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

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

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

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.

Yiru Wei | Object Detection | Best Researcher Award

Dr. Yiru Wei | Object Detection | Best Researcher Award

Lecturer at Shenyang University of Technology, China

Dr. Wei Yiru is an accomplished researcher specializing in image processing and artificial intelligence, with a dedicated focus on deep learning applications for real-time threat detection and saliency analysis. With a Ph.D. in Software Engineering from Northeastern University, she has transitioned from a skilled engineer to a passionate academician. Currently serving as a faculty member at Shenyang University of Technology, she has published extensively in top-tier journals such as Physics Letters A and Journal of Real-Time Image Processing. Dr. Wei demonstrates a strong ability to independently identify and solve complex problems, underpinned by her rigorous academic background and applied industrial experience. Her research contributions focus on enhancing the accuracy and speed of X-ray image analysis, particularly in public security. She has also actively contributed to national research projects and has led university-level initiatives. Her career reflects a consistent trajectory of growth, innovation, and commitment to advancing artificial intelligence applications in imaging.

Professional Profile 

Education🎓

Dr. Wei Yiru has pursued a comprehensive and progressive academic path in the field of computer science and engineering. She earned her Ph.D. in Software Engineering from Northeastern University between 2017 and 2021, where she conducted advanced research in deep learning and real-time image analysis. Prior to that, she completed her Master’s degree in Computer System Architecture at North China Electric Power University in Beijing from 2010 to 2013, building a strong foundation in system design and computational frameworks. Her undergraduate studies in Software Engineering were completed at Wuhan Institute of Technology, from 2006 to 2010, during which she demonstrated academic excellence and began her early engagement with programming and intelligent systems. This educational journey has equipped Dr. Wei with a robust theoretical background, practical software development expertise, and a solid grounding in both traditional computing architectures and modern artificial intelligence technologies, positioning her strongly for both academic research and industry applications.

Professional Experience📝

Dr. Wei Yiru brings a well-rounded blend of academic and industrial experience to her research endeavors. Since December 2021, she has been serving as a full-time faculty member at Shenyang University of Technology, where she teaches, mentors students, and conducts cutting-edge research in AI-based image processing. Before her academic appointment, she accumulated valuable industry experience. From 2014 to 2017, she worked as a software engineer at Shenyang Blu-ray Group, where she was involved in developing practical software applications. Prior to that, she served as a database engineer at Schneider Electric (China) Co., Ltd. from 2013 to 2014, where she gained experience in data management and enterprise systems. These roles have given her a deep understanding of real-world computing challenges and solutions, which she effectively integrates into her research. Her professional journey reflects a consistent dedication to technical innovation, system development, and academic advancement in the computing and artificial intelligence domains.

Research Interest🔎

Dr. Wei Yiru’s research interests lie at the intersection of artificial intelligence, image processing, and real-time detection systems. Her primary focus is on developing deep learning models for real-time threat detection in X-ray baggage inspection systems, which is crucial for enhancing public safety and security. She has explored various deep convolutional architectures, including anchor-free detection networks, depthwise separable convolutional layers, and bidirectional feature fusion networks. In addition, Dr. Wei is actively researching saliency detection using lightweight models, emphasizing computational efficiency and accuracy for deployment in resource-constrained environments. Her research demonstrates a balanced approach between theoretical innovation and practical application, particularly in the domain of intelligent surveillance and automated visual analysis. She is also interested in chaotic video encryption and compressed sensing, showcasing a broader interest in data security and multimedia processing. These interconnected themes reflect her long-term commitment to leveraging AI for intelligent perception and real-time decision-making systems.

Award and Honor🏆

Dr. Wei Yiru has received numerous awards and honors throughout her academic journey, reflecting her excellence and dedication to research and learning. During her master’s studies, she was awarded the prestigious National Scholarship and a Special Scholarship, in addition to being named an Outstanding Graduate Student. She also received a Second-Class Scholarship, recognizing her academic performance and contributions. As an undergraduate, Dr. Wei secured the National Encouragement Scholarship and First-Class Scholarships on three separate occasions. She was honored as an Outstanding Graduate and twice recognized as an Outstanding Student Leader, underscoring both her academic and leadership capabilities. She has also passed the National College English Test Level 6 (CET6) and National Computer Rank Examination Level 3, reflecting her well-rounded skills in communication and technical proficiency. These accolades highlight her consistent track record of achievement, leadership, and commitment to personal and professional development across all stages of her academic career.

