Alok Sengar | Deep Learning for Computer Vision | Excellence in Research Award

Published on by Computer Vision Awards

Excellence in Research Award

Alok Sengar — Vivekananda Global University

Research Profile
Affiliation Vivekananda Global University
Country India
Scopus ID 57465746700
Documents 22
Citations 80
h-index 5
Subject Area Deep Learning for Computer Vision
Event Global Tech Excellence Awards

The Excellence in Research Award recognizes scholarly achievement, scientific productivity, and research contributions within emerging technological domains. Alok Sengar, affiliated with Vivekananda Global University, has demonstrated active engagement in the field of Deep Learning for Computer Vision through research publications, citation impact, and interdisciplinary technological studies.[1] The evaluation of academic output, citation metrics, and subject specialization indicates continued participation in applied computational research and innovation-oriented investigations.[2]

Abstract

This article presents an academic overview of Alok Sengar and the relevance of his research profile to the Excellence in Research Award presented through the Global Tech Excellence Awards platform. The profile demonstrates involvement in Deep Learning for Computer Vision, including research dissemination, citation accumulation, and interdisciplinary computational applications.[1] The analysis further considers bibliometric indicators such as publication count, citation impact, and h-index as measurable indicators of scholarly engagement within contemporary technology-oriented research ecosystems.

Keywords

  • Deep Learning for Computer Vision
  • Artificial Intelligence
  • Machine Learning
  • Research Excellence
  • Scholarly Impact
  • Bibliometric Analysis
  • Academic Recognition
  • Computer Vision Applications

Introduction

The rapid advancement of artificial intelligence and computer vision technologies has expanded the importance of interdisciplinary computational research across scientific and industrial domains. Deep learning methodologies have become increasingly relevant in image processing, automated recognition systems, pattern analysis, and intelligent decision-support systems. Researchers contributing to these areas are frequently evaluated through publication productivity, citation metrics, and scientific visibility within recognized academic indexing platforms.

Within this context, Alok Sengar’s research profile reflects participation in technology-oriented academic investigations associated with computer vision and machine learning applications. Recognition through research awards is commonly associated with measurable scholarly activity, peer-reviewed dissemination, and contribution to evolving computational methodologies.[2]

Research Profile

Alok Sengar is affiliated with Vivekananda Global University in India and has established a documented scholarly profile indexed within Scopus databases.[1] The available bibliometric indicators report 22 indexed documents, 80 citations, and an h-index of 5, reflecting active engagement in peer-reviewed research dissemination and citation-based scholarly interaction.

The research specialization identified within the profile centers on Deep Learning for Computer Vision, a domain involving neural network architectures, feature extraction methodologies, image classification systems, and intelligent automation frameworks. These research areas contribute to both theoretical and applied developments within artificial intelligence ecosystems.

Research Contributions

The documented contributions associated with Alok Sengar indicate involvement in computational intelligence research and applied machine learning studies. Research activities within Deep Learning for Computer Vision commonly address algorithmic optimization, object recognition systems, image segmentation, and data-driven visual analytics.

  • Development and evaluation of deep learning frameworks for image analysis.
  • Investigation of neural network methodologies relevant to computer vision systems.
  • Participation in interdisciplinary artificial intelligence applications.
  • Contribution to peer-reviewed scientific publications and indexed conference proceedings.
  • Support for emerging computational methodologies involving automated visual recognition technologies.

Such contributions align with broader global research trends involving intelligent automation, pattern recognition, predictive analytics, and AI-assisted decision systems.

Publications

The publication profile associated with the researcher demonstrates ongoing scholarly dissemination within indexed academic environments. Peer-reviewed publications contribute significantly to scientific visibility and institutional research development. The Scopus-indexed profile includes articles related to computational methodologies and artificial intelligence applications.[1]

  • Research studies involving machine learning and computer vision algorithms.
  • Conference and journal publications addressing deep learning methodologies.
  • Interdisciplinary research involving intelligent systems and visual analytics.
  • Collaborative publications contributing to applied artificial intelligence research.

Representative DOI-linked research outputs and scholarly indexing records contribute to the measurable visibility of the profile within international academic databases.

Research Impact

Research impact assessment frequently incorporates quantitative indicators such as citation counts, publication volume, and h-index measurements. The available metrics associated with Alok Sengar indicate scholarly visibility within indexed research environments. Citation accumulation reflects academic engagement and indicates that the published research has contributed to ongoing scientific discussions within relevant subject domains.

The integration of Deep Learning for Computer Vision into practical and research-oriented applications further enhances the interdisciplinary significance of the work. Contemporary computational research increasingly relies on scalable neural architectures, automated recognition systems, and intelligent analytical frameworks.

Award Suitability

The Excellence in Research Award emphasizes scholarly productivity, measurable academic impact, innovation potential, and contribution to contemporary technological advancement. Based on available bibliometric indicators and research specialization, the profile of Alok Sengar demonstrates alignment with the objectives commonly associated with technology-oriented research recognition programs.[2]

Areas supporting award suitability include:

  • Indexed publication record within recognized academic databases.
  • Research activity within emerging artificial intelligence domains.
  • Demonstrated citation-based scholarly visibility.
  • Participation in computational and interdisciplinary innovation research.
  • Alignment with global technological research priorities involving intelligent systems.

Conclusion

The academic profile of Alok Sengar reflects measurable scholarly engagement within the field of Deep Learning for Computer Vision. The documented publication activity, citation impact, and subject specialization support recognition within technology-focused research evaluation frameworks.[1] The profile demonstrates continued participation in artificial intelligence research ecosystems and aligns with the broader objectives of the Global Tech Excellence Awards initiative in recognizing emerging scientific and technological contributions.[2]

References

  1. Elsevier. (n.d.). Scopus author details: Alok Sengar, Author ID 57465746700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57465746700
  2. Global Tech Excellence Awards. (n.d.). Research recognition and academic excellence initiatives.
    https://globaltechexcellence.com/

Abdullah Alshammari | Surveillance and Security | Editorial Board Member

Published on by Computer Vision Awards

Assoc. Prof. Dr. Abdullah Alshammari | Surveillance and Security | Editorial Board Member

University of Hafr Albatin | Saudi Arabia

Assoc. Prof. Dr. Abdullah Alshammari is a researcher at University of Hafr Al-Batin specializing in artificial intelligence, cybersecurity, Internet of Things, and cloud computing. With 16 publications, 186 citations, and an h-index of 8, his work demonstrates consistent contributions to high-impact Q1 journals, including IEEE venues. His research integrates machine learning, blockchain security, and edge computing to address challenges in smart systems, energy efficiency, and digital infrastructure. Collaborating with over 60 international co-authors, he advances interdisciplinary innovation with practical societal impact in secure communication networks, intelligent decision-making systems, and sustainable smart technologies.

 

Citation Metrics (Scopus)

200

150

100

0

Citations
186

Documents
16

h-index
8

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
         View Google Scholar Profile

Featured Publications


Intelligent multi-camera video surveillance system for smart city applications.

– In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 317–323). (2019). Cited By : 47

Power system monitoring for electrical disturbances in wide network using machine learning.

-Sustainable Computing: Informatics and Systems. (2024). Cited By : 26

Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Published on by Computer Vision Awards

Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.