Guanxun Cui | Computer Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Guanxun Cui
Affiliation Chongqing University of Technology
Country China
Scopus ID 35777666500
Documents 10
Citations 5
h-index 1
Subject Area Computer Science
Event  International Research Excellence and Best Paper Awards

Guanxun Cui— Chongqing University of Technology

Guanxun Cui is affiliated with Chongqing University of Technology in China and is featured in this Best Paper Award profile in the field of Computer Science. The profile highlights the researcher’s scholarly publication record and research activity as represented by the supplied Scopus metrics. The profile provides a starting point for presenting research contributions, publication information, and academic achievements. Specific paper details and award decisions should be added when verified information is available.[1]

Abstract

This profile presents Guanxun Cui, affiliated with Chongqing University of Technology, China, in connection with the Best Paper Award and the field of Computer Science. According to the supplied information, the researcher’s Scopus profile records 10 documents, 5 citations, and an h-index of 1. These indicators provide a quantitative overview of the available publication record. Further information about individual research papers, methodologies, findings, and their applications is required to describe specific scientific contributions accurately.[1]

Keywords

Best Paper Award, Guanxun Cui, Computer Science, Chongqing University of Technology, China, Computer Science Research, Academic Publications, Scholarly Research, Research Evaluation, Research Excellence, Scientific Publications, Citation Analysis, h-index, Research Contributions, Digital Technologies, Computing Research, Academic Recognition, Research Impact, Scholarly Communication, Best Paper Awards.[1]

Introduction

Computer Science encompasses the study of computing systems, algorithms, software, data processing, and computational methods. Research in this field supports technological development and provides approaches to complex problems across academic and industrial settings. This profile introduces Guanxun Cui of Chongqing University of Technology, China, using the supplied academic information and Scopus publication indicators. Detailed research topics and individual paper findings can be incorporated once the corresponding publications are identified and verified.[1]

Research Profile

Guanxun Cui is associated with Chongqing University of Technology in China and is listed under the subject area of Computer Science for this profile.  The provided metrics indicate 10 documents, 5 citations, and an h-index of 1. These figures summarize the supplied publication and citation information; they do not independently establish research quality, award eligibility, or the outcome of an award selection process.[1]

Research Contributions

Research contributions in Computer Science may include the development of algorithms, software systems, computational models, data analysis methods, and practical computing applications. Guanxun Cui’s individual research contributions should be described with reference to verified publications and their documented findings. The available information establishes the researcher’s institutional affiliation and broad subject area but does not specify a particular paper, technical method, experimental result, or research outcome. These details can be added after the relevant publication is confirmed.

Publication Record

The supplied Scopus information lists 10 documents associated with Guanxun Cui, along with 5 citations and an h-index of 1. These indicators provide a snapshot of the researcher’s indexed scholarly output at the time the information was supplied. Individual publication titles, co-authors, journal names, publication years, and DOI links have not been provided for this profile. Verified bibliographic details should be added before presenting a specific article as the subject of the Best Paper Award.[1]

Research Impact

Research impact in Computer Science can be considered through scholarly citations, methodological contributions, reproducibility, practical applications, and the adoption of research outcomes by other researchers or practitioners. The supplied Scopus metrics offer a limited quantitative view of Guanxun Cui’s indexed publication record. A more detailed assessment would require examination of the researcher’s individual publications, their subject-specific significance, and documented evidence of academic or practical influence.

Award Recognition

The Best Paper Award profile highlights Guanxun Cui’s academic affiliation with Chongqing University of Technology and research area in Computer Science. The Best Paper Awards website provides the event’s official online reference. To document a confirmed award, the profile should include the official award announcement, the recognized paper title, and any available publication or selection details. The supplied information alone does not establish a specific winning paper or independently verify an award decision.[2]

Conclusion

Guanxun Cui of Chongqing University of Technology, China, is presented in this Best Paper Award profile under Computer Science. The supplied academic indicators list 10 documents, 5 citations, and an h-index of 1. These details establish a basic publication profile. Adding a verified paper title, complete bibliographic information, and documented award confirmation would allow the page to provide a more specific account of the researcher’s work and its significance.

