Samaneh Eftekhari Mahabadi | Data Science and Analytics | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Samaneh Eftekhari Mahabadi
University of Tehran, Iran

Samaneh Eftekhari Mahabadi
Affiliation University of Tehran
Country Iran
Google Scholar ID nKYIHE8AAAAJ&hl
Documents 47
Citations 238
h-index 8
Subject Area Data Science and Analytics
Event Computer Scientists Awards
ORCID 0000-0001-8938-5864

Samaneh Eftekhari Mahabadi is a researcher affiliated with the University of Tehran whose reported scholarly record includes work in statistical modelling, missing-data methodology, machine learning, longitudinal inference, and applications involving clinical and financial data. The documented publication portfolio provides a basis for academic recognition in data science and analytics.

Abstract

The research record associated with Samaneh Eftekhari Mahabadi spans statistical computation, machine learning, missing-data imputation, competing-risks analysis, and Bayesian sensitivity analysis. Her publications address methodological questions that arise when incomplete, masked, or non-ignorable data affect statistical inference. These topics are relevant to modern data science because reliable analysis increasingly depends on methods capable of accounting for uncertainty and incomplete observations. [1]

Keywords

Data science; statistical modelling; machine learning; missing data; multiple imputation; Bayesian inference; longitudinal analysis; competing risks; sensitivity analysis; clinical trial data.

Introduction

Contemporary statistical research frequently combines computational techniques with rigorous inferential frameworks. Mahabadi’s documented publications reflect this intersection, particularly through supervised learning approaches to missing-data imputation and machine-learning methods for masked competing-risks data. [2] Such work contributes to methodological discussions concerning the validity and robustness of conclusions derived from incomplete datasets.

Research Profile

The supplied scholarly record reports 47 documents, 238 citations, and an h-index of 8. The stated subject area is Data Science and Analytics. Her research profile is characterized by methodological statistics and applications involving complex datasets, with publications appearing in journals and scholarly book collections. [1]

Research Contributions

  • Development and evaluation of approaches for missing-data imputation using supervised learning methods. [4]
  • Application of machine-learning algorithms to masked competing-risks data. [2]
  • Bayesian second-order sensitivity analysis addressing non-ignorability in longitudinal inference. [1]
  • Analysis of monetary policy and Islamic-bank stability under differing governance models. [3]

Publications

  1. Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data. The International Journal of Biostatistics, 2024.  [1]
  2. Multiple imputation of masked competing risks data using machine learning algorithms. Journal of Statistical Computation and Simulation, 2022. [2]
  3. The Effects of Monetary Policy on the Stability of Islamic Banks with Different Governance Models: Case of Islamic Republic of Iran. 2021. [3]
  4. Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications, 2021.  [4]

Research Impact

The reported citation count and h-index indicate that the publication record has received scholarly citations. More specifically, the documented research addresses recurring challenges in statistical analysis where missingness, masking, competing risks, or non-ignorability can influence inference. The supplied profile reports 238 citations across 47 documents, providing quantitative context for the recognition of this research record.

Award Suitability

For the Innovative Research Award, the documented combination of statistical methodology, machine learning, and applied research provides relevant evidence for consideration. The publication portfolio demonstrates engagement with methodological problems and their applications rather than relying on a single research theme. Final award decisions remain subject to the criteria and review procedures of the Computer Scientists Awards.

Conclusion

Samaneh Eftekhari Mahabadi’s documented research profile reflects sustained scholarly activity in data science and analytics, with particular emphasis on statistical inference, machine learning, and incomplete-data methodology. The cited publications provide a structured basis for evaluating her research contributions in the context of an academic recognition program.

References

  1. Eftekhari Mahabadi, S. et al. (2024). Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data. The International Journal of Biostatistics.
    https://doi.org/10.1515/ijb-2022-0014
  2. Eftekhari Mahabadi, S. et al. (2022). Multiple imputation of masked competing risks data using machine learning algorithms. Journal of Statistical Computation and Simulation.
    https://doi.org/10.1080/00949655.2022.2063864
  3. Eftekhari Mahabadi, S. et al. (2021). The Effects of Monetary Policy on the Stability of Islamic Banks with Different Governance Models: Case of Islamic Republic of Iran. Monetary Policy, Islamic Finance, and Islamic Corporate Governance.
    https://doi.org/10.1108/978-1-80043-786-920211009
  4. Eftekhari Mahabadi, S. (2021). Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications.
    http://jsm.yazd.ac.ir/article_2049.html
  5. ORCID. (n.d.). Samaneh Eftekhari Mahabadi, ORCID record.
    https://orcid.org/0000-0001-8938-5864
  6. Computer Scientists Awards. (n.d.). Computer Scientists Awards official website.
    https://computerscientists.net/

Firouz Matloubi Moghaddam | Decision Sciences | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Firouz Matloubi Moghaddam
Sharif University of Technology, Iran

Firouz Matloubi Moghaddam
Affiliation Sharif University of Technology
Country Iran
Scopus ID 7003600166
Documents 178
Citations 4,224
h-index 33
Subject Area Decision Sciences
Event Computer Scientists Awards
ORCID 0000-0002-8418-9888

Firouz Matloubi Moghaddam is a researcher affiliated with Sharif University of Technology, Iran, whose bibliographic profile is identified by Scopus Author ID 7003600166. The supplied research record reports 178 documents, 4,224 citations and an h-index of 33. These indicators provide a quantitative overview of the publication and citation record associated with the researcher. [1]

Abstract

This academic recognition profile summarizes the reported research record of Firouz Matloubi Moghaddam in the context of the Best Researcher Award. The supplied publication evidence includes recent studies involving cycloaddition chemistry, ionic liquids, nanocatalysis, heterocyclic synthesis, biological activity and photoluminescent materials. The record indicates continuing scholarly activity across chemically oriented research topics relevant to interdisciplinary decision and research sciences. [2]

Keywords

Decision Sciences; Organic Chemistry; Cycloaddition; Ionic Liquids; Nanocatalysis; Heterocyclic Chemistry; Biological Activity; Photoluminescence; Research Impact; Scholarly Publications.

