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

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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

Yingxia Chen | Computer Science | Research Excellence Award

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Dr.Yingxia Chen | Computer Science | Research Excellence Award 

Professor at Yangtze University | China 

Dr. Yingxia Chen is a respected academic and researcher in computer science with strong expertise in software engineering and intelligent computing. Dr. Yingxia Chen received advanced education in computer science and has developed a solid academic foundation that supports both teaching and research leadership. With extensive professional experience in higher education, Dr. Yingxia Chen has contributed actively to scholarly publishing and research supervision. Research interests include artificial intelligence, computer vision, pattern recognition, and deep learning. With 13 documents, 66 citations, and an h-index of 6, Dr. Yingxia Chen demonstrates sustained research impact and academic excellence.

Citation Metrics (Scopus)

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Citations
66

Documents
13

h-index
6

Featured Publications

Mohd Khaled Shambour | Computer Science | Best Paper Award

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Prof. Mohd Khaled Shambour | Computer Science | Best Paper Award  

Professor at Middle East University | Jordan

Prof. Mohd Khaled Shambour is an accomplished academic and researcher in the field of computer science, widely recognized for his contributions to intelligent systems and computational optimization. He holds advanced academic qualifications in computer science and intelligent system techniques and has gained extensive experience through senior academic and research roles within higher education and research institutions. His professional work reflects strong expertise in artificial intelligence, optimization techniques, metaheuristic algorithms, data science, scheduling, and timetabling, with a clear emphasis on solving complex real-world problems through innovative computational approaches. Prof. Mohd Khaled Shambour has produced a substantial body of high-impact scholarly work, authoring 40 research documents that have attracted 1,189 citations across 1,039 citing documents, resulting in an h-index of 16, underscoring both productivity and sustained research influence. His research is noted for methodological rigor, interdisciplinary relevance, and practical applicability, and he continues to contribute meaningfully to advancing knowledge, innovation, and academic excellence in computer science.

Citation Metrics (Verified)

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Citations
1,189

Documents
40

h-index
16

Featured Publications

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
1,379

Documents
161

h-index
21

Top Publications

Eshaa Gogia | Computer Science | Best Researcher Award

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Ms. Eshaa Gogia | Computer Science | Best Researcher Award 

Student Researcher at Rutgers University | United States

Ms. Eshaa Gogia is an emerging professional in the field of data science, combining a strong academic foundation with diverse industry and research experiences. She has developed expertise in machine learning, data engineering, and applied analytics, with contributions spanning healthcare, computational biology, and natural language processing. Her projects highlight a blend of technical rigor and innovation, addressing complex real-world challenges through scalable solutions. With a consistent record of academic excellence and practical achievements, Ms. Eshaa Gogia demonstrates both leadership and creativity in data-driven problem-solving, making her a strong candidate for recognition in the field of computational sciences.

Profile:

Google Scholar

Education:

Ms. Eshaa Gogia pursued her Master’s in Data Science at Rutgers University, where she built advanced competencies in statistical modeling, predictive analytics, and data systems. She also completed her Bachelor of Technology in Computer Science from Bhilai Institute of Technology, developing her foundational skills in algorithms, programming, and database management. Throughout her education, she demonstrated consistent academic performance and complemented coursework with applied research. Her training spans theoretical foundations, cutting-edge machine learning frameworks, and practical implementation across multiple platforms, equipping her with a robust educational background to excel in multidisciplinary data-driven environments.

Experience:

Ms. Eshaa Gogia has worked across academic research centers, corporate firms, and healthcare organizations. As a Data Engineer at Florida Blue, she designed scalable ETL pipelines and automated ingestion systems, enhancing predictive analytics for healthcare. At the Center of Computational & Integrative Biology, she served as a Program Analyst, managing large ecological datasets and developing dashboards to visualize biodiversity and precipitation trends. Her internship at CGI involved building innovative generative AI tools, including a drug label translation chatbot and a data insights bot. This blend of research and industry practice illustrates her ability to bridge theory with impactful execution.

Research Interest:

Ms. Eshaa Gogia’s research interests focus on applied machine learning, bioinformatics, and healthcare analytics. She is particularly drawn to projects at the intersection of data science and human well-being, with experience in neuroimaging for early detection of brain aging and predictive modeling for ecological systems. Her work demonstrates a passion for advancing scientific discovery through computational innovation, from analyzing drug interactions with AI-driven models to uncovering mortality trends through census health data. These interests reveal her commitment to using data for societal impact, advancing knowledge in health, sustainability, and biomedical research through computational approaches.

Awards and Honors:

Ms. Eshaa Gogia has earned recognition for her consistent excellence, reflected in academic performance and professional certifications. She has completed the AWS Foundational Certificate, Google Data Analytics, and Docker Foundations Professional Certification, highlighting her commitment to continuous professional growth. Her achievements in industry roles, including efficiency improvements in large-scale data processing and innovative AI-driven tools, earned appreciation from research teams and corporate mentors. These honors emphasize her adaptability, technical expertise, and dedication to building impactful solutions. With her achievements spanning both academia and industry, Ms. Eshaa Gogia represents an outstanding candidate for award consideration.

Publications:

  • Title: Automated Subregional Hippocampus Segmentation Using 3D CNNs: A Computational Framework for Brain Aging Biomarker Analysis
    Year of Publication: 2025

  • Title: A Review on Technological Innovation in Business and Organization Continuity
    Year of Publication: 2022

  • Title: A Review on Security Sensor Alert System
    Year of Publication: 2021

Conclusion:

Ms. Eshaa Gogia exemplifies the qualities of an emerging leader in data science through her educational achievements, research innovation, and impactful professional experience. Her ability to integrate advanced computational methods with pressing real-world challenges reflects a rare blend of technical expertise and social responsibility. With publications spanning health, ecology, and artificial intelligence, she demonstrates intellectual versatility and a forward-looking vision. Her certifications, research accomplishments, and applied problem-solving further enhance her profile, positioning her as a strong nominee for recognition. Ms. Eshaa Gogia’s trajectory underscores her potential to continue making meaningful contributions to science and society.