Xiaoling Shu | Large Language Models | Best Researcher Award

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Xiaoling Shu | Large Language Models | Best Researcher Award

Ms. Xiaoling Shu, Northwest Normal University , China.

Xiaoling Shu is a dedicated researcher and graduate student at Northwest Normal University in Lanzhou, China. Her work focuses on the innovative application of large language models (LLMs) and natural language processing (NLP) techniques in the fault diagnosis of mine hoists, contributing to the advancement of hyper-relational knowledge graphs. Xiaoling’s research explores hierarchical reinforcement learning and link prediction methods, emphasizing their role in enhancing industrial operations. Passionate about the intersection of technology and practical problem-solving, she has authored multiple impactful publications. Outside her academic pursuits, Xiaoling is inspired by the rich historical and cultural heritage of Tianshui. 🌟📚

Publication Profiles

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Education and Experience

  • 🎓 Graduate Student in Progress (Computer Science and Engineering)
    Northwest Normal University, Lanzhou, China (Since 1999-02)
  • 🔬 Researcher in Mine Hoist Fault Analysis and Knowledge Graphs
    Specializing in advanced NLP and hierarchical learning techniques.

Suitability For The Award

Ms. Xiaoling Shu, a graduate student at Northwest Normal University, specializes in applying large language models and natural language processing for fault diagnosis in mine hoists. Her innovative research, including hyper-relational knowledge graphs and reinforcement learning, contributes significantly to advancements in fault prediction and analysis. Ms. Shu’s impactful work positions her as a deserving candidate for the Best Researcher Award.

Professional Development

Xiaoling Shu is continuously advancing her expertise in cutting-edge computational techniques, leveraging the power of large language models and NLP. Her work integrates artificial intelligence with industrial fault diagnostics, focusing on predictive algorithms and hyper-relational knowledge graphs. With an eye on technological evolution, she engages in workshops, seminars, and collaborations aimed at fostering innovation in hierarchical reinforcement learning. Xiaoling’s dedication to problem-solving has earned her a place among emerging experts in AI-driven industrial applications. Beyond her academic endeavors, she actively participates in cross-disciplinary exchanges to promote innovative thinking in fault diagnosis systems. 🚀🖥️

Research Focus

Xiaoling Shu’s research is centered on applying advanced computational models to optimize fault diagnosis systems for mine hoists. Her focus includes utilizing large language models to construct hyper-relational knowledge graphs, enabling precise and efficient fault analysis. She explores hierarchical reinforcement learning techniques to enhance decision-making in industrial operations and develops methodologies like HyperKGLinker for effective link prediction. Her work aligns with the broader goal of integrating AI with practical applications, addressing complex challenges in mining industries. Xiaoling’s innovative approach contributes to smarter, safer, and more reliable industrial systems. 🤖⚙️

Awards and Honors

  • 🏅 Best Research Contribution Award for advancements in NLP-based fault diagnostics.
  • 🏆 Innovation in AI Award for hyper-relational knowledge graph applications.
  • 🎖️ Outstanding Researcher for publications on hierarchical reinforcement learning.
  • 📜 Certificate of Excellence for contributions to link prediction methods.
  • 🌟 Technology Pioneer Award for integrating LLMs in industrial applications.

Publication Top Notes

  • 📘 “Utilizing Large Language Models for Hyper Knowledge Graph Construction in Mine Hoist Fault Analysis” – 2024, cited by 0,  ✍️
  • 📕 “Research on Fault Diagnosis of Mine Hoists Based on Hierarchical Reinforcement Learning” – 2024, cited by 0. 

Mr. Tohid Sharifi | Machine Learning Award | Best Researcher Award

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Mr. Tohid Sharifi | Machine Learning Award | Best Researcher Award

Mr. Tohid Sharifi, Niroo Research Institute, Iran

Mr. Tohid Sharifi is a proficient electrical engineer with an M.Sc. in Electrical Machines and Power Electronics from Amirkabir University of Technology and a B.Sc. in Electrical Power Engineering from Urmia University. His research encompasses notable projects such as a hybrid estimation model for real-time temperature monitoring in electric motors, published in Case Studies in Thermal Engineering, and he is actively working on heat transfer investigations for advanced motor designs. With industrial experience as a CFD Specialist and Cooling System Design Engineer, he has contributed to thermal analysis for a 100kW flywheel energy storage system and optimized heat transfer for a 200kW water-cooled motor using artificial neural networks. His research interests include power electronics, electrical machines, electric vehicles, and metaheuristics, and he holds a patent for a hybrid excited flux switching permanent magnet motor for electric vehicle applications.

Professional Profile:

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Google Scholar

Suitability for the Best Researcher Award:

Mr. Tohid Sharifi’s extensive research and industrial contributions make him an ideal candidate for the Best Researcher Award. His focus on heat transfer and cooling systems for electric motors, coupled with his work in metaheuristic optimization for motor efficiency, reflects his forward-thinking approach to solving key challenges in power electronics and energy systems. His innovative contributions to electric vehicle motor design and the optimization of thermal systems using advanced algorithms showcase his potential for significant future impact in the field.

🎓 Education:

Mr. Tohid Sharifi holds an M.Sc. in Electrical Machines and Power Electronics from Amirkabir University of Technology (Tehran Polytechnic) and a B.Sc. in Electrical Power Engineering from Urmia University.

🛠️ Academic Projects:

His research includes significant projects such as a hybrid estimation model for real-time temperature monitoring in electric motors, published in Case Studies in Thermal Engineering. He has also worked on heat transfer investigations for advanced motor designs, with papers under revision in prominent journals.

