Daniel Condurache | Augmented Analytics | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Daniel Condurache
Affiliation Technical University Of Iasi
Country Romania
Scopus ID 15841500000
Documents 91
Citations 749
h-index 16
Subject Area Augmented Analytics
Event Research Data Analysis Awards
ORCID 0000-0001-9287-8387

Daniel Condurache
Technical University Of Iasi, Romania

Daniel Condurache is affiliated with the Technical University Of Iasi and has contributed to research in augmented analytics and related computational disciplines. His scholarly output demonstrates sustained academic engagement through peer-reviewed publications, citation impact, and interdisciplinary collaborations that support innovation in data-driven research methodologies.[1]

Abstract

This article summarizes the academic profile of Daniel Condurache, highlighting measurable research achievements, publication activity, and scholarly influence. The profile reflects recognized contributions to augmented analytics and related computational research supported by bibliometric indicators.[2]

Keywords

Augmented Analytics, Data Analysis, Machine Intelligence, Computational Methods, Artificial Intelligence, Scientific Research, Research Metrics, Bibliometrics.

Introduction

Daniel Condurache has established an active academic career through multidisciplinary investigations that integrate analytical techniques with engineering applications. His publications demonstrate continued engagement with evolving research challenges and international scientific communication.[3]

Research Profile

With 91 indexed publications, 749 citations, and an h-index of 16, his academic record reflects consistent scholarly productivity. These indicators demonstrate sustained visibility within the international research community and continued citation recognition.[1]

Research Contributions

His research contributes to augmented analytics by combining computational methodologies with practical engineering solutions. These studies encourage improved analytical performance, efficient data interpretation, and broader interdisciplinary collaboration.[4]

Publications

The publication portfolio includes peer-reviewed journal articles and conference papers addressing computational intelligence, engineering analysis, and data-centric methodologies. These works collectively demonstrate sustained scientific productivity and knowledge dissemination.[5]

Research Impact

Citation performance and publication consistency indicate meaningful academic influence within relevant research communities. The documented metrics provide quantitative evidence supporting the significance and continued visibility of his scholarly contributions.[1]

Award Suitability

The documented publication record, citation impact, and interdisciplinary research activities align with the evaluation criteria commonly associated with the Best Researcher Award. His scholarly achievements illustrate consistent academic excellence and professional dedication.

Conclusion

Daniel Condurache’s research profile reflects continuous scholarly development supported by recognized publications and measurable academic impact. His sustained contributions to augmented analytics position him as a noteworthy researcher within contemporary scientific research.

References

  1. Elsevier. (n.d.). Scopus author details: Daniel Condurache, Author ID 15841500000.
    https://www.scopus.com/authid/detail.uri?authorId=15841500000
  2. ORCID. (n.d.). Researcher Identifier Record.
    https://orcid.org/0000-0001-9287-8387
  3. Condurache, D. (2026). A unified theory of generalized Bresse properties in higher-order kinematics of rigid body and multibody systems. Mechanism and Machine Theory. Advance online publication.
  4. Condurache, D., Cojocari, M., & Popa, I. (2025). Higher-order kinematics of planar rigid motion by Euclidean tensors and complex algebra: An overview. In Higher-Order Kinematics of Planar Rigid Motion by Euclidean Tensors and Complex Algebra
    https://link.springer.com/chapter/10.1007/978-3-031-87537-3_6
  5. Condurache, D., & Cojocari, M. (2025). Hyper-state of multibody systems and trident quaternions. In Volume 5: IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA); Mechanisms and Robotics Conference (MR).

Gosha Colquhoun | Innovation in Data Analysis | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Gosha Colquhoun
Edinburgh Napier University, United Kingdom

Gosha Colquhoun
Affiliation Edinburgh Napier University
Country United Kingdom
Scopus ID 60079888200
Documents 5
Citations 1
h-index 1
Subject Area Innovation in Data Analysis
Event Research Data Analysis Awards
ORCID 0000-0003-3857-2090

Gosha Colquhoun is affiliated with Edinburgh Napier University and contributes to research associated with innovation in data analysis and interdisciplinary academic investigation. The available scholarly profile demonstrates participation in internationally indexed publications and emerging research visibility through Scopus-indexed outputs and citation records.[1]

Abstract

This article summarizes the academic profile of Gosha Colquhoun and highlights scholarly activities related to innovation in data analysis. The overview reflects publication records, research engagement, and academic visibility documented through recognized indexing platforms.[1]

Keywords

Innovation, Data Analysis, Research Methods, Academic Publications, Scopus, Knowledge Discovery, Interdisciplinary Research, Research Impact.

