Yingjie Yang | Data Science | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Yingjie Yang – De Montfort University

Yingjie Yang
Affiliation De Montfort University
Country United Kingdom
Scopus ID 7409384730
Documents 236
Citations 5720
h-index 38
Subject Area Data Science
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0003-4525-5624

Yingjie Yang is a researcher affiliated with De Montfort University whose scholarly profile is associated with Data Science and interdisciplinary research involving data-driven methods. The available Scopus indicators record 236 documents, 5720 citations and an h-index of 38, providing a quantitative basis for assessing publication activity and scholarly influence in relation to the Best Researcher Award. [1]

Abstract

This article presents an academic recognition profile of Yingjie Yang, affiliated with De Montfort University, for consideration under the Best Researcher Award. Available bibliometric indicators, including 236 documents, 5720 citations and an h-index of 38, are considered alongside research activity in Data Science and related scholarly contributions. [1]

Keywords

Best Researcher Award; Yingjie Yang; De Montfort University; Data Science; Research Data Analysis; Bibliometric Assessment; Scholarly Publications; Citation Impact; Research Excellence; Academic Recognition. [2]

Introduction

Academic recognition commonly considers research productivity, citation performance, publication continuity and contribution to a specialist field. Yingjie Yang’s available scholarly indicators provide a measurable profile for examining research achievement within Data Science. Such evaluation supports transparent comparison of documented academic output and influence using established bibliometric information. [3]

Research Profile

Yingjie Yang is affiliated with De Montfort University in the United Kingdom and is associated with research in Data Science. The available Scopus profile records 236 documents, 5720 citations and an h-index of 38. These indicators collectively describe sustained scholarly activity and a documented record of academic visibility. [1]

Research Contributions

The research profile demonstrates contributions represented through a substantial body of indexed scholarly documents. Within the Data Science context, such work contributes to the continuing development, application and evaluation of data-driven knowledge. The accumulated publication record also indicates engagement with research communication and dissemination across relevant academic channels. [1]

Publications

The available Scopus record lists 236 documents associated with the researcher profile, indicating sustained publication activity. Indexed publications provide an important basis for evaluating scholarly productivity because they document research dissemination and enable subsequent citation analysis. Specific publication details should be interpreted through the linked author profile and corresponding publisher records.[2]

Research Impact

The profile records 5720 citations and an h-index of 38, indicating that the published work has received measurable scholarly attention. Citation indicators are not complete measures of research quality, but they provide useful evidence of academic visibility and uptake. Their interpretation is strengthened when considered with disciplinary context and documented research outputs. [3]

Award Suitability

Based on the available publication and citation indicators, Yingjie Yang presents a documented academic profile relevant to consideration for the Best Researcher Award. The combination of publication volume, citation performance and an h-index of 38 provides objective evidence for assessment. Final recognition should remain subject to the event’s eligibility criteria and review process. [1]

Conclusion

The available research profile of Yingjie Yang reflects sustained scholarly publication activity and measurable citation impact in association with Data Science. With 236 documents, 5720 citations and an h-index of 38, the profile provides evidence suitable for structured academic evaluation. These documented indicators support informed consideration for research recognition. [1]

 References

  1. Predicting the number of care beds for older people by a novel grey Verhulst cosine self-memory model: two case studies of Jiangsu and Shanghai, China.
    https://link.springer.com/article/10.1186/s12877-026-07337-6
  2. Interpretable Temporal Graph Attention Network and Cross-Modal Fusion for Early Rumor Detection.
    https://www.researchgate.net/publication/405011816_Interpretable_Temporal_Graph_Attention_Network_and_Cross-Modal_Fusion_for_Early_Rumor_Detection
  3. A novel time-varying Wiener process for adaptive RUL prediction under multiple uncertainties
    https://www.researchgate.net/publication/401712852_A_novel_time-varying_Wiener_process_for_adaptive_RUL_prediction_under_multiple_uncertainties