External Links

References

  1. Scopus. (n.d.). Scopus Author Profile: Guanxun Cui.
    https://www.scopus.com/authid/detail.uri?authorId=35777666500
  2. Best Paper Awards. (n.d.). Official website.
    https://bestpaperawards.com/

Md Rashidunnabi | Computer Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Md Rashidunnabi
Affiliation University of Beira Interior
Country Portugal
Google Scholar ID 0_6ryVoAAAAJ
Documents 13
Citations 29
h-index 3
Subject Area Computer Science
Event International Research Excellence and Best Paper Awards

Md Rashidunnabi — University of Beira Interior

Md Rashidunnabi of the University of Beira Interior, Portugal, is recognized with the Best Paper Award for research in Computer Science. His recognized paper, “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production”, focuses on a real-world dataset for fabric-defect detection collected in an active textile production environment. The research connects computer vision, machine learning, industrial inspection, and intelligent manufacturing.
[1]

Abstract

This article recognizes Md Rashidunnabi with the Best Paper Award for research in Computer Science. His recognized work, “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production”, introduces a high-resolution fabric-defect dataset collected at an active textile inspection station using an industrial line-scan camera and directional illumination. The dataset contains 25,120 native images, including both defect-containing and defect-free fabric, together with 18,557 class-agnostic bounding boxes.[1]

Keywords

Best Paper Award, Md Rashidunnabi, Computer Science, LUSITANOv2, Fabric Defect Detection, Textile Production, Computer Vision, Machine Learning, Artificial Intelligence, Industrial Inspection, Automated Quality Control, Deep Learning, Smart Manufacturing, Intelligent Manufacturing, Textile Industry, Industrial AI, Visual Inspection, Manufacturing Automation.[1]

Introduction

Automated fabric-defect detection is an important application of computer vision and artificial intelligence in modern textile manufacturing. Reliable inspection can be challenging because production-line imagery may differ from controlled laboratory samples in textile appearance, illumination, imaging conditions, and defect characteristics.[1]

Research Profile

Md Rashidunnabi is affiliated with the University of Beira Interior in Portugal and works within the field of Computer Science. The supplied Google Scholar profile records 13 documents, 29 citations, and an h-index of 3.[2]

Research Contributions

The recognized paper contributes a real-world dataset for fabric-defect detection collected directly from an active textile production environment. LUSITANOv2 contains 25,120 native images and 18,557 localized defect instances, providing research material for computer-vision applications. The dataset supports supervised object detection as well as one-class anomaly-detection research. The paper evaluates YOLOv12n, Faster R-CNN, and RT-DETR-L for supervised detection and nine one-class anomaly-detection methods for anomaly-based inspection.[1]

Publications

The principal publication associated with this award profile is “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production”, authored by Rui Carrilho, Md Rashidunnabi, and Hugo Proença. The article was published in Electronics, Volume 15, Issue 19, Article 4403, on 24 September 2026. The article DOI is 10.3390/electronics15194403.[1]

Research Impact

LUSITANOv2 provides research material for automated visual inspection of textile products. Its real-world production imagery enables researchers to investigate computer-vision systems under operational conditions rather than only controlled laboratory environments. The dataset is relevant to defect localization, anomaly detection, deep learning, domain generalization, and industrial computer vision. The benchmark experiments provide baseline information that can support future evaluation of fabric-inspection methods.[1]

Award Recognition

Md Rashidunnabi is recognized with the Best Paper Award in Computer Science for the research contribution represented by “LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production.” The research addresses real-world fabric-defect detection and provides a dataset for evaluating computer-vision and artificial-intelligence methods in active textile production.[1]

Conclusion

Md Rashidunnabi of the University of Beira Interior, Portugal, is recognized with the Best Paper Award for research in Computer Science. His recognized paper presents LUSITANOv2, a real-world dataset for fabric-defect detection in active textile production.[1]

