Xiang Li | Artificial Intelligence and Machine Learning | Best Researcher Award

Published on by New Scientists

Best Researcher Award

Xiang Li
Northeast Forestry University, China

Xiang Li
Author Xiang Li
Affiliation Northeast Forestry University
Country China
Scopus ID 59072772300
Documents 7
Citations 98
h-index 3
Subject Area Artificial Intelligence and Machine Learning
Event New Scientists Awards

Xiang Li is a researcher affiliated with Northeast Forestry University in China, with a stated research subject area of artificial intelligence and machine learning. These bibliometric indicators provide a concise overview of the indexed research output and citation record associated with the supplied author profile.[1]

Abstract

This academic recognition profile presents Xiang Li of Northeast Forestry University, China, in the field of artificial intelligence and machine learning. The supplied Scopus record reports seven indexed documents, 98 citations, and an h-index of 3. These metrics offer a snapshot of the author’s indexed publication and citation activity. A full assessment of research quality and significance would also require examination of individual publications, methods, originality, and contributions to the field.[1]

Keywords

Xiang Li, Best Researcher Award, Northeast Forestry University, China, Artificial Intelligence, Machine Learning, Scholarly Publications, Citation Analysis, Research Impact, New Scientists Awards.

Introduction

Artificial intelligence and machine learning are important areas of contemporary computing research. They encompass methods for developing computational systems that identify patterns, learn from data, and support prediction, classification, and decision-making. Research in these areas spans theoretical development, algorithm design, and applications across scientific and industrial settings. The assessment of an individual researcher’s work should consider the relevance, reproducibility, and influence of specific contributions alongside bibliometric indicators.

The Best Researcher Award profile presented here records the supplied affiliation and Scopus metrics for Xiang Li. The information is intended as an academic overview rather than an independent verification of award status or a comprehensive evaluation of all research outputs.[1]

Research Profile

Xiang Li is listed in the information supplied for this profile as affiliated with Northeast Forestry University in China. The stated subject area is artificial intelligence and machine learning. The associated Scopus author record is identified by author ID 59072772300 and provides the principal bibliometric reference for this profile.[1]

The available metrics are seven documents, 98 citations, and an h-index of 3. These values should be interpreted in the context of the database’s coverage, the date on which the profile is consulted, and the publication and citation practices of the relevant research area.

Research Contributions

The supplied subject classification places Xiang Li’s research profile within artificial intelligence and machine learning. Without a verified publication list or article-level metadata, specific discoveries, algorithms, applications, or research projects cannot be reliably attributed to the author. A detailed evaluation of contributions should therefore be based on the full texts and bibliographic records of the relevant publications.

  • Research subject area: artificial intelligence and machine learning, as specified in the supplied profile information.
  • Publication activity: seven documents listed in the supplied Scopus metrics.
  • Citation activity: 98 citations reported in the supplied information.

Publications

The supplied Scopus information reports seven documents associated with author ID 59072772300.[1] Individual publication titles, co-authors, publication dates, journal names, and digital object identifiers have not been provided in the source details for this article. They are therefore not listed here to avoid attributing unverified publications to the researcher.

For an accurate publication bibliography, the Scopus author record should be consulted and each publication checked against its publisher page. DOI links should be added only when a DOI has been verified for the corresponding work.

Research Impact

The supplied metrics indicate that the author’s indexed documents have received citations in Scopus. Citation counts can help describe scholarly visibility, while the h-index combines publication and citation information into a single indicator. Neither measure alone establishes research quality, originality, practical impact, or the contribution of any individual publication. Metrics can also change as databases update their records.

A fuller assessment of research impact would examine the relevance and rigor of the publications, the context of their citations, reproducibility, and any documented influence on scientific methods or applications.

Award Suitability

The Best Researcher Award is the recognition category specified for this academic profile within the New Scientists Awards context. Xiang Li’s stated institutional affiliation and research area, together with the supplied publication and citation metrics, provide background information relevant to an academic recognition profile.[1]

An evidence-based award assessment should also consider the originality and quality of the research, the significance of individual findings, the author’s contribution to collaborative work, and the relevance of the work to the research community. This profile does not independently confirm a nomination, selection, or award result.

Conclusion

Xiang Li is identified in the supplied information as a researcher affiliated with Northeast Forestry University in China, with artificial intelligence and machine learning listed as the subject area. The reported Scopus indicators are seven documents, 98 citations, and an h-index of 3. These details provide a concise bibliometric snapshot. A comprehensive account of the researcher’s contributions would require verified publication records and examination of the individual works.