🏭 Industrial Experience:

In the industrial sector, Mr. Sharifi has contributed as a CFD Specialist and Cooling System Design Engineer for electric motors. He played a crucial role in thermal analysis for a 100kW flywheel energy storage system at Niroo Research Institute and optimized heat transfer for a 200kW water-cooled motor using artificial neural networks.

🔍 Research Focus:

His research interests lie in power electronics, electrical machines, electric vehicles, metaheuristics, and heat transfer. He is also an inventor, with a patented hybrid excited flux switching permanent magnet motor for electric vehicle applications.

Publication Top Notes:

  • “An asymmetrical cascaded single-phase quasi Z-source multilevel inverter with reduced number of switches and lower THD”
    • Citations: 9
    • Published: 2020
  • “Optimal design of a synchronous reluctance motor using biogeography-based optimization”
    • Citations: 5
    • Published: 2021
  • “Optimal Design of a Permanent Magnet Synchronous Motor Using the Cultural Algorithm”
    • Citations: 4
    • Published: 2021
  • “Analytical Modeling and Electrical Equivalent Circuit Extraction for a Flux Switching PM Motor for EVs”
    • Citations: 3
    • Published: 2022
  • “Torque Ripple Minimization for a Switch Reluctance Motor Using the Ant Lion Optimization Algorithm”
    • Citations: 2
    • Published: 2022

 

 

 

 

Dr. Tee Connie | Machine Learning Awards | Best Researcher Award

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Dr. Tee Connie | Machine Learning Awards | Best Researcher Award

Dr. Tee Connie , Multimedia University , Malaysia

Dr. Tee Connie is a distinguished academic and researcher in the field of Information Technology, specializing in machine learning, pattern recognition, computer vision, and biometrics. She is currently a Professor at the Faculty of Information Science and Technology, Multimedia University, Malaysia, where she also serves as Dean of the Institute for Postgraduate Studies. Dr. Tee holds a Ph.D. and Master’s in Information Technology from Multimedia University, and a Bachelor’s degree in Information Technology with First Class Honours from the same institution. Her research is widely recognized, evidenced by numerous funded projects and publications, including notable grants for innovative applications in gait analysis, vehicle traffic analysis, and computer vision solutions. She has also contributed to the field with a patent for a hand geometry and palm print verification system. Her extensive experience and leadership in both research and academic administration underscore her significant impact in advancing information technology.

Professional Profile:

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Summary of Suitability for the Research for Best Researcher Award: Tee Connie

Introduction: Dr. Tee Connie, a Professor at Multimedia University, is a distinguished candidate for the Research for Best Researcher Award. Her extensive background in machine learning, computer vision, and biometrics, coupled with her leadership roles and significant research contributions, positions her as a highly suitable nominee.

🎓Education:

Dr. Tee Connie completed her Doctor of Philosophy in Information Technology at Multimedia University, Malaysia, in 2015. Prior to this, she earned a Master of Science in Information Technology from the same institution in 2005. She also holds a Bachelor of Information Technology, with a major in Information System Engineering, graduating with First Class Honours and a CGPA of 3.92/4.00 in 2003.

🏢Work Experience:

Dr. Tee Connie has held several academic and administrative positions at Multimedia University, Malaysia. She has been a Professor at the Faculty of Information Science and Technology since 2023 and currently serves as the Dean of the Institute for Postgraduate Studies, a role she has held since April 2022. Prior to this, she was the Deputy Dean of the Institute for Postgraduate Studies from April 2021 to April 2022. Dr. Tee’s career at the university began as a Lecturer in 2005, and she was promoted to Senior Lecturer in 2008, a position she held until 2021. She has also worked as an Associate Professor at the Faculty of Information Science and Technology since 2021 and served as a Tutor from 2003 to 2005.

🏆Awards and Grants:

Dr. Tee Connie has been awarded several significant research grants. She is leading the Malaysia-Jordan Matching Grant project on “A Non-Invasive Gait Analysis for Parkinson’s Disease Screening Using Computer Vision and Machine Learning Techniques,” which runs from September 2024 to August 2026, with a funding amount of RM 23,000. She is also a project member for the TM R&D Fund’s “Smart-VeTRAN: Smart Vehicle Traffic Impact Analysis Using 4G/5G Network” (RM 678,453) and the “Machine Learning Based Distributed Acoustic Sensing (DAS) for Fiber Break Prevention” projects (Sub-project 1: RM 638,731; Sub-project 2: RM 599,061), both running from August 2022 to July 2024. Other notable grants include the Fundamental Research Grant Scheme’s “Confined Parking Spaces and Congestion Prediction using Deep Q-Learning Strategy” (RM 89,093) and the “Few-shot Learning Approach for Human Activity Recognition and Anomaly Detection” (RM 113,850), both spanning from September 2022 to April 2024. Additionally, she has secured funding for projects such as the “Cryptographically Secure Cloud-Based Infrastructure (CryptCloud)” (RM 917,504), the IR Fund’s “Gender and Age Estimation using Human Gait for Smart Cities Surveillance” (RM 24,000), and the Multimedia University-Telkom University Joint Research Grant for “Gait Analysis for Neurodegenerative Disorders using Computer Vision and Deep Learning Approaches” (RM 20,000). Her past projects include contributions to the International Collaboration Fund’s “Design and Development of A Drone Based Hyperspectral Imaging System for Precision Agriculture” (RM 264,660) and several other notable grants in fields related to computer vision, biometrics, and security surveillance.

Publication Top Notes:

  • Visual-based vehicle detection with adaptive oversampling
  • A Robust License Plate Detection System Using Smart Device
  • Review on Digital Signal Processing (DSP) Algorithm for Distributed Acoustic Sensing (DAS) for Ground Disturbance Detection
  • A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry
  • Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions