Mr.Mohammad Nadeem Rahimi | Data Mining | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

Mr. Mohammad Nadeem Rahimi
Kurukshetra University, India

Mr. Mohammad Nadeem Rahimi
Affiliation Kurukshetra University
Country India
Scopus ID 60592866100
Documents 1
Subject Area Data Mining
Event Global CSE Awards

Mr. Mohammad Nadeem Rahimi is a researcher affiliated with Kurukshetra University, India, whose available Scopus author record identifies Data Mining as his subject area. The indexed profile currently reports one document, providing a concise bibliometric snapshot of his documented scholarly activity. [1][2][3]

Abstract

Mr. Mohammad Nadeem Rahimi is a researcher affiliated with Kurukshetra University, India, with a Scopus author profile identifying Data Mining as his subject area. The currently available record lists one document .These bibliometric indicators provide a limited snapshot of his indexed scholarly activity and should be interpreted alongside verified publications, institutional records, research quality, and future outputs. The profile offers a basis for recognition under the Best Researcher Award, while transparent evaluation remains important. Related literature demonstrates the importance of data-driven analysis in renewable energy and environmental research, providing broader contextual relevance. [1][2][3]

Keywords

Data Mining;Bibliometrics; Research Profile; Scholarly Publications; Research Impact; Computational Research.

Introduction

Mr. Mohammad Nadeem Rahimi is a researcher affiliated with Kurukshetra University, India, whose Scopus-indexed profile identifies Data Mining as a subject area. His academic record currently lists one document . The profile provides a basis for documenting his research identity and scholarly development. [1][2][3]

Research Profile

Rahimi’s available Scopus author record identifies Kurukshetra University as his institutional affiliation and Data Mining as his subject area. The profile reports one indexed document.These indicators describe the currently visible bibliometric record rather than the complete scope of academic activity.[1][2][3]

Research Contributions

The available record provides limited evidence for assessing Rahimi’s research contributions directly. His listed subject area, Data Mining, places his profile within computational methods for extracting patterns and knowledge from data. Broader research themes should therefore be evaluated from verified publications, institutional records, and indexed scholarly outputs as additional evidence becomes available.[1][2][3]

Publications

The Scopus record currently associated with Rahimi lists one document, while the supplied scholarly references provide contextual literature concerning renewable-energy diffusion and carbon-dioxide emissions. These studies illustrate quantitative, data-oriented research themes relevant to contemporary analysis, although they should not be attributed to Rahimi without direct authorship verification. [1][2][3]

Research Impact

Rahimi’s present bibliometric profile records,indicating that measurable Scopus citation impact is currently limited. Such indicators can change as publications are indexed and cited. Consequently, research impact should be interpreted cautiously and supplemented with publication quality, collaboration, practical relevance, and subsequent scholarly recognition. [1][2][3]

Award Suitability

The Best Researcher Award may be considered in relation to Rahimi’s documented research identity, institutional affiliation, and subject-area classification in Data Mining. However, award suitability should be determined through transparent assessment of verified publications, originality, research quality, contributions, and broader scholarly evidence rather than relying solely on current citation indicators. [1][2][3]

Conclusion

Mr. Mohammad Nadeem Rahimi has a documented Scopus author profile associated with Kurukshetra University and Data Mining. The currently visible record contains one document.Continued publication and indexing may provide additional evidence for evaluating his research trajectory, influence, and future recognition.[1][2][3]

References

  1. Elsevier. (2026). Drivers of renewable energy diffusion in global economy: Long-run panel evidence. ScienceDirect.
    https://www.sciencedirect.com/science/article/pii/S2666759226001344
  2. Springer Nature. (2026). Drivers of carbon dioxide emissions in Brazil, Russia, India, China, and South Africa: A panel vector auto-regression approach. Journal of Economic Structures.
    https://link.springer.com/article/10.1007/s41685-026-00425-w
  3. Elsevier. (n.d.). Scopus author details: Mohammad Nadeem Rahimi, Author ID 60592866100. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60592866100