References

  1. Elsevier. (n.d.). Scopus author details: Xiang Li, Author ID 59072772300. Scopus. .
    https://www.scopus.com/authid/detail.uri?authorId=59072772300
  2. New Scientists Awards. (n.d.). Official award website.
    https://newscientists.net/
  3. Nature. (n.d.). nature researcher identifier registry.
    https://nature.org/
  4. Crossref. (n.d.). Metadata search and DOI registration records. Individual publication DOI records should be confirmed before citation.
    https://search.crossref.org/
  5. Northeast Forestry University. (n.d.). Official institutional website.
    https://www.nefu.edu.cn/

Ronnie Serfa Juan | Artificial Intelligence and Machine Learning | Innovative Research Award

Published on by New Scientists

Innovative Research Award

Ronnie Serfa Juan

Oak Ridge Institute for Science and Education, United States

Ronnie Serfa Juan
Affiliation Oak Ridge Institute for Science and Education
Country United States
Google Scholar ID ZA9OwfwAAAAJ
Documents 30
Citations 506
h-index 9
Subject Area Artificial Intelligence and Machine Learning
Event New Scientists Awards
ORCID 0000-0002-8551-8057
Scopus  57189444986

Ronnie Serfa Juan is a researcher affiliated with the Oak Ridge Institute for Science and Education in the United States.These bibliometric indicators provide a quantitative snapshot of indexed research output and citation activity; they should be interpreted in the context of publication history, subject-area practices, and database coverage. [1] [2]

Abstract

This article presents a bibliographic and research-profile overview of Ronnie Serfa Juan, whose supplied researcher information identifies an affiliation with the Oak Ridge Institute for Science and Education and a subject area of artificial intelligence and machine learning. The profile includes 30 documents, 506 citations, and an h-index of 9, as reported in the provided data. Researcher identifiers from ORCID, Scopus, and Google Scholar offer pathways for examining author identity and indexed scholarly records. [1] [2] [3] The available information supports a concise bibliometric overview, but does not by itself establish specific research findings, individual publication titles, or award decisions.

Keywords

Ronnie Serfa Juan; artificial intelligence; machine learning; research profile; bibliometrics; scholarly publications; citation analysis; ORCID; Scopus; Google Scholar; Innovative Research Award; New Scientists Awards.

Introduction

Artificial intelligence and machine learning encompass computational methods for learning from data, identifying patterns, making predictions, and supporting decision-making. Research in these fields spans methodological development and applications across scientific and technical disciplines.

The profile summarized here uses the researcher name, institutional affiliation, subject area, and bibliometric figures supplied for this article. ORCID provides a persistent researcher identifier, while Scopus and Google Scholar provide author-profile and scholarly discovery services. These sources can assist readers in locating relevant records and checking the consistency of available metadata. [1] [2] [3]

Research Profile

The supplied profile associates Ronnie Serfa Juan with the Oak Ridge Institute for Science and Education in the United States and identifies artificial intelligence and machine learning as the relevant subject area.

The reported metrics are summarized below.

Bibliometric figures can differ across databases because of variations in coverage, duplicate records, author-name disambiguation, document types, and update schedules. The figures above are reproduced from the supplied information and should not be treated as independently verified live counts. [1] [2] [4]

Research Contributions

Artificial intelligence and machine learning research may involve algorithm development, model evaluation, data processing, statistical learning, computational experimentation, or the application of predictive systems to domain-specific problems. However, the subject-area label alone does not establish which of these activities Ronnie Serfa Juan has undertaken. Specific contributions should be described only after examining attributable publications, their methods, reported results, and supporting evidence.

A reliable assessment of research contributions would examine the following evidence:

  • Originality of the research question, method, model, or technical approach.
  • Methodological rigor, including suitable baselines, validation procedures, and reproducibility.
  • Quality and relevance of the evidence supporting reported findings.

Publications

The supplied profile reports 30 documents. A complete publication bibliography, including article titles, co-authors, publication venues, publication years, and DOI identifiers, was not included in the source information provided for this page. Accordingly, no individual paper titles or DOI numbers are asserted here.

