Mª del Carmen de Castro-Cabrera | Computer Science | Innovative Research Award

Posted on by Phenomenological Research

Innovative Research Award

Mª del Carmen de Castro-Cabrera
University of Cádiz, Spain

Mª del Carmen de Castro-Cabrera
Affiliation University of Cádiz
Country Spain
Scopus 49862833800
Documents 18
Citations 58
h-index 4
Subject Area Computer Science
Event International Phenomenological Research Awards
ORCID 0000-0003-4622-5275

Mª del Carmen de Castro-Cabrera is an Associate Professor in the Department of Computer Science and Engineering at the University of Cádiz, Spain, and a member of the UCASE software engineering research group. Her documented research activity centers on software engineering, particularly software testing, metamorphic testing, model-based testing, and related computational methods. Her publication record also includes work involving educational technologies and collaborative learning. [1]

Abstract

Mª del Carmen de Castro-Cabrera’s academic profile reflects sustained engagement with software engineering research at the University of Cádiz. Her work addresses software testing and associated techniques, including metamorphic testing, test-case prioritization, model-based testing, and constraint-based approaches. Her research record also extends to educational innovation and collaborative learning, providing an interdisciplinary connection between software engineering research and higher-education practice. [2]

Keywords

Software engineering; Software testing; Metamorphic testing; Model-based testing; Test-case prioritization; Constraint programming; Service-oriented architectures; Educational innovation.

Introduction

The research profile of Mª del Carmen de Castro-Cabrera is situated within computer science and software engineering, with particular attention to methods for improving software quality and testing processes. Her work has included research on testing service compositions, metamorphic testing, test-case prioritization, and combinations of model-based testing with constraint programming. Bibliographic records also document contributions to educational technology and assessment in higher education. [3]

Research Profile

Mª del Carmen de Castro-Cabrera is associated with the UCASE software engineering research group at the University of Cádiz. Her research activity has included participation in regional and national research projects concerned with software architectures, software design, formal modeling, testing, service-oriented systems, search-based software engineering, and data-processing applications. University research records identify her as a university professor and document continued collaboration within software engineering research. [1]

Research Contributions

The research contributions focus on software engineering, including metamorphic testing, WS-BPEL service testing, test-case prioritization, model-based testing, and constraint-based approaches for software verification and quality assurance. [3] [4] Her research also includes educational technology, focusing on wiki-based learning assessment, educational innovation, and software-testing practices that support collaborative and self-assessment in higher education.. [2] [5]

Publications

Mª del Carmen de Castro-Cabrera’s publications address software testing, metamorphic testing, test-case prioritization, model-based testing, constraint programming, service-oriented architectures, and educational technologies. Representative publications include research on wiki-based learning assessment, test-case prioritization, metamorphic testing with constraint solvers, WS-BPEL compositions, and model-based testing combined with constraint programming. These works illustrate continuity between methodological software-engineering research and applied educational or systems contexts. [2] [3] [4] [5] [6]

Research Impact

The supplied bibliometric profile records 18 Scopus documents, 58 citations, and an h-index of 4 for author ID 49862833800. A separate Google Scholar profile supplied for the researcher reports 59 documents, 115 citations, and an h-index of 5. Because bibliometric databases apply different coverage and counting methods, these figures should be interpreted as database-specific indicators rather than interchangeable measures of research output. [1] Her publication record demonstrates research activity extending across software testing and educational applications. Bibliographic records also document continuing collaboration within the University of Cádiz software engineering environment, including publications on model-based testing, constraint programming, and metamorphic testing. [6]

Award Suitability

The Innovative Research Award category aligns with her documented research in software engineering, software testing, metamorphic testing, test-case prioritization, and model-based approaches. Her participation in multiple research projects and publications demonstrates sustained academic engagement across software engineering and educational technology, supporting consideration within an innovation-focused research recognition framework based on the supplied record.

Conclusion

Mª del Carmen de Castro-Cabrera’s academic profile integrates university teaching and research in software engineering. Her work includes software testing, metamorphic testing, model-based approaches, constraint programming, service-oriented systems, and educational technology. Participation in research projects and scholarly publications demonstrates sustained contributions to software quality, testing methodologies, and applied computer science research. [1] [6]