Alfatma Salama | Data Analysis Innovation | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Alfatma Salama
Researcher Alfatma Salama
Affiliation Princess Nourah bint Abderhman University
Country Saudi Arabia
Scopus ID 57226770697
Documents 1
Citations 1
h-index 1
Subject Area Data Analysis Innovation
Event Research Data Analysis Awards
ORCID 0000-0002-9591-0853

Alfatma Salama
Princess Nourah bint Abderhman University

Alfatma Salama is affiliated with Princess Nourah bint Abderhman University, Saudi Arabia. Her scholarly activities focus on data analysis innovation, emphasizing analytical methodologies that contribute to evidence-based research and practical decision-making. Her publication record demonstrates an emerging contribution to interdisciplinary data-driven studies and academic collaboration.[1]

Abstract

This article summarizes the academic profile of Alfatma Salama, highlighting contributions to data analysis innovation and research methodology. Her work supports systematic interpretation of research data and promotes reliable analytical practices across scientific disciplines.[2]

Keywords

Data Analysis Innovation, Statistical Modeling, Research Analytics, Quantitative Analysis, Decision Support Systems, Data Interpretation, Scientific Research, Academic Analytics, Information Management, Digital Research.

Introduction

Modern research increasingly depends on advanced analytical approaches to transform complex datasets into meaningful knowledge. Contributions in this area strengthen research quality, reproducibility, and evidence-based scientific decision-making.[3]

Research Profile

Alfatma Salama has developed a focused research profile centered on innovative data analysis techniques. Her academic affiliation supports interdisciplinary collaboration and encourages the application of analytical methods to diverse research challenges.[1]

Research Contributions

Her scholarly contribution emphasizes improving analytical accuracy, research transparency, and interpretation of scientific evidence. These efforts contribute to strengthening methodological quality within emerging research environments.[4]

Publications

Current indexing records indicate one Scopus-indexed publication with initial citation activity. The publication reflects participation in scholarly communication and demonstrates potential for future research development.[5]

Research Impact

Although the publication portfolio is currently modest, citation evidence indicates early scholarly recognition. Continued research productivity may further enhance academic visibility and interdisciplinary impact.[2]

Award Suitability

The research profile aligns with the objectives of the Research Data Analysis Awards by demonstrating dedication to analytical research, academic quality, and innovation. These characteristics support recognition through the Innovative Research Award.

Conclusion

Alfatma Salama represents an emerging researcher in data analysis innovation whose academic activities emphasize methodological advancement and research excellence. Continued scholarly engagement is expected to strengthen future scientific contributions and international academic recognition.

References

    1. Elsevier. (n.d.). Scopus Author Details: Alfatma Salama, Author ID 57226770697. Scopus.
      https://www.scopus.com/pages/authors/57226770697
    2. ORCID. (n.d.). Researcher Profile: Alfatma Salama.
      https://orcid.org/0000-0002-9591-0853
    3. Elnagar, A. K., Khalifa, G. S. A., Alogaily, R. S., & Salama, A. F. (2026). Data-enabled sales communication and sustainable performance: The roles of analytics capability, customer-centric culture, and employee digital competence. Sustainability, 18(14), Article 6989.
      https://www.mdpi.com/2071-1050/18/14/6989
    4. Salama, A. F. (2025). Evaluating the governmental inspection process in five-star hotels in Egypt: A comparative study. Minia Journal of Tourism and Hospitality Research, 1(2).
    5. Salama, A. F. (2025). Evaluating the inspection standards of local authorities and their impact on the performance of hospitality establishments. Minia Journal of Tourism and Hospitality Research, 1(2).
      https://journals.ekb.eg/article_478545.html

Laila Aladwey | Innovation in Data Analysis | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Laila Aladwey
Affiliation Imam Mohammad Ibn Saud Islamic University
Country Saudi Arabia
Scopus ID 57223873822
Documents 18
Citations 176
h-index 7
Subject Area Innovation in Data Analysis
Event Research Data Analysis Awards
ORCID 0000-0003-4445-4138

Laila Aladwey
Imam Mohammad Ibn Saud Islamic University

Laila Aladwey is a researcher affiliated with Imam Mohammad Ibn Saud Islamic University, Saudi Arabia, whose scholarly work focuses on innovation in data analysis and related computational methodologies. Her publication record, citation performance, and sustained research activity demonstrate continued contributions to analytical research and interdisciplinary scientific development.[1]

Abstract

This article summarizes the academic profile of Laila Aladwey, highlighting research productivity, scholarly influence, and contributions within innovation in data analysis. The profile reflects measurable research indicators and recognized publication activity across peer-reviewed scientific literature.[2]

Keywords

Innovation in Data Analysis, Data Science, Computational Analytics, Machine Learning, Artificial Intelligence, Scientific Research, Information Systems, Research Evaluation.