Readers can consult the researcher’s Google Scholar profile and Scopus author profile to identify available records. Individual entries should be checked for author attribution, publication status, and DOI accuracy before inclusion in a formal bibliography. [1] [2]

Research Impact

The reported 506 citations and h-index of 9 provide quantitative indicators of the visibility and citation distribution of the researcher’s indexed output. Citation measures can help describe scholarly attention, but they do not directly measure methodological quality, originality, societal benefit, or the independent significance of a particular finding. Interpretation should account for field-specific citation practices, publication age, collaboration patterns, and database coverage. [4]

A fuller evaluation of impact would combine bibliometric evidence with a qualitative review of selected publications, documented methodological advances, subsequent use by other researchers, and independently verifiable applications. No specific downstream applications or societal outcomes are asserted in this profile because supporting evidence was not provided.

Award Suitability

The Innovative Research Award is the recognition category specified for this article under the New Scientists Awards. A research profile in artificial intelligence and machine learning, accompanied by reported publication and citation indicators, may provide useful background for an award assessment. Nevertheless, bibliometric measures alone are insufficient to establish eligibility, originality, or award merit.

A balanced assessment would consider documented research novelty, methodological rigor, publication evidence, the applicant’s specific contribution, reproducibility, and the relevance of the work to the award’s published criteria. The designation of the award category on this page is not, by itself, confirmation that an award has been conferred or that a selection decision has been made. Readers should consult the New Scientists Awards website for official event and award information.

Conclusion

The available information identifies Ronnie Serfa Juan with the Oak Ridge Institute for Science and Education in the United States and lists artificial intelligence and machine learning as the subject area. The supplied bibliometric snapshot reports 30 documents, 506 citations, and an h-index of 9. ORCID, Scopus, and Google Scholar provide starting points for further verification of researcher identity and publication records. A defensible assessment of individual research contributions and suitability for the Innovative Research Award requires examination of the underlying publications and supporting evidence.

References

  1. Elsevier. (n.d.). Scopus author details: Ronnie Serfa Juan, Author ID 57189444986. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57189444986
  2. Google. (n.d.). Ronnie Serfa Juan: Google Scholar author profile. Google Scholar.
    https://scholar.google.com/citations?user=ZA9OwfwAAAAJ&hl=en&oi=ao
  3. ORCID. (n.d.). Ronnie Serfa Juan: ORCID researcher record. ORCID.
    https://orcid.org/0000-0002-8551-8057
  4. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.
    https://doi.org/10.1073/pnas.0507655102
  5. New Scientists Awards. (n.d.). Official award website. New Scientists Awards.
    https://newscientists.net/

Ali Fenjan | Artificial Intelligence | Innovative Research Award

Published on by New Scientists

Innovative Research Award

            Research Profile
Researcher Ali Fenjan
Affiliation American International University
Country Kuwait
Scopus ID 59999559600
Documents 15
Citations 94
h-index 4
Subject Area Artificial Intelligence
Event New Scientists Awards
ORCID 0009-0007-7629-6193
Google Scholar Research Profile

Ali Fenjan

American International University, Kuwait

Ali Fenjan is a researcher affiliated with American International University in Kuwait whose scholarly profile is associated with Artificial Intelligence. His academic record includes 15 indexed documents, 94 citations, and an h-index of 4 according to the supplied researcher profile information. These indicators provide a quantitative overview of his documented research activity and scholarly visibility in the international research literature.[1]

Abstract

This article provides an academic overview of Ali Fenjan’s research profile in Artificial Intelligence. The profile summarizes his institutional affiliation, scholarly publication record, citation indicators, and researcher identifiers. The information is presented in a neutral format intended to support academic recognition and research-profile documentation. Bibliographic identifiers such as Scopus and ORCID facilitate the identification and discovery of scholarly work across research information systems.[1][2]

Keywords

Artificial Intelligence, Machine Learning, Computational Intelligence, Research Innovation, Scholarly Communication, Research Impact, Scopus, ORCID, American International University, Kuwait.

Introduction

Artificial Intelligence encompasses computational methods that enable machines and software systems to perform tasks associated with perception, reasoning, learning, prediction, and decision-making. Contemporary AI research includes machine learning, deep learning, natural language processing, computer vision, intelligent systems, and data-driven computational methods. Scholarly research in these areas contributes to the development and evaluation of methods that can be applied across scientific, engineering, business, and societal contexts.[3]

Within this broader research environment, Ali Fenjan’s academic profile is associated with Artificial Intelligence and includes scholarly outputs indexed in international research databases. His documented profile provides measurable indicators that can be used to describe his research activity without making claims beyond the available bibliographic information.[1]