Introduction

Innovation in data analysis supports evidence-based research across multiple disciplines by enabling accurate interpretation of complex information. Researchers working in this area contribute to methodological development and practical applications supported by scholarly publication.[2]

Research Profile

Gosha Colquhoun’s research profile includes five Scopus-indexed documents with an h-index of one and an emerging citation record. The available metrics indicate active participation in academic research and ongoing scholarly development.[1]

Research Contributions

The published work reflects contributions to innovation-oriented research with emphasis on analytical approaches and collaborative academic inquiry. These studies enhance understanding within relevant research domains while supporting future investigations.[3]

Publications

The publication portfolio demonstrates participation in peer-reviewed scholarly communication indexed through Scopus. These publications contribute to the dissemination of research findings and encourage continued academic collaboration.[1]

Research Impact

Current citation indicators represent an early stage of measurable academic impact while providing a foundation for future scholarly recognition. Continued publication activity is expected to strengthen research visibility and academic influence.[4]

Award Suitability

The research profile demonstrates commitment to innovation and scholarly dissemination, aligning with the objectives of the Research Data Analysis Awards. Academic productivity, institutional affiliation, and indexed publications provide a suitable basis for recognition.[5]

Conclusion

Gosha Colquhoun represents an emerging researcher contributing to innovation in data analysis through scholarly publications and academic engagement. Continued research activity is expected to expand both research impact and professional recognition within the academic community.

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Gosha Colquhoun, Author ID 60079888200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60079888200
  2. ORCID. (n.d.). Research profile of Gosha Colquhoun.
    https://orcid.org/0000-0003-3857-2090
  3. Al Bayrakdar, A., Colquhoun, G., Dragone, M., McConnell, A., & Paterson, R. (2026). Co-designing a socially assistive robot intervention for diabetes self-management: Perspectives of adults living with diabetes. International Journal of Social Robotics. Advance online publication.
    https://link.springer.com/article/10.1007/s12369-026-01383-1
  4. Mawson, P., Morton, M., Walmsley, Z., Wafer, R., Hancock, H. C., Mossop, H., Al-Ashmori, S., Emerson, L. M., Smith, J., Colquhoun, G., et al. (2026). SHORTER trial: Protocol for a pragmatic, multicentre, randomised controlled trial of short-duration antibiotic therapy for critically ill patients with sepsis. BMJ Open. Advance online publication.
    https://bmjopen.bmj.com/content/16/3/e117142
  5. Colquhoun, G., Smith, J., & Ring, N. (2026). Invisible yet indispensable: Why clinical research nursing remains a neglected policy priority. Journal of Clinical Nursing. Advance online publication.

Raghavendran Prabakaran | Machine Learning | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Raghavendran Prabakaran
Easwari Engineering College, India

Raghavendran Prabakaran
Affiliation Easwari Engineering College
Country India
Scopus ID 58670546100
Documents 53
Citations 310
h-index 11
Subject Area Machine Learning
Event Research Data Analysis Awards
ORCID 0009-0001-7333-6555

Raghavendran Prabakaran recognizes scholarly excellence demonstrated through sustained research productivity, scientific impact, and contributions to the advancement of machine learning. Raghavendran Prabakaran has established an active research profile through peer-reviewed publications, interdisciplinary collaboration, and measurable citation performance. His academic achievements reflect continued engagement in applied artificial intelligence and data-driven research methodologies.[1]

Abstract

Raghavendran Prabakaran has contributed to machine learning research through scholarly publications, citation impact, and interdisciplinary collaboration. His work reflects a consistent focus on computational intelligence, predictive analytics, and intelligent systems while supporting practical applications across engineering disciplines.[2]

Keywords

Machine Learning, Artificial Intelligence, Predictive Analytics, Data Science, Intelligent Systems, Pattern Recognition, Research Analytics.

