Alok Singh Chauhan | Machine Learning | Innovative Research Award

Innovative Research Award
Alok Singh Chauhan
Galgotias University, Greater Noida, India
Alok Singh Chauhan
Affiliation Galgotias University, Greater Noida, India
Country India
Scopus ID 57924434100
Documents 57
Citations 409
h-index 10
Subject Area Machine Learning
Event Indian Scientist Awards
ORCID 0000-0002-9144-4381

Alok Singh Chauhan is a researcher affiliated with Galgotias University, Greater Noida, India, whose Scopus profile records research activity in areas associated with machine learning and related computational methods. The available Scopus Preview profile lists 57 documents, 409 citations and an h-index of 10, providing a bibliometric overview of the researcher’s indexed scholarly output and citation impact. The profile also lists recent work involving deep learning, machine learning classification, graph neural networks, parameter-efficient fine-tuning and predictive modelling. [1]

This academic recognition page presents information derived from the supplied Scopus profile and selected publications displayed within it. The article describes the documented research profile without making claims beyond the available source material.

Abstract

Alok Singh Chauhan is affiliated with Galgotias University, Greater Noida, India, and is represented in the supplied Scopus Preview profile as a researcher with a documented publication and citation record. The profile reports 57 indexed documents, 409 citations and an h-index of 10. Recent publications shown in the profile include studies of deep learning-based cognitive workload classification, supervised machine learning classifiers for monkeypox disease classification, graph neural networks for fraudulent transaction detection, parameter-efficient fine-tuning for continual image classification, and machine learning for shipment delay prediction. [1]

Keywords

Machine Learning; Deep Learning; Graph Neural Networks; Classification; Predictive Modelling; Cognitive Workload; Fraud Detection; Image Classification; Shipment Delay Prediction; Artificial Intelligence.

Introduction

Machine learning and deep learning are represented across the research works listed in the supplied Scopus profile. Chauhan’s recent indexed publications demonstrate application-oriented research involving classification, predictive modelling and neural-network-based approaches. The listed works span domains including cognitive workload analysis, healthcare-related classification, financial fraud detection, image classification and logistics prediction. [1]

The available profile indicates collaborative research activity, with multiple publications involving co-authors from different research and academic settings. The Scopus Preview page reports 154 co-authors associated with the profile. [1]

Research Profile

The supplied Scopus Preview profile identifies Chauhan, Alok Singh and associates the researcher with Galgotias University, Greater Noida, India. The profile provides the Scopus Author ID 57924434100 and ORCID identifier 0000-0002-9144-4381. At the time represented in the supplied document, the profile records 57 documents, 409 citations and an h-index of 10. [1]

Bibliometric indicator Value reported in supplied profile
Documents 57
Citations 409
h-index 10
Co-authors 154

Research Contributions

The publication titles displayed in the supplied profile indicate several identifiable areas of application within machine learning and deep learning:

  • Deep learning-based classification of cognitive workload using functional connectivity features, representing an application of learning methods to cognitive workload analysis. [1]
  • Comparative analysis of supervised machine learning classifiers integrated with deep learning models for monkeypox disease classification.
  • Development of a risk-sensitive graph neural network model for fraudulent transaction detection. [1]
  • Evaluation of parameter-efficient fine-tuning strategies for continual image classification.
  • Application of machine learning modelling to shipment delay prediction. [1]

Collectively, these publications indicate an application-oriented research profile in which machine learning and deep learning techniques are examined for classification, prediction and detection tasks across multiple domains. The supplied profile does not provide sufficient methodological detail to independently assess the comparative performance of the models described in the publication titles.

Publications

The supplied Scopus Preview document displays several recent publications associated with Alok Singh Chauhan. The following list preserves the publication information available in that source:

