Dr. Hadeel Saadany | Computer Science | Best Researcher Award

Dr. Hadeel Saadany | Computer Science | Best Researcher Award

Birmingham City University | United Kingdom

Author Profile 

GOOGLE SCHOLAR 

Summary


Hadeel Saadany is a distinguished researcher and lecturer specializing in natural language processing, computational linguistics, and data science. she holds a phd in computer science (nlp) and an ma in computational linguistics from the university of wolverhampton, uk. her professional journey spans roles as a lecturer in data science, research fellow, and research assistant, contributing to machine learning model development, large language model applications, knowledge graph construction, sentiment analysis, and multilingual dataset curation. she has supervised numerous undergraduate and postgraduate projects, collaborated with industrial partners, published in peer-reviewed journals, and participated in nlp shared tasks and editorial work.

Early academic pursuits


Hadeel Saadany pursued her phd in computer science with a focus on natural language processing at the university of wolverhampton, uk, completing it in 2023 with honours pass with no correction. prior to her phd, she earned an ma in computational linguistics from the same university in 2019, achieving distinction. her academic journey reflects a deep commitment to language technologies and computational methods, providing a solid foundation for her research and professional endeavors in nlp and ai.

Professional endeavors


Hadeel currently serves as a lecturer in data science at birmingham city university, uk, where she leads and develops modules on ai and machine learning for undergraduate students, while also teaching postgraduate courses in python programming, applied machine learning, and ai. she supervises final year projects and master’s dissertations in computer science and collaborates on faculty research projects focusing on large language models and knowledge graph implementation.

Contributions and research focus


Hadeel’s research is centered on natural language processing, machine learning, and data science applications. she specializes in large language models, knowledge graph implementations, sentiment analysis, information retrieval, and custom language model construction for domain-specific speech recognition. her work extends to compiling and curating multilingual datasets, building relational networks, and employing statistical analysis and visualization techniques for textual data. she actively publishes in peer-reviewed nlp and data science journals and participates in shared tasks, workshops, and conferences.

Impact and influence


Hadeel’s work has influenced both academic and industrial applications of nlp. through her research fellow role and collaboration with industrial partners, she has implemented practical machine learning solutions for commercial pipelines. her supervision of undergraduate and postgraduate students has fostered new talent in computational linguistics and data science. her participation in conferences, workshops, and peer-reviewed publications has contributed to advancing nlp methodologies, multilingual data analysis, and sentiment detection technologies.

Academic cites


Hadeel’s research output spans multiple peer-reviewed journals and conferences in nlp, computational linguistics, and data science. her publications include work on large language models, knowledge graph integration, sentiment analysis, and information retrieval systems. her editorial work with the natural language engineering journal further highlights her engagement in scholarly communication and peer review processes.

Legacy and future contributions


Hadeel Saadany is poised to continue shaping the field of nlp and data science through her teaching, research, and collaborative projects. her future contributions are expected to focus on enhancing large language model applications, improving multilingual data processing, and developing knowledge graph-based solutions for real-world problems. she remains dedicated to mentoring emerging researchers and integrating innovative ai and nlp methods into both academic and industrial settings.

Publications 

Title: BLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-oriented Text
Author(s): H. Saadany, C. Orasan
Journal: Proceedings of TRITON (TRanslation and Interpreting Technology ONline), 48-56, 2021

Title: Fake or Real? A Study of Arabic Satirical Fake News
Author(s): H. Saadany, E. Mohamed, C. Orasan
Journal: Proceedings of the 3rd International Workshop on Rumours and Deception, 2020

Title: Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews
Author(s): H. Saadany, C. Orasan
Journal: Proceedings of the Fifth Arabic Natural Language Processing Workshop, 24-37, 2020

Title: RGCL at IDAT: deep learning models for irony detection in Arabic language
Author(s): T. Ranasinghe, H. Saadany, A. Plum, S. Mandhari, E. Mohamed, C. Orasan, …
Journal: IDAT, 2019

Title: Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study
Author(s): H. Saadany, C. Orasan, R.C. Quintana, F. Carmo, L. Zilio
Journal: arXiv preprint arXiv:2106.10719

Conclusion


Hadeel’s work has made a significant impact on both academic research and practical applications of nlp and ai. through her teaching, mentoring, and collaborative projects, she continues to advance the fields of computational linguistics and data science. her dedication to innovation, research excellence, and student development positions her as a leading contributor to the future of natural language processing and machine learning, shaping the next generation of researchers and practitioners.

