Cybersecurity – Blockchain as First Securitisation Layer of an IoT Network.” In Introduction to IoT in Management Science and Operations Research. Cited by 25.

Conclusion

Arturo Benayas Ayuso’s career exemplifies a blend of practical expertise and research-driven innovation. His contributions to naval digitalization, particularly through his work on the “El Cano” platform, highlight his commitment to integrating advanced technologies in shipbuilding. Arturo’s focus on IoT and cybersecurity, coupled with his passion for teaching, positions him as a forward-thinking leader in his field. As he continues to contribute to the academic and professional spheres, his research has the potential to reshape naval engineering, making him a strong candidate for the Best Researcher Award. His work reflects a dedication to innovation, resilience in navigating complex projects, and a vision for the future of naval architecture and digital integration.

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.