Introduction

Machine learning continues to influence modern engineering, healthcare, automation, and business analytics by enabling intelligent decision-making from complex datasets. Researchers with sustained publication records contribute to both theoretical understanding and practical innovation while strengthening scientific collaboration.[3]

Research Profile

The research profile demonstrates 53 indexed publications, 310 citations, and an h-index of 11 according to Scopus metrics. These indicators reflect sustained scholarly activity and growing academic visibility within the machine learning research community.[1]

Research Contributions

Research emphasizes predictive modelling and intelligent algorithm development for solving practical engineering problems while improving computational efficiency through data-driven learning approaches. Contributions explore AI-based decision support systems integrating analytical models with automation techniques to enhance reliability, scalability, and real-world implementation.

Publications

The publication portfolio consists of peer-reviewed journal articles and conference papers indexed in international scholarly databases. The body of work demonstrates continuing engagement with emerging topics in artificial intelligence and machine learning.[4]

Research Impact

Citation performance, publication consistency, and interdisciplinary collaborations indicate measurable academic influence. The research outputs contribute to knowledge dissemination while supporting future developments in intelligent computing technologies.

Award Suitability

Based on publication metrics, citation record, research quality, and ongoing scholarly engagement, the profile aligns with evaluation criteria commonly applied for academic innovation and research excellence awards. The combination of productivity and scientific impact supports recognition within international research communities.[6]

Conclusion

Raghavendran Prabakaran demonstrates sustained academic productivity through quality publications, measurable citation impact, and contributions to machine learning research. The overall scholarly profile reflects continued commitment to research excellence, innovation, and knowledge advancement within engineering and computational sciences.

References

  1. Elsevier. (n.d.). Scopus author details: Raghavendran Prabakaran, Author ID 58670546100.
    https://www.scopus.com/authid/detail.uri?authorId=58670546100
  2. ORCID. (n.d.). ORCID record for Raghavendran Prabakaran.
    https://orcid.org/0009-0001-7333-6555
  3. Parthiban, Y., Prabakaran, R., Thakur, D., & Madhumitha, S. (2026). Application of Upadhyaya transforms with machine learning for predictive and analytical solutions in complex systems. Transactions on Computational Modeling and Intelligent Systems.
    https://tcmis.org/index.php/files/article/view/23
  4. Tripathi, S., Gochhait, S., & Prabakaran, R. (2026). Neuromarketing applications and ethical implications in consumer behavior analysis. In Book chapter.
    https://www.igi-global.com/gateway/chapter/404055
  5. Prabakaran, R., Parthiban, Y., Thiravidarani, J., & Madhumitha, S. (2026). Application of fractional integro-differential equations in paracetamol drug release modeling. Oriental Journal of Chemistry.
    http://dx.doi.org/10.13005/ojc/420208

Dimitris Kavroudakis | Machine Learning Applications | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Dimitris Kavroudakis
University of the Aegean, Greece

Dimitris Kavroudakis
Affiliation University of the Aegean
Country Greece
Scopus ID 54966735900
Documents 53
Citations 490
h-index 12
Subject Area Machine Learning Applications
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0001-5782-3049

The Innovative Research Award recognizes researchers whose scholarly contributions demonstrate methodological advancement, interdisciplinary relevance, and measurable impact. Dimitris Kavroudakis of the University of the Aegean has established a research profile centered on machine learning applications, geospatial analytics, environmental monitoring, visualization technologies, and geoeducation. His publication record reflects sustained engagement with data-driven approaches for addressing contemporary scientific and societal challenges.[1]

Abstract

This article presents an overview of the academic achievements of Dimitris Kavroudakis, highlighting contributions in machine learning, environmental sensing, spatial analysis, and geospatial education. Through a combination of applied research and interdisciplinary collaboration, his work demonstrates the integration of advanced analytical techniques into real-world decision-making environments. Recent studies emphasize predictive modeling, sensor analytics, virtual reality applications, and geospatial visualization methodologies.[2]

Keywords

Machine Learning, Geospatial Analytics, Environmental Monitoring, Geoeducation, Data Visualization, Heritage Conservation, Spatial Intelligence, Predictive Modeling.

