Alexander Pukhov | Algorithm Development | Best Researcher Award

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Best Researcher Award

Alexander Pukhov
Heinrich Heine University of Dusseldorf, Germany

Alexander Pukhov
Affiliation Heinrich Heine University of Dusseldorf
Country Germany
Scopus ID 7006039283
Documents 400
Citations 21,488
h-index 72
Subject Area Algorithm Development
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0001-5043-960X

Alexander Pukhov is a researcher affiliated with Heinrich Heine University of Dusseldorf whose scholarly record includes extensive computational and theoretical research. Public researcher records associate his work with computational physics, plasma modelling, particle acceleration, laser-plasma interactions, and algorithmic approaches to scientific simulation. His research profile is documented through persistent identifiers and bibliographic databases. [1]

Abstract

This academic recognition profile presents Alexander Pukhov in the context of the Best Researcher Award. The assessment considers the supplied bibliometric indicators, institutional affiliation, persistent researcher identification, publication activity, and documented contributions to computational and theoretical research. Particular attention is given to algorithmic and simulation-oriented approaches relevant to advanced scientific investigation.[2]

Keywords

Alexander Pukhov; Best Researcher Award; Algorithm Development; Computational Physics; Scientific Computing; Plasma Physics; Particle Acceleration; Laser-Plasma Interaction; Research Impact; Bibliometrics; Academic Publications; Heinrich Heine University of Dusseldorf.

Introduction

Alexander Pukhov’s research profile reflects sustained engagement with computational and theoretical approaches to advanced physical systems. His scholarly record connects numerical modelling, scientific algorithms, plasma physics, and particle acceleration. Bibliographic records identify Heinrich Heine University of Dusseldorf as his affiliation and associate his publications with computationally intensive scientific investigations. [1]

Research Profile

The research profile of Alexander Pukhov encompasses computational methods, plasma modelling, laser-plasma interactions, particle acceleration, and related theoretical investigations. His ORCID record links his identity with a substantial body of scholarly works and confirms the Scopus Author ID supplied for this profile. These records provide a persistent basis for bibliographic evaluation. [3]

Research Contributions

Pukhov’s contributions include development and application of computational techniques for modelling complex physical phenomena. His documented work includes particle-in-cell simulation methods, hybrid computational models, and algorithms supporting plasma and particle-acceleration studies. Such contributions demonstrate the role of algorithm development in enabling numerical investigation of systems that are difficult to examine solely through analytical approaches. [2]

Publications

The publication record associated with Alexander Pukhov includes articles addressing computational physics, particle-in-cell modelling, laser-driven particle acceleration, and high-energy plasma phenomena. Representative publications include work on dispersionless Maxwell solvers and plasma-based particle acceleration, as well as recent studies of laser-driven radiation sources. These works illustrate continuity across computational and applied research.[3]

Research Impact

The supplied profile reports 400 documents, 21,488 citations, and an h-index of 72, indicating substantial bibliometric visibility. Independent bibliographic records also associate Pukhov with a large publication and citation portfolio. Such indicators provide quantitative evidence of scholarly reach, although citation measures should be interpreted alongside research quality, authorship, collaboration, and field-specific practices. [1]

Award Suitability

Based on the supplied bibliometric information and documented research activity, Alexander Pukhov presents a profile consistent with consideration for a Best Researcher Award. The combination of extensive publication output, citation visibility, a substantial h-index, and contributions to computational scientific research provides measurable evidence for scholarly recognition, subject to the award’s formal evaluation criteria.[2]

Conclusion

Alexander Pukhov’s profile demonstrates an established research presence involving computational methods, scientific algorithms, plasma physics, and particle acceleration. The reported bibliometric indicators, persistent researcher identification, and documented publications collectively support recognition of sustained scholarly activity. Final award decisions should nevertheless incorporate independent verification and the complete criteria established by the awarding organization. [3]

References

  1. Magnetized plasma rotator for relativistic mid-infrared pulses via frequency-variable Faraday rotation.
    https://www.nature.com/articles/s41377-025-02047-x
  2. Universal power-law spectral feature in laser-driven proton acceleration.
    https://www.researchgate.net/publication/410970527_Universal_power-law_spectral_feature_in_laser-driven_proton_acceleration
  3. Preservation of ³ He ion polarization after laser-driven acceleration in plasma
    https://www.researchgate.net/publication/403915347_Preservation_of_He_ion_polarization_after_laser-driven_acceleration_in_plasma

 