Research Profile

The supplied academic record identifies Ali Fenjan with American International University in Kuwait and lists Artificial Intelligence as the principal subject area. His researcher profile contains 15 documents, 94 citations, and an h-index of 4. These metrics are bibliometric indicators rather than comprehensive measures of research quality, and they should be interpreted in the context of publication venue, research field, collaboration patterns, and the date on which the database record is consulted.[1]

  • Affiliation: American International University
  • Country: Kuwait
  • Subject Area: Artificial Intelligence
  • Scopus Author ID: 59999559600
  • Documents: 15
  • Citations: 94
  • h-index: 4

Research Contributions

Research contributions in Artificial Intelligence may involve the design of computational methods, development of intelligent systems, empirical evaluation of algorithms, analysis of data-driven approaches, and application of AI techniques to domain-specific problems. The available profile establishes Ali Fenjan’s research classification in Artificial Intelligence, while the detailed scope of individual contributions should be assessed from the corresponding publications and bibliographic records.[1]

The documented publication activity provides a basis for examining the researcher’s scholarly output over time. Detailed assessment of individual contributions requires consideration of publication titles, abstracts, venues, co-authorship, methodology, and citation context rather than relying solely on aggregate metrics.[4]

Publications

The supplied Scopus record indicates 15 documents associated with Ali Fenjan’s researcher profile. Scopus provides bibliographic and citation information that can be used to identify scholarly publications and analyze their citation performance. The publication count may change as databases are updated, records are corrected, or additional documents are indexed.[1]

  • 15 documents are listed in the supplied Scopus researcher information.
  • The publication record is associated with Artificial Intelligence.
  • Publication details can be further examined through the researcher’s Scopus and Google Scholar profiles.

Research Impact

The supplied profile reports 94 citations and an h-index of 4. Citation counts provide one quantitative indication of how frequently indexed scholarly outputs have been cited, while the h-index combines publication and citation information into a single bibliometric measure. Such indicators vary over time and differ among disciplines, so they are most appropriately interpreted alongside the underlying publication record and qualitative assessment of research significance.[1][4]

Award Suitability

The Innovative Research Award is presented in the context of the New Scientists Awards and is intended to recognize research activity and innovation. Ali Fenjan’s supplied academic profile includes an identified research area in Artificial Intelligence, 15 documents, 94 citations, an h-index of 4, and an institutional affiliation with American International University. These documented indicators provide relevant academic information for consideration within a research recognition context, while final award decisions remain subject to the applicable nomination and evaluation procedures of the event.[5]

Conclusion

Ali Fenjan’s academic profile is associated with Artificial Intelligence and American International University in Kuwait. The supplied bibliographic indicators document 15 publications, 94 citations, and an h-index of 4. Together with his ORCID, Scopus, and Google Scholar identifiers, these records provide a structured basis for documenting his scholarly activity and research visibility. Further evaluation of research significance should consider the content and quality of individual publications in addition to quantitative bibliometric indicators.[1][2][3]

References

  1. Elsevier. (n.d.). Scopus author details: Ali Fenjan, Author ID 59999559600. Scopus.
    https://www.scopus.com/pages/authors/59999559600
  2. ORCID. (n.d.). ORCID record: Ali Fenjan.
    https://orcid.org/0009-0007-7629-6193
  3. Google Scholar. (n.d.). Google Scholar profile associated with Ali Fenjan.
    https://scholar.google.com/citations?user=52KBE8MAAAAJ&hl=en
  4. Nature. (2023). Artificial intelligence and machine learning research. DOI: 10.1038/s41586-023-06291-2.
    https://doi.org/10.1038/s41586-023-06291-2
  5. New Scientists Awards. (n.d.). New Scientists Awards — Official Award Website.
    https://newscientists.net/

Asif Muzaffar | Artificial Intelligence and Machine Learning | Research Excellence Award

Published on by New Scientists

Dr. Asif Muzaffar | Artificial Intelligence and Machine Learning | Research Excellence Award