Introduction

Research in data-intensive disciplines increasingly depends on the integration of machine learning with spatial and environmental datasets. Dimitris Kavroudakis has contributed to this evolving landscape through studies that connect computational methods with geographic and environmental applications. His scholarly output reflects an emphasis on evidence-based analysis, innovation in visualization, and practical implementation of analytical frameworks.[3]

Research Profile

Affiliated with the University of the Aegean, Kavroudakis has developed a multidisciplinary research portfolio spanning machine learning applications, geospatial information systems, environmental monitoring, educational technologies, and spatial visualization. His Scopus-indexed publication record and citation metrics indicate consistent academic engagement and influence across related research communities.[1]

Research Contributions

  • Development of machine learning models for forecasting indoor microclimate conditions in heritage conservation environments.
  • Research on spatio-temporal approaches for distinguishing sensor anomalies from environmental events.
  • Applications of virtual reality technologies in geoeducation and geoscience communication.
  • Advancement of multiscale visualization methods for spatial motion and geospatial datasets.

Publications

  • Machine Learning-Based Forecasting of Indoor Microclimate Conditions for Heritage Conservation.
  • Distinguishing Sensor Errors from Environmental Events During Wildfire Pollution in Athens.
  • Virtual Reality in Geoeducation: The Case of the Lesvos Geopark.
  • Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry.

Research Impact

The impact of Kavroudakis’s work is reflected in its applicability to environmental assessment, cultural heritage management, geospatial education, and data visualization. By incorporating machine learning and advanced analytics into practical contexts, his research contributes to the broader adoption of intelligent systems for scientific and policy-oriented decision support.[4]

Award Suitability

Dimitris Kavroudakis demonstrates characteristics commonly associated with recognition through the International Research Data Analysis Excellence & Awards program. These include interdisciplinary scholarship, measurable research impact, methodological innovation, and continued contribution to machine learning applications within environmental and geospatial domains. His publication portfolio provides evidence of both academic rigor and practical relevance.[5]

Conclusion

The academic record of Dimitris Kavroudakis reflects a sustained commitment to advancing machine learning applications and geospatial research methodologies. Through contributions spanning environmental analytics, educational innovation, and spatial intelligence, his work represents a noteworthy example of contemporary interdisciplinary scholarship deserving of professional recognition within international research communities.

References

  1. Elsevier. (n.d.). Scopus author details: Dimitris Kavroudakis, Author ID 54966735900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=54966735900
  2. Applied Sciences. (2026). Machine Learning-Based Forecasting of Indoor Microclimate Conditions for Heritage Conservation.
    DOI: https://doi.org/10.3390/app16126092
  3. European Journal of Geography. (2026). Distinguishing Sensor Errors from Environmental Events.
    DOI: https://doi.org/10.48088/ejg.s.zaf.17.1.212.230
  4. Interactive Learning Environments. (2024). Virtual Reality in Geoeducation: The Case of the Lesvos Geopark.
    DOI:https://doi.org/10.1080/10494820.2024.2374399
  5. ISPRS International Journal of Geo-Information. (2024). Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry.
    DOI: https://doi.org/10.3390/ijgi13070236
  6. International Research Data Analysis Excellence & Awards. (n.d.). Award program information.
    researchdataanalysis.com

Noureddine Mhadhbi | Data Analysis | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award


Noureddine Mhadhbi
Faculty of Sciences of Sfax, Tunisia

Noureddine Mhadhbi
Affiliation Faculty of Sciences of Sfax
Country Tunisia
Scopus ID 55062432600
Documents 53
Citations 416
h-index 11
Subject Area Data Analysis
Event International Research Data Analysis Excellence & Awards

The Best Researcher Award recognition highlights the scholarly achievements of Noureddine Mhadhbi, a researcher affiliated with the Faculty of Sciences of Sfax, Tunisia. His academic profile demonstrates sustained contributions to data analysis, crystallographic characterization, materials science, molecular modeling, and interdisciplinary research involving structural chemistry and computational investigations. Through a growing publication portfolio and measurable citation impact, his work reflects continued engagement with contemporary scientific challenges and international research collaboration.[1]

Abstract

Noureddine Mhadhbi has established a research profile characterized by multidisciplinary scientific inquiry and quantitative analytical methodologies. His publications emphasize structural characterization, crystallography, computational chemistry, molecular docking, materials analysis, and interpretation of complex experimental datasets. The combination of laboratory investigations with advanced analytical techniques has contributed to the understanding of inorganic and hybrid materials while supporting broader scientific applications.[2]

Keywords

Data Analysis, Crystallography, Materials Science, Molecular Docking, Structural Chemistry, Computational Modeling, Research Excellence.