References

  1. Elsevier. (n.d.). Scopus author details: Mª del Carmen de Castro-Cabrera, Author ID 49862833800. Scopus. https://www.scopus.com/authid/detail.uri?authorId=49862833800
  2. Palomo-Duarte, M., Dodero, J. M., García-Domínguez, A., Neira-Ayuso, P., et al. (2014). Scalability of assessments of wiki-based learning experiences in higher education. Computers in Human Behavior, 31, 638–650. DOI: https://doi.org/10.1016/j.chb.2013.07.033
  3. de Castro-Cabrera, M. C., García-Domínguez, A., & Medina-Bulo, I. (2020). Trends in prioritization of test cases: 2017–2019. Proceedings of the 35th Annual ACM Symposium on Applied Computing, 2005–2011. DOI: https://doi.org/10.1145/3341105.3374036
  4. de Castro-Cabrera, M. C., García-Domínguez, A., & Medina-Bulo, I. (2019). Using constraint solvers to support metamorphic testing. 2019 IEEE/ACM 4th International Workshop on Metamorphic Testing, 32–39. DOI: https://doi.org/10.1109/MET.2019.00013
  5. Castro-Cabrera, C., & Medina-Bulo, I. (2011). An approach to metamorphic testing for WS-BPEL compositions. Proceedings of the International Conference on e-Business, 137–142. DOI: https://doi.org/10.5220/0003611401370142
  6. Castro-Cabrera, C., & Medina-Bulo, I. (2012). Application of Metamorphic Testing to a Case Study in Web Services Compositions. Communications in Computer and Information Science, 168–181. DOI: https://doi.org/10.1007/978-3-642-35755-8_13

Ghulam Masudh Mohamed | Computer Science | Research Excellence Award

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Mr.Ghulam Masudh Mohamed | Computer Science | Research Excellence Award 

Lecturer at Durban University of Technology | South Africa 

Mr. Ghulam Masudh Mohamed is a Lecturer in the Department of Information Technology at the Durban University of Technology, where he is actively involved in teaching, research, and student development. He holds advanced qualifications in Information and Communications Technology and is currently pursuing doctoral studies, reflecting a strong academic foundation and commitment to continuous scholarly growth. His professional experience spans lecturing, postgraduate supervision, programme coordination, and student support, with a focus on innovative, people-centred teaching practices and curriculum development. His research interests lie in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, and data-driven applications addressing real-world challenges. As both a graduate and academic staff member of the Durban University of Technology, he brings valuable institutional insight and a deep understanding of the student experience. Through teaching excellence, research contribution, and community engagement, Mr. Ghulam Masudh Mohamed plays a meaningful role in advancing the university’s academic mission and strategic vision.

Citation Metrics (Scopus)

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Citations
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Documents
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h-index
21

Top Publications

Pengfei Wei | Computer Science | Best Researcher Award

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Dr. Pengfei Wei | Computer Science | Best Researcher Award 

Senior Engineer at Guangdong University of Technology | China

Dr. Pengfei Wei is a Senior Engineer at Guangdong University of Technology, recognized for his pioneering contributions to the field of computer science, particularly in multimodal learning, knowledge tracing, edge artificial intelligence, and task-oriented dialogue systems. He holds a Ph.D. in Computer Science, where his research focused on integrating deep learning models with practical applications in intelligent education and human–machine interaction. Combining academic rigor with industrial innovation, he brings substantial experience from both enterprise research and academic development, bridging the gap between theory and real-world technology deployment. His work encompasses advanced methods such as visual-enhanced transformers for multimodal named entity recognition, genetic-inspired relation extraction, and the introduction of Kolmogorov–Arnold representations in knowledge tracing, which have improved model interpretability and performance in AI-based learning systems. In addition to his theoretical advancements, he has successfully led projects on real-time lab-safety analytics and large-scale AI deployment using Huawei Ascend, Nvidia, and TPU platforms, contributing to the broader industrial adoption of edge AI technologies. Dr. Pengfei Wei has authored numerous peer-reviewed papers in top-tier international journals and conferences, including Neural Networks, ICMR, and IJCAI, and serves as a reviewer for several prestigious publications such as Neural Networks, Pattern Recognition Letters, AAAI, and IJCNN. His collaborative initiatives with research teams and institutions have fostered multidisciplinary innovation, emphasizing the integration of AI with blockchain, big data, and education systems. A dedicated mentor and research leader, he actively supports student-led research and fosters the development of next-generation AI scholars. His professional memberships with the China Computer Federation (CCF) and the Association for Computing Machinery (ACM) reflect his strong engagement in the global computing community. Dr. Pengfei Wei’s research continues to push the boundaries of multimodal understanding and intelligent systems, driving transformative progress in computational learning and applied artificial intelligence. Through his sustained contributions, he remains committed to advancing the capabilities of intelligent technologies that enhance human productivity, knowledge discovery, and digital transformation.

Profile: Orcid | Google Scholar

Featured Publications:

  • Liao, W., B. Zeng, Yin, X., & Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence, 51(6), 3522–3533.

  • Liao, W., Zeng, B., Liu, J., Wei, P., Cheng, X., & Zhang, W. (2021). Multi-level graph neural network for text sentiment analysis. Computers & Electrical Engineering, 92, 107096.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Fang, J. (2022). Image-text interaction graph neural network for image-text sentiment analysis. Applied Intelligence, 52(10), 11184–11198.

