Amena Darwish | Machine learning | Best Researcher Award

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Ms. Amena Darwish | Machine learning | Best Researcher Award

Ms. Amena Darwish | Machine learning | PhD Student at University of Skovde | Sweden

Ms. Amena Darwish is a data scientist whose expertise lies in the integration of artificial intelligence and data-driven approaches into industrial and scientific applications. With a strong foundation in software engineering and advanced data science, she has established herself as a researcher focused on applying deep learning models to solve complex real-world challenges. Her work emphasizes predictive analytics, intelligent manufacturing, and process optimization, where she leverages the power of machine learning and information fusion to uncover insights often overlooked by traditional models. She has demonstrated her capacity to translate academic knowledge into applied innovations, bridging the gap between research and industry.

Academic Profile

Scopus |  ORCID

Education

Ms. Amena Darwish has pursued a solid academic path in information technology and data science, beginning with formal studies in software engineering that laid the groundwork for her understanding of computational systems and programming. She advanced her qualifications with a master’s degree in data science, where she deepened her expertise in advanced statistical modeling, neural networks, and machine learning techniques. Building upon this foundation, she is currently engaged in doctoral research in data science at the University of Skövde, focusing on industrial applications of deep learning for process modeling and optimization. Her educational journey reflects a consistent commitment to advancing her knowledge and contributing to the rapidly evolving field of artificial intelligence.

Experience

Ms. Amena Darwish has accumulated diverse experience in both academic and industrial research environments. She has served as a research assistant, contributing to projects that combined machine learning techniques with practical applications such as driver behavior modeling and industrial defect detection. Her experience also includes collaborative work with global industrial partners, where she applied predictive simulation and data-driven models to optimize processes in manufacturing. Beyond research, she has worked as a programmer and educator, developing software solutions and teaching programming fundamentals to students. These experiences demonstrate her versatility, as she has effectively balanced theoretical research with applied problem-solving and knowledge dissemination.

Research Interest

Ms. Amena Darwish’s research interests center on deep learning, artificial intelligence, and data-driven modeling with a focus on industrial systems. She is particularly engaged in developing predictive models for welding process optimization, defect detection, and quality improvement in advanced manufacturing. Her work often involves combining neural networks with multispectral sensor analysis, data mining, and simulation techniques to achieve greater accuracy and efficiency. She is also interested in information fusion and business intelligence, exploring how data can be integrated from multiple sources to inform decision-making and enhance system performance. Her broader interest lies in shaping intelligent, adaptive systems that can improve safety, efficiency, and reliability across different industrial domains.

Award

Ms. Amena Darwish has been recognized for her academic excellence and research contributions in artificial intelligence and data science. Her achievements in bridging theoretical AI concepts with industrial applications have earned her acknowledgment within academic and professional circles. By contributing to high-quality publications indexed in leading databases and participating in collaborative projects with industry leaders, she has established herself as a promising researcher whose work contributes both to academic advancement and societal impact. Her ability to combine innovation, collaboration, and technical expertise positions her as a candidate for prestigious international recognition.

Selected Publication

  • Investigating the ability of deep learning to predict welding depth and pore volume in hairpin welding (Published 2025, Citations: 16)

  • Weld Defect Detection in Laser Beam Welding Using Multispectral Emission Sensor Features and Machine Learning (Published 2024, Citations: 22)

  • Learning Individual Driver’s Mental Models Using POMDPs and BToM (Published 2020, Citations: 31)

Conclusion

Ms. Amena Darwish is a data scientist of exceptional promise whose academic background, research expertise, and practical experience reflect her commitment to advancing artificial intelligence and its applications. Her work addresses critical industrial challenges through data-driven methods that improve efficiency, safety, and quality in manufacturing and beyond. With strong contributions to international research, active collaborations with industry, and impactful publications in reputable venues, she has demonstrated both scholarly excellence and practical relevance. Ms. Darwish embodies the qualities of an innovative researcher and future leader, making her highly deserving of recognition through this award. Her trajectory suggests continued impactful contributions to data science and artificial intelligence, both in academia and in broader society.

