Ao Guo | Artificial Intelligence | Best Researcher Award

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Mr. Ao Guo | Artificial Intelligence | Best Researcher Award

Master’s student, Xinjiang University, China

📚 Ao Guo is a dedicated postgraduate researcher at Xinjiang University with a focus on the innovation, optimization, and application of object detection technology. Currently pursuing a master’s degree in Electronic Information, Ao Guo has a robust background in computer vision, deep learning, pattern recognition, and image processing. He is committed to enhancing the accuracy and efficiency of object detection algorithms, contributing to both academia and industry.

Profile

Google Scholar

 

Education

🎓 Master’s Degree in Electronic Information – Xinjiang University, Urumqi, China
Ao Guo is advancing his studies in Electronic Information, focusing on the intersection of computer vision and deep learning to address real-world problems.

Experience

Ao Guo has been deeply involved in research aimed at optimizing deep learning models for intelligent weed management in agricultural environments. His work on a lightweight weed detection model, which incorporates global contextual features, is recognized for its high detection speed and accuracy, particularly suited for resource-constrained edge devices.

Research Interests

Ao Guo’s research interests encompass weed detection, deep learning, YOLO (You Only Look Once) models, attention mechanisms, and the development of lightweight networks. His innovative approach to integrating global information capture mechanisms into detection algorithms stands out in his field.

Awards

Ao Guo’s contributions to the field have been acknowledged through his publications and patent. Notably, he has published a paper in the highly reputed journal “Engineering Applications of Artificial Intelligence,” and he holds a patent for a lightweight weed detection method and device.

Publications

A lightweight weed detection model with global contextual joint features. Engineering Applications of Artificial Intelligence, 136, 108903. Link – Cited by: Article on Engineering Applications of Artificial Intelligence.

Nabi Mehri Khansari | Machine Learning | Best Researcher Award

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Dr. Nabi Mehri Khansari | Machine Learning | Best Researcher Award

University Professor, Sahand University of Technology, Iran

Dr. Nabi Mehri-Khansari is an esteemed Assistant Professor at the Sahand University of Technology. With a rich academic background in Mechanical and Aerospace engineering from prestigious institutions like Iran University of Science and Technology and the University of Tehran, he has made significant contributions to the field. His research spans failure analysis, damage and fracture mechanics in lightweight composite structures, leveraging machine learning and deep learning. Dr. Mehri-Khansari has collaborated with various international research centers and industries, enhancing his expertise and impact in the field.

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Scopus

Education

🎓 Dr. Nabi Mehri-Khansari obtained his B.Sc. degree in Mechanical Engineering from the Iran University of Science and Technology in 2011. He pursued his M.Sc. and Ph.D. degrees in Aerospace Engineering from the University of Tehran, completing them in 2014 and 2018, respectively. His academic excellence is marked by being ranked 2nd in M.Sc. and 1st in Ph.D., earning acceptance with quotas for talented students. He also served as a research fellow at NTNU University, Trondheim, Norway, further broadening his academic horizons.

Experience

🔧 Dr. Mehri-Khansari has an extensive professional background. He has been a faculty member at the Sahand University of Technology since January 2019. Prior to this, he was a lecturer at the University of Tehran – North Branch, a research assistant at NTNU University in Norway, and a technical expert at the Iranian Space Institute. His diverse roles reflect his versatile expertise and commitment to advancing engineering education and research.

Research Interests

🔬 Dr. Mehri-Khansari’s research interests are vast and interdisciplinary. They include wind turbine technology, multi-scale fracture mechanics of composites and inhomogeneous media, multi-scale damage mechanics, aeroelasticity, and defect detection methods. His innovative work often incorporates machine learning and deep learning techniques, pushing the boundaries of traditional engineering research.

Awards

🏅 Dr. Mehri-Khansari has received numerous accolades throughout his career. These include the prestigious Ph.D. acceptance with quotas for talented students, being ranked 1st in his Ph.D. program at the University of Tehran, and the Best Teacher Award from the Sahand University of Technology in June 2024. His membership in professional organizations such as the American Society of Mechanical Engineering and the Iranian Composites Scientific Association further underscores his professional excellence.

Publications

Orthotropic failure criteria based on machine learning and micro-mechanical matrix adapting coefficient
Mixed-modes (I/III) fracture of aluminum foam based on micromechanics of damage
Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
Numerical & experimental assessment of mixed-modes (I/II) fracture of PMMA/hydroxyapatite nanocomposite

Ali Raza | artificial intelligence | Best Researcher Award

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Mr. Ali Raza | artificial intelligence | Best Researcher Award

Lecturer, The University of Lahore, Pakistan

Ali Raza is a dedicated research scholar specializing in data science, known for his expertise in machine learning and deep learning applications. With a strong academic background and extensive professional experience in software development, he has contributed significantly to research in artificial intelligence and health informatics.

Profile

Google Scholar

📚 Education:

Ali completed his Bachelor of Science in Computer Science at KFUEIT after graduating from Iqra Degree College with a degree in Pre-Engineering. He further pursued his passion for computer science by earning a Master’s degree in Computer Science from KFUEIT, where his research focused on novel approaches in deep learning for image detection.

💼 Experience:

Ali’s professional journey includes roles as a Research Assistant at KFUEIT, where he published research articles on artificial intelligence. He has also worked as a Desktop App Developer at DexDevs Company and as a Full Stack Python Developer at BuiltinSoft Company, gaining expertise in business application development and machine learning frameworks.

🔬 Research Interests:

Ali’s research interests revolve around data science, particularly in machine learning model optimization, health informatics, and artificial intelligence applications in diverse domains such as pregnancy health analysis and network security.

🏆 Awards:

Ali has contributed significantly to research, evident from his publications and contributions as a peer reviewer for IEEE Access and PLOS ONE, highlighting his recognition in the academic community.

📄 Publications:

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, Plos one, 2022 (cited 46 times)

A novel deep learning approach for deepfake image detection, Applied Sciences, 2022 (cited 58 times)

Predicting employee attrition using machine learning approaches, Applied Sciences, 2022 (cited 44 times)

A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence, Technologies, 2023 (cited 23 times)

Novel class probability features for optimizing network attack detection with machine learning, IEEE Access, 2023