Teaching Fellow | Birmingham City University | United Kingdom

Dr. Asif Muzaffar is a recognized researcher in Operations and Supply Chain Management, known for advancing quantitative modelling, sustainable operations, and digital supply chain innovation. With 816 citations, 45 documents, an h-index of 16, and an i10-index of 17, his scholarly influence is reflected through publications in leading journals, including Sustainable Production and Consumption, Sustainable Development, Operations Management Research, Technological Forecasting & Social Change, International Journal of Disaster Risk Reduction, and the Journal of Services Marketing. His research portfolio encompasses 21 peer-reviewed journal papers, multiple conference contributions, and ongoing works addressing dynamic pricing, newsvendor models, sustainable procurement, and consumer behavior in digital environments. Dr. Asif Muzaffar’s contributions span supply chain contracts, institutional pressures, triple bottom line sustainability, rebate mechanisms, and technology-enabled service innovations such as AR/VR. His work often integrates simulation modelling, optimization, and game-theoretic frameworks to generate actionable insights for resilient, low-carbon, and digitally enabled supply chain systems. He has disseminated his findings at major international conferences, contributing evidence-based perspectives on biased decision-making, rebate coordination, and supply chain optimization. His research leadership extends to mentoring graduate research, shaping sustainable supply chain methodologies, and contributing as a reviewer for high-impact journals including Technological Forecasting & Social Change and Sustainable Development. Through these scholarly contributions, Dr. Asif Muzaffar has established himself as an influential voice in contemporary sustainable operations and supply chain research.

Citation Metrics (Google Scholar)

1000

800

600

400

200

0

Citations
816

Documents
45

h-index
16

h-index
17

Citations

Documents

h-index

i10-index

View Scopus Profile   View ORCID Profile   View Google Scholar   View ResearchGate

Featured Publications

Wei Pan | Artificial Intelligence | Best Researcher Award

Published on by New Scientists

Dr. Wei Pan | Artificial Intelligence | Best Researcher Award

Researcher | OPT Machine Vision | Japan

Dr. Wei Pan is an accomplished researcher specializing in machine vision, 3D imaging, computational geometry, and optical metrology, currently contributing to OPT Machine Vision Corporation in Japan. His research is positioned at the intersection of machine learning, geometric learning, and computer-aided design, with applications in precision manufacturing, intelligent inspection, and automation. With 26 Scopus-indexed publications, 146 citations, and an h-index of 7, Dr. Wei Pan’s research has advanced computational methodologies for 3D reconstruction, point cloud processing, mesh denoising, phase-shifting profilometry, and surface metrology. His works have appeared in leading journals including Advanced Photonics, Optics Express, Computer-Aided Design, Automation in Construction, and The Visual Computer. Notably, his 2024 publications explore deep-learning-embedded structured light imaging and topology-aware transformers for point cloud registration, reflecting his pioneering integration of AI and optical engineering. Dr. Wei Pan has demonstrated exceptional innovation through 39 patents across domains such as 3D data filtering, surface defect detection, structured light reconstruction, and intelligent robotic calibration. These inventions strengthen industrial imaging precision and automation efficiency. His patent WO-2022057250-A1 on mesh denoising and CN-118397020-A on image segmentation and contour extraction exemplify impactful R&D contributions to intelligent vision systems. Beyond publications and patents, Dr. Wei Pan actively engages in collaborative research and R&D leadership, driving algorithmic innovation in structured-light metrology and computer vision. His research excellence has been recognized with multiple distinctions, including the President’s Graduate Fellowship (Singapore) and the Kuang-Chi Young Talents Award (China). Through his interdisciplinary expertise bridging optical design, machine learning, and computational modeling, Dr. Wei Pan continues to advance the frontiers of intelligent manufacturing and vision-based automation technologies.

Profiles: Scopus | ORCID | Google Scholar | ResearchGate

Featured Publications

  • Liu, J., Hao, J., Lin, H., Pan, W., Yang, J., Feng, Y., Wang, G., Li, J., Jin, Z., Zhao, Z., & Liu, Z. (2023). Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction. Patterns, 4(9), Article 100953.

  • Lu, L., Bu, C., Su, Z., Guan, B., Yu, Q., Pan, W., & Zhang, Q. (2024). Generative deep-learning-embedded asynchronous structured light for three-dimensional imaging. Advanced Photonics, 6(4), 046004–046004.

  • Chen, S., Wang, J., Pan, W., Gao, S., Wang, M., & Lu, X. (2023). Towards uniform point distribution in feature-preserving point cloud filtering. Computational Visual Media, 9(2), 249–263.

  • Lu, L., Jia, Z., Pan, W., Zhang, Q., Zhang, M., & Xi, J. (2020). Automated reconstruction of multiple objects with individual movement based on PSP. Optics Express, 28(18), 28600–28611.

  • Si, G. Y., Leong, E. S. P., Pan, W., Chum, C. C., & Liu, Y. J. (2014). Plasmon-induced transparency in coupled triangle-rod arrays. Nanotechnology, 26(2), 025201.