Introduction

Researcher profiles combine bibliographic indicators with evidence from individual scholarly publications. In this case, the supplied record identifies Sharif University of Technology as the institutional affiliation and Decision Sciences as the designated subject area. Recent publications demonstrate work spanning synthetic methodologies, catalytic systems and biological evaluation. [3]

Research Profile

The reported Scopus record comprises 178 documents and 4,224 citations, with an h-index of 33. These values are bibliometric descriptors rather than independent measures of research quality and should be interpreted alongside publication content, authorship, venue, and disciplinary context. [1]

Research Contributions

The supplied publications indicate contributions to synthetic organic chemistry and related interdisciplinary applications. Topics include 1,3-dipolar cycloaddition in ionic liquids, nanocatalyst-assisted synthesis, PARP-1 inhibitory evaluation, regio- and diastereoselective reactions, and photoluminescent properties. [4]

Publications

  1. “Synthesis of spiro acenaphthylene pyrrolizidine and pyrrolidine compounds via 1,3-dipolar cycloaddition in ionic liquid and the investigation of their biological activity as potent α-glucosidase inhibitors.” Journal of Ionic Liquids, 2026.
  2. “Design and synthesis of novel phthalazine and benzofuran scaffolds using CoFe₂O₄@CNSCu nanocatalyst: Evaluation of PARP-1 inhibitory activity.” Journal of Organometallic Chemistry, 2026.
  3. “Regio- and diastereoselective synthesis of indeno-quinoxaline-thiazolidine-2-thione and bis-indeno-quinoxaline-dicarbamodithioate derivatives, along with an investigation of their photoluminescent properties and potential applications.” Tetrahedron, 2025.
  4. “Base catalyzed abnormal [3+2]-cycloaddition reaction of isatin N,N′-cyclic azomethine imine with malononitrile via C-3 umpolung of oxindole.” Journal of Molecular Structure, 2024.

Research Impact

The reported citation count and h-index provide measurable indicators of scholarly visibility within the indexed record. The publication list further shows research appearing in established chemistry journals and addressing methodological as well as application-oriented questions. Individual article impact should be assessed using article-level citation and usage data in addition to aggregate indicators. [5]

Award Suitability

For the Best Researcher Award profile, the documented indicators and supplied publications provide identifiable evidence for consideration of research productivity, citation activity and recent scholarly contributions. Award assessment remains dependent on the applicable criteria and review process of Computer Scientists Awards rather than on bibliometric indicators alone.

Conclusion

Firouz Matloubi Moghaddam’s supplied academic record combines a substantial indexed publication history with recent research addressing synthetic chemistry, catalytic methodologies and biological applications. The reported Scopus indicators and publication evidence form the principal documented basis of this academic recognition profile.

References

  1. Elsevier. (n.d.). Scopus author details: Firouz Matloubi Moghaddam, Author ID 7003600166. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=7003600166
  2. Journal of Ionic Liquids. (2026). Synthesis of spiro acenaphthylene pyrrolizidine and pyrrolidine compounds via 1,3-dipolar cycloaddition in ionic liquid and the investigation of their biological activity as potent α-glucosidase inhibitors.
    https://doi.org/10.1016/j.jil.2026.100187
  3. Journal of Organometallic Chemistry. (2026). Design and synthesis of novel phthalazine and benzofuran scaffolds using CoFe₂O₄@CNSCu nanocatalyst: Evaluation of PARP-1 inhibitory activity.
    https://doi.org/10.1016/j.jorganchem.2025.123997
  4. Tetrahedron. (2025). Regio- and diastereoselective synthesis of indeno-quinoxaline-thiazolidine-2-thione and bis-indeno-quinoxaline-dicarbamodithioate derivatives, along with an investigation of their photoluminescent properties and potential applications.
    https://doi.org/10.1016/j.tet.2025.134708
  5. Journal of Molecular Structure. (2024). Base catalyzed abnormal [3+2]-cycloaddition reaction of isatin N,N′-cyclic azomethine imine with malononitrile via C-3 umpolung of oxindole.
    https://doi.org/10.1016/j.molstruc.2023.137206
  6. ORCID. (n.d.). Firouz Matloubi Moghaddam, ORCID 0000-0002-8418-9888.
    https://orcid.org/0000-0002-8418-9888

Mohsen Goodarzi | Engineering | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Mohsen Goodarzi
Bu-Ali Sina University, Iran

Mohsen Goodarzi
Affiliation Bu-Ali Sina University
Country Iran
Google Scholar ID zCHbDWIAAAAJ
Documents 55
Citations 946
h-index 16
Subject Area Engineering
Event Computer Scientists Awards
ORCID 0000-0002-0321-2135

Mohsen Goodarzi is an engineering researcher affiliated with Bu-Ali Sina University in Iran. The supplied research record identifies 55 documents, 946 citations, and an h-index of 16. His recent publication activity addresses thermal engineering, natural-draft dry cooling systems, wind-energy conversion, and numerical analysis of rotating devices.

Abstract

The research profile presented here concerns engineering studies involving thermal performance, natural-draft dry cooling towers, Savonius wind turbines, and computational assessment of fluid-flow systems. Recent publications demonstrate a continuing focus on numerical investigation and performance evaluation. The record includes work published in Results in Engineering, Ocean Engineering, and Energy Sources, Part A, together with a 2026 preprint. [1]

Keywords

Natural draft dry cooling; thermal engineering; crosswind; Savonius turbine; computational fluid dynamics; renewable energy; engineering performance analysis.

Introduction

Goodarzi’s recent research is situated at the intersection of thermal-fluid engineering and renewable-energy technologies. Studies of dry cooling towers consider how geometric arrangements and environmental conditions influence thermal performance, while turbine research examines aerodynamic behavior, power generation, and force fluctuations. [2]

Research Profile

The supplied bibliometric record reports 55 documents, 946 citations, and an h-index of 16. These indicators provide quantitative context for the research record but do not independently measure methodological quality or the significance of individual contributions.

Research Contributions

  • Assessment of radiator-periphery arrangements in side-by-side natural-draft dry cooling towers under crosswind conditions.
  • Comparative numerical analysis of tandem natural-draft dry cooling towers with elliptical cross sections.
  • Investigation of end-plate effects on finite-height Savonius turbine performance.
  • Numerical examination of power generation and force fluctuations in side-by-side Savonius rotors.