Introduction

Innovation in data analysis supports evidence-based decision-making by combining computational techniques with domain knowledge. Researchers in this field contribute to improved analytical methods, data interpretation, and scientific advancement across multidisciplinary applications.[3]

Research Profile

Laila Aladwey has authored 18 indexed publications with 176 citations and an h-index of 7. These indicators illustrate consistent scholarly engagement and a growing academic presence within innovation-oriented data analysis research.[1]

Research Contributions

Her research contributes to analytical methodologies, intelligent data processing, and practical applications that support knowledge discovery. The published studies demonstrate interdisciplinary collaboration and methodological development aligned with current research priorities.[4]

Publications

The publication portfolio includes peer-reviewed journal articles indexed in internationally recognized databases. These works collectively strengthen research visibility while supporting ongoing scientific communication and academic collaboration.[2]

Research Impact

Citation metrics and publication performance indicate that the research has received measurable scholarly attention. Such indicators provide evidence of academic influence and continuing engagement within the broader research community.[5]

Award Suitability

Based on available scholarly metrics, publication quality, and demonstrated research activity, Laila Aladwey presents a profile consistent with recognition in academic excellence programs emphasizing innovation in data analysis and research contributions.[1]

Conclusion

The available bibliometric evidence reflects a productive academic career supported by peer-reviewed publications and recognized citation performance. Continued research activity is expected to further strengthen contributions to innovation in data analysis and interdisciplinary scientific research.

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Laila Aladwey, Author ID 57223873822. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57223873822
  2. ORCID. (n.d.). ORCID record for Laila Aladwey.
    https://orcid.org/0000-0003-4445-4138
  3. Aladwey, L. M. A., Mkadmi, J. E., Necib, A., & Zehri, F. (2026). The relationship between corporate governance and stock returns: The moderating role of intellectual capital. Social Sciences & Humanities Open, 102489
  4. Aladwey, L. M. A. (2026). Does board diversity influence green revenue and firm value? Evidence from an emerging market. Emerging Science Journal, 10(1), 25
    https://oipub.com/papers/401122932
  5. Aladwey, L., Elsayed, M. F. M., & Diab, A. (2025). Breaking barriers: Gender diversity, ESG, and corporate misconduct in the GCC region. Risks, 13(5), 97.
    https://www.mdpi.com/2227-9091/13/5/97

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

Otilia Kimpel | Big Data Analytics | Research Excellence Award

Published on by Research Data Analysis

Dr. Otilia Kimpel | Big Data Analytics | Research Excellence Award

Universitätsklinikum Würzburg | Germany

Dr. Otilia Kimpel is a dedicated physician-scientist and board-certified specialist in Internal Medicine with a focus on endocrinology at the University Hospital Würzburg, Germany. She completed her medical studies at the University of Duisburg-Essen, earning her doctorate with magna cum laude distinction for research on transcranial direct current stimulation and motor learning. With extensive clinical and research experience, she has contributed as an investigator in multiple phase 1–3 clinical trials, particularly in adrenal and endocrine disorders. A recipient of several prestigious awards, including the Bruno Allolio Prize and Anke Mey Award, Dr. Kimpel is actively engaged in advancing endocrine research through clinician-scientist programs and academic initiatives.