External Links

References

  1. Carrilho, R., Rashidunnabi, M., & Proença, H. (2026).
    LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production.
    Electronics, 15(19), 4403.
    https://doi.org/10.3390/electronics15194403
  2. Google Scholar. (n.d.). Google Scholar Profile: Md Rashidunnabi.
    https://scholar.google.com/citations?user=0_6ryVoAAAAJ&hl=en
  3. Best Paper Awards. (n.d.). International Research Excellence and Best Paper Awards.
    https://bestpaperawards.com/

 

Fuyuan Song | Computer Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Fuyuan Song
Affiliation Nanjing University of Information Science and Technology
Country China
Scopus ID
57210288154
Documents 29
Citations 253
h-index 8
Subject Area Computer Science
Event International Research Excellence and Best Paper Awards
ORCID 0000-0002-0784-7007

Fuyuan Song

Fuyuan Song of Nanjing University of Information Science and Technology, China is recognized with the Best Paper Award for research excellence in the field of Computer Science. The recognized research, titled “Privacy-Preserving Collaborative Task Allocation for Multi-Skill Mobile Crowdsensing”, addresses privacy-preserving approaches to collaborative task allocation in mobile crowdsensing environments, with particular emphasis on multi-skill participants and the efficient assignment of sensing tasks.

Abstract

This article recognizes Fuyuan Song with the Best Paper Award for research excellence in Computer Science. The recognized paper, “Privacy-Preserving Collaborative Task Allocation for Multi-Skill Mobile Crowdsensing”, investigates privacy-preserving mechanisms for collaborative task allocation in mobile crowdsensing systems. The research focuses on the challenge of assigning sensing tasks to participants who possess different skills while protecting sensitive information during the collaborative allocation process [1].

Keywords

Best Paper Award, Fuyuan Song, Computer Science, Mobile Crowdsensing, Privacy-Preserving Task Allocation, Collaborative Task Allocation, Multi-Skill Workers, Crowdsensing Systems, Privacy Protection, Mobile Computing, Task Assignment, Distributed Sensing, Data Privacy, Intelligent Computing, Participatory Sensing.

Introduction

Mobile crowdsensing enables large numbers of mobile users or sensing devices to collaboratively collect information from physical environments. By combining the sensing capabilities of multiple participants, such systems can support applications involving urban monitoring, environmental sensing, transportation, public services, and other data-intensive applications [2].

Research Profile

Fuyuan Song is affiliated with Nanjing University of Information Science and Technology in China and is associated with the subject area of Computer Science. According to the supplied academic information, the researcher’s indexed record includes 29 documents, 253 citations, and an h-index of 8 [1].

Research Contributions

The recognized paper contributes to the field of privacy-preserving mobile crowdsensing by examining collaborative task allocation in environments involving participants with multiple or differentiated skills. The multi-skill dimension is important because sensing tasks may require particular capabilities, expertise, resources, or combinations of participant competencies.

Publications

The principal publication associated with this recognition is “Privacy-Preserving Collaborative Task Allocation for Multi-Skill Mobile Crowdsensing.” The supplied information identifies this paper as the research basis for the Best Paper Award recognition in Computer Science.

Research Impact

The supplied academic information records 253 citations across 29 documents, together with an h-index of 8 [1]. These bibliometric indicators provide evidence of citation activity associated with the researcher’s indexed scholarly record.

The focus on multi-skill mobile crowdsensing also supports research directions involving smart cities, Internet of Things applications, participatory sensing, distributed intelligence, mobile computing, and privacy-aware artificial intelligence. These areas continue to require scalable approaches for coordinating heterogeneous sensing resources.

Award Suitability

The Best Paper Award recognizes research demonstrating academic quality, originality, relevance, and meaningful contribution to its respective discipline. The recognized work by Fuyuan Song aligns with these objectives through its focused investigation of privacy-preserving collaborative task allocation for multi-skill mobile crowdsensing.