Publications

  • Impact of radiators periphery arrangement on thermal performance of side-by-side natural draft dry cooling towers under crosswind conditions
    Publication date: 2026-06
    Journal: Results in Engineering
    Type: journal article
  • Comparative study on performance of two tandem natural draft dry cooling towers with elliptical cross sections
    Publication date: 2026
    Type: preprint
  • Numerical assessment of the effect of different end-plates on the performance of a finite-height Savonius turbine
    Publication date: 2025-06-16
    Journal: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
    Type: journal article
  • Numerical study on power generation versus force fluctuation of two side-by-side Savonius rotors
    Publication date: 2024-03
    Journal: Ocean Engineering
    Type: journal article

Research Impact

The reported citation count and h-index indicate measurable bibliometric visibility. The publication topics also address practical engineering questions involving cooling infrastructure and wind-energy conversion. Citation indicators should, however, be interpreted alongside publication quality, field norms, authorship, and research context. [5]

Award Suitability

For a Best Researcher Award profile, the documented publication record, engineering subject focus, reported bibliometric indicators, and recent peer-reviewed research provide identifiable criteria for academic consideration. Final recognition remains dependent on the applicable award committee’s eligibility requirements and assessment procedures. [6]

Conclusion

Mohsen Goodarzi’s supplied academic record reflects sustained engineering research spanning thermal systems, dry cooling technology, and wind-energy conversion. His recent publications provide a focused basis for documenting research activity and bibliometric visibility within engineering.

References

  1. Elsevier. (n.d.). Research publication record for Mohsen Goodarzi.
    Author profile
  2. Goodarzi, M. et al. (2026). Impact of radiators periphery arrangement on thermal performance of side-by-side natural draft dry cooling towers under crosswind conditions. Results in Engineering.
    https://doi.org/10.1016/j.rineng.2026.110134
  3. Goodarzi, M. et al. (2026). Comparative study on performance of two tandem natural draft dry cooling towers with elliptical cross sections. Preprint.
    https://doi.org/10.2139/ssrn.6800350
  4. Goodarzi, M. et al. (2025). Numerical assessment of the effect of different end-plates on the performance of a finite-height Savonius turbine. Energy Sources, Part A.
    https://doi.org/10.1080/15567036.2021.1976324
  5. Goodarzi, M. et al. (2024). Numerical study on power generation versus force fluctuation of two side-by-side Savonius rotors. Ocean Engineering.
    https://doi.org/10.1016/j.oceaneng.2024.117086
  6. Computer Scientists Awards. (2026). Awards and researcher recognition information.
    https://computerscientists.net/

Luis Manuel Díaz Angulo | Cosmology and Physics | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Luis Manuel Díaz Angulo
Universidad de Granada, Spain

Luis Manuel Díaz Angulo is a researcher affiliated with the Universidad de Granada, Spain, whose documented research profile includes work in cosmology and physics and computational methods for electromagnetic problems. His publication record includes recent studies of finite-difference time-domain (FDTD), discontinuous Galerkin time-domain (DGTD), multiconductor transmission-line methods, and computational analysis of Maxwell’s equations.

Luis Manuel Díaz Angulo
Affiliation Universidad de Granada
Country Spain
Scopus ID 35866858300
Documents 53
Citations 723
h-index 14
Subject Area Cosmology and Physics
Event Computer Scientists Awards
ORCID 0000-0002-1690-7892

Abstract

This article presents a scholarly recognition profile for Luis Manuel Díaz Angulo in connection with the Best Researcher Award. The profile records the supplied bibliometric indicators and selected recent publications concerning computational electromagnetics, numerical schemes, and transmission-line modelling. The available record identifies 53 documents, 723 citations, and an h-index of 14 under Scopus author identifier 35866858300. [1]

Keywords

Computational electromagnetics; Maxwell’s equations; FDTD; DGTD; multiconductor transmission lines; numerical computation; electromagnetic compatibility; LTI systems; circuit simulation; cosmology and physics.

Introduction

Díaz Angulo’s supplied publication record demonstrates activity at the intersection of mathematical modelling, numerical computation, and electromagnetic simulation. His recent work addresses the stability, accuracy, computational performance, and coupling of numerical methods used to represent electromagnetic systems. These subjects are relevant to computational physics and engineering applications involving Maxwell’s equations and transmission-line models. [2]

Research Profile

The supplied bibliometric profile associates the researcher with Universidad de Granada and the subject area of Cosmology and Physics. The reported Scopus record contains 53 documents, 723 citations, and an h-index of 14. These indicators provide quantitative information about the indexed publication and citation record but do not independently measure the quality or significance of individual research contributions. [1]

Research Contributions

  • Analysis of local stability for FDTD and DGTD schemes using linear time-invariant systems theory.
  • Evaluation of accuracy and computational performance across different DGTD implementations for Maxwell’s equations.
  • Numerical computation of in-cell parameters for unshielded multiwire structures and coupling between FDTD and multiconductor transmission-line methods.
  • Hybrid integration of a multiconductor transmission-line solver with Ngspice for line interconnections and terminations.

Publications

  1. Local stability of FDTD and DGTD schemes for Maxwell’s equations using LTI systems theory
    • Publication date: November 2026
    • Journal: Computers & Mathematics with Applications
    • Type: Journal Article
  2. An Accuracy and Computational Performance Study of Basis, Operator, and Curvilinear Implementations of DGTD for Maxwell’s Equations
    • Publication date: September 2026
    • Journal: IEEE Transactions on Antennas and Propagation
    • Type: Journal Article
  3. Numerical Computation of Unshielded Multiwires In-Cell Parameters for Coupling Between FDTD and MTL Methods Using Multipolar Expansions
    • Publication date: 2026
    • Journal: IEEE Transactions on Electromagnetic Compatibility
    • Type: Journal Article
  4. Hybridization of Multiconductor Transmission Line Solver With Circuit Solver Ngspice to Treat Line Interconnections and Terminations
    • Publication date: 2025
    • Journal: IEEE Transactions on Electromagnetic Compatibility
    • Type: Journal Article

Research Impact

The reported citation count and h-index indicate that the indexed research has accumulated measurable scholarly attention. The recent publications further show continuity in numerical electromagnetic modelling, including stability analysis, implementation comparisons, electromagnetic compatibility, and circuit coupling. [1]

Award Suitability

For the Best Researcher Award profile, the supplied record provides several documented evaluation dimensions: an indexed publication record, citation activity, h-index, institutional affiliation, and recent peer-reviewed research outputs. These materials can support an award committee’s independent assessment of research productivity, relevance, and scholarly contribution. The numerical indicators should be considered together with publication quality, authorship, originality, and other criteria established by the relevant award process.