  1. V. Khemchandani, V. A. S. Chauhan, Alok Singh Chauhan, S. Fatima, D. S. Srivastava, V. H. Abdullayev, et al. Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features. Advance Sustainable Science, Engineering and Technology, 2026. The supplied profile records 1 citation for this publication. [1]
  2. N. Chauhan, S. Yadav, Alok Singh Chauhan. Comparative analysis of supervised machine learning classifiers for classification of Monkeypox disease integrated with deep learning models. International Journal of System Assurance Engineering and Management, 2026. [1]
  3. S. Srivastava, V. Gupta, Alok Singh Chauhan, S. Kumar, S. Rani, et al. ECCFD-GNN: A Novel Risk-Sensitive Graph Neural Network Model for Fraudulent Transaction Detection. Advance Sustainable Science, Engineering and Technology, 2026. [1]
  4. N. Agarwal, Alok Singh Chauhan, P. A. H. Bours. Comparative Evaluation of Parameter-Efficient Fine-Tuning Strategies for Continual Image Classification. Advance Sustainable Science, Engineering and Technology, 2026. [1]
  5. P. Vishwari, R. N. Thakker, S. Verma, Alok Singh Chauhan. Machine Learning Model for Shipment Delay Prediction. Proceedings of the International Conference on Circuit, Power and Computing Technologies, ICCPCT 2026, 2026. [1]

No DOI identifiers are provided for these publications in the supplied PDF. Accordingly, no DOI has been inferred or added to avoid introducing bibliographic information not supported by the source.

Research Impact

The supplied Scopus profile reports 409 citations across 57 documents and an h-index of 10. These figures provide quantitative indicators of the indexed publication and citation record represented by the profile.  The profile also reports 154 co-authors, indicating substantial collaborative activity within the indexed record.

The recent publications shown in the source demonstrate research applications across cognitive workload analysis, disease classification, financial fraud detection, continual image classification and logistics prediction. The supplied document does not provide sufficient evidence to attribute specific real-world deployments, commercial outcomes or societal effects to these studies.

Award Suitability

For consideration under the Innovative Research Award associated with the Indian Scientist Awards, the available profile provides documented evidence of research activity in machine learning and related computational methods. The record includes 57 Scopus-indexed documents, 409 citations and an h-index of 10, together with recent publications addressing multiple machine-learning applications. [1]

The documented publication themes are relevant to an award category focused on research innovation, particularly because the listed works address diverse computational problems using supervised learning, deep learning, graph neural networks and parameter-efficient fine-tuning. Final award decisions, however, should be based on the applicable award criteria and the complete submitted research record rather than bibliometric indicators alone.

Conclusion

Alok Singh Chauhan’s supplied Scopus profile documents an active research record affiliated with Galgotias University, Greater Noida, India. The profile reports 57 documents, 409 citations and an h-index of 10, alongside recent research addressing machine learning and deep learning applications in areas such as cognitive workload classification, disease classification, fraud detection, image classification and shipment-delay prediction. [1] These documented characteristics provide the principal academic information presented on this recognition page.

References

  1. Elsevier B.V. Scopus Preview Author Profile: Chauhan, Alok Singh. Galgotias University, Greater Noida, India. Scopus Author ID: 57924434100. Supplied profile records 57 documents, 409 citations, h-index 10 and 154 co-authors, with selected publications from 2026.
    https://www.scopus.com/authid/detail.uri?authorId=57924434100
  2. SQL injection attack: Quick view
    https://map.researchcommons.org/mjcs/vol3/iss1/6/
  3. Artificial intelligence techniques based on federated learning in smart healthcare
    https://www.taylorfrancis.com/chapters/edit/10.1201/9781003489368-5/
  4. Comparative analysis of supervised machine and deep learning algorithms for kyphosis disease detection
    https://www.mdpi.com/2076-3417/13/8/5012

Anushka Masand | Pure Mathematics | Innovative Research Award

Innovative Research Award

Anushka Masand
Shanti Niketan Public School, India

Researcher Profile
Name Anushka Masand
Affiliation Shanti Niketan Public School
Country India
Subject Area Pure Mathematics
Event Indian Scientist Awards
ORCID 0009-0002-6968-5346

Anushka Masand is associated with Shanti Niketan Public School, India, with Pure Mathematics identified as the subject area for recognition under the Innovative Research Award at the Indian Scientist Awards. This academic recognition profile presents the available affiliation and subject information in a structured format while distinguishing supplied profile information from bibliographic indicators that have not been provided.

Abstract

This academic recognition profile concerns Anushka Masand of Shanti Niketan Public School, India, whose stated subject area is Pure Mathematics. The profile is presented in the context of the Innovative Research Award associated with the Indian Scientist Awards. Pure Mathematics encompasses the study of abstract mathematical structures, logical relationships, proof, and foundational concepts, with applications extending across mathematics and related scientific disciplines. The available information establishes the researcher’s institutional affiliation, country, subject area, award category, event, and ORCID identifier. Bibliometric indicators such as Scopus documents, citations, and h-index have not been supplied and are therefore not represented as verified measurements.