Dong Xia | Big Data | Best Innovator Award

Published on by Global CSE Awards

Best Innovator Award

                  Dong Xia
Affiliation Chongqing Jiaotong University
Country China
Documents 10
Subject Area Big Data
Event Global CSE Awards
ORCID 0000-0002-0156-3345

Dong Xia, affiliated with Chongqing Jiaotong University, has contributed to research in the field of big data applications for intelligent transportation systems. His published studies investigate vehicle trajectory analysis, transportation planning, urban mobility, and traffic simulation using large-scale datasets. These research activities demonstrate continued academic engagement with practical transportation challenges supported by modern data-driven analytical methods.[1]

Abstract

Dong Xia has conducted research focusing on transportation engineering supported by big data technologies and intelligent traffic analysis. His published studies explore electronic vehicle registration identification data, commuter behavior recognition, traffic estimation, simulation, and customized urban transportation planning. These investigations contribute to evidence-based mobility management and demonstrate practical applications of data analytics in transportation systems. Such scholarly work reflects interdisciplinary integration between transportation engineering, computational analytics, and urban planning while supporting innovation relevant to smart city development and intelligent transportation research.[1][2][3]

Keywords

Big Data, Intelligent Transportation, Traffic Simulation, Urban Mobility, Electronic Registration Identification, Transportation Planning, Smart Cities, Vehicle Analytics, Data Mining, Transportation Engineering.

Introduction

Dong Xia’s research emphasizes the application of big data technologies to transportation engineering, particularly through large-scale vehicle information analysis and intelligent mobility planning. His studies support improved transportation efficiency by integrating computational methods with practical urban traffic management strategies for modern smart city environments.[1]

Research Profile

Affiliated with Chongqing Jiaotong University, Dong Xia has authored scholarly publications addressing transportation data analytics, intelligent transportation systems, and commuter behavior recognition. His academic profile demonstrates sustained interest in applying advanced computational techniques to solve practical transportation planning and urban mobility challenges.[3]

Research Contributions

His contributions include methodologies for traffic estimation, commuter identification, transportation simulation, and customized bus route design using electronic registration identification datasets. These studies illustrate how large-scale transportation data can support informed decision-making and enhance sustainable urban transportation planning initiatives.[1][2]

Publications

Published research includes studies on road network traffic estimation, customized public transportation services, and recognition of private vehicle commuting behaviors. These publications demonstrate the practical application of transportation data science while contributing knowledge relevant to intelligent transportation systems and urban infrastructure development.[1][2][3]

Research Impact

The research supports data-driven transportation management by providing analytical approaches for understanding traffic dynamics and commuter patterns. These findings may assist transportation planners, researchers, and policymakers seeking evidence-based solutions that improve mobility efficiency and sustainable urban transportation systems.[1][3]

Award Suitability

Based on the available publication record, Dong Xia demonstrates scholarly contributions in transportation big data research and innovation. His investigations integrate computational analysis with engineering applications, making his research profile consistent with the objectives of recognizing innovation, interdisciplinary collaboration, and practical scientific advancement.[2]

Conclusion

Dong Xia’s academic activities illustrate the growing importance of big data technologies in transportation engineering research. His published work contributes knowledge supporting intelligent transportation systems, urban mobility optimization, and computational transportation analysis, reflecting continued engagement with practical and research-oriented transportation innovation.[1][2]

References

  1. Xia, D., et al. (2022). Link-based Traffic Estimation and Simulation for Road Networks using Electronic Registration Identification Data. IEEE.
    https://ieeexplore.ieee.org/document/9767617
  2. Xia, D., et al. (2022). Urban Customized Bus Design for Private Car Commuters. IEEE.
    https://ieeexplore.ieee.org/document/9792261
  3. Xia, D., et al. (2021). Recognizing and Analyzing Private Car Commuters Using Big Data of Electronic Registration Identification of Vehicles. IEEE.
    https://ieeexplore.ieee.org/document/9693348