Conclusion

Luis Manuel Díaz Angulo’s supplied profile documents research activity associated with computational electromagnetics and physics, supported by 53 indexed documents, 723 citations, and an h-index of 14. The listed 2025–2026 publications demonstrate continuing work on numerical methods for Maxwell’s equations and electromagnetic-system modelling. [1]

References

  1. Elsevier. (n.d.). Scopus author details: Luis Manuel Díaz Angulo, Author ID 35866858300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=35866858300
  2. Universidad de Granada. (n.d.). Research and institutional information.
  3. Díaz Angulo, L. M. (2026). Local stability of FDTD and DGTD schemes for Maxwell’s equations using LTI systems theory. Computers & Mathematics with Applications.
    https://doi.org/10.1016/j.camwa.2026.09.006
  4. Díaz Angulo, L. M. (2026). An Accuracy and Computational Performance Study of Basis, Operator, and Curvilinear Implementations of DGTD for Maxwell’s Equations. IEEE Transactions on Antennas and Propagation.
    https://doi.org/10.1109/TAP.2026.3701027
  5. Díaz Angulo, L. M. (2026). Numerical Computation of Unshielded Multiwires In-Cell Parameters for Coupling Between FDTD and MTL Methods Using Multipolar Expansions. IEEE Transactions on Electromagnetic Compatibility.
    https://doi.org/10.1109/TEMC.2026.3713195
  6. Díaz Angulo, L. M. (2025). Hybridization of Multiconductor Transmission Line Solver With Circuit Solver Ngspice to Treat Line Interconnections and Terminations. IEEE Transactions on Electromagnetic Compatibility.
    https://doi.org/10.1109/TEMC.2025.3560361

Adedoyin Adeyemi | Machine Learning | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Adedoyin Adeyemi
Federal University of Technology, Akure, Nigeria

Adedoyin Adeyemi
Affiliation Federal University of Technology, Akure
Country Nigeria
Scopus ID 59368133300
Documents 3
Citations 31
h-index 2
Subject Area Machine Learning
Event Computer Scientists Awards
ORCID 0009-0007-4701-1259

Adedoyin Adeyemi is a researcher affiliated with the Federal University of Technology, Akure, Nigeria, whose documented research activity includes work related to environmental modelling, hydrological analysis, land-use change and computational approaches relevant to machine learning. Available bibliographic information records three documents, 31 citations and an h-index of 2. These indicators provide a quantitative overview of the researcher’s indexed scholarly output and citation activity.

Abstract

The research profile of Adedoyin Adeyemi includes scholarly contributions addressing environmental and hydrological systems, with particular relevance to computational modelling of land-use change and flood-related processes. Recent collaborative research examines urbanization-driven land-cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models, as well as hydrological responses and flood hazards under projected climate and land-use change. These studies demonstrate an interdisciplinary connection between environmental science, spatial modelling, hydrology and computational analysis. [1] [2]

Keywords

  • Machine Learning
  • Hydrological Modelling
  • Land-Use Change
  • Flood Hazard Mapping
  • Ogun River Basin

Introduction

Research on flood risk increasingly combines hydrological modelling with spatial representations of land-use and environmental change. The Ogun River Basin provides an important setting for examining how urbanization and changing land cover can influence hydrological behaviour. Adeyemi’s collaborative publications contribute to this research area through computational modelling and assessment of projected environmental conditions. The work connects empirical geographical information with models capable of representing terrain, land-cover transitions and hydrological response. [1] [2]

Research Profile

Bibliographic records identify Adeyemi with three indexed documents and 31 citations, with a reported h-index of 2. The available publication evidence places his research within multidisciplinary environmental and computational investigations. Such work involves collaboration across hydrology, environmental modelling, geography and data-driven analytical methods.

Research Contributions

A notable contribution is the study of urbanization-driven land-cover change and floodplain transformation in the Ogun River Basin using Height Above Nearest Drainage (HAND) and Cellular Automata–Markov (CA-Markov) models. The approach provides a framework for examining relationships between terrain, projected land-cover transitions and floodplain characteristics. [1]

A second contribution concerns hydrological response and flood hazard mapping under projected climate and land-use change. Published in Water Science & Technology in 2026, the study examines how changing environmental conditions may influence hydrological behaviour and flood hazards in the basin. [2]

Publications

  1. “Urbanization driven land cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models.
  2. “Hydrological response and flood hazard mapping of the Ogun River Basin under projected climate and land-use change.” Water Science & Technology, 2026-05-15.

Research Impact

The documented citation record indicates that Adeyemi’s indexed publications have received scholarly attention. The research also has practical relevance to understanding flood hazards, land-use transformation and environmental planning. By combining spatial and hydrological modelling approaches, the reported studies contribute evidence that can support further research into basin-scale flood assessment and climate-sensitive land-use planning. [2]

Award Suitability

For an Innovative Research Award assessment, the documented profile presents relevant evidence of interdisciplinary research involving computational models, environmental change and flood-risk analysis. The use of HAND and CA-Markov approaches, together with hydrological hazard assessment under projected scenarios, provides a substantive basis for considering the research within an innovation-oriented academic recognition framework. Award decisions, however, should be based on the complete nomination record and independently verified evidence.

Conclusion

Adedoyin Adeyemi’s documented research profile reflects participation in multidisciplinary studies of hydrology, land-use change and flood hazards. His publications concerning the Ogun River Basin demonstrate the application of computational and spatial modelling to contemporary environmental questions. The available bibliographic indicators and publication record provide a concise scholarly basis for evaluating his research contributions in the context of the Innovative Research Award.

References

  1. Springer Nature. (2026). Urbanization driven land cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models.
    https://doi.org/10.1007/s44288-026-00529-y
  2. IWA Publishing. (2026). Hydrological response and flood hazard mapping of the Ogun River Basin under projected climate and land-use change. Water Science & Technology.
    https://doi.org/10.2166/wst.2026.274
  3. Elsevier. (n.d.). Scopus author details: Adedoyin Adeyemi, Author ID 59368133300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59368133300
  4. ORCID. (n.d.). Adedoyin Adeyemi, ORCID iD 0009-0007-4701-1259.
    https://orcid.org/0009-0007-4701-1259
  5. Computer Scientists Awards. (n.d.). Award and nomination information.
    https://computerscientists.net/

Saravanan S | Engineering | Excellence in Research Award

Published on by Computer Scientists Awards

Excellence in Research Award

Saravanan S
K.K Wagh Institute Of Engineering Education and Research, Nashik, India

