Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

Jinglin Li | Computer Science | Best Researcher Award

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Mr. Jinglin Li | Computer Science | Best Researcher Award

Engineer | China National Nuclear Corporation | China

Li Jinglin is a researcher specializing in intelligent systems, reinforcement learning, and energy-efficient technologies for industrial and service applications. He holds advanced degrees in Instrument Science and Technology, Electrical Engineering, and Vehicle Engineering with a focus on new energy systems. His research encompasses the development of intelligent interactive service technologies for elderly care, optimization of energy-harvesting wireless sensor networks, and multi-task scheduling for energy-secured unmanned vehicles. He has led projects on digital twin platform technologies and vertical displacement control of nuclear fusion plasma, applying deep reinforcement learning to enhance system performance and replace traditional control methods. Li has extensive experience in algorithm design, including MATLAB-based reinforcement learning, adaptive dynamic programming, and multi-level exploration deep Q-network scheduling, with applications in optimal microgrid transmission, mobile charging sequence scheduling, and network monitoring. His work has resulted in multiple first-author publications in high-impact journals covering reinforcement learning, wireless sensor networks, and energy management, as well as conference contributions in control and automation. Beyond his technical expertise, he demonstrates strong analytical, problem-solving, and team collaboration skills, with experience in summarizing complex research findings and implementing practical solutions. Li actively engages in academic presentations and has earned recognition for his research achievements. In addition to his research, he maintains leadership roles in university sports teams, reflecting his commitment to teamwork, discipline, and resilience. His professional approach combines a proactive mindset, logical thinking, and a dedication to advancing intelligent and sustainable technological solutions across both industrial and service domains.

Profile: Scopus

Featured Publications

Li, J. (2024). A deep reinforcement learning approach for online mobile charging scheduling with optimal quality of sensing coverage in wireless rechargeable sensor networks. Ad Hoc Networks, 156, 103431.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks. Journal of Ambient Intelligence and Humanized Computing, 15(6), 2869–2881.

Li, J. (2023). Mobile charging sequence scheduling for optimal sensing coverage in wireless rechargeable sensor networks. Applied Sciences, 13(5), 2840.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. IEEE Transactions on Network and Service Management.

Li, J. (2024). A swarm deep reinforcement learning based on-demand mobile charging-scheduling and charging-time control joint algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. Expert Systems with Applications.

Nooshin Nemati | Computer Science | Best Researcher Award

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Ms. Nooshin Nemati | Computer Science | Best Researcher Award

Ankara University, Turkey

Dr. Nooshin Nemati is a dedicated researcher in the fields of Artificial Intelligence, Deep Learning, and Medical Image Processing, currently pursuing her PhD in Computer Engineering at Ankara University, where she also contributes to multiple AI-based cancer detection projects. She holds a Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University and a Bachelor’s from Qazvin Azad University.

Profile:

Educational Background:

Nooshin Nemati is currently a PhD candidate in Computer Engineering at Ankara University. She earned her Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University with a completed her undergraduate studies at Qazvin Azad University in Iran.

Research Areas:

Her main research interests lie in Artificial Intelligence, Deep Learning, Medical Image Processing, and Computer Vision, particularly applied to cancer detection in histopathology images. She focuses on segmentation, classification, and detection tasks using advanced deep learning frameworks.

Projects and Contributions:

She has actively contributed to significant research initiatives such as the TUBITAK 1001 Project, focused on deep learning methodologies for breast cancer detection, and the BAP Project, which deals with cancer region detection in histopathology images. She has also been involved in the development of important datasets such as NuSeC and MiDeSeC, aimed at supporting machine learning in medical imaging. In addition, she has applied her technical skills in software development projects including system analysis and automation tools for banks.

Technical Skills:

Nooshin is proficient in AI, Machine Learning, Deep Learning, and programming frameworks such as ASP.NET and WordPress. She also holds certifications like Network+ and CCNA, showcasing her broad technical competence.

Citation Metrics:

  • Total Citations: 75

  • Citations Since 2020: 71

  • h-index: 6

  • h-index Since 2020: 5

  • i10-index: 3

  • i10-index Since 2020: 3

Publication Top Notes:

  • An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
    2023
    Citations: 22

  • Detection of colorectal cancer with vision transformers
    2022
    Citations: 11

  • Effect of color normalization on nuclei segmentation problem in H&E stained histopathology images
    2022
    Citations: 10

  • A hybridized deep learning methodology for mitosis detection and classification from histopathology images
    2023
    Citations: 8

  • CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
    2024
    Citations: 7

 

Belal Hamed | Computer Science | Best Researcher Award

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Dr. Belal Hamed | Computer Science | Best Researcher Award