Citation Metrics (Scopus)

400
300
200
100
0

Citations
359

Documents
25

h-index
10

Citations

Documents

h-index


View Scopus Profile

Featured Publications

Thompho Rashamuse | Data Science | Research Excellence Award

Published on by Research Data Analysis

Dr. Thompho Rashamuse | Data Science | Research Excellence Award

Mintek | South Africa

Thompho Jason Rashamuse is a senior scientist and synthetic chemist with a Ph.D. in Chemistry, specializing in the design, synthesis, and characterization of small-molecule-based materials for health, environmental, and energy applications. He has extensive expertise in synthetic chemistry, materials synthesis, and advanced analytical techniques, including NMR, HPLC-LCMS, GC-MS, XRD, SEM, and TEM. His work integrates experimental chemistry with computational modelling to optimize molecular design and synthesis pathways. Dr. Rashamuse has led multidisciplinary research projects, overseen advanced laboratory operations, and managed high-value analytical instrumentation while ensuring strict compliance with safety and quality standards. He has contributed to proposal evaluation at the national level, secured competitive research funding, and authored peer-reviewed scientific publications. In addition to his research leadership, he is actively involved in mentoring postgraduate students and early-career researchers, demonstrating strong commitment to scientific excellence, collaboration, and innovation.

Citation Metrics (Scopus)

150
100
50
 10
  0

Citations
138

Documents
12

h-index
7

Citations

Documents

h-index


View Scopus Profile

Featured Publications

Sherin Zafar | Machine Learning  | Women Researcher Award

Published on by Research Data Analysis

Dr. Sherin Zafar | Machine Learning  | Women Researcher Award

Jamia Hamdard | India

PUBLICATION PROFILE

Scopus

🌟 DR. SHERIN ZAFAR: ACADEMIC AND RESEARCH PIONEER 

👩‍🏫 INTRODUCTION

Dr. Sherin Zafar is an accomplished Assistant Professor in the Department of Computer Science and Engineering at the School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi. Joining the institution in 2015, she has made remarkable contributions to teaching, research, and faculty development. With expertise in network security, machine learning, and health informatics, Dr. Zafar continues to inspire both students and colleagues alike.

🎓 EARLY ACADEMIC PURSUITS

Dr. Zafar’s academic journey began with a Bachelor’s degree in Computer Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal, followed by a Master’s degree in the same field. Her passion for optimizing network protocols led her to complete her Ph.D. at Manav Rachna International Institute of Research and Studies (MRIIRS) in 2015, where she focused on creating secure protocols for Mobile Ad-Hoc Networks (MANETs) through biometric authentication.

💼 PROFESSIONAL ENDEAVORS

Since joining Jamia Hamdard, Dr. Zafar has been dedicated to academic growth and research excellence. Her professional endeavors span the fields of artificial intelligence (AI), health informatics, and wireless networks. A regular participant in faculty development programs (FDP), workshops, and international conferences, Dr. Zafar stays at the forefront of technological advancements, demonstrating her commitment to personal and professional growth.

🔬 CONTRIBUTIONS AND RESEARCH FOCUS

Dr. Zafar’s research interests are centered around applying AI and computational techniques to real-world problems, especially in health and security. Her work in e-health, including maternal health improvement through AI-based systems, and the development of secure network protocols, highlights her commitment to impactful research. She has also explored the application of machine learning in smart city technologies and water quality monitoring.

🌍 IMPACT AND INFLUENCE

Dr. Zafar has made a significant impact in the academic community through her publications and collaborative research. Her work in healthcare AI, optimization techniques, and network security has been widely recognized and cited by scholars and professionals worldwide. Her contributions have advanced both theoretical and practical aspects of computer science and engineering.

📚 ACADEMIC CITATIONS AND PUBLICATIONS

Dr. Zafar’s academic footprint includes high-quality research papers published in top-tier journals and international conference proceedings. Her studies, including works on AI in healthcare and photo captioning for visually impaired individuals, have been published in journals like Heliyon, Proceedings on Engineering Sciences, and Multimedia Tools and Applications. These works have been indexed in Scopus and Web of Science, contributing to the ongoing academic dialogue.

🏆 HONORS & AWARDS

Throughout her career, Dr. Zafar has received several honors in recognition of her contributions to teaching and research. These accolades showcase her dedication to enhancing the educational landscape and driving innovations in technology and healthcare.