The combination of mobile crowdsensing, collaborative computing, privacy preservation, and multi-skill task allocation demonstrates a research direction with potential relevance to secure distributed sensing applications and next-generation intelligent computing environments.

Conclusion

Fuyuan Song is recognized with the Best Paper Award for research addressing privacy-preserving collaborative task allocation for multi-skill mobile crowdsensing. The recognized publication, “Privacy-Preserving Collaborative Task Allocation for Multi-Skill Mobile Crowdsensing,” focuses on the integration of privacy protection and collaborative task assignment within mobile crowdsensing environments.

External Links

References

  1. Fuyuan Song – Scopus Author Profile. Scopus Author ID 57210288154.
    https://www.scopus.com/authid/detail.uri?authorId=57210288154
  2. Fuyuan Song – ORCID Profile. ORCID identifier 0000-0002-0784-7007.
    https://orcid.org/0000-0002-0784-7007
  3. Best Paper Awards. Best Paper Awards website and award information.
    https://bestpaperawards.com/

Mr. MD Shadman Soumik | Computer Science | Best Researcher Award

Published on by Best Paper Awards

Mr. MD Shadman Soumik | Computer Science | Best Researcher Award

Graduate Student | Washington University of Science & Technology | United States

MD Shadman Soumik is a dedicated IT Support and Data Security professional and researcher with extensive expertise in cybersecurity, data analytics, cloud computing, and enterprise IT systems, combining practical experience with innovative research contributions. He earned his Master of Science in Information Technology from Washington University of Science and Technology, Virginia (2025) and a Bachelor of Science in Electrical and Electronic Engineering from North South University, Bangladesh (2017), establishing a strong academic foundation for both technical and analytical proficiency. Over his career, he has worked with organizations including Reliable Home Care Services Corp, Frontier Semiconductor Bangladesh Ltd, and Poran Agro Products Ltd, where he provided comprehensive IT support, implemented cybersecurity protocols, conducted security audits, managed secure data integration, and optimized IT operations to ensure operational efficiency and data protection. His research interests encompass artificial intelligence applications in cybersecurity, network intrusion detection, secure and usable system design, early disease diagnosis using healthcare data, and AI-based financial market prediction. Soumik’s research skills include machine learning, neural networks, cybersecurity operations, database management, cloud computing, data analysis, technical writing, and project management, allowing him to bridge applied IT work and academic research effectively. He has contributed to high-impact publications in journals and conferences such as IEEE ICDSIS, International Journal of Computer Applications (IJCA), International Journal of Scientific Research and Applications (IJSRA), and International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), and he actively participates in professional organizations including the International Society for Data Science and Analytics (USA) and the Institute of Engineers, Bangladesh. His achievements have been recognized through research awards and professional honors that highlight his contributions to cybersecurity, IT management, and applied analytics. In conclusion, MD Shadman Soumik exemplifies a researcher and professional whose combined expertise, leadership, publications, and dedication to innovation make him highly deserving of recognition, with significant potential to advance IT security, artificial intelligence applications, and data analytics on a global scale.

Profile: LinkedIn

Featured Publications

  1. Soumik, M. S., Mahmud, M. R., Sami, S. I., & Tanim, M. K. B. S. (2025). Designing secure and usable systems: The intersection of human-computer interaction, cybersecurity, and machine learning. International Journal of Computer Applications (IJCA).

  2. Tarafdar, R., Soumik, M. S., & Venkateswaranaidu, K. (2025). Applying artificial intelligence for enhanced precision in early disease diagnosis from healthcare dataset analytics. IEEE International Conference on Data Science and Information Systems (ICDSIS).

  3. Soumik, M. S. (2024). A comparative analysis of network intrusion detection (NID) using artificial intelligence techniques for increased network security. International Journal of Scientific Research and Applications (IJSRA).

  4. Tanim, M. K. B. S., Parash, M. H., Soumik, M. S., & Shakib, M. (2024). Enhanced network anomaly detection using convolutional neural networks in cybersecurity operations. International Journal of Computer Applications (IJCA).

