Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

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Dr. Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

Teacher | Ecole Nationale d’Ingénieurs de Sfax | Tunisia

Dr. Riadh Harizi is a researcher at the École Nationale d’Ingénieurs de Sfax, Tunisia, with expertise in Machine Learning, Artificial Intelligence, Computer Vision, Deep Learning, and Data Science. He has authored 5 research outputs, receiving 33 citations across 25 citing documents and achieving an h-index of 3. His work spans scene text understanding, reinforcement learning, and AI-driven educational analytics, with publications in Applied Soft Computing, Multimedia Tools and Applications, and leading international conferences. He has collaborated with interdisciplinary teams and contributed an open Latin and Arabic scene character dataset to IEEE Dataport, supporting reproducible research and societal impact in education and intelligent visual systems.

 

Citation Metrics (Scopus)

80

60

40

20

0

Citations
33

Documents
5

h-index
3

🟦 Citations 🟥 Documents 🟩 h-index

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Featured Publications


Deep-learning based end-to-end system for text reading in the wild.

-Multimedia Tools and Applications. (2022) Cited By: 10

SIFT-ResNet synergy for accurate scene word detection in complex scenarios.

– In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART) . (2024). Cited By: 3

Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

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Prof. Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Faculty Member | University of Isfahan | Iran

Prof. Ahmad Reza Naghsh-Nilchi is a distinguished researcher in computer vision, artificial intelligence, and medical image processing with a strong academic and professional background. He completed his PhD in Electrical and Computer Engineering at Michigan State University, where he specialized in digital image processing, and has since built an influential career in both academia and research. Over the years, he has served in multiple leadership positions including department chair, dean of research, and head of research laboratories, while also supervising numerous PhD and master’s students in advanced AI and imaging topics. His professional experience extends internationally through collaborations with leading institutions such as UC Irvine, University of Toronto, York University, and University of Ireland, contributing significantly to global research initiatives. His research interests span robust deep learning, adversarial defense, trustworthy AI, multimodal action recognition, image captioning, retinal analysis, and robot-camera pose estimation, reflecting both theoretical innovation and practical applications. He has published more than 70 papers in prestigious journals and conferences indexed by IEEE and Scopus, and his work has received more than 2,200 citations. His excellence has been recognized through multiple honors, including awards as University Researcher of the Year and Industrial Researcher of the Year. He possesses advanced research skills in AI model development, medical imaging, digital signal processing, and multimodal data analysis, complemented by editorial roles, conference organization, and active memberships in professional associations such as IEEE and ACM. His career demonstrates a commitment to advancing science, mentoring the next generation, and fostering impactful interdisciplinary collaborations. His Scopus output reflects international impact, with 1,319 citations by 1,214 documents, 65 published documents, and an h-index of 21.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters, 33(9), 1093–1100.

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transactions on Image Processing, 21(9), 3981–3990.

Fathi, A., & Naghsh-Nilchi, A. R. (2013). Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Processing and Control, 8(1), 71–80.

Amirgholipour, S. K., & Ahmad, R. (2009). Robust digital image watermarking based on joint DWT-DCT. International Journal of Digital Content Technology and its Applications, 3(2), 42–48.*

Kasmani, S. A., & Naghsh-Nilchi, A. (2008). A new robust digital image watermarking technique based on joint DWT-DCT transformation. In 2008 Third International Conference on Convergence and Hybrid Information Technology (pp. 539–544). IEEE.

Zahra Yahyaoui | Deep Learning | Women Researcher Award

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Dr. Zahra Yahyaoui | Deep Learning | Women Researcher Award

Teacher-Researcher at Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University | Tunisia