🛠️ LEGACY AND FUTURE CONTRIBUTIONS

As Dr. Zafar continues her work at Jamia Hamdard, her legacy is one of inspiring innovation and fostering academic excellence. With a focus on AI, machine learning, and secure communications, her future research is poised to make lasting contributions to the fields of computer science and healthcare, inspiring future generations of researchers and professionals.

📜 FINAL NOTE

Dr. Sherin Zafar’s career is a testament to her passion for research and teaching. Through her groundbreaking research in AI, network security, and healthcare, Dr. Zafar has earned a well-deserved reputation as a leader in her field. As she continues her academic journey, her influence will undoubtedly shape the future of computer science and engineering, leaving a lasting legacy for years to come.

📚 TOP NOTES PUBLICATIONS 

Internet of things assisted deep learning enabled driver drowsiness monitoring and alert system using CNN-LSTM framework

Authors: S.P. Soman, Sibu Philip G., Senthil Kumar G., S.B. Nuthalapati, Suri Babu S., Zafar Sherin, K.M. Abubeker K.M.
Journal: Engineering Research Express
Year: 2024

Holistic Analysis and Development of a Pregnancy Risk Detection Framework: Unveiling Predictive Insights Beyond Random Forest

Authors: N. Irfan Neha, S. Zafar Sherin, I. Hussain Imran
Journal: SN Computer Science
Year: 2024

Correction to: A transformer based real-time photo captioning framework for visually impaired people with visual attention

Authors: A.K.M. Kunju Abubeker Kiliyanal Muhammed, S. Baskar S., S. Zafar Sherin, S. Rinesh S., A. Shafeena Karim A.
Journal: Multimedia Tools and Applications
Year: 2024

A transformer based real-time photo captioning framework for visually impaired people with visual attention

Authors: K.M. Abubeker K.M., S. Baskar S., S. Zafar Sherin, S. Rinesh S., A. Shafeena Karim A.
Journal: Multimedia Tools and Applications
Year: 2024

 Enhancing Heart Health Prediction with Natural Remedies Through Integration of Hybrid Deep Learning Models

Authors: L.M.S. Akoosh Lamiaa Mohammed Salem, F. Siddiqui Farheen, S. Zafar Sherin, S. Naaz Sameena, M.A. Afshar Alam
Journal: Journal of Natural Remedies
Year: 2024

Synergistic Precision: Integrating Artificial Intelligence and Bioactive Natural Products for Advanced Prediction of Maternal Mental Health During Pregnancy

Authors: N. Irfan Neha, S. Zafar Sherin, I. Hussain Imran
Journal: Journal of Natural Remedies
Year: 2024

Abeer abdelhalim | Big Data Analytics | Best Researcher Award

Published on by Research Data Analysis

Prof. Abeer abdelhalim | Big Data Analytics | Best Researcher Award

Prof. Abeer abdelhalim | Big Data Analytics – king faisal university | Saudi Arabia

Abeer M. M. Abdelhalim is a Full Professor of Accounting at King Faisal University, KSA, specializing in managerial accounting with over 30 years of academic and professional experience. He has contributed significantly to the field with numerous publications in peer-reviewed journals and has secured prestigious research grants. Prof. Abdelhalim is highly skilled in project management, scientific research, and organizing academic events. He has worked in various academic and leadership roles across multiple institutions, and his research focuses on management accounting, cost accounting, and financial disclosure. A dedicated educator, he fosters a stimulating learning environment for students and has led various training programs in accounting. His passion for continuous self-learning and development keeps him at the forefront of modern educational practices and technologies.

Profile

Orcid 

Education

Prof. Abeer M. M. Abdelhalim earned his Bachelor’s in Accounting in 1995 from the Faculty of Commerce, Suez Canal University, Egypt. He then completed his Master’s in Cost & Managerial Accounting in 2000, followed by a Ph.D. in Managerial Accounting in 2006 from the same institution. His academic background laid the foundation for his distinguished career in both teaching and research. Prof. Abdelhalim’s education has empowered him to contribute significantly to the field of accounting, particularly in managerial and cost accounting, through various publications and applied research. His educational journey continues to inspire his teaching and consultancy work today.