Saravanan S
Affiliation K.K Wagh Institute Of Engineering Education and Research, Nashik
Country India
Scopus ID 57217569884
Documents 11
Citations 91
h-index 5
Subject Area Engineering
Event Computer Scientists Awards
ORCID 0000-0002-4671-1727

Saravanan S is an engineering researcher affiliated with K.K Wagh Institute Of Engineering Education and Research, Nashik. The documented research profile comprises 11 Scopus-indexed documents, 91 citations, and an h-index of 5. The publication record is particularly associated with power electronics, electric-vehicle charging systems, power-quality improvement, converter technologies, and intelligent control methods. [1]

Abstract

This academic recognition profile presents the research record of Saravanan S in engineering, with emphasis on power electronics and electric-vehicle energy systems. His documented publications address converter evaluation, wireless charging, fuzzy logic control, active power filtering, and power-quality enhancement. These topics represent practical engineering challenges involving efficient energy conversion, intelligent control, and improved electrical performance. [2]

Keywords

  • Power Electronics
  • Electric Vehicles
  • Power Quality
  • DC–DC Converters
  • Intelligent Control

Introduction

Modern electrical engineering increasingly requires power-conversion systems that combine efficiency, controllability, and power-quality performance. Saravanan S’s publication record reflects this research direction through studies of SEPIC converters, wireless EV charging, fuzzy logic control, and active power filters. [3]

Research Profile

The available bibliographic profile records 11 documents and 91 citations, with an h-index of 5. The research is situated within Engineering and demonstrates a publication focus on applied electrical and power-electronic systems. [1]

Research Contributions

  • Evaluation of a bridgeless DCM SEPIC converter using a sliding-mode control approach for power-factor correction. [4]
  • Investigation of wireless charging systems for electric vehicles using DC–DC conversion and fuzzy logic control. [5]
  • Application of optimal active power filtering to mitigate harmonics and improve EV charging-station power quality. [6]

Publications

  1. “Evaluation and Improvement of a Bridgeless DCM SEPIC Converter for Power Factor Correction Employing a SMC Controller.” MAPAN, 2026. DOI.
  2. “Evaluation and improvement of wireless charging system with DC–DC converter for EV employing the fuzzy logic controller.” Multiscale and Multidisciplinary Modeling, Experiments and Design, 2024. DOI.
  3. “Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station.” e-Prime – Advances in Electrical Engineering, Electronics and Energy, 2024. DOI.

Research Impact

The reported citation count of 91 and h-index of 5 provide quantitative indicators of scholarly visibility. The research themes also align with engineering priorities surrounding electrified transportation, efficient power conversion, charging infrastructure, and power-quality management. [1]

Award Suitability

The Excellence in Research Award profile is supported by a documented engineering publication record addressing contemporary power-electronic and electric-vehicle applications. The combination of indexed research output, citations, and focused technical contributions provides an objective basis for academic recognition within the stated subject area. [2]

Conclusion

Saravanan S’s documented research demonstrates sustained engagement with applied engineering problems involving power conversion, EV charging, intelligent control, and power quality. The available scholarly metrics and publications provide a concise evidence base for an Excellence in Research Award recognition profile.

References

  1. Elsevier. (n.d.). Scopus author details: Saravanan S, Author ID 57217569884. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57217569884
  2. ORCID. (n.d.). Saravanan S, ORCID 0000-0002-4671-1727.
    https://orcid.org/0000-0002-4671-1727
  3. Computer Scientists Awards. (n.d.). Computer Scientists Awards.
    https://computerscientists.net/
  4. Saravanan S. (2026). Evaluation and Improvement of a Bridgeless DCM SEPIC Converter for Power Factor Correction Employing a SMC Controller. MAPAN.
    https://doi.org/10.1007/s12647-026-00955-w
  5. Saravanan S. (2024). Evaluation and improvement of wireless charging system with DC–DC converter for EV employing the fuzzy logic controller. Multiscale and Multidisciplinary Modeling, Experiments and Design.
    https://doi.org/10.1007/s41939-024-00491-7
  6. Saravanan S. (2024). Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station. e-Prime – Advances in Electrical Engineering, Electronics and Energy.
    https://doi.org/10.1016/j.prime.2024.100527

Om Sahu | Computer Science & Artificial Intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Om Sahu
University Of Petroleum & Energy Studies (UPES) Dehradoon, India

Om Sahu
Affiliation University Of Petroleum & Energy Studies (UPES) Dehradoon
Country India
Scopus ID 57215754785
Documents 10
Citations 47
h-index 4
Subject Area Computer Science & Artificial Intelligence
Event Computer Scientists Awards
ORCID 0000-0003-0515-399X

Om Sahu is a researcher affiliated with the University Of Petroleum & Energy Studies (UPES) Dehradoon, India, whose documented research activity is associated with computer science and artificial intelligence. The available scholarly record identifies 10 documents, 47 citations, and an h-index of 4. These indicators provide a bibliometric snapshot of the researcher’s indexed publication activity and citation impact.

Abstract

Om Sahu’s research profile reflects activity in computer science and artificial intelligence, with published work applying deep learning to practical detection problems. His documented publications include research on multi-disease detection and smart-factory product defect detection. The available bibliometric information records 10 documents, 47 citations, and an h-index of 4. [1]

Keywords

Artificial intelligence; deep learning; computer science; machine learning; medical image analysis; disease detection; smart manufacturing; product defect detection; computer vision; intelligent systems.

Introduction

Deep learning is increasingly used for classification, recognition, and automated detection across scientific and industrial applications. Within this broader area, Sahu’s documented work addresses both healthcare-oriented detection and manufacturing-oriented quality inspection. His publications indicate an applied research orientation in which computational methods are used to address domain-specific recognition and decision-support problems. [2]

Research Profile

The researcher’s indexed profile is associated with the University Of Petroleum & Energy Studies (UPES) Dehradoon and the subject area of computer artificial intelligence. The reported Scopus identifier is 57215754785, while the ORCID identifier is 0000-0003-0515-399X. The bibliometric record supplied for this recognition page comprises 10 documents, 47 citations, and an h-index of 4. [1]

Research Contributions

Sahu’s recent publications demonstrate the application of deep learning to two distinct detection contexts. The first examines multi-disease detection, while the second focuses on identifying product defects in smart-factory environments. Together, these studies illustrate the use of computational intelligence and pattern-recognition techniques across healthcare and industrial settings. [2] [3]

Publications

  • Multi-Disease Detection Using Deep Learning. O.P. Sahu, Om Prakash, A. Singh, Arya, D. Prakash, Deo. ISED 2025 – 13th International Conference on Intelligent Systems and Embedded Design, Proceedings, 2025. IEEE Xplore. [2]
  • Smart Factory Product Defect Detection Using Deep Learning. Prakash, D.; Singh, A.; Bais, Y.D.S.; Majumdar, V.; Patel, E.; Sahu, O.P. Lecture Notes in Mechanical Engineering, conference paper, 2025. [3]

Research Impact

The reported 47 citations and h-index of 4 indicate measurable scholarly attention to the researcher’s indexed output. [1] The subject applications also demonstrate relevance beyond a single domain, connecting artificial intelligence methods with medical detection and smart manufacturing. The available evidence supports describing the profile as an emerging applied research contribution rather than making broader claims about field-wide influence.