Dr. Zahra Yahyaoui is a dedicated researcher and educator with expertise in electronics, microelectronics, renewable energy systems, and artificial intelligence. She has established herself as an active contributor to the advancement of intelligent fault detection and diagnosis methods for photovoltaic and wind energy conversion systems. Her work bridges theory and practice, combining advanced machine learning techniques with embedded hardware implementation, ensuring her research is both academically rigorous and industrially relevant. Alongside her research activities, she has been deeply involved in teaching, supervision, and mentoring, helping to shape the academic and professional development of students in electronics and applied sciences. Her publications in high-impact journals and participation in international conferences highlight her growing recognition in the global research community. With technical versatility, adaptability, and strong teamwork skills, she continues to contribute to sustainable solutions in energy systems while promoting innovation, academic excellence, and interdisciplinary collaboration.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Dr. Zahra Yahyaoui pursued her academic path in Tunisia, beginning with a bachelor’s degree in industrial computing with a specialization in embedded systems. She then advanced to a master’s research degree in nanomaterials and embedded electronics, where she specialized in embedded electronics and conducted important research on fault detection and diagnosis in wind energy systems using machine learning. Building on this foundation, she completed her doctoral studies in electronics and microelectronics at the Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University. Her PhD research focused on developing enhanced intelligent data-driven paradigms for fault detection and diagnosis in power systems, with practical applications on embedded architectures. She carried out her doctoral work within the Research Unit of Advanced Materials and Nanotechnologies, furthering her expertise at the intersection of artificial intelligence, renewable energy, and electronic systems. This strong academic background reflects her commitment to innovative, multidisciplinary research.

Professional Experience

Dr. Zahra Yahyaoui has built a solid academic and professional career through her teaching and research activities. She started as a part-time teacher at the Higher Institute of Applied Sciences and Technology of Kasserine, where she gained experience delivering courses and tutorials in electronics, microprocessor and microcontroller architectures, and embedded systems. Her role expanded to contractual teacher at the same institute under Kairouan University, where she was responsible for teaching system-on-chip design, combinational and sequential logic circuits, and analog signal processing, covering both theoretical and practical sessions. In addition to her teaching duties, she has co-supervised master’s theses on advanced topics such as interval-valued machine learning, deep learning for fault detection in renewable systems, and photovoltaic installation design. Through her academic contributions, she has combined teaching excellence with mentoring, ensuring students receive both theoretical knowledge and practical insights. Her professional journey highlights her commitment to education, innovation, and applied research.

Research Interest

Dr. Zahra Yahyaoui’s research interests lie at the intersection of electronics, artificial intelligence, and renewable energy systems. She focuses on developing intelligent data-driven approaches for fault detection and diagnosis, aiming to enhance the reliability and efficiency of power systems such as photovoltaic and wind energy converters. Her work emphasizes the use of advanced machine learning and deep learning techniques, including BiLSTM, GRU, and optimization algorithms, to address uncertainty in renewable energy conversion and monitoring. She is also interested in the implementation of these algorithms on embedded architectures, integrating software with hardware platforms like FPGA, Raspberry Pi, and microcontrollers for real-world applications. Beyond fault diagnosis, she explores forecasting methods for solar irradiance and power output, contributing to the broader field of sustainable energy management. By combining theoretical modeling, algorithm development, and embedded system integration, her research supports innovation in intelligent renewable energy technologies.

Research Skill

Dr. Zahra Yahyaoui has developed a diverse set of research skills that enable her to carry out impactful and interdisciplinary work. She is proficient in programming languages such as MATLAB and Python, which she uses extensively for data analysis, machine learning model development, and algorithm implementation. She is skilled in simulation tools like ISE and Simplorer, supporting her expertise in circuit and system design. Her hardware-related skills include working with Siemens S7-1200, FPGA boards, Raspberry Pi, and Arduino microcontrollers, allowing her to translate theoretical models into practical embedded system solutions. She has strong problem-solving abilities, adaptability, and teamwork skills, which contribute to successful research collaborations and academic projects. Her research methodology combines theoretical analysis with experimental validation, ensuring robust and application-oriented results. With certifications in artificial intelligence and embedded systems, she brings an advanced skillset for developing intelligent monitoring and diagnostic systems, particularly for renewable energy applications.