Research Skill🔬

Dr. Wei Yiru possesses a robust suite of research skills that make her highly effective in academic and applied research environments. She has strong expertise in deep learning, particularly in developing and deploying real-time detection models for image and video analysis. Her proficiency spans convolutional neural networks (CNNs), salient object detection, threat object recognition, and feature fusion techniques. Dr. Wei is skilled in using advanced algorithms to enhance the speed and accuracy of image classification and has a proven ability to design lightweight and scalable models suitable for real-time deployment. She also has hands-on experience with chaotic video encryption, compressed sensing, and data security frameworks. Her ability to independently manage end-to-end research—from problem identification to solution implementation and publication—demonstrates strong critical thinking, project management, and technical writing abilities. These capabilities position her to contribute meaningfully to interdisciplinary collaborations and complex problem-solving in artificial intelligence and computer vision.

Conclusion💡

Dr. Wei Yiru demonstrates a strong, focused, and consistent research profile in AI-based image processing, particularly in real-time threat detection and saliency detection. Her solid publication record, project leadership, and academic rigor make her a highly suitable candidate for the Best Researcher Award at a national or institutional level.

To strengthen her candidacy further, she may consider pursuing larger-scale grants, international collaborations, patents, and mentorship roles in the near future.

Publications Top Noted✍

  • Title: A Cross Dual Branch Guidance Network for Salient Object Detection

  • Authors: Yiru Wei, Zhiliang Zhu, Hai Yu, Wei Zhang

  • Year: 2025

Assist Prof Dr. Anna Szczerba-Turek | Surveillance | Research Impact Award

Assist Prof Dr. Anna Szczerba-Turek | Surveillance | Research Impact Award

Anna Szczerba-Turek at University of Warmia and Mazury, Poland

Profiles

Scopus

Orcid

Summary

Assist Prof Dr. Anna Szczerba-Turek is a dedicated researcher with expertise in microbiology, zoonotic diseases, and veterinary sciences. Based at the University of Warmia & Mazury since 2004, she has significantly contributed to the field with research on pathogenic Escherichia coli and its impact on wildlife and humans. Her recent studies focus on understanding zoonotic transmission, particularly Shiga toxin-producing strains. Alongside her research, she is highly active in peer reviewing manuscripts, reflecting her commitment to advancing the scientific community.

Education

Assist Prof Dr. Anna Szczerba-Turek earned her PhD in Epizootiology from the University of Warmia and Mazury in Olsztyn, Faculty of Veterinary Medicine, located in Olsztyn, Poland. Her doctoral studies spanned from October 1, 2004, to January 21, 2009. Prior to this, she obtained her MSc in Biotechnology from the Intercollegiate Faculty of Biotechnology of the University of Gdansk and the Medical University of Gdansk (IFB UG & MUG) in Gdańsk, Poland, where she studied from October 1, 1997, to June 17, 2002.

💼 Professional Experience

  • University of Warmia & Mazury (2004–present)
    Researcher focused on microbiology, zoonotic diseases, and veterinary sciences.

🔬 Research Interests

  • Microbiology and Zoonotic Diseases
    Concentrating on Escherichia coli strains, particularly those with pathogenic potential like Shiga toxin-producing Escherichia coli (STEC) from wildlife such as wild boars, deer, and foxes.
  • Food Safety and Veterinary Sciences
    Evaluating the prevalence of zoonotic bacteria in livestock and companion animals, contributing to studies in food microbiology and antibiotic resistance

 

Publications

Prevalence of Aleutian Mink Disease Virus (AMDV) in Free-Ranging American Mink from Biebrza and Narew National Parks (Poland)—An Epidemiological Concern

  • Authors: Przywara, K., Siemionek, J., Jakubowski, T., Konczyk-Kmiecik, K., Szczerba-Turek, A.
  • Journal: Animals
  • Year: 2024

Shiga Toxin-Producing Escherichia coli in Faecal Samples from Wild Ruminants

  • Authors: Szczerba-Turek, A., Chierchia, F., Socha, P., Szweda, W.
  • Journal: Animals
  • Year: 2023

The Prevalence of Salmonella spp. in Two Arctic Fox (Alopex lagopus) Farms in Poland

  • Authors: Siemionek, J., Przywara, K., Szczerba-Turek, A.
  • Journal: Animals
  • Year: 2020

Fallow Deer (Dama dama) as a Reservoir of Shiga Toxin-Producing Escherichia coli (STEC)

  • Authors: Szczerba-Turek, A., Kordas, B.
  • Journal: Animals
  • Year: 2020

Shiga Toxin-Producing Escherichia coli Isolates from Red Deer (Cervus elaphus), Roe Deer (Capreolus capreolus) and Fallow Deer (Dama dama) in Poland

  • Authors: Szczerba-Turek, A., Siemionek, J., Socha, P., Lipczyńska-Ilczuk, K., Szweda, W.
  • Journal: Food Microbiology
  • Year: 2020