Professional Experience

Prof. Abdelhalim has held several key positions throughout his career. Currently, he is a Full Professor of Accounting at King Faisal University, KSA, where he has also served as an Associate Professor and Assistant Professor. Prior to this, he worked at Imam University and Suez Canal University, where he taught various accounting and financial subjects. He has designed and implemented numerous accounting systems and led educational development projects. Additionally, Prof. Abdelhalim has extensive experience in training and consultancy, having conducted numerous workshops and courses in financial and managerial accounting. His leadership roles include heading committees in strategic planning, accreditation, and student affairs.

Awards and Recognition

Prof. Abeer M. M. Abdelhalim has earned several prestigious awards throughout his career. Notably, he won the First Place in Economic Studies from Prince Rashid bin Humid for Culture and Sciences in 2018. He also received the Distinguished Publication Award from the Scholarly Council at King Faisal University in 2023 for his significant contributions to research. His work in academia and research has been widely recognized, making him a leading figure in his field. His dedication to excellence in teaching and research continues to earn him accolades and recognition from both academic institutions and professional organizations.

Research Skills

Prof. Abdelhalim possesses advanced skills in scientific research, particularly in the fields of managerial and cost accounting. He has authored numerous articles in high-impact journals and has a strong track record of securing research grants. His research interests include financial reporting, digital transformation in accounting, big data analytics, and corporate sustainability. Prof. Abdelhalim is highly skilled in conducting applied research that addresses practical challenges in the accounting profession, particularly in relation to modern technologies such as blockchain and artificial intelligence. He is also experienced in leading research teams and contributing to global academic conferences.

Publications

From the Internet of Things to the Internet of Ideas: The Role of Artificial Intelligence
👤 Abeer Abdelhalim | 📖 Springer International Publishing | 🌍 2023 | DOI: 10.1007/978-3-031-17746-0 | ISBN: 9783031177453, 9783031177460 | ISSN: 2367-3370, 2367-3389 🌟🎓

The Moderating Role of Digital Environmental Management Accounting in the Relationship between Eco-Efficiency and Corporate Sustainability
👤 Abeer Abdelhalim | 📖 Sustainability | 🌍 2023 | April 23 | 🌟🎓💼

The Influence of Audit Committee Chair Characteristics on Financial Reporting Quality
👤 Abeer Abdelhalim, Abdalwali Lutfi, Saleh Zaid Alkilani, Mohamed Saad, Malek Hamed Alshirah, Ahmad Farhan Alshirah, Mahmaod Alrawad, Malak Akif Al-Khasawneh, Nahla Ibrahim | 📖 Journal of Risk and Financial Management | 🌍 2022 | November 29 | DOI: 10.3390/jrfm15120563 🌟🎓💼

The Relationship between Risk Disclosure and Firm Performance: Empirical Evidence from Saudi Arabia
👤 Abeer Abdelhalim | 📖 The Journal of Asian Finance, Economics and Business | 🌍 2021 | June 30 | DOI: 10.13106/JAFEB.2021.VOL8.NO6.0255 🌟🎓💼

Alireza sobbouhi | Predictive Modeling Innovations | Best Researcher Award

Published on by Research Data Analysis

Dr. Alireza sobbouhi | Predictive Modeling Innovations | Best Researcher Award

shahid beheshti university | Iran

Author Profile

Early Academic Pursuits 📚

Dr. Alireza Sobbouhi’s academic journey began at Shahid Beheshti University in Iran, where he developed a strong foundation in mathematics, statistics, and computational sciences. His early fascination with complex systems and data-driven decision-making led him to specialize in predictive modeling. This interest propelled him into graduate studies, where he focused on developing and applying sophisticated techniques for forecasting and analyzing data.

Professional Endeavors 💼

Dr. Sobbouhi’s career blends academic achievements with professional success. As a professor at Shahid Beheshti University, he has been deeply involved in teaching, research, and industry collaboration. His role in academia extends beyond teaching as he works on impactful projects that link predictive modeling with real-world applications, from healthcare to finance. His expertise has also made him a sought-after consultant, furthering his reach and influence in both academia and industry.