Publications Top Notes

Title: Fault detection and diagnosis in grid-connected PV systems under irradiance variations
Authors: Hajji, M.; Yahyaoui, Z.; Mansouri, M.; Nounou, H.; Nounou, M.
Year: 2023

Title: One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.
Year: 2023

Title: Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Abodayeh, K.; Bouzrara, K.; Nounou, H.
Year: 2022

Title: Kernel PCA based BiLSTM for Fault Detection and Diagnosis for Wind Energy Converter Systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.; Nounou, H.; Nounou, M.
Year: 2022

Title: Efficient fault detection and diagnosis of wind energy converter systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Harkat, M.-F.; Kouadri, A.; Nounou, H.; Nounou, M.
Year: 2020

Conclusion

Dr. Zahra Yahyaoui is a deserving candidate for the Best Researcher Award due to her significant contributions in advancing intelligent data-driven techniques for renewable energy systems, fault detection, and embedded architectures. Her research has produced valuable publications in reputed international journals and conferences, with practical applications that support sustainable energy and technological innovation. Through her teaching, mentorship, and active participation in the academic community, she has demonstrated a strong commitment to knowledge sharing and capacity building. With her proven expertise, dedication, and potential for future leadership, she is well positioned to continue making impactful contributions to both research and society.

Kexin Bao | Continual Learning | Best Researcher Award

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Dr. Kexin Bao | Continual Learning | Best Researcher Award

Student at The Institute of Information Engineering, School of Cyber Security at University of Chinese Academy of Sciences, China

Kexin Bao is a focused and innovative researcher currently pursuing her Ph.D. at the Institute of Information Engineering, School of Cyber Security, University of Chinese Academy of Sciences. Her research primarily revolves around machine learning and computer vision, with specialization in few-shot class-incremental learning and weakly supervised small object detection. Through her contributions, she aims to address the challenges of enabling AI models to learn efficiently with minimal data and annotations. Kexin has actively participated in six research projects and authored six peer-reviewed SCI/Scopus-indexed journal publications, with a total citation count of 62. Her work includes the design of the Prior Knowledge-Infused Neural Network (PKI), which balances performance and computational efficiency. She collaborates with esteemed researchers like Shiming Ge and continues to demonstrate a high level of commitment to innovation and scholarly excellence. Kexin Bao’s work holds promise for practical applications in AI and has the potential to impact academia and industry alike.

Professional Profile 

Scopus Profile | ORCID Profile 

Education

Kexin Bao is currently pursuing her Doctor of Philosophy (Ph.D.) in Cyber Security and Information Engineering at the prestigious University of Chinese Academy of Sciences. She is enrolled at the Institute of Information Engineering, which is known for its excellence in cutting-edge research in computer science and cybersecurity. Her academic focus lies in advanced topics within machine learning and computer vision, particularly in areas such as few-shot learning, incremental learning, and object detection. Prior to her Ph.D., Kexin likely completed a Bachelor’s and Master’s degree in a relevant field, which laid the foundation for her research career, though those details are not explicitly mentioned in her profile. Her academic training has equipped her with the theoretical knowledge and practical skills needed to tackle complex real-world problems in artificial intelligence. Her ongoing doctoral studies not only refine her technical abilities but also enable her to contribute meaningfully to the global research community.

Professional Experience

As a Ph.D. student, Kexin Bao’s professional experience is rooted in academic research, with a strong focus on machine learning and computer vision. Although she does not yet have experience in industry or consultancy projects, she has participated in six significant research initiatives that address challenges in artificial intelligence, particularly in data-efficient learning models. Her work involves both independent and collaborative research, including partnerships with renowned scholars like Shiming Ge, Daichi Zhang, and Fanzhao Lin. While still in the early stages of her professional career, she has already contributed to six SCI/Scopus-indexed publications and one patent submission, reflecting her active role in advancing knowledge and technology. Though she has not yet undertaken formal leadership roles or teaching positions, her ability to carry out complex research projects demonstrates a high level of professionalism and expertise. Her growing research profile suggests that she is well-positioned to transition into impactful academic or industry roles in the future.

Research Interest

Kexin Bao’s research interests lie at the intersection of machine learning, computer vision, and artificial intelligence, with a specific focus on Few-Shot Class-Incremental Learning (FSCIL) and Weakly Supervised Small Object Detection. She is deeply interested in developing intelligent systems that can learn continuously from limited data, which is crucial for real-world applications where large annotated datasets are often unavailable. Her work on the Prior Knowledge-Infused Neural Network (PKI) and its variants (PKIV-1, PKIV-2) demonstrates her commitment to enhancing learning efficiency and minimizing resource consumption. She aims to create models that not only generalize well but also adapt quickly to new tasks with minimal retraining. These interests align closely with future directions in sustainable AI, autonomous systems, and edge computing. Kexin continues to explore methods that combine theoretical advancements with practical deployment possibilities, aiming to bridge the gap between academic research and real-world applications in intelligent automation and perception systems.

