Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

University of Saskatchewan, Canada.

Golshid Ranjbaran is a PhD Candidate in Computer Science at the University of Saskatchewan (USASK), specializing in Artificial Intelligence, Machine Learning, and Interpretability. With a Bachelor's degree in Software Engineering and a Master's in Artificial Intelligence from the Science and Research Branch in Iran, he has accumulated several awards, including the Best Paper Award at the IKT Conference in 2021 and Best Researcher at ITRC in 2022. Golshid's research is aimed at advancing AI methodologies and improving machine learning models for real-world applications. He was also a research associate at the Data Science & Big Data Lab in Seville, Spain, in 2023. 🌐

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Education 🎓

Golshid holds a Bachelor's degree in Software Engineering and a Master's degree in Artificial Intelligence from the Science and Research Branch in Iran. He is currently pursuing a Ph.D. in Computer Science at the University of Saskatchewan (USASK), Canada, where he focuses on AI, machine learning, and interpretability, aiming to bridge the gap between theoretical advancements and practical applications.

Experience 🏢

Golshid has been awarded several prestigious positions and accolades, including a research position at the Data Science & Big Data Lab in Seville, Spain (2023), and was recognized as the Best Researcher at ITRC (2022). He has also contributed to various consultancy projects and industry collaborations, such as working on AI systems at ITRC, smart meters algorithms, and data governance in Iran.

Research Interests 🔍

Enhancing model interpretability through methods like SHAP.

Exploring sentiment analysis for stock market prediction.

Developing augmented techniques for unbalanced tasks in the financial domain.

Improving network security through Moving Target Defense technology.

Investigating Federated Learning for wearable health devices and ontology-based text summarization for efficient information processing.

Awards 🏆

Best Paper Award at the IKT Conference (2021)

Best Researcher Award at the Iran Telecommunication Research Center (ITRC) (2022)

Research Position at the Data Science & Big Data Lab in Seville, Spain (2023)

Nomination for the Gala GSA Award at the University of Saskatchewan (2025).

Selected Publications 📚

C-SHAP: A Hybrid Method for Fast and Efficient InterpretabilityApplied Sciences (Q2 Journal), Published 2025.

Leveraging Augmentation Techniques for Tasks with Unbalancedness within the Financial DomainEPJ Data Science (Q1 Journal), Published 2023.

Investigating Sentiment Analysis of News in Stock Market PredictionInternational Journal of Information and Communication Technology Research, Published 2024.

Unsupervised Learning Ontology-Based Text Summarization Approach with Cellular Learning AutomataJournal of Theoretical and Applied Information Technology, Published 2023.

Analyzing the Effect of News Polarity on Stock Market PredictionProceedings of the 12th International Conference on Information and Knowledge Technology (IKT), Published 2021.

 

 

Mr. Jin Qilin  | Computer vision | Best Researcher Award

Mr. Jin Qilin  | Computer vision | Best Researcher Award

Hohai University, China.

Qilin Jin is a passionate researcher specializing in the application of artificial intelligence and software engineering. With a focus on integrating AI with real-world engineering challenges, he has made significant strides in areas such as sonar image analysis, defect detection, and structural monitoring. His innovative work has been recognized through impactful publications and contributions to the scientific community.

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🎓 Education

Qilin Jin is currently pursuing a Ph.D. in Computer Science, specializing in the applications of artificial intelligence and software engineering to solve complex real-world problems. His doctoral research focuses on leveraging advanced AI algorithms for defect detection and structural analysis. He holds a Master's Degree in Engineering, where his studies centered on intelligent systems and structural monitoring, providing a strong foundation for his innovative contributions to these fields.

💼 Experience

Qilin Jin is an accomplished researcher with extensive experience in developing AI-driven solutions tailored for industrial and engineering applications. His work focuses on creating innovative methods for defect detection and segmentation, leveraging cutting-edge artificial intelligence technologies to enhance accuracy and efficiency. As a dedicated collaborator, Qilin actively contributes to multidisciplinary projects, combining expertise from various domains to address complex challenges in structural monitoring and intelligent systems.

🔍 Research Interests

Artificial Intelligence Applications: Advanced algorithms for defect detection and structural analysis.

Software Engineering: Innovative methods to enhance engineering software development.

Deep Learning: Leveraging deep neural networks for real-time segmentation and detection tasks.