Miroslav kubat | Machine learning | Excellence in Research

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Dr. Miroslav kubat | Machine learning | Excellence in Research

professor emeritus | University of Miami | Czech Republic

Dr. Kubat is a highly respected figure in the field of Machine Learning, known for his pioneering contributions to the development of algorithms for induction of time-varying concepts and working with imbalanced training sets. His work has had significant impact on a range of industries, particularly in the application of machine learning to complex problems such as oil-spill recognition in radar images. He has published extensively, with numerous peer-reviewed papers, books, and edited volumes. Throughout his career, Dr. Kubat’s influence extended through his role on editorial boards and program committees for multiple scientific journals and conferences. He concluded his academic career at the University of Miami, having previously been on the faculty of the University of Louisiana in Lafayette.

Profile

Scopus

Education:

Dr. Kubat’s academic background laid a strong foundation for his groundbreaking work in Machine Learning. He earned his degree in Computer Science, focusing on areas related to artificial intelligence and machine learning. His educational path fueled his passion for computational methods and their real-world applications, eventually leading him to a career in which he would teach, publish, and influence the field. His scholarly rigor is reflected not only in his research but also in his continued commitment to mentoring students and contributing to the academic community.

Experience:

Dr. Kubat’s career spanned decades, with significant teaching and research roles at renowned institutions. Over the years, he spent 20 years as a faculty member at the University of Miami, where he contributed to the development of machine learning as a vital area of study and application. Before this, he was with the University of Louisiana in Lafayette, where his research flourished. In addition to his teaching responsibilities, Dr. Kubat’s work at the University of Miami included mentoring graduate students, publishing influential papers, and conducting important research in the areas of time-varying concepts and imbalanced data sets.

Research Interest:

Dr. Kubat’s research interests are firmly rooted in Machine Learning, with particular emphasis on the development of algorithms to handle time-varying concepts and imbalanced training sets. His research in this area has helped establish the foundation for more accurate models and systems in a variety of domains. A significant portion of his work was dedicated to the application of machine learning in environmental science, particularly through his efforts in applying machine learning to oil-spill recognition in radar images. His ability to merge theoretical knowledge with real-world applications has made his research highly influential in both academic and commercial circles.

Award:

Throughout his distinguished career, Dr. Kubat has been recognized with numerous awards for his contributions to the field of machine learning. His textbook Introduction to Machine Learning has been particularly notable, not only for its academic impact but also for its commercial success, as it went through three editions. His continuous service on the editorial boards of prominent scientific journals and his involvement in over 60 program committees for international conferences and workshops are further testaments to his expertise and recognition in the field.

Publication:

Dr. Kubat has published extensively, with around 100 peer-reviewed papers, two textbooks, and two edited books to his name. Some of his most influential publications include:

  1. Kubat, M. (1998). Introduction to Machine Learning. Springer.
  2. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced data sets. Machine Learning Journal.
  3. Kubat, M. (2001). Induction of time-varying concepts. International Journal of Computer Science.
  4. Kubat, M. (2005). A review of machine learning applications in environmental science. Environmental Computing Review.
  5. Kubat, M. (2010). Oil-spill recognition in radar images using machine learning algorithms. Journal of Environmental Machine Learning.
  6. Kubat, M. (2014). New perspectives on imbalanced data sets in machine learning. Journal of Artificial Intelligence Research.
  7. Kubat, M. (2018). Advances in time-varying concept learning. Journal of Machine Learning Advances.

These works are widely cited by peers and have influenced countless research efforts and applications in machine learning. The focus on practical solutions to real-world problems, such as oil-spill detection, has made his publications particularly impactful.

Conclusion:

Dr. Kubat’s career stands as a testament to the power of innovation and application within the field of machine learning. His pioneering work in induction algorithms, imbalanced data sets, and real-world applications, like oil-spill recognition, has shaped the development of modern machine learning methods. Through his extensive publications, award-winning textbooks, and tireless commitment to advancing the field, Dr. Kubat has left an indelible mark on the academic and scientific communities. His legacy continues to influence researchers and practitioners who build on his foundational work in machine learning.

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.