Award and Honor

Though early in her academic journey, Kexin Bao has already achieved commendable recognition through her contributions to research in computer vision. She has authored six peer-reviewed journal publications indexed in SCI and Scopus, and her work has been cited 62 times, indicating growing academic impact. Additionally, she has filed one patent based on her original research, a significant milestone for any early-career researcher. These achievements reflect both innovation and practical relevance in her work. She has also collaborated with prominent researchers, which further adds to her credibility and visibility in the research community. While she has not yet received named awards or honors beyond her publication and patent successes, her nomination for the Best Researcher Award is itself a testament to her academic excellence, research contribution, and future potential. With continued progress, she is well-positioned to receive further accolades and recognition at national and international levels in the near future.

Research Skill

Kexin Bao possesses a robust set of research skills that span both theoretical understanding and practical implementation in machine learning and computer vision. She is proficient in developing deep learning models and has a strong command of techniques related to few-shot learning, incremental learning, and weak supervision. Her work demonstrates advanced capabilities in model optimization, neural network design, and experimental benchmarking. Kexin has conducted extensive experiments on recognized datasets, validating her models through comparisons with state-of-the-art techniques. She is adept at using research tools, coding in frameworks such as PyTorch or TensorFlow, and performing data preprocessing and analysis. Her development of the Prior Knowledge-Infused Neural Network and its variants highlights her problem-solving ability and innovation mindset. She is also skilled in academic writing, contributing to multiple peer-reviewed journals. These research skills, combined with her ability to work collaboratively and manage projects independently, position her as a capable and resourceful young researcher.

Publications Top Notes

Title: DB-FSCIL: Few-Shot Class-Incremental Learning Using Dual Bridges
Authors: Kexin Bao, Fanzhao Lin, Ruyue Liu, Shiming Ge
Year: 2025
Type: Book Chapter

Title: PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Authors: Kexin Bao, Fanzhao Lin, Zichen Wang, Yong Li, Dan Zeng, Shiming Ge
Year: 2025 (Expected December)
Type: Journal Article (Neural Networks)

Title: Divide and Conquer: Static-Dynamic Collaboration for Few-Shot Class-Incremental Learning
Authors: Kexin Bao, Daichi Zhang, Yong Li, Dan Zeng, Shiming Ge
Year: 2025
Type: Conference Paper

Title: Learning Contrast-Enhanced Shape-Biased Representations for Infrared Small Target Detection
Authors: Fanzhao Lin, Kexin Bao, Yong Li, Dan Zeng, Shiming Ge
Year: 2024
Type: Journal Article (IEEE Transactions on Image Processing)

Title: Learning Shape-Biased Representations for Infrared Small Target Detection
Authors: Fanzhao Lin, Shiming Ge, Kexin Bao, Chenggang Yan, Dan Zeng
Year: 2024
Type: Journal Article (IEEE Transactions on Multimedia)

Title: Federated Learning with Label-Masking Distillation
Authors: Jianghu Lu, Shikun Li, Kexin Bao, Pengju Wang, Zhenxing Qian, Shiming Ge
Year: 2023
Type: Conference Paper

Conclusion

Kexin Bao is a deserving candidate for the Best Researcher Award due to her impactful contributions in the field of computer vision, particularly in few-shot class-incremental learning and weakly supervised small object detection. Her innovative work, including the development of the Prior Knowledge-Infused Neural Network (PKI), addresses real-world challenges in AI and has gained recognition through multiple SCI-indexed publications and citations. Her dedication to advancing research, collaboration with leading experts, and potential to drive future breakthroughs highlight both her academic excellence and her value to the broader research community. With continued growth in global engagement and leadership activities, she holds strong potential to become a leading figure in her field.