  • Liao, W., Zeng, B., Liu, J., Wei, P., & Cheng, X. (2022). Taxi demand forecasting based on the temporal multimodal information fusion graph neural network. Applied Intelligence, 52(10), 12077–12090.

  • Wei, P., Zeng, B., & Liao, W. (2022). Joint intent detection and slot filling with wheel-graph attention networks. Journal of Intelligent & Fuzzy Systems, 42(3), 2409–2420.

  • Wei, P., Ouyang, H., Hu, Q., Zeng, B., Feng, G., & Wen, Q. (2024). VEC-MNER: Hybrid transformer with visual-enhanced cross-modal multi-level interaction for multimodal NER. Proceedings of the International Conference on Multimedia Retrieval (ICMR 2024).

  • Wen, S., Zeng, B., Liao, W., Wei, P., & Pan, Z. (2021). Research and design of credit risk assessment system based on big data and machine learning. Proceedings of the IEEE 6th International Conference on Big Data Analytics (ICBDA 2021), 9–13.

Kai Jin | Computer Science | Best Researcher Award

Posted on by Phenomenological Research

Dr. Kai Jin | Computer Science | Best Researcher Award

Lecturer at Sanya Research Institute of Hunan University of Science and Technology | China

Dr. Kai Jin is an accomplished researcher and academic whose work bridges the fields of computer science, artificial intelligence, and information engineering. With a strong educational foundation culminating in a Ph.D. in Computer Science and Technology from Hunan University, he has built a research career characterized by innovation, interdisciplinary collaboration, and practical impact. His professional experience spans both academia and industry, having served as a lecturer and researcher at the Sanya Research Institute of Hunan University of Science and Technology, as well as a software engineer in technology firms where he developed expertise in system architecture and Java-based solutions. Dr. Kai Jin’s scholarly contributions focus on network measurement, image recognition, and deep learning areas that are pivotal to advancing intelligent computing and data-driven technologies. He has authored six scientific papers published in high-impact journals and international conferences, including IEEE Transactions on Network Science and Engineering, Connection Science, and Scientific Reports. His work has earned 70 citations by 60 documents, with an h-index of 5, reflecting the growing influence of his research within the global academic community. In addition to publications, Dr. Kai Jin has secured four invention patents covering innovations in network traffic measurement, remote sensing image detection, brain tumor identification, and predictive maintenance for industrial IoT systems. His research projects, supported by national and provincial grants, such as the National Natural Science Foundation of China and the Hunan Provincial Key R&D Program, demonstrate a commitment to technological progress and societal benefit. Beyond his technical achievements, Dr. Kai Jin’s leadership in research collaborations and mentorship reflects his dedication to fostering the next generation of computer scientists. His current research continues to explore the integration of deep learning models with real-world systems, optimizing intelligent network management, and enhancing computational efficiency. Through his scientific rigor, creativity, and contributions to both theoretical and applied computing, Dr. Kai Jin has established himself as a leading voice in modern computer science, shaping innovations that address the complex challenges of today’s interconnected digital world.

Profile: Scopus

Featured Publications:

1. Jin, K., Xie, K., Wang, X., Tian, J., Xie, G., & Wen, J. (2022). Low-cost online network traffic measurement with subspace-based matrix completion. IEEE Transactions on Network Science and Engineering, 10(1), 53–67.

2. Jin, K., Xie, K., Tian, J., Liang, W., & Wen, J. (2023). Low-cost network traffic measurement and fast recovery via redundant row subspace-based matrix completion. Connection Science, 35(1), 2218069.

3. Jin, K., Banizaman, H., Gharehveran, S. S., & Jokar, M. R. (2025). Robust power management capabilities of integrated energy systems in the smart distribution network including linear and non-linear loads. Scientific Reports, 15(1), 6615.

4. Zhu, M., Rasheed, R. H., Albahadly, E. J. K., Zhang, J., Alqahtani, F., Tolba, A., & Jin, K.* (2025). Application of fixed and mobile battery energy storage flexibilities in robust operation of two-way active distribution network. Electric Power Systems Research, 244, 111556.

5. Wen, J., Chen, Y., & Jin, K.* (2023, June). Revolutionizing network performance: The active and passive service path performance monitoring analysis method. In 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud) / 2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom) (pp. 1–6). IEEE.

6. Huo, Y., Jin, K., Cai, J., Xiong, H., & Pang, J. (2023). Vision Transformer (ViT)-based applications in image classification. In Proceedings of the 9th IEEE International Conference on High Performance and Smart Computing (HPSC 2023) (pp. 135–140). IEEE.

7. Jin, K., Xie, K., Tian, J., Liang, W., & Wen, J. (2024). A acylthiourea based ion-imprinted membrane for selective removal of Ag⁺ from aqueous solution. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2024, Article 9 citations.