Venkatraman Ethirajan | Smart Grid | Innovative Research Award

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Innovative Research Award

Venkatraman Ethirajan  – University College of Engineering Villupuram, India

Venkatraman Ethirajan
Affiliation University College of Engineering Villupuram
Country India
Scopus ID 59477247900
Documents 2
Citations 2
h-index 1
Subject Area Smart Grid
Event International Research Data Analysis Excellence & Awards
ORCID 0009-0001-8538-3500

Venkatraman Ethirajan is a researcher affiliated with the University College of Engineering Villupuram, India, with research interests in the area of Smart Grid. Available bibliographic information records two documents, two citations, and an h-index of one, providing a concise basis for academic recognition and profile assessment. [1]

Abstract

This article presents an academic profile of Venkatraman Ethirajan, affiliated with the University College of Engineering Villupuram, India. The profile focuses on Smart Grid research and summarizes available bibliographic indicators, including documents, citations, and h-index. The information provides a concise foundation for evaluating research activity and suitability for academic recognition. [1]

Keywords

Smart Grid; Electrical Engineering; Research Data Analysis; Academic Research; Energy Systems; Intelligent Power Systems; Research Impact; Scholarly Publications; Innovative Research Award; Academic Recognition. [2]

Introduction

Smart Grid research integrates digital technologies, communication systems, monitoring, automation, and electrical power infrastructure to support more responsive energy networks. Venkatraman Ethirajan’s listed subject area is Smart Grid, placing the profile within an interdisciplinary technological domain concerned with modernizing electricity systems, improving operational intelligence, and supporting data-informed energy management. [3]

Research Profile

Venkatraman Ethirajan is affiliated with University College of Engineering Villupuram in India. The available Scopus information identifies author ID 59477247900 and records two documents, two citations, and an h-index of one. The researcher also maintains an ORCID identifier, providing an additional persistent mechanism for scholarly identity and research record management. [2]

Research Contributions

The available profile indicates research activity associated with Smart Grid systems, a field that combines electrical engineering with sensing, communication, computation, and control. With two indexed documents and two citations, the documented contribution remains relatively focused. These indicators establish measurable scholarly activity while avoiding conclusions beyond the available bibliographic evidence. [1]

Publications

Scopus records two documents under the supplied author identifier. These indexed outputs constitute the currently documented publication activity used for this profile. Specific publication titles, journals, publication years, co-authors, and DOI identifiers were not supplied in the input data; therefore, no unsupported publication-level claims or DOI assignments are made here. [2]

Research Impact

The available bibliometric record reports two citations and an h-index of one. These measures indicate that the indexed publications have received a limited but measurable level of scholarly citation. Such indicators should be interpreted in relation to publication age, disciplinary citation patterns, collaboration, and career stage when a broader assessment of research influence is undertaken. [1]

Award Suitability

The Innovative Research Award profile recognizes documented research activity in Smart Grid-related work. The available record demonstrates an identifiable scholarly profile, institutional affiliation, indexed documents, citations, and a persistent ORCID identity. Final award suitability should be determined according to the event’s published eligibility criteria, submitted evidence, and independent review procedures rather than bibliometric indicators alone.[3]

Conclusion

Venkatraman Ethirajan’s available academic record identifies Smart Grid as a principal subject area and documents two Scopus-indexed publications, two citations, and an h-index of one. The profile provides evidence of active scholarly participation while remaining limited in scope. Further publication details and research outputs would support a more comprehensive evaluation. [1]

References

  1. Review Challenges and Barriers Regarding Electric Vehicles in Modern India with Grid Optimization.
    https://www.researchgate.net/publication/389697656_Review_Challenges_and_Barriers_Regarding_Electric_Vehicles_in_Modern_India_with_Grid_Optimization
  2. An in-depth survey of latest progress in smart grids: paving the way for a sustainable future through renewable energy resources.
    https://link.springer.com/article/10.1186/s43067-025-00195-z
  3. IMPLEMENTATION & COMPARISON OF DIFFERENT SEGMENTATION ALGORITHMS FOR MEDICAL IMAGING.
    https://www.researchgate.net/publication/376522897_IMPLEMENTATION_COMPARISON_OF_DIFFERENT_SEGMENTATION_ALGORITHMS_FOR_MEDICAL_IMAGING

Sibel Cevik Bektas l Energy Management/Optimization | Research Excellence Award

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Dr. Sibel Cevik Bektas l Energy Management/Optimization | Research Excellence Award