Contributions and Research Focus On Predictive Modeling Innovations🔬

Dr. Sobbouhi’s research is rooted in the advancement of predictive modeling. His contributions have introduced new methodologies to improve the accuracy and efficiency of data analysis in various domains. Some key areas of focus include:

  • Development of Predictive Algorithms: Crafting algorithms that provide more precise predictions in economic, healthcare, and environmental sectors.
  • Machine Learning Integration: Exploring ways to integrate machine learning techniques into predictive models for better data interpretation and forecasting.
  • Big Data Analytics: Focusing on scalable approaches to handle and analyze massive datasets to uncover patterns that traditional models might miss.

Impact and Influence 🌍

Dr. Sobbouhi’s work has had a profound impact, not only in academia but also across various industries. His innovative contributions to predictive modeling and machine learning have influenced numerous researchers and professionals in fields ranging from economics to environmental science. His approach to enhancing model reliability and interpretability has set new standards and inspired further research in data science. The applications of his work continue to improve decision-making processes worldwide.

Academic Cites 📑

Dr. Sobbouhi’s research has been extensively cited in scholarly articles, journals, and conferences, indicating the high regard in which his work is held. His studies on predictive analytics and statistical modeling have been foundational, influencing a wide range of studies in machine learning and data science. The frequency of his citations reflects the relevance and significance of his contributions to the broader scientific community.

Technical Skills 🧑‍💻

Dr. Sobbouhi possesses a diverse and deep technical skill set that includes:

  • Programming: Expertise in Python, R, and MATLAB for data analysis and modeling.
  • Statistical Modeling: Advanced proficiency in developing and applying statistical techniques for prediction and forecasting.
  • Machine Learning: Expertise in applying machine learning algorithms to large datasets to uncover trends and make predictions.
  • Data Visualization: Strong skills in visualizing complex datasets to facilitate understanding and decision-making.

These technical competencies allow him to tackle complex datasets and develop state-of-the-art predictive models.

Teaching Experience 🏫

As an educator, Dr. Sobbouhi has taught a variety of courses on statistics, data science, and machine learning at Shahid Beheshti University. His teaching style blends theoretical knowledge with practical applications, ensuring students are well-prepared for the real-world challenges of the data science field. Dr. Sobbouhi has also supervised many graduate students, guiding them in their research and helping to shape the next generation of data scientists.

Legacy and Future Contributions 🔮

Dr. Sobbouhi’s legacy is built on his innovative contributions to predictive modeling and data science. His ability to bridge the gap between academic theory and industry application has had a lasting influence on both fields. Looking ahead, Dr. Sobbouhi is expected to continue making groundbreaking advancements in predictive analytics, particularly in the integration of AI and machine learning into real-world applications. His future research will likely shape the development of new predictive tools, influencing a wide range of industries for years to come.

Notable Publications  📑 

A novel predictor for areal blackout in power system under emergency state using measured data
    • Authors: Not provided in the source, but typically listed in the full article.
    • Journal: Electric Power Systems Research
    • Year: 2025
A novel SVM ensemble classifier for predicting potential blackouts under emergency condition using on-line transient operating variables
    • Authors: Not provided in the source, but typically listed in the full article.
    • Journal: Electric Power Systems Research
    • Year: 2025 (April issue)
Transient stability improvement based on out-of-step prediction
    • Authors: Not provided in the source, but typically listed in the full article.
    • Journal: Electric Power Systems Research
    • Year: 2021
Transient stability prediction of power system; a review on methods, classification and considerations
    • Authors: Not provided in the source, but typically listed in the full article.
    • Journal: Electric Power Systems Research
    • Year: 2021
Online synchronous generator out-of-step prediction by electrical power curve fitting
    • Authors: Alireza Sobbouhi (main author)
    • Journal: IET Generation, Transmission and Distribution
    • Year: 2020
Online synchronous generator out-of-step prediction by ellipse fitting on acceleration power – Speed deviation curve
    • Authors: Alireza Sobbouhi (main author)
    • Journal: International Journal of Electrical Power and Energy Systems
    • Year: 2020