  5. Soumik, M. S. (2024). Prediction of the financial stock market: A comprehensive analysis of artificial intelligence. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 4(2).

Jamshir Qureshi | Cybersecurity | Best Researcher Award

Published on by Best Paper Awards

Mr. Jamshir Qureshi | Cybersecurity | Best Researcher Award 

Vice President, at Purdue University Global, United States.

Summary

Jamshir Qureshi is a seasoned Solution Architect based in Dallas/Fort Worth, Texas, with over two decades of experience in FinTech and regulated industries. Specializing in developing resilient, secure, and scalable enterprise architectures, he has led transformative projects in modern application platforms, cloud-native solutions, and cybersecurity frameworks. His extensive knowledge extends to healthcare information systems and compliance-driven solutions, supporting operational excellence in highly regulated environments. In addition to his technical contributions, Jamshir actively engages in thought leadership, authoring works on AI-driven innovations and their impact on digital transformation. His recent paper, “AI-Powered Cloud-Based E-Commerce,” presented at the IACIS 2024 Conference, showcases his commitment to integrating AI into cloud technologies for digital business growth. Recognized for his leadership in AI-powered cybersecurity solutions, he also served as a judge for the Globee Awards in Cybersecurity. Jamshir is passionate about mentoring and driving continuous improvement across teams and aligning strategies with business priorities.

Professional Profile

Education

🎓 Jamshir Qureshi holds a Master of Science in Information Technology from Purdue University Global, USA. This advanced degree has equipped him with the knowledge and technical prowess to tackle complex architectural and technological challenges in today’s fast-evolving digital landscape. Beyond his formal education, Jamshir is dedicated to continuous learning, demonstrated by his professional certifications. He is an AWS Certified Solutions Architect – Professional, showcasing his commitment to cloud excellence and proficiency in designing and implementing solutions on Amazon Web Services. His memberships in prominent organizations like the National Society of Leadership and Success (NSLS) and the American Association for the Advancement of Science reflect his passion for professional development and leadership in technology. These credentials, combined with his diverse experiences, have empowered Jamshir to build solutions that meet rigorous standards in highly regulated industries, including FinTech and healthcare.

Experience

💼 Over his 20+ year career, Jamshir Qureshi has built a strong foundation in enterprise architecture, focusing on scalable, secure, and resilient systems across various global firms. Currently, he serves as Vice President of Software Engineering at MUFG Bank in Dallas, Texas, where he drives technology initiatives and leads engineering teams. His career includes significant roles, such as Architect at Charles Schwab (2016-2021) and Lead Software Engineer at CVS Caremark (2011). He has also worked with prestigious organizations like Target and OM Software Ltd., spanning locations from India to South Africa and the U.S. Jamshir’s expertise in cloud-native platforms, microservices, DevOps practices, and cybersecurity has allowed him to implement successful strategies that align with business objectives, compliance standards, and operational excellence. His leadership in regulated industries has been recognized as transformative, focusing on innovation and team mentorship.

Research Interests

🔬 Jamshir’s research interests center on the transformative applications of artificial intelligence (AI) and cloud technology in FinTech, e-commerce, and healthcare systems. His research addresses emerging trends in AI-driven solutions, exploring how AI impacts human cognition, emotion, and healthcare. His paper “AI-Powered Cloud-Based E-Commerce,” presented at the IACIS 2024 Conference, delves into how AI integration with cloud technologies can enhance digital business strategies. His interest in AI deepfakes, illustrated in his work “Deciphering Deception: The Impact of AI Deepfakes on Human Cognition and Emotion,” explores the ethical and psychological implications of deepfake technology. Jamshir is also intrigued by the role of AI in healthcare, analyzing both risks and benefits in publications such as “AI Deepfakes in Healthcare: A Double-Edged Sword.” His work underscores his dedication to leveraging AI for innovation while recognizing the ethical considerations tied to this technology.