Karadeniz Technical University | Turkey

Dr. Sibel Cevik Bektas is an electrical and electronics engineering researcher specializing in energy systems and renewable integration. Education includes undergraduate, postgraduate, and doctoral degrees in electrical engineering from Karadeniz Technical University. Professional experience centers on academic research, peer-reviewed publications, conference contributions, and leadership of a TUBITAK-funded project. Research interests cover optimal energy management, power systems, load forecasting, solar irradiance prediction, and data-driven optimization. Research skills include machine learning, deep learning, time-series forecasting, optimization, MATLAB modeling, and power system analysis. Awards and honors include competitive national research funding. Overall, Dr. Sibel Cevik Bektas contributes impactful, applied solutions advancing energy systems.

Citation Metrics (Scopus)

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13

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5

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Featured Publications

Scenario-Based Data-Driven Approaches for Short-Term Load Forecasting
Energy and Buildings, 2026

Ammar Khaleel l Reinforcement Learning | Research Excellence Award

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Mr. Ammar Khaleel l Reinforcement Learning | Research Excellence Award

Széchenyi István Egyetem | Hungary

Mr. Ammar Khaleel PhD-level researcher in computer science with ongoing doctoral training, focusing on reinforcement learning–based decision making for autonomous vehicles. Experience includes designing, training, and evaluating deep reinforcement learning and control algorithms for autonomous driving, particularly lane-changing, within large-scale traffic simulations using SUMO and the TraCI Python API. Research interests span reinforcement learning, deep learning, model predictive control, intelligent transportation systems, and traffic modeling. Technical expertise covers Python, C/C++, simulation frameworks, and reproducible research workflows. Academic contributions emphasize simulation-driven experimentation and algorithmic innovation; no formal awards are listed. Overall, the work aims to advance safe, efficient, and intelligent mobility systems.

Citation Metrics (Scopus)

40
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12

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5

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2

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View Scopus Profile View Orcid Profile

Featured Publications


A Multi-Levels RNG Permutation

– Indonesian Journal of Electrical Engineering and Computer Science, 2019

A New Permutation Method for Sequence of Order 28

– Journal of Theoretical and Applied Information Technology, 2019

N/A and Signature Analysis for Malwares Detection and Removal

– Indian Journal of Science and Technology, 2019

Shaymaa Sorour | Artificial Intelligence | Best Researcher Award

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Dr. Shaymaa Sorour | Artificial Intelligence | Best Researcher Award

King Faisal University | Saudi Arabia

Dr. Shaymaa E. Sorour is an Assistant Professor of Computer Science specializing in Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with a strong focus on educational technologies and intelligent learning systems. She earned her Ph.D. in Computer Science from Kyushu University, Japan (2016), following an M.Sc. in Computer Education and a B.Sc. in Computer Teacher Preparation with honors. Dr. Sorour has extensive academic experience across teaching, research, quality assurance, and academic advising, serving in faculty roles at King Faisal University, Saudi Arabia, and Kafrelsheikh University, Egypt. Her research integrates data mining, learning analytics, student performance prediction, adaptive and intelligent educational systems, and technology-enhanced learning, with publications in leading international journals and conferences. She has actively contributed to global scholarly communities through sustained participation in IEEE, LNCS, and international education and AI venues. Her scholarly impact includes 486 citations, 49 documents, and an h-index of 11, reflecting consistent contributions to AI in education and learning analytics (486 citations by 441 documents). Among her recognitions, she received a Best Paper Award at an international conference. Dr. Sorour’s work continues to bridge advanced computational intelligence with practical, scalable educational innovation, supporting data-driven decision-making and improved learning outcomes worldwide.

Citation Metrics (Scopus)

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486

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49

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View Scopus Profile
View Orcid Profile
View Google Scholar Profile

Featured Publications

George Vlontzos | Agricultural Data Analysis | Research Excellence Award

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Prof. George Vlontzos | Agricultural Data Analysis | Research Excellence Award