Assistant Lecturer at Department of Computer Science, Faculty of Science, Minia University, Egypt

Belal Ahmed Mohammed Hamed is an Assistant Lecturer at the Department of Computer Science, Faculty of Science, Minia University, and at the Department of Artificial Intelligence, Minia National University, Egypt. He holds a master’s degree in Computer Science, with research expertise in bioinformatics, machine learning, and graph-based disease prediction models. His work focuses on developing advanced algorithms for pattern recognition in DNA sequences and medical data analysis. He has published in Scopus and SCI-indexed journals, and contributed to six research projects, including two funded ones. He also serves as a reviewer for journals such as Scientific Reports and The Journal of Supercomputing. His notable contributions include a high-accuracy Graph Convolutional Network model for Alzheimer’s gene prediction.

Profile:

Academic Background:

Belal holds a Master’s degree in Computer Science. His academic training and research work are rooted in computer science, with a focus on interdisciplinary applications in healthcare and genomics.

Research Areas:

  • Bioinformatics

  • Machine Learning

  • SNP-based Disease Prediction

  • Graph Neural Networks

  • DNA Pattern Matching Algorithms

Research Contributions:

Belal developed a deep learning model that integrates SNP data and Graph Convolutional Networks (GCNs) to predict gene-disease associations, specifically in Alzheimer’s disease. The model achieved 98.04% accuracy and AUROC of 0.996, identifying both known and novel genes. His framework is adaptable for use in other diseases, supporting personalized medicine and clinical research.

Publications & Impact:

  • 4 research papers in SCI/Scopus-indexed journals (Springer Nature, Wiley)

  • Google Scholar Citations: 73

  • h-index: 3

Research & Projects:

  • Participated in 6 research projects, including 2 funded

  • Contributed to 1 industry-academic collaboration in medical data analysis

Editorial Roles:

  • Reviewer for The Journal of Supercomputing, Scientific Reports, and Medical Data Mining Journal

  • Young Scientist – Medical Data Mining Journal

Collaborations:

Active in interdisciplinary research teams, particularly in genomics and artificial intelligence.

Publication Top Notes:

Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Xiang Ma | Computer Science and Artificial Intelligence | Best Researcher Award

Postgraduate sichuan unviersity China

📖 Xiang Ma is a student at Sichuan University specializing in Electronic Information and Control Engineering. His research focuses on developing innovative solutions for image super-resolution reconstruction in construction site scenarios. By leveraging computer vision, machine learning, and engineering principles, Xiang’s work aims to improve image quality, safety, and monitoring efficiency in real-world construction environments.

Profile

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Education

🎓 Xiang Ma is pursuing a degree in Electronic Information and Control Engineering at Sichuan University. With a strong academic foundation, he integrates principles of electronic systems, computer vision, and machine learning in his research.

Experience

🔧 Xiang Ma has contributed to three completed and ongoing research projects, including collaborations with CSCEC First Bureau Technology R&D Program and the Sichuan Province Major Special Project on Intelligent Manufacturing and Robotics. His work bridges academic research with industrial applications in safety and automation technologies for construction sites.

Research Interest

🔍 Xiang Ma is passionate about Image Super-Resolution Reconstruction, with a focus on enhancing low-resolution images affected by noise in construction scenarios. His research includes proposing the Lightweight Feature Enhancement Network (LFEN) to improve visual perception, edge detection, and noise immunity using advanced machine learning techniques.

Awards

🏆 Xiang Ma is applying for the Best Researcher Award for his contributions to image processing technologies in construction scenarios. His work has been recognized for its innovative approach to leveraging lightweight network designs for practical applications.

Publications Top Notes: 

📚 Xiang Ma has published three research papers in prestigious journals:

Liu, Y., Ma, X. & Cheng, J. (2024). Lightweight Feature Enhancement Network for Image Super-Resolution Reconstruction at Construction Sites. Arab Journal of Science and Engineering. Published Year: 2024. Cited by: 15 articles.

 

Quanzeng Liu | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Quanzeng Liu | Computer Science and Artificial Intelligence | Best Researcher Award

Member Chinese Association of Automation China

Quanzeng Liu is a dedicated researcher and a CPC member, specializing in intelligent robot technology. Currently holding a Master’s degree in Control Science and Engineering from Anhui University of Technology, he has actively contributed to meta-heuristic algorithms, robot control, and path planning. With five research publications and numerous awards in academic competitions, Quanzeng’s work advances innovative solutions in robotics and automation systems.

Profile

Orcid

Education 🎓

Quanzeng Liu holds a Master’s degree in Control Science and Engineering from Anhui University of Technology, where his focus was on intelligent robot technology. His academic training has provided a robust foundation in control systems and advanced robotics, enabling significant contributions to both theory and practical applications.