🏆 Awards and Recognitions

Young Innovator Award for contributions to AI-based defect detection in sonar imaging.

Recognized for excellence in research by leading journals like Applied Sciences and Journal of Sound and Vibration.

🖋️ Publications

  1. Jin, Qilin, Han Qingbang, Qian Jianhua, et al. "Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images," Applied Sciences, 2025, 15(2): 597.
    Cited by 5 articles.
  2. Jin Qilin, Han QingBang, Su NaNa, et al. "A deep learning and morphological method for concrete cracks detection," Journal of Circuits, Systems, and Computers, 2023, 4.
    Cited by 3 articles.
  3. Wu Yang, Han QingBang, Jin Qilin, et al. "LCA-YOLOv8-Seg: An Improved Lightweight YOLOv8-Seg for Real-Time Pixel-Level Crack Detection of Dams and Bridges," Applied Sciences, 2023, 13: 10583.
    Cited by 7 articles.
  4. Han YuFeng, Han QingBang, Zhang ShiGong, Su NaNa, Jin Qilin, Shan MingLei. "Semianalytical numerical iterative analysis for a one-dimensional second harmonic wave," Journal of Sound and Vibration, 2022, 8.
    Cited by 2 articles.

 

 

 

Prof. Wen Jiang | Artificial Intelligence | Best Researcher Award

Prof. Wen Jiang | Artificial Intelligence | Best Researcher Award

Northwestern Polytechnical University, China.

Prof. Wen Jiang is a distinguished researcher and academic with a Ph.D. from Northwestern Polytechnical University, Xi’an, China (2009). She currently serves as a professor in the School of Electronics and Information at Northwestern Polytechnical University. Her work focuses on cutting-edge areas like information fusion, artificial intelligence, remote sensing image processing, and intelligent algorithm security, making her a leader in her field.

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Education 🎓

Prof. Wen Jiang has an impressive academic background in information systems and engineering. She earned her Ph.D. in Information Systems from Northwestern Polytechnical University, Xi’an, China, in 2009, where her research focused on innovative data systems and intelligent technologies. Prior to that, she completed her Master’s degree in Information Engineering at Information Engineering University, Zhengzhou, China, in 1997, gaining in-depth knowledge of advanced engineering concepts. She began her academic journey with a Bachelor’s degree in Information Engineering from the same university in 1994, building a strong foundation for her pioneering contributions to the field.

Experience 🏫

Prof. Wen Jiang is a Professor at the School of Electronics and Information, Northwestern Polytechnical University, where she has made a significant impact in academia. She is highly regarded for mentoring aspiring researchers and leading innovative projects in advanced technologies. Her leadership and expertise have been instrumental in driving forward research in areas like artificial intelligence, information fusion, and algorithm security..

Research Interests 🔍

Information Fusion:
Integrating data from diverse sources to enable smarter and more efficient decision-making processes, crucial for applications in defense, healthcare, and industry.

Artificial Intelligence:
Advancing machine learning and intelligent systems to solve complex problems and enhance automation across various domains.

Remote Sensing Image Processing:
Developing cutting-edge tools for environmental monitoring, urban planning, disaster management, and mapping applications.

Intelligent Algorithm Security:
Ensuring the robustness, reliability, and safety of AI-driven solutions to address vulnerabilities in critical systems.

Publications Top Notes 📚

A New Data Augmentation Method Based on Mixup and Dempster-Shafer Theory IEEE Transactions on Multimedia, 2024
Contributors: Zhuo Zhang, Hongfei Wang, Jie Geng, Xinyang Deng, Wen Jiang. Link

A Novel Air Target Intention Recognition Method Based on Sample Reweighting and Attention-Bi-GRU IEEE Systems Journal, 2024
Contributors: Yu Zhang, Weichen Ma, Fanghui Huang, Xinyang Deng, Wen Jiang. Link

Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Weichen Ma, Wen Jiang. Link

CMSE: Cross-Modal Semantic Enhancement Network for Classification of Hyperspectral and LiDAR Data IEEE Transactions on Geoscience and Remote Sensing, 2024, Contributors: Wenqi Han, Wang Miao, Jie Geng, Wen Jiang. Link

Dual-Path Feature Aware Network for Remote Sensing Image Semantic Segmentation IEEE Transactions on Circuits and Systems for Video Technology, 2024, Contributors: Jie Geng, Shuai Song, Wen Jiang. Link