University of Thessaly | Greece

Prof. George Vlontzos is a distinguished agricultural economist and Full Professor at the University of Thessaly, Greece, where he directs the Laboratory of Agricultural Economics and Consumer Behavior. He holds a Ph.D. in Planning and Regional Development (University of Thessaly), an MBA in Agribusiness (University of Wales, Aberystwyth) and a BSc in Agriculture (Aristotle University of Thessaloniki). With a strong international academic profile, he has served as a visiting professor at the University of Florida and contributed to graduate programs at the Mediterranean Agronomic Institute of Montpellier. Prof. Vlontzos has authored 62 documents indexed on Scopus with 952 citations by 886 documents and an h‑index of 17, reflecting significant research impact in agricultural economics, efficiency and sustainability analysis, consumer behavior, optimization and AI applications in agriculture. His research employs advanced quantitative and behavioral models—such as Data Envelopment Analysis, Stochastic Frontier Analysis, the Theory of Planned Behavior and Artificial Neural Networks—to address economic and environmental efficiency, consumer decision processes, and sustainable agri‑food systems. He has published extensively in refereed journals, participated in major international conferences, and contributed to multiple competitive projects co‑funded by the EU, national and private sources. His leadership in research and education underscores his influence in shaping sustainable agricultural policy and practice.

Profile : Scopus

Featured Publications

Assessing the economic impact of insect pollination on the agricultural sector: A department-level case study in France

– Environmental and Sustainability Indicators, 2025

Emerging Technologies for Investigating Food Consumer Behavior: A Systematic Review

– Review, 2025

A hybrid remotely operated underwater vehicle for maintenance operations in aquaculture: Practical insights from Greek fish farms

– Computers and Electronics in Agriculture, 2025

Digital Transformation of Food Supply Chain Management Using Blockchain: A Systematic Literature Review Towards Food Safety and Traceability

– Business and Information Systems Engineering, 2025

Variable RTS in hierarchical network DEA: Enhancing efficiency in higher education systems

– Socio Economic Planning Sciences, 2024

Helmi Ayari | Data processing | Research Excellence Award

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Mr. Helmi Ayari | Data processing | Research Excellence Award

Université Ibn khaldoun | Tunisia

Helmi Ayari is a doctoral researcher in Artificial Intelligence at the École Polytechnique de Tunisie, focusing on machine learning, deep learning, and intelligent imaging systems, with a research track record that includes 3 published documents, an h-index of 1, and citations from 33 documents. He holds a Master of Research in Intelligent Systems for Imaging and Computer Vision and a fundamental Bachelor’s degree in Computer Science. His work includes contributions to medical image analysis, explainable AI, and optimization techniques, with notable publications such as a comparative study of classical versus deep learning-based computer-aided diagnosis systems published in Knowledge and Information Systems (Q2), and a study integrating genetic algorithms with ensemble learning for enhanced credit scoring presented at the International Conference on Business Information Systems (Class B). Professionally, he has served as a Maître Assistant and teaching assistant, delivering courses in machine learning, deep learning, Python, R, computer architecture, and automata theory, while supervising Master’s research, coordinating academic programs, and contributing to hackathons and university events. His research interests span AI-driven decision systems, interpretable machine learning, evolutionary optimization, and applied deep learning. Recognized for both academic and pedagogical engagement, he continues advancing impactful AI research and education.

Profile : Scopus

Featured Publications

Computer-Aided Diagnosis Systems: A Comparative Study of Classical Machine Learning Versus Deep Learning-Based Approaches. Knowledge and Information Systems, published May 23, 2023. https://doi.org/10.1007/s10115-023-01894-7

Integrating Genetic Algorithms and Ensemble Learning for Improved and Transparent Credit Scoring. International Conference on Business Information Systems, June 25, 2025. (Class B)

Umme Habiba | Machine Learning and AI Applications | Research Excellence Award

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Mrs. Umme Habiba | Machine Learning and AI Applications | Research Excellence Award

North Dakota State University(NDSU) | United States

Umme Habiba is a dedicated researcher and educator in computer science whose work centers on machine learning, deep learning, transformer-based architectures, explainable AI, and swarm intelligence, particularly within medical data analysis. She is pursuing her Ph.D. and M.Sc. in Computer Science at North Dakota State University, where she has gained extensive teaching experience as an instructor of record and graduate teaching assistant across several undergraduate courses. Her research contributions span clinical text mining, medical risk prediction, brain–computer interface modeling, IoT security, and usability analysis of mHealth applications, with publications in reputable international journals. She has also collaborated on projects involving mobile sensor–based spatial analysis, voice-controlled IoT automation systems, and hybrid ML models for network intrusion detection. Her academic journey began with a bachelor’s degree in Computer Science and Engineering, where she developed a strong foundation in data-driven system design and intelligent applications. She has received consistently high teaching evaluations and has demonstrated a commitment to interdisciplinary research and student learning. Her long-term goal is to contribute impactful solutions at the intersection of artificial intelligence and healthcare while advancing as both a researcher and an educator.