Introduction

Modern scientific research increasingly relies on robust data interpretation and interdisciplinary collaboration. Noureddine Mhadhbi’s scholarly activities illustrate the integration of structural analysis, spectroscopy, computational approaches, and experimental validation. His work contributes to the evaluation of chemical systems and provides evidence-based insights relevant to materials development and functional characterization.[3]

Research Profile

According to available scholarly metrics, the researcher has accumulated 53 indexed documents, 416 citations, and an h-index of 11. His academic portfolio demonstrates sustained productivity across peer-reviewed journals and collaborative research projects. Areas of specialization include crystal engineering, hybrid materials, inorganic chemistry, computational studies, and data-driven evaluation of structure–property relationships.[1]

Research Contributions

  • Investigation of zinc-based and copper-based hybrid compounds through crystallographic and spectroscopic methods.
  • Application of molecular docking and computational analysis for biological activity assessment.
  • Development of structure–property relationships using advanced analytical and modeling techniques.
  • Contribution to electrochemical and environmental remediation studies involving complex materials.

Publications

  • Structure–biological activity relationships in a zinc(II) pyrazole halide complex via noncovalent interactions, molecular docking, and antimicrobial studies (RSC Advances, 2026).
  • Integrated structural, vibrational, thermal, and optical characterization of a zinc-based organic–inorganic hybrid with DFT and molecular docking insights (RSC Advances, 2026).
  • Structural, optical, and electrochemical properties of a new 1D copper(II) halometalate for dopamine detection (Dalton Transactions, 2026).
  • Structural, optical, and biological investigations of a hybrid tetrachloridozincate compound (Journal of Molecular Structure, 2026).
  • High-efficiency electro-Fenton mineralization of triclosan using a novel iron(III) complex (RSC Advances, 2026).

Research Impact

The citation performance and publication record indicate meaningful engagement within the scientific community. The research output demonstrates relevance to analytical sciences, materials characterization, environmental applications, and computational investigations. Through collaborative publications and interdisciplinary methodologies, the researcher contributes to the dissemination of reproducible scientific knowledge and quantitative evaluation practices.[4]

Award Suitability

Noureddine Mhadhbi’s profile aligns with the objectives of the International Research Data Analysis Excellence & Awards program. His documented research productivity, citation impact, interdisciplinary collaborations, and application of analytical methodologies demonstrate characteristics commonly associated with scholarly excellence. The breadth of contributions and sustained publication activity support consideration for recognition within an international academic framework.[5]

Conclusion

The academic record of Noureddine Mhadhbi reflects a commitment to rigorous scientific investigation, data analysis, and collaborative research. His contributions across crystallography, materials science, computational modeling, and analytical methodologies provide evidence of sustained scholarly engagement. These accomplishments support his profile as a noteworthy candidate for academic recognition and research excellence awards.[6]

References

  1. Elsevier. (n.d.). Scopus author details: Noureddine Mhadhbi, Author ID 55062432600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=55062432600
  2. RSC Publishing. (2026). Structure–biological activity relationships in a zinc(II) pyrazole halide complex.
  3. Royal Society of Chemistry. (2026). Integrated structural and computational characterization studies.
  4. Dalton Transactions. (2026). Structural, optical, and electrochemical properties of copper halometalates.
  5. International Research Data Analysis Excellence & Awards. (n.d.). Award evaluation framework and recognition criteria.
  6. Journal of Molecular Structure. (2026). Research contributions in hybrid materials and structural analysis.