Award Suitability

Based on the supplied publication and bibliometric information, Om Sahu presents a documented research profile relevant to the Best Researcher Award category at the Computer Scientists Awards. The combination of indexed publications, citations, and research activity in artificial intelligence provides objective material for consideration. Final award decisions should remain subject to the applicable evaluation criteria and review by the responsible award committee.

Conclusion

Om Sahu’s academic profile reflects research activity in computer artificial intelligence, particularly the application of deep learning to disease detection and smart-factory quality inspection. With 10 indexed documents, 47 citations, and an h-index of 4 in the supplied record, the profile provides a measurable basis for academic recognition. [1] His documented publications further indicate an applied orientation connecting artificial intelligence with practical research challenges.

References

  1. Elsevier. (n.d.). Scopus author details: Om Sahu, Author ID 57215754785. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57215754785
  2. Sahu, O.P.; Singh, A.; Prakash, D. et al. (2025). Multi-Disease Detection Using Deep Learning. ISED 2025 – 13th International Conference on Intelligent Systems and Embedded Design, Proceedings.
    https://ieeexplore.ieee.org/document/11405003
  3. Prakash, D.; Singh, A.; Bais, Y.D.S.; Majumdar, V.; Patel, E.; Sahu, O.P. (2025). Smart Factory Product Defect Detection Using Deep Learning. Lecture Notes in Mechanical Engineering. DOI: 10.1007/978-981-95-0063-5_34.
    https://doi.org/10.1007/978-981-95-0063-5_34
  4. ORCID. (n.d.). Om Sahu, ORCID iD 0000-0003-0515-399X.
    https://orcid.org/0000-0003-0515-399X
  5. IEEE. (2025). IEEE Xplore Digital Library: Multi-Disease Detection Using Deep Learning.
    https://ieeexplore.ieee.org/document/11405003
  6. Springer Nature. (2025). Lecture Notes in Mechanical Engineering: Smart Factory Product Defect Detection Using Deep Learning.
    https://link.springer.com/chapter/10.1007/978-981-95-0063-5_34

Lin Wang | Cybersecurity and Cryptography | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Lin Wang
China University of Petroleum (East China), China

Lin Wang
Affiliation China University of Petroleum (East China)
Country China
Scopus ID 57215074985
Documents 35
Citations 1,443
h-index 19
Subject Area Cybersecurity and Cryptography
Event Computer Scientists Awards
ORCID 0000-0001-5644-8865

Lin Wang is a researcher affiliated with China University of Petroleum (East China), China. His reported scholarly record includes 35 documents, 1,443 citations, and an h-index of 19. The research publications supplied for this profile address catalytic materials, single-atom catalysts, photocatalytic carbon dioxide conversion, oxygen evolution, and membrane-based ion transport. These topics demonstrate a research portfolio situated within advanced materials and chemical-energy technologies. The following article presents a structured academic overview based on the supplied bibliographic information and does not constitute an independent verification of authorship, metrics, or award eligibility.

Abstract

This academic profile examines the reported research activities of Lin Wang in the context of the Innovative Research Award. The supplied publications describe investigations into covalent triazine frameworks, ruthenium-based single-site catalysts, manganese-doped oxide nanoarrays, two-dimensional metal–organic framework membranes, and iridium single-atom catalysts. Collectively, these studies concern the design of functional materials for energy conversion and selective transport. The profile summarizes the available research themes, publication record, and reported bibliometric indicators while distinguishing documented information from award assessment.

Keywords

Innovative Research Award; Lin Wang; China University of Petroleum; photocatalytic CO2 reduction; single-atom catalysts; ruthenium catalysis; oxygen evolution; metal–organic frameworks; membrane separation; advanced materials.

Introduction

Contemporary materials research increasingly combines nanoscale structural engineering with catalytic and separation technologies. Research into carbon dioxide conversion, water oxidation, and selective ion transport seeks to improve efficiency, stability, and material functionality. The publications supplied for Lin Wang identify several studies within this broad scientific landscape. Their reported approaches include coordination-environment engineering, template-directed synthesis, and the construction of hybrid membrane architectures. [2]

Research Profile

The supplied profile records 35 documents, 1,443 citations, and an h-index of 19, associated with Scopus author identifier 57215074985. These figures are presented as reported values and may change as bibliographic databases are updated. The listed subject area is Cybersecurity and Cryptography, whereas the supplied publication titles primarily concern catalysis and advanced materials. Consequently, subject classification and publication authorship should be independently checked before formal institutional or award use.

Research Contributions

The supplied studies indicate several recurring research directions:

  • Single-site ruthenium coordination within covalent triazine frameworks for photocatalytic carbon dioxide reduction. [1]
  • Manganese-doped ruthenium–titanium oxide nanoarrays designed for acidic oxygen evolution. [2]
  • Two-dimensional metal–organic framework and graphene oxide hybrid membranes for selective ion transport. [3]
  • Coordination engineering of iridium single-atom catalysts for carbon dioxide hydrogenation to formic acid. [4]

Publications

The following publication titles and author lists are reproduced from the supplied information. Lin Wang appears among the listed authors of each work; the exact contribution and publication metadata require verification from the original records.