Profile : Orcid

Featured Publication

PSO-optimized TabTransformer architecture with feature engineering for enhanced cervical cancer risk prediction, 2026

Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

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Dr. Mebarka Allaoui | Machine Learning and AI Applications | Best Paper Award

Bishop’s University | Canada

Dr. Mebarka Allaoui dedicated computer science researcher with a strong background in machine learning, manifold learning, and computer vision, this scholar holds a PhD in Computer Science focused on embedding techniques and their applications to visual data analysis. Their academic journey includes a master’s degree in industrial computer science and a bachelor’s degree in information systems, all completed with high distinction. Professionally, they have served as a Postdoctoral Fellow contributing to industry-funded research on anomaly detection, developing novel embedding, deep learning, and clustering methods to enhance the interpretability of latent representations and improve fraud detection in real-world financial datasets. Prior experience includes working as a computer engineer supporting system administration, software development, data analysis, and network configuration, alongside several teaching appointments delivering practical courses in software engineering, algorithmics, and web development. Their research contributions span dimensionality reduction, clustering, optimization, document analysis, and scientific information retrieval, with publications in reputable journals and conferences. Collaborative work further extends to studies on optimizers, object detection, and embedding initialization strategies. Recognized for high-quality academic performance and impactful research outputs, they continue to advance data-driven methodologies, aiming to bridge theoretical innovation with practical applications in intelligent systems and decision-support technologies.

Profile : Google Scholar

Featured Publications

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “Considerably improving clustering algorithms using UMAP dimensionality reduction technique” in International Conference on Image and Signal Processing, 317–325.

Drid, K., Allaoui, M., & Kherfi, M. L. (2020). “Object detector combination for increasing accuracy and detecting more overlapping objects” in International Conference on Image and Signal Processing, 290–296.

Allaoui, M., Belhaouari, S. B., Hedjam, R., Bouanane, K., & Kherfi, M. L. (2025). “t-SNE-PSO: Optimizing t-SNE using particle swarm optimization” in Expert Systems with Applications, 269, 126398.

Allaoui, M., Kherfi, M. L., Cheriet, A., & Bouchachia, A. (2024). “Unified embedding and clustering” in Expert Systems with Applications, 238, 121923.

Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). “International Conference on Image and Signal Processing” in Springer.

Naima Rahiel | Public Health Analytics | Women Researcher Award

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Mrs. Naima Rahiel | Public Health Analytics | Women Researcher Award

QARTZ, Université Paris 8 | France

Naima Rahiel is a doctoral researcher in Industrial Engineering and Productics at the University of Paris 8, specializing in the modeling and optimization of complex systems, with a particular focus on hospital logistics and supply chain resilience. She holds a Master’s and a Bachelor’s degree in Industrial Engineering from the University of Oran 2, Algeria, where she built strong foundations in probabilistic analysis, production systems, and decision-making under uncertainty. Her professional experience includes academic teaching at IUT de Montreuil and practical research in industrial and healthcare environments, such as Tosyali Algeria and the Canastel Pediatric Hospital in Oran. Her research explores the resilience of healthcare supply chains through analytical and simulation-based approaches, leading to several international conference presentations and peer-reviewed publications, including contributions to Springer’s book series and the journal Environmental Systems and Decision. Passionate about innovation, data analysis, and system reliability, she aims to bridge theoretical modeling with real-world decision support tools for sustainable and adaptive supply chain management. Her academic achievements and active participation in scientific events demonstrate her commitment to advancing research on healthcare logistics and resilience engineering, contributing valuable insights to the industrial and operational research community.

Profile : Google Scholar

Featured Publication

Rahiel, N., El Mhamedi, A., & Hachemi, K. (2024). Healthcare Supply Chain: Resilience Qualitative Evaluation. In Hospital Supply Chain: Challenges and Opportunities for Improving Healthcare.

Rahiel, N., El Mhamedi, A., Hachemi, K., Aouffen, N., & Rahiel, I. (2025). Resilience of the hospital supply chain: a case study-based approach on safety stock. Environment Systems and Decisions, 45 (4), 56.

Rahiel, N., Addouche, S.A., El Mhamedi, A., & Hachemi, K. (2025). Function-Based Modeling for Reactive Optimization of Healthcare Resource Reallocation. In Proceedings of the 16th International Conference on Logistics and Supply Chain Management (LOGISTIQUA 2025).