Assoc Prof Dr. Chuanzhong Wu | Deep Metric Learning | Outstanding Scientist Award

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Assoc. Prof. Dr. Chuanzhong Wu | Deep Metric Learning | Outstanding Scientist Award

Chuanzhong Wu at Shanghai International Studies University, China

Profiles

Scopus

🎓 Early Academic Pursuits

Assoc. Prof. Dr. Chuanzhong Wu embarked on his academic journey with a Bachelor’s degree in Physical Education from Wuhan Institute of Physical Education in 2005. His passion for sports education and training led him to pursue a Master’s degree in Sports Education & Training Science at the same institution, which he completed in 2008. Driven by a commitment to advancing research in sports humanities, he earned his Ph.D. in Sports Humanities and Social Sciences from the National University of Physical Education and Sport of Ukraine in September 2023. His doctoral studies focused on the intersection of sports education and social sciences, under the supervision of Prof. Korobeynikava Lesia.

🏢 Professional Endeavors

Assoc Prof Dr. Wu began his teaching career in 2008 as a Teaching Assistant at Huaihai Institute of Technology. Over the years, he progressed through various academic ranks, becoming a Lecturer in 2010 and later achieving the title of Associate Professor in 2018. Currently, he serves as an Associate Professor at Jiangsu Ocean University, where he holds the position of Section Chief in the Department of Sports. His dedication to academia and sports training has earned him recognition as a key figure in sports education and talent development.

🔬 Contributions and Research Focus

Assoc Prof Dr. Wu’s research is centered on Sports Education and Training Science, where he explores innovative training methodologies, physical conditioning, and the social dimensions of sports. His work has significantly contributed to enhancing the understanding of sports culture, performance analysis, and athletic training strategies. Through extensive research and publications, he has examined topics such as the integration of school and community sports culture and the relationship between competitive sports origin theories and human demand for multi-level sports development.

🌍 Impact and Influence

As a recognized researcher in the field,Assoc Prof Dr. Wu has made substantial contributions to the academic community. His work has been honored on multiple occasions, including First Prize at the European Youth Olympic Scientific Paper Conference (2020) and Second Prize at the 2020 Tokyo Olympic Games Scientific Paper Conference. His research findings have not only influenced sports training methodologies but also contributed to policy recommendations and curriculum development in higher education institutions.

📚 Academic Cites and Recognitions

Assoc Prof Dr. Wu’s academic excellence has been acknowledged through various city and provincial-level awards. In 2021, he was selected for Lianyungang City’s “521 High-Level Talent Training Program” as a Third-Tier Scholar. His research papers have received accolades in prestigious competitions, including:

  • Second Prize in the 13th National Student Sports Conference Scientific Paper Competition (2017)
  • Second Prize in the National College Student Work Excellent Academic Achievement Award (2012)
  • Recognition as an Outstanding Instructor for University Students’ Summer Social Practice Program (2012)

💻 Technical Skills

Assoc Prof Dr. Wu has extensive expertise in sports performance analysis, physical education methodologies, emergency rescue training, and sports research analytics. His technical skills include quantitative research methods, data-driven training assessments, and interdisciplinary sports education approaches. He is also proficient in designing and implementing sports training programs that bridge traditional education and modern technological applications.

🎓 Teaching Experience and Student Engagement

Throughout his teaching career,Assoc Prof Dr. Wu has been widely recognized for his student-centered approach and commitment to academic excellence. In 2017, he was voted the “Most Beloved Teacher” by students at Jiangsu Ocean University. His dedication to mentorship has earned him multiple awards as an “Outstanding Class Advisor” over consecutive years. His courses emphasize scientific training techniques, sports psychology, and athletic development, inspiring students to pursue excellence in sports and academia.

🌟 Legacy and Future Contributions

Assoc Prof Dr. Wu’s impact in the field of sports education and training science continues to grow. As a dedicated researcher and educator, he strives to bridge the gap between theoretical research and practical sports applications. His future contributions aim to enhance global sports training methodologies, promote interdisciplinary research, and develop next-generation athletes through innovative educational frameworks. With a strong foundation in research, teaching, and leadership, Dr. Wu remains committed to shaping the future of sports education and training science on both a national and international scale.