Dr. Wenli yang – Computer Science and Artifical intelligence – Best Researcher Award 

Dr. Wenli yang - Computer Science and Artifical intelligence - Best Researcher Award 

University of Tasmania  - Australia

Author Profile 

GOOGLE SCHOLAR

🎓 Early academic pursuits

Dr. Wenli yang’s academic journey is rooted in strong interdisciplinary training, having earned dual phds that laid the foundation for her career in artificial intelligence. her early focus on knowledge representation and algorithmic modeling sparked her interest in scalable systems. through rigorous academic training and research exposure, she developed a deep understanding of power electronics, data engineering, and image analysis. this multidisciplinary education shaped her distinctive approach to solving real-world ai challenges using technically grounded methodologies.

👩‍🏫 Professional endeavors

Currently a lecturer at the school of ict, university of tasmania, dr. yang has quickly become a recognized figure in the artificial intelligence research community. she has actively led and contributed to over 10+ research projects and 8+ consultancy/industry projects, showcasing her technical capabilities across domains. her dedication extends beyond academia, with involvement in field-based data collection initiatives and industry-driven sustainability projects. her expertise also spans power electronics, where her data-driven methodologies enhance system efficiency and smart energy solutions.

🧠 Contributions and research focus

Dr. Yang’s research areas include knowledge representation, ai-powered image analysis, explainable ai, and generative ai. she is passionate about building interpretable and robust ai Computer Science and Artifical intelligence models that perform reliably under real-world conditions. having authored 29 peer-reviewed publications, with 20 as first author, she has made consistent contributions to high-impact q1 journals. her recent studies integrate power electronics with intelligent systems, creating scalable algorithms for energy-efficient computing, smart imaging, and automation across scientific domains.

🌐 Impact and influence

Wenli yang’s work has impacted both academic and applied sectors. her h-index of 9 (scopus) and h-index of 12 (google scholar), with over 647 citations, highlight her growing influence in the ai research community. her collaborative initiatives with australia seafood industries (asi), imas, csiro, and sense-t reflect her commitment to applying ai for public good. through ai- Computer Science and Artifical intelligencedriven oyster genotyping and sustainable fisheries management, she has contributed to ecological and operational advancements across australia’s marine industries.

🔮 Legacy and future contributions

Dr. Wenli yang’s vision is to create scalable, transparent, and adaptable ai systems that serve real-world applications. she continues to expand her research in explainable ai and data Computer Science and Artifical intelligence engineering, with future goals focused on integrating power electronics into intelligent systems for sustainable smart environments. her legacy lies in bridging theoretical research with field impact, inspiring future generations to pursue responsible and innovative ai solutions across multidisciplinary domains.

Notable Publications 

  • Title: A survey on blockchain-based internet service architecture: requirements, challenges, trends, and future
    Author(s): W. Yang, E. Aghasian, S. Garg, D. Herbert, L. Disiuta, B. Kang
    Journal: IEEE Access

  • Title: Survey on explainable AI: From approaches, limitations and applications aspects
    Author(s): W. Yang, Y. Wei, H. Wei, Y. Chen, G. Huang, X. Li, R. Li, N. Yao, X. Wang, X. Gu, ...
    Journal: Human-Centric Intelligent Systems

  • Title: Blockchain: Trends and future
    Author(s): W. Yang, S. Garg, A. Raza, D. Herbert, B. Kang
    Journal: Knowledge Management and Acquisition for Intelligent Systems: 15th Pacific …

  • Title: Design of intelligent transportation system supported by new generation wireless communication technology
    Author(s): W. Yang, X. Wang, X. Song, Y. Yang, S. Patnaik
    Journal: International Journal of Ambient Computing and Intelligence (IJACI)

  • Title: A decision model for blockchain applicability into knowledge-based conversation system
    Author(s): W. Yang, S. Garg, Z. Huang, B. Kang
    Journal: Knowledge-Based Systems