  1. Lu Wang, Lin Wang, Saifei Yuan, Liping Song, Hao Ren, Yuankang Xu, Manman He, Yuheng Zhang, Hang Wang, Yichao Huang, Tong Wei, Jiangwei Zhang, Yuichiro Himeda, and Zhuangjun Fan. Covalently-bonded single-site Ru-N2 knitted into covalent triazine frameworks for boosting photocatalytic CO2 reduction.
  2. Xinyuan Qin, Ruili Gao, Yuan Li, Junyu Zhang, Lin Wang, Yan Zhou, Lianming Zhao, Chuande Wu, and Yichao Huang. In-situ construction of Mn-doped RuTiOx nanoarray via template-directed replacement reaction for highly efficient and durable acidic oxygen evolution.
  3. Yu Cheng, Hang Wang, Longyu Wang, Zhong Wang, Zhibang Liu, Ziyi Zhang, Qiang Ma, Chao Zhang, Xiaoxue Zhang, Dongmin Bian, Lin Wang, and Chuan-De Wu. Interlayer-engineered 2D MOF-GO hybrid membranes with sub-nanometer channels for selective ion transport.
  4. Yuankang Xu, Lin Wang, Yuanying Liu, Linghao Liu, Yanzhuo Zhao, Tao Shu, Fanqiang Meng, Hang Wang, Tong Wei, Yichao Huang, and Zhuangjun Fan. Coordination engineering directs d-band center optimization for efficient CO2 hydrogenation to formic acid on Ir–Nx single-atom catalysts.

Research Impact

The reported citation count and h-index suggest measurable scholarly visibility. The supplied publication themes are relevant to energy conversion, catalytic reaction engineering, and membrane-based separations. However, the practical, industrial, or societal impact of these studies cannot be established solely from titles and bibliometric indicators. Such assessment would require analysis of experimental results, independent citations, technological adoption, and verified research contributions.

Award Suitability

The supplied research themes may be relevant to an Innovative Research Award because they concern the development and optimization of advanced functional materials. Evidence potentially relevant to an award assessment includes originality, methodological rigor, reproducibility, documented outcomes, and the significance of the research problem. Final suitability should be determined by the Computer Scientists Awards committee using verified publications, eligibility requirements, and an independent evaluation of the nominee’s contribution.

Conclusion

Lin Wang’s supplied profile combines reported bibliometric indicators with publications addressing catalytic materials, carbon dioxide conversion, oxygen evolution, and selective ion transport. The listed works provide a basis for documenting research activity in advanced materials and energy-related technologies. Verification of author identity, database metrics, institutional affiliation, and individual contributions remains necessary before the profile is used as a definitive academic or award record.

References

  1. Wang, L., Wang, L., et al. Covalently-bonded single-site Ru-N2 knitted into covalent triazine frameworks for boosting photocatalytic CO2 reduction. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S0926337322010384
  2. Qin, X., Gao, R., et al. In-situ construction of Mn-doped RuTiOx nanoarray via template-directed replacement reaction for highly efficient and durable acidic oxygen evolution. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S0021979726013111
  3. Cheng, Y., Wang, H., et al. Interlayer-engineered 2D MOF-GO hybrid membranes with sub-nanometer channels for selective ion transport. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S1005030226002100
  4. Xu, Y., Wang, L., et al. Coordination engineering directs d-band center optimization for efficient CO2 hydrogenation to formic acid on Ir–Nx single-atom catalysts. ScienceDirect.
    https://www.sciencedirect.com/science/article/pii/S2772834X26000576
  5. Elsevier. (n.d.). Scopus author details: Lin Wang, Author ID 57215074985. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57215074985
  6. ORCID. (n.d.). ORCID record: Lin Wang, ORCID iD 0000-0001-5644-8865. ORCID.
    https://orcid.org/0000-0001-5644-8865

Sara Amjad | Computer Science | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Sara Amjad
Aligarh Muslim University, India

Sara Amjad
Affiliation Aligarh Muslim University
Country India
Scopus ID 57222341800
Documents 2
Citations 483
h-index 2
Subject Area Computer Science
Event Computer Scientists Awards
Google Scholar ID MCnRe20AAAAJ&hl

Sara Amjad is a researcher associated with Aligarh Muslim University whose scholarly record includes work spanning cellular metabolism, molecular signaling, genetics, neuroscience, and computationally relevant biomedical research. Her documented publications include collaborative review research examining NAD+-dependent cellular processes and the genetics of glutamate signaling in autism spectrum disorder. These works provide evidence of interdisciplinary engagement across molecular biology, health sciences, and data-informed research.

Abstract

Sara Amjad’s research profile is represented by collaborative publications addressing biological signaling, metabolism, genetics, and neurodevelopment. Her work on NAD+ reviews its importance in energy metabolism, DNA repair, gene expression, cellular stress, aging, cancer, and neurodegeneration. [1] A second publication examines genetic alterations involving glutamate and its receptors in autism spectrum disorder, connecting molecular genetics with neuroscience and non-invasive imaging. [2]

Keywords

  • Computer Science
  • NAD+ metabolism
  • Cellular signaling
  • Genetics
  • Neuroscience
  • Autism spectrum disorder

Introduction

The supplied publication record indicates an interdisciplinary research orientation. In the NAD+ review, the authors describe NAD+ as a critical coenzyme involved in bioenergetics and cellular signaling, while discussing its relationship with aging, metabolic disorders, cancer, and neurodegeneration. [1] The autism spectrum disorder review evaluates genetic changes affecting glutamate and glutamate receptors and considers imaging approaches for investigating altered glutamatergic pathways. [2]

Research Profile

The provided Scopus information lists two documents, 483 citations, and an h-index of 2. These supplied metrics should be interpreted as a profile snapshot because citation databases can change over time. The publications also demonstrate collaboration across institutions and disciplines, including molecular medicine, genetics, neuroscience, imaging, and biomedical research.

Research Contributions

Amjad’s documented contribution includes participation in research that synthesizes evidence across complex biological systems. The NAD+ article identifies cellular energy metabolism, DNA damage repair, gene expression, and stress response as major areas influenced by NAD+ biology. [1] The genetics review addresses glutamatergic signaling and receptor biology in relation to autism spectrum disorder, emphasizing interactions among genetics, neuronal function, and imaging. [2]

Publications

  • Role of NAD+ in regulating cellular and metabolic signaling pathways, Molecular Metabolism, 2021, 49:101195.. [1]
  • Genetics of glutamate and its receptors in autism spectrum disorder, Molecular Psychiatry, 2022, 27:2380–2392. . [2]

Research Impact

The supplied citation count of 483 indicates that the documented publication set has received substantial scholarly attention relative to its size. The cited research addresses topics with broad biomedical relevance, including metabolism, cellular signaling, genetics, and neurodevelopment. The Nature publication also records citation and access metrics on its publisher page, illustrating continuing visibility of the research area. [2]

Award Suitability

Based on the supplied profile and publication evidence, Sara Amjad can be considered for an Innovative Research Award in recognition of interdisciplinary scholarly participation and research addressing contemporary questions in cellular metabolism, molecular genetics, neuroscience, and biomedical science. The assessment should remain subject to the award committee’s independent verification of affiliation, authorship, bibliographic records, and current research metrics.