Experience 💼

Quanzeng Liu has valuable research experience, participating in three major scientific research projects, including the collaborative innovation project of Anhui Province (GXXT-2023-068) and chairing the postgraduate innovation fund project (2023CX2086) at Anhui University of Technology. His research engagements reflect a strong capability in designing and improving robotic systems, particularly for multi-machine cooperative operations.

Research Interests 🔍

Quanzeng Liu’s primary research areas include meta-heuristic algorithms, robot control, and path planning. His work focuses on improving the performance of intelligent robots, including quadruped robots and weeding robots, as well as optimizing algorithms for visual SLAM and real-world robotic applications.

Awards 🏆

Quanzeng Liu has received five awards in prestigious academic competitions, showcasing his excellence in research and innovative problem-solving. These recognitions underscore his ability to translate complex theories into impactful solutions in robotics and automation.

Publications Top Notes:📚

Quanzeng Liu has published five influential papers in recognized journals and conferences, contributing to advancements in robotics and algorithms.

CMGWO: Grey wolf optimizer for fusion cell-like P systems
Heliyon, 2024. Read here

An Evaluation System for Multi-Machine Cooperative Operation of Weeding Robots Based on Fuzzy Combination Weight
China Automation Congress (CAC), 2024.

Robust visual SLAM algorithm based on target detection and clustering in dynamic scenarios
Frontiers in Neurorobotics, 2024. Read here

A hypergraph cell membrane computing network model for soybean disease identification
Scientific Reports, 2024. Read here

Conclusion ✅

Quanzeng Liu is an exceptional researcher whose work in robotics and intelligent systems contributes to solving complex challenges in automation and control. His innovative approach to meta-heuristic algorithms and robot path planning makes him a highly deserving candidate for the Best Researcher Award. With continued focus on industrial applications and broader collaborations, Quanzeng is poised to make even greater impacts in the future of robotics and automation.

Yunfei Zì | Computer Science | Best Researcher Award

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Dr. Yunfei Zì | Computer Science | Best Researcher Award

Researcher Wuhan University of Technology China

Zi Yunfei is a distinguished researcher specializing in voiceprint recognition and artificial intelligence, affiliated with the Wuhan University of Technology. His expertise lies in developing advanced speaker verification systems and acoustic feature extraction methods, especially within IoT contexts. Currently, he is concluding his Ph.D. under the guidance of Professor Xiong Shengwu.

Profile

Orcid

Google scholar

Education 🎓

  • Ph.D. in Computer Science and Technology (2019–2023) – Wuhan University of Technology
  • M.Eng. in Information and Communication Engineering (2016–2019) – Beijing University of Graphic Arts
  • B.Eng. in Computer Science and Technology (2011–2015) – Northeast Petroleum University

Experience 💼

Zi has led and contributed to various research initiatives, including a Huawei NRE project and significant AI advancements in IoT voiceprint recognition and military voice monitoring. His technical contributions have been instrumental in enhancing acoustic feature extraction and system integration on Huawei’s deep learning platform, MindSpore.

Research Interests 🔍

  • Voiceprint Recognition
  • Short Utterance Speaker Verification
  • Artificial Intelligence & Deep Learning
  • Acoustic Feature Enhancement
  • IoT Smart Services

Awards 🏆

  • Outstanding Academic Achievement Award – Beijing University of Graphic Arts, 2018
  • Outstanding Master’s Thesis Award – Beijing University of Graphic Arts, 2019
  • Huawei Smart Base Future Star Award – Ministry of Education-Huawei, 2021
  • Outstanding Doctoral Thesis Award – Wuhan University of Technology, 2024

Publications Top Notes📚:

Multi-Fisher and Triple-Domain Feature Enhancement-Based Short Utterance Speaker Verification for IoT Smart Service – IEEE Internet of Things Journal (2024) [DOI:10.1109/JIOT.2023.3309659]

Joint Filter Combination-based Central Difference Feature Extraction – Expert Systems with Applications (2023) [DOI:10.1016/j.eswa.2023.120995]

Fisher Ratio-Based Multi-Domain Frame-Level Feature Aggregation – Engineering Applications of Artificial Intelligence (2024) [DOI:10.1016/j.engappai.2024.108063]

Short-Duration Speaker Verification by Joint Filter Superposition – IEEE Transactions on Consumer Electronics (2024) [DOI:10.1109/TCE.2024.3411116]

Aggregating Discriminative Embedding by Triple-Domain Feature Joint Learning – Biomedical Signal Processing and Control (2023) [DOI:10.1016/j.bspc.2023.104703]