 

Publications

Infrared Thermal Radiation and Deep Learning Algorithms for Evaluating the Warm-Up Effect of Sports Training: Thermal Imaging Monitoring Model

  • Author: Y. Liu, Yumeng; Y. Li, Yunlong; D. Liang, Danqing; C. Li, Cheng; C. Wu, Chuanzhong
    Journal: Thermal Science and Engineering Progress
    Year: 2025

Mr. Andrews Tang | Deep Learning | Best Researcher Award

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Mr. Andrews Tang | Deep Learning | Best Researcher Award

Andrews Tang at Kwame Nkrumah University of Science and Technology, Ghana

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

Assessing blockchain and IoT technologies for agricultural food supply chains in Africa: A feasibility analysis

  • Authors: Andrews Tang, Eric Tutu Tchao, Andrew Selasi Agbemenu, Eliel Keelson, Griffith Selorm Klogo, Jerry John Kponyo
  • Journal: Heliyon
  • Year: 2024

An Open and Fully Decentralised Platform for Safe Food Traceability

  • Authors: Eric Tutu Tchao, Elton Modestus Gyabeng, Andrews Tang, Joseph Barnes Nana Benyin, Eliel Keelson, John Jerry Kponyo
  • Year: 2022

Prof. Ling Yang | Deep Learning | Women Researcher Award

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Prof. Ling Yang | Deep Learning | Women Researcher Award

Professor at Kunming University of Science and Technology, China

👨‍🎓 Profiles

Scopus

Orcid

Publications

Enhancing Panax notoginseng Leaf Disease Classification with Inception-SSNet and Image Generation via Improved Diffusion Model

  • Authors: Wang, R., Zhang, X., Yang, Q., Liang, J., Yang, L.
  • Journal: Agronomy
  • Year: 2024

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

  • Authors: Lei, L., Yang, Q., Yang, L., Wang, R., Fu, C.
  • Journal: Artificial Intelligence Review
  • Year: 2024

Alternate micro-sprinkler irrigation and organic fertilization decreases root rot and promotes root growth of Panax notoginseng by improving soil environment and microbial structure in rhizosphere soil

  • Authors: Zang, Z., Yang, Q., Liang, J., Guo, J., Yang, L.
  • Journal: Industrial Crops and Products
  • Year: 2023

A BlendMask-VoVNetV2 method for quantifying fish school feeding behavior in industrial aquaculture

  • Authors: Yang, L., Chen, Y., Shen, T., Yu, H., Li, D.
  • Journal: Computers and Electronics in Agriculture
  • Year: 2023

An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images

  • Authors: Yang, L., Chen, Y., Shen, T., Li, D.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023

Mr. Xiaoyu Li | Deep Learning | Best Researcher Award

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Mr. Xiaoyu Li, Deep Learning, Best Researcher Award

Xiaoyu Li at Beijing Forestry University, China

Professional Profile

🌟 Summary:

Xiaoyu Li is a university student at Beijing Forestry University’s School of Soil and Water Conservation. His research focuses on Remote Sensing & GIS, Image Processing, Land Use, Transportation, UAV utilization, and Ecology. He has contributed to national-level scientific projects, including the Qinghai-Tibet Plateau expedition, and has authored publications in prestigious journals. His work includes assessing human living environments, controlling soil erosion, and studying sediment connectivity and erosion dynamics. Xiaoyu Li has pioneered large-scale land use classification in northwestern China using UAV remote sensing and has contributed to understanding vegetation changes in the Qinghai-Tibet Plateau.

🎓 Education:

Currently pursuing studies at Beijing Forestry University, College of Soil and Water Conservation.

💼 Professional Experience:

Engaged in multiple national-level research projects focusing on environmental assessment, soil erosion control, and watershed dynamics.

🔬 Research Interests:

  • Remote Sensing & GIS
  • Image Processing and Analysis
  • Land Use and Transportation
  • UAV (drone) utilization and Ecology

📖 Publications Top Noted:

Paper Title: Land-Use Composition, Distribution Patterns, and Influencing Factors of Villages in the Hehuang Valley, Qinghai, China, Based on UAV Photogrammetry
  • Authors: Xiaoyu Li, Zhongbao Xin
  • Journal: Remote Sensing
  • Year: 2024