Conclusion

Sara Amjad’s documented research contributions demonstrate engagement with interdisciplinary biomedical questions and collaborative scientific scholarship. Her publications on NAD+ signaling and glutamate genetics provide a foundation for evaluating her research profile within an innovation-focused academic recognition framework.

References

  1. Amjad, S. et al. (2021). Role of NAD+ in regulating cellular and metabolic signaling pathways. Molecular Metabolism, 49, 101195.
    https://doi.org/10.1016/j.molmet.2021.101195
  2. Nisar, S. et al. (2022). Genetics of glutamate and its receptors in autism spectrum disorder. Molecular Psychiatry, 27, 2380–2392.
    https://doi.org/10.1038/s41380-022-01506-w
  3. PubMed. (2021). Role of NAD+ in regulating cellular and metabolic signaling pathways. PMID 33609766.
    https://pubmed.ncbi.nlm.nih.gov/33609766/
  4. PubMed. (2022). Genetics of glutamate and its receptors in autism spectrum disorder. PMID 35296811.
    https://pubmed.ncbi.nlm.nih.gov/35296811/
  5. Elsevier. (n.d.). Role of NAD+ in regulating cellular and metabolic signaling pathways. Molecular Metabolism.
    https://www.sciencedirect.com/science/article/pii/S2212877821000351
  6. Springer Nature. (2022). Genetics of glutamate and its receptors in autism spectrum disorder. Molecular Psychiatry.
    https://www.nature.com/articles/s41380-022-01506-w

Saifal Abbas  | Engineering | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Saifal Abbas
Chang’an University, China

Saifal Abbas
Affiliation Chang’an University
Country China
Scopus ID 60811683500
Documents 3
Subject Area Engineering
Event Computer Scientists Awards
ORCID 0009-0005-6121-5309

Saifal Abbas is an engineering researcher affiliated with Chang’an University, China. The available scholarly profile records three documents, with zero citations and an h-index of zero at the stated profile snapshot. His listed publications address pavement engineering, asphalt sustainability, pavement condition assessment, artificial intelligence, and computer-vision-based infrastructure monitoring. These themes provide an interdisciplinary basis for consideration under an Innovative Research Award.

Abstract

The research profile of Saifal Abbas is centered on engineering applications involving transportation infrastructure, pavement assessment, sustainable asphalt materials, and artificial-intelligence-assisted condition monitoring. His documented publications demonstrate a progression from neural-network-based pavement management to high-reclaimed-asphalt-pavement mixtures and lightweight computer-vision approaches for crack detection. [1] [2] [3]

Keywords

  • Pavement engineering
  • Artificial intelligence
  • Computer vision
  • Sustainable asphalt
  • Infrastructure monitoring

Introduction

Modern transportation engineering increasingly combines materials science, data-driven assessment, and automated infrastructure inspection. Abbas’s publication record reflects this convergence, particularly through research examining pavement condition, reclaimed asphalt mixtures, and machine-learning-supported crack detection. [1] [2]

Research Profile

The available profile identifies Engineering as the principal subject area. The three documented works cover artificial neural networks for pavement condition and maintenance management, sustainability and performance of high-RAP asphalt mixtures, and a lightweight YOLO26s-based approach for multi-class pavement crack detection. [1] [2] [3]

Research Contributions

The publication portfolio connects infrastructure management with computational methods and sustainable materials. The artificial-neural-network study addresses data-driven pavement condition assessment, while the high-RAP study examines performance, durability, sustainability, and emerging technologies. The 2026 Sensors article further applies lightweight object detection to automated pavement crack identification, with an emphasis on edge deployment. [1] [2] [3]

Publications

  1. Evaluating YOLO26s for Multi-Class Pavement Crack Detection: A Lightweight Approach for Sustainable Edge Deployment.
    Sensors, 12 August 2026.
  2. High-RAP Asphalt Mixtures (>40%): Mechanical Performance, Durability, Sustainability, and Emerging Technologies.
    Scientific Journal of Engineering Research, 5 December 2025.
  3. Evaluating Pavement Condition Index and Maintenance Management using Artificial Neural Networks.
    European Journal of Applied Science, Engineering and Technology, 1 March 2024.

Research Impact

The current bibliometric profile records three documents, zero citations, and an h-index of zero. These metrics should be interpreted in the context of the documented publication record and its recent chronology rather than as a standalone assessment of research quality. The research topics nevertheless address practical engineering challenges involving pavement maintenance, material sustainability, and automated inspection.

Award Suitability

For the Innovative Research Award category, the profile presents a relevant combination of engineering research and computational innovation. In particular, the integration of lightweight deep-learning-based pavement inspection with sustainable infrastructure objectives provides a coherent basis for academic recognition, subject to the award committee’s independent evaluation and eligibility criteria.

Conclusion

Saifal Abbas’s documented research portfolio demonstrates work at the intersection of pavement engineering, sustainable materials, artificial intelligence, and automated infrastructure assessment. The three listed publications establish a focused research trajectory with potential relevance to data-driven and sustainable transportation engineering.

References

  1. Elsevier. (n.d.). Scopus author details: Saifal Abbas, Author ID 60811683500. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60811683500
  2. MDPI. (2026). Evaluating YOLO26s for Multi-Class Pavement Crack Detection: A Lightweight Approach for Sustainable Edge Deployment. Sensors. DOI: https://doi.org/10.3390/s26165113
  3. Scientific Journal of Engineering Research. (2025). High-RAP Asphalt Mixtures (>40%): Mechanical Performance, Durability, Sustainability, and Emerging Technologies.
    DOI: https://doi.org/10.64539/sjer.v1i4.2025.321
  4. European Journal of Applied Science, Engineering and Technology. (2024). Evaluating Pavement Condition Index and Maintenance Management using Artificial Neural Networks.
    DOI: https://doi.org/10.59324/ejaset.2024.2(2).15
  5. ORCID. (n.d.). ORCID record: Saifal Abbas. ORCID.
    https://orcid.org/0009-0005-6121-5309
  6. Computer Scientists Awards. (n.d.). Computer Scientists Awards official website.
    https://computerscientists.net/