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/

Alireza Goli | Computer Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Alireza Goli
Affiliation University of Isfahan
Country Iran
Scopus ID 57090489100
Documents 111
Citations 4,382
h-index 39
Subject Area Computer Science
Event International Research Excellence and Best Paper Awards
ORCID 0000-0001-9535-9902

Alireza Goli

Alireza Goli of the University of Isfahan, Iran is recognized with the Best Paper Award for research excellence in the field of Computer Science. The recognized research, titled “Performance Evaluation of Emerging Meta-Heuristic Algorithms on Vehicle Routing Problem,” focuses on evaluating emerging meta-heuristic optimization algorithms for solving complex vehicle routing problems and advancing computational approaches to logistics and transportation optimization.

Abstract

This article recognizes Alireza Goli for the Best Paper Award in recognition of research excellence in computer science and optimization. The recognized paper, titled “Performance Evaluation of Emerging Meta-Heuristic Algorithms on Vehicle Routing Problem,” investigates the application and performance of emerging meta-heuristic algorithms for addressing the Vehicle Routing Problem (VRP). Vehicle routing is a major computational optimization challenge involving the design of efficient routes for transportation and distribution systems. The study contributes to the evaluation of advanced computational strategies for solving complex routing problems and supports the development of effective optimization methodologies.

Keywords

Vehicle Routing Problem, Meta-Heuristic Algorithms, Emerging Meta-Heuristics, Optimization Algorithms, Combinatorial Optimization, Transportation Optimization, Logistics Optimization, Route Optimization, Computational Intelligence, Operations Research, Artificial Intelligence, Optimization, Algorithm Performance Evaluation, Supply Chain Optimization, Computer Science.

Introduction

The Vehicle Routing Problem is a fundamental optimization problem in computer science, operations research, transportation, and logistics. It involves determining efficient routes for a fleet of vehicles while satisfying operational constraints and minimizing objectives such as travel distance, transportation cost, or route duration.

Research Profile

Alireza Goli is affiliated with the University of Isfahan, Iran, and is associated with the subject area of Computer Science. The provided academic information records 111 documents, 4,382 citations, and an h-index of 39. The researcher’s Scopus Author ID is 57090489100, while the ORCID identifier is 0000-0001-9535-9902.

Research Contributions

The recognized study contributes to the computational study of the Vehicle Routing Problem by focusing on the performance evaluation of emerging meta-heuristic algorithms. Such evaluation is important because different optimization algorithms can exhibit different behavior in terms of solution quality, computational efficiency, convergence, and robustness.

Research Impact

The provided academic profile records 111 documents, 4,382 citations, and an h-index of 39 for Alireza Goli. These supplied indicators provide a quantitative view of the researcher’s indexed scholarly output and citation activity.

Research on meta-heuristic algorithms for vehicle routing has broad relevance to logistics, transportation management, supply chain systems, fleet planning, distribution networks, and intelligent optimization. Efficient routing can support better utilization of transportation resources and improved decision-making in complex distribution environments.

Award Suitability

The Best Paper Award recognizes research demonstrating academic quality, originality, relevance, methodological value, and meaningful contribution to its respective discipline. Alireza Goli’s recognized research aligns with these objectives through its investigation of emerging meta-heuristic algorithms and their performance in solving the Vehicle Routing Problem.

Conclusion

Alireza Goli represents a significant research profile in the field of Computer Science, with scholarly activity relevant to optimization, computational intelligence, and intelligent transportation systems. The recognized paper, “Performance Evaluation of Emerging Meta-Heuristic Algorithms on Vehicle Routing Problem,” addresses the important computational challenge of vehicle routing through the evaluation of advanced meta-heuristic optimization approaches.

External Links

References

  1. Scopus Author Profile: Alireza Goli, Author ID 57090489100.
    Scopus. Scopus Author Profile
  2. ORCID Research Profile: Alireza Goli.
    ORCID. ORCID Research Profile
  3. Best Paper Awards.
    Best Paper Awards

Mercy Mulwa | Computer Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Mercy Mulwa
Affiliation Jomo Kenyatta University of Agriculture and Technology (JKUAT)
Country Kenya
Scopus ID 58922109600
Documents 1
Citations 1
h-index 16
Subject Area Computer Science
Event International Research Excellence and Best Paper Awards

Mercy Mulwa

Mercy Mulwa of Jomo Kenyatta University of Agriculture and Technology (JKUAT), Kenya is recognized with the Best Paper Award for research excellence in the field of Computer Science. The recognized research, titled “GMM-LIME explainable machine learning model for interpreting sensor-based human gait,” focuses on explainable machine learning techniques for interpreting sensor-based human gait data and improving the transparency and understanding of machine learning models.

Abstract

This article recognizes Mercy Mulwa for research excellence in Computer Science and for the paper titled “GMM-LIME explainable machine learning model for interpreting sensor-based human gait.” The research focuses on the application of explainable machine learning to sensor-based human gait analysis. By combining a Gaussian Mixture Model (GMM) approach with Local Interpretable Model-agnostic Explanations (LIME), the work addresses the need for greater interpretability in machine learning systems that analyze human movement data. The research contributes to the growing field of explainable artificial intelligence, machine learning interpretation, sensor-based computing, and human gait analysis.

Keywords

Explainable Machine Learning, GMM-LIME, Gaussian Mixture Model, LIME, Human Gait Analysis, Sensor-Based Gait Recognition, Explainable Artificial Intelligence, Machine Learning Interpretability, Sensor Data, Computer Science.

Introduction

Machine learning has become increasingly important for analyzing complex sensor data and identifying meaningful patterns in human movement. Human gait analysis is an important area of research because gait characteristics can provide valuable information for applications involving movement recognition, activity analysis, intelligent sensing, and human-centered computing.

The awarded paper, “GMM-LIME explainable machine learning model for interpreting sensor-based human gait,” addresses the challenge of understanding how machine learning models interpret sensor-based gait information. The integration of explainability techniques provides a pathway toward making machine learning predictions more understandable to researchers, practitioners, and end users.

Research Profile

Mercy Mulwa is affiliated with Jomo Kenyatta University of Agriculture and Technology (JKUAT), Kenya, and works within the broad field of Computer Science. The provided academic information identifies the researcher with Scopus Author ID 58922109600. The supplied profile information records 1 document, 1 citation, and an h-index of 16.

Research Contributions

The research makes a contribution to explainable artificial intelligence and sensor-based machine learning by focusing on the interpretation of human gait data. The use of GMM-LIME provides an explainability-oriented framework for examining machine learning behavior and identifying patterns that may influence model outputs.

Research Impact

The research has relevance to several areas of modern computing, including explainable AI, machine learning, sensor analytics, gait recognition, human activity analysis, and intelligent systems. Explainable models can help researchers better understand complex relationships within sensor-generated data and support the development of more transparent computational systems.

Award Suitability

The Best Paper Award recognizes research that demonstrates academic relevance, methodological value, originality, and contribution to its field. Mercy Mulwa’s research aligns with these objectives through its focus on GMM-LIME explainable machine learning for interpreting sensor-based human gait.

Conclusion

Mercy Mulwa represents research excellence in the field of Computer Science through the recognized paper “GMM-LIME explainable machine learning model for interpreting sensor-based human gait.” The research addresses an important intersection between sensor-based computing, human gait analysis, machine learning, and explainable artificial intelligence.

External Links

References

  1. Scopus Author Profile: Mercy Mulwa, Author ID 58922109600.
    Scopus.https://www.scopus.com/pages/authors/58922109600
  2. Bet Paper Awards.
    https://bestpaperawards.com/

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