Baliira Kalyebara | Financial Data Analysis | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Baliira Kalyebara
American University of Ras Al Khaimah, United Arab Emirates

Baliira Kalyebara
Affiliation American University of Ras Al Khaimah
Country United Arab Emirates
Scopus ID 56413038400
Documents 20
Citations 33
h-index 4
Subject Area Financial Data Analysis
Event Research Data Analysis Awards

Baliira Kalyebara recognizes scholarly achievement and sustained contributions to research and knowledge development. This article presents an academic overview of Baliira Kalyebara, whose work has contributed to the field of financial data analysis through research activities, publications, and scholarly engagement. The profile has been prepared in the context of the Research Data Analysis Awards and follows a neutral academic style supported by publicly accessible scholarly sources.[1]

Abstract

Baliira Kalyebara is a researcher affiliated with the American University of Ras Al Khaimah whose academic work is associated with financial data analysis, business analytics, and evidence-based decision-making. Scholarly metrics indicate a developing research profile supported by peer-reviewed publications, citation activity, and interdisciplinary engagement. This article summarizes the research background, scholarly contributions, publication record, and potential suitability for recognition through the Innovative Research Award.[1][2]

Keywords

Financial Data Analysis, Research Analytics, Business Intelligence, Scholarly Research, Data-Driven Decision Making, Innovation in Research, Predictive Analytics, Statistical Modeling, Quantitative Research, Financial

Introduction

Financial data analysis has become an important component of modern research, enabling organizations and institutions to evaluate trends, risks, and opportunities using quantitative evidence. Researchers operating in this domain frequently contribute to analytical frameworks, statistical methodologies, and applied business intelligence solutions. Baliira Kalyebara’s scholarly activities reflect participation in this evolving research landscape and demonstrate engagement with contemporary analytical challenges.[1]

Research Profile

Baliira Kalyebara is affiliated with the American University of Ras Al Khaimah in the United Arab Emirates. According to scholarly indexing databases, the researcher has produced twenty indexed documents and accumulated citation activity that reflects academic visibility within relevant subject areas. The documented h-index indicates measurable research influence and engagement with scholarly communities.[1]

Research Contributions

The research contributions associated with Baliira Kalyebara are aligned with analytical approaches used in finance, management, and organizational decision-making. Such contributions support the broader objective of transforming complex datasets into actionable knowledge. Through scholarly dissemination and participation in academic discourse, the researcher has contributed to the development of analytical understanding within applied research environments.[1][2]

Publications

The research record includes multiple scholarly publications indexed in international databases. These works collectively contribute to discussions surrounding analytical methodologies, business research, and financial data interpretation. Representative publications are discoverable through the author’s Scopus and Google Scholar profiles.[1][3]

Research Impact

Research impact may be evaluated using publication productivity, citation indicators, scholarly visibility, and influence on future studies. Available metrics show that Baliira Kalyebara’s work has received citations from other researchers, indicating engagement with the published literature and recognition within academic networks.[1]

Award Suitability

The Innovative Research Award recognizes researchers whose scholarly activities contribute to knowledge advancement and analytical excellence. Based on available publication records, citation metrics, institutional affiliation, and engagement with financial data analysis, Baliira Kalyebara demonstrates characteristics commonly associated with academic recognition programs. Evaluation would ordinarily consider originality, scholarly rigor, research dissemination, and measurable academic impact.[1][2]

Conclusion

Baliira Kalyebara’s academic profile reflects active participation in research related to financial data analysis and associated analytical disciplines. The documented publication output, citation record, and institutional affiliation indicate ongoing engagement with scholarly inquiry. As a candidate considered within the framework of the Research Data Analysis Awards, the researcher represents a profile characterized by academic productivity, evidence-based investigation, and contributions to analytical knowledge development.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Baliira Kalyebara, Author ID 56413038400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56413038400
  2. Research Data Analysis Awards. (n.d.). Innovative Research Award evaluation framework and recognition criteria.
    https://researchdataanalysis.com/
  3. Google Scholar. (n.d.). Scholar profile and citation metrics for Baliira Kalyebara.
    https://scholar.google.com/citations?user=R_oLej4AAAAJ&hl=en&oi=ao
  4. Aziz, M. I. A., Chebab, D., Kalyebara, B., & Nor, S. M. (2026). Safe havens in turbulent times: Assessing the role of gold and the USD against global stock market indices. Journal of Risk and Financial Management, 19(5), 308.
    https://www.mdpi.com/1911-8074/19/5/308
  5. Alafeef, M. A., Kalyebara, B., Kalbouneh, N. Y., Abuoliem, N., Bani Yousef, A. N., & Al-Afeef, M. A. M. (2024). The impact of FINTECH on banking performance: Evidence from Middle Eastern countries. International Journal of Data and Network Science, 8(4), 2219–2230. https://www.growingscience.com/ijds/Vol8/ijdns_2024_115.pdf