Keywords

Anushka Masand; Pure Mathematics; Mathematical Research; Innovative Research Award; Indian Scientist Awards; Shanti Niketan Public School; Academic Recognition; Mathematics.

Introduction

Pure Mathematics is concerned primarily with the development and investigation of mathematical concepts, structures, relationships, and proofs. Its major areas include algebra, analysis, geometry, number theory, topology, logic, and related fields. Mathematical results are commonly evaluated through the clarity of definitions, rigor of proofs, consistency of arguments, and significance of the problems addressed. Standard scholarly practice therefore places emphasis on verifiable research outputs and clearly documented contributions [1].

Within an academic recognition context, an individual’s research profile may be considered through information such as institutional affiliation, subject specialization, publications, scholarly impact, and persistent researcher identifiers. ORCID provides a persistent identifier designed to distinguish researchers and connect them with their scholarly activities [2].

Research Profile

Anushka Masand is identified in the supplied recognition information as being affiliated with Shanti Niketan Public School in India and working within the subject area of Pure Mathematics. The profile also provides the ORCID identifier 0009-0002-6968-5346. ORCID identifiers are intended to provide persistent and unique identification of researchers across scholarly systems [2].

Profile element Available information
Researcher Anushka Masand
Institution Shanti Niketan Public School
Country India
Subject area Pure Mathematics
ORCID 0009-0002-6968-5346

Research Contributions

The available profile information identifies Pure Mathematics as the relevant subject area but does not provide a detailed list of research projects, mathematical results, datasets, patents, grants, or specific scholarly contributions. Accordingly, no particular theorem, methodology, publication, or research finding is attributed to Anushka Masand without supporting bibliographic evidence.

  • Subject specialization: Pure Mathematics.
  • Institutional affiliation: Shanti Niketan Public School.
  • Researcher identifier: ORCID 0009-0002-6968-5346.
  • Specific research outputs: not provided in the supplied information.

Publications

No publication list, article titles, journal information, conference records, or DOI identifiers were supplied with the profile information available for this page. Consequently, specific publications are not attributed to Anushka Masand in order to avoid creating unverifiable bibliographic records. Publication information can be incorporated when supported by an institutional profile, ORCID record, publisher page, or other authoritative bibliographic source.

Research Impact

Research impact can be documented using multiple forms of evidence, including scholarly publications, citations, collaboration, adoption of research findings, educational contributions, and other demonstrable outcomes. Citation counts and related bibliometric indicators should be interpreted in relation to field, publication age, database coverage, and author identity matching rather than treated as standalone measures of research quality [3].

For the present profile, Scopus ID, document count, citation count, and h-index were not provided. These fields are therefore marked as unavailable rather than estimated. The absence of a supplied bibliometric value should not be interpreted as evidence for or against research impact.

Award Suitability

The supplied information associates Anushka Masand with the Innovative Research Award under the Indian Scientist Awards and identifies Pure Mathematics as the relevant subject area. The profile documents the stated institutional affiliation, country, and researcher identifier. A complete scholarly assessment would ordinarily require evidence concerning research outputs, originality, methodological or mathematical contribution, and other criteria specified by the relevant award process.

This page is a structured academic recognition profile rather than an independent bibliometric evaluation. The award event is identified as Indian Scientist Awards, and the official event website is provided in the External Links section.

Conclusion

Anushka Masand is presented in the supplied information as a researcher associated with Shanti Niketan Public School, India, with Pure Mathematics as the stated subject area and the Innovative Research Award as the recognition category. The profile includes an ORCID identifier, while detailed bibliometric and publication information remains unspecified. Further scholarly documentation can provide a more comprehensive account of research contributions and impact.

References

  1. The Encyclopaedia Britannica, “Mathematics,” provides general background on mathematics as a discipline concerned with quantity, structure, space, and change. .https://www.britannica.com/science/mathematics
  2. ORCID, “About ORCID,” describes ORCID as a persistent identifier and infrastructure for distinguishing researchers and connecting research activities. https://orcid.org/0009-0002-6968-5346
  3. 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. DOI: https://doi.org/10.1073/pnas.0507655102.
  4. Diagonal Cofactor Relations and Low rank structure in Sequential Integer matrices
    https://doi.org/10.1073/pnas.0507655102.