Awards

🏆 Jamshir Qureshi has received notable recognition for his contributions to technology and innovation. He was honored as a judge for the prestigious Globee Awards in Cybersecurity, reflecting his expertise in AI-powered cybersecurity solutions. His leadership in advancing secure, resilient, and scalable architectures in highly regulated environments has set a standard for excellence. Jamshir’s work on integrating AI into enterprise systems has garnered industry attention, positioning him as a thought leader in cloud-based e-commerce and AI-driven digital transformation. Additionally, his influence extends to the academic and professional communities, where he has shared his expertise through publications and conference presentations. His accolades underscore his commitment to enhancing technology-driven solutions and his ability to shape the future of cybersecurity, artificial intelligence, and enterprise architecture.

Top Noted Publications

📚 Below are some of Jamshir Qureshi’s notable publications, illustrating his contributions to AI and digital transformation:

  • “AI-Powered Cloud-Based E-Commerce: Driving Digital Business Transformation Initiatives”
    • Published in: IACIS 2024 Conference
    • Summary: This paper explores how artificial intelligence (AI) integrated with cloud technologies can drive digital business transformation in e-commerce. It discusses the potential of AI in enhancing business operations, customer experience, and overall business agility, with an emphasis on cloud-native platforms, scalability, and resilience. The paper provides insights into how AI applications can reshape business strategies and models in the digital age.
    • Link: IACIS 2024 Conference Paper
  • “Deciphering Deception: The Impact of AI Deepfakes on Human Cognition and Emotion”
    • Published in: Journal of Applied Artificial Intelligence, 2024
    • Summary: This paper delves into the psychological and cognitive implications of AI-generated deepfakes. It explores how deepfakes influence human perception, trust, and emotions, addressing the ethical concerns and risks associated with their use. The paper discusses both the dangers and potential applications of deepfakes in various sectors, including media and healthcare.
    • Link: Journal of Applied Artificial Intelligence
  • “Artificial Intelligence (AI) Deepfakes in Healthcare Systems: A Double-Edged Sword? Balancing Opportunities and Navigating Risks”
    • Published in: Preprints, 2024
    • Summary: This paper examines the dual nature of AI deepfakes in healthcare. While deepfakes offer transformative potential in areas like medical imaging and patient simulation, they also pose significant risks related to misinformation, privacy, and ethical concerns. The paper explores strategies for navigating these challenges while harnessing the benefits of AI in healthcare.
    • Link: Preprints – AI Deepfakes in Healthcare
  • “How Artificial Intelligence Technology Can Be Used to Treat Diabetes”
    • Published in: Preprints, 2024
    • Summary: This paper investigates how AI technologies can be applied in the treatment of diabetes, focusing on data analytics, predictive modeling, and personalized medicine. It reviews AI-driven advancements in monitoring glucose levels, improving diagnostic accuracy, and designing tailored treatment plans to enhance patient outcomes.
    • Link: Preprints – AI in Diabetes Treatment
  • “How Artificial Intelligence and Machine Learning Can Impact Market Design”
    • Published in: OPAST Publishers, 2024
    • Summary: This paper explores the role of AI and machine learning in transforming market design. It discusses how these technologies can improve market efficiency, enhance decision-making processes, and optimize resource allocation. The paper highlights the future of AI-driven market solutions, particularly in areas such as pricing strategies, demand forecasting, and dynamic pricing models.
    • Link: OPAST Publishers – AI and Market Design

Conclusion

Jamshir Qureshi is a strong candidate for the Best Researcher Award, given his extensive background, contributions to AI and cybersecurity, and demonstrated thought leadership in highly regulated industries. His career reflects an impressive blend of industry expertise, technical knowledge, and commitment to emerging research areas like AI ethics, cybersecurity, and market design. While a more focused publication strategy and deeper academic collaborations could enhance his research impact, his contributions already make a significant case for recognition. His dedication to innovation and the ethical challenges of technology use strengthens his candidacy, aligning well with the goals of a Best Researcher Award.