Zahra Lakdawala | Machine Learning and AI Applications | Research Excellence Award

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Dr. Zahra Lakdawala | Machine Learning and AI Applications | Research Excellence Award

Fraunhofer IWES | Germany

Zahra Lakdawala is an accomplished industrial mathematician and Senior Research Scientist at Fraunhofer Institute for Wind Energy Systems, with extensive expertise in applied mathematics, computational fluid dynamics, and AI-driven modeling. She earned her Ph.D. from the Technical University of Kaiserslautern, focusing on multiscale filtration problems. Her research integrates numerical methods, physics-informed neural networks, and large-scale simulations for industrial and environmental applications, including groundwater management and wind energy. With strong academic, industry, and international research experience, she has contributed to advanced software development, interdisciplinary projects, and high-impact scientific publications.

Citation Metrics (Scopus)

400
300
200
100
0

Citations
122

Documents
17

h-index
6

Citations

Documents

h-index


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Prof. Dr. Xiaoxiao Cheng | Innovation In Data Analysis | Research Excellence Award

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Prof. Dr. Xiaoxiao Cheng | Innovation In Data Analysis | Research Excellence Award

Soochow University | China

Dr. Xiaoxiao Cheng is a Professor at Soochow University specializing in polymer synthesis, living polymerization, and chiral assembly. He earned his Ph.D. in Materials Science from Soochow University, with international research experience at Hokkaido University and Nagoya University in Japan. Following postdoctoral research, he served as Assistant Professor and Principal Investigator at the Nano Life Science Institute, Kanazawa University. Dr. Cheng has published nearly 60 high-impact papers, many in leading journals, and holds multiple patents in advanced polymer materials. A recipient of several prestigious awards and research grants, he is actively involved in editorial roles and international scientific collaborations.

Citation Metrics (Scopus)

3000
2000
1000
50
0

Citations
1,351

Documents
58

h-index
22

Citations

Documents

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Nasser Ahmed | Machine Learning Applications | Worldwide Excellence in Research Analytics Advancement Award

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National Research Institute of Astronomy and Geophysics | Egypt

Assist. Prof. Dr. Nasser Ahmed | Machine Learning Applications | Worldwide Excellence in Research Analytics Advancement Award

National Research Institute of Astronomy and Geophysics | Egypt

Assoc. Prof. Dr. Nasser Mohamed Ahmed is an accomplished astrophysicist at the National Research Institute of Astronomy and Geophysics (NRIAG), Egypt, with extensive expertise in computational astrophysics and X-ray astronomy. He earned his Ph.D. from the University of Groningen, focusing on simulations of cooling flows in galaxy clusters using advanced hydrodynamic modeling. His research spans plasma dynamics, galaxy formation, and data analysis using modern tools such as Python, FLASH, and X-ray observatories. Dr. Ahmed has contributed to numerous international projects, established computational facilities, and published widely in reputable journals, demonstrating significant impact in both theoretical and observational astronomy.

Citation Metrics (Scopus)

50
35
30
15
0

Citations
47

Documents
16

h-index
4

Citations

Documents

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Sun Vertical Depressions and Their Effects on the Morning Twilight Phases in Egypt
– Springer Proceedings in Physics, 2025

KAMURAN GÖRGÜN | Interdisciplinary Data Insights | Women Researcher Award

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Mrs. KAMURAN GÖRGÜN | Interdisciplinary Data Insights | Women Researcher Award

Skisehir Osmangazi University | Turkey

Assoc. Prof. Dr. Kamuran Görgün is a distinguished academic in chemistry at Eskişehir Osmangazi University, with extensive expertise in organic synthesis, nanomaterials, and spectroscopic analysis. She earned her PhD, MSc, and BSc in Chemistry from the same institution and has progressed from research assistant to associate professor. Her research focuses on semiconductor materials, dye-sensitized solar cells, and advanced molecular design. With over 90 publications, significant citations, and multiple funded projects, she has made notable contributions to materials science and renewable energy research. Dr. Görgün is also actively involved in teaching undergraduate and postgraduate courses and mentoring emerging researchers.

Citation Metrics (Scopus)

300
200
100
10
0

Citations
258

Documents
25

h-index
9

Citations

Documents

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