Corresponding Author, at ferdowsi University of mashhad, Iran📖
Prof. Hadi Sadoghi Yazdi is an accomplished academic and researcher in the field of electronic engineering, with extensive experience in pattern recognition, machine learning, and signal processing. As a Professor at Ferdowsi University of Mashhad, he leads cutting-edge research in artificial intelligence, overseeing projects that have resulted in numerous patents and products in diverse industries. His expertise extends to both academic and industrial sectors, where he has made significant contributions to the development of smart systems, including applications in health, security, and automation. Dr. Yazdi is also a key figure in advancing technology in the military and defense sectors, with his work in missile tracking and vision-based systems influencing both national and international technological advancements.
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Education Background🎓
Prof. Hadi Sadoghi Yazdi has a strong educational foundation in electronic engineering, having completed his PhD in Electronic Engineering at Tarbiat Modares University, Tehran in 2005. His doctoral research focused on advanced topics in electronic systems, which significantly contributed to his expertise in areas such as pattern recognition and machine learning. Prior to his PhD, he earned a Master’s degree in Electronic Engineering from the same university in 1996, where he honed his skills in signal processing and electronics applications. Dr. Yazdi’s journey in engineering began with a Bachelor’s degree in Electronic Engineering from Ferdowsi University of Mashhad, which he completed in 1994. This educational background laid the groundwork for his distinguished career in both academia and industry, where he has been at the forefront of research in machine vision, signal processing, and artificial intelligence.
Dr. Yazdi is currently a Professor and Deputy of Research and Technology at Ferdowsi University of Mashhad, a position he has held since 2014. He has served in various academic roles, including Associate Professor (2009-2014) and Assistant Professor (2008-2009) at the same institution. Additionally, Dr. Yazdi supervises the Pattern Recognition Lab at Ferdowsi University, a leading research facility in the field. Prior to his tenure at Ferdowsi University, he held faculty positions at Hakim Sabzevari University (2005-2008), where he was also the Head of the Engineering Department, as well as teaching roles at several other prestigious institutions, including Kashmar University, Tabriz University, Tehran University, Arak University, and Shariati University.
In addition to his academic work, Dr. Yazdi has a strong background in research and development, having worked in industry on numerous projects involving artificial intelligence, electronic systems, and military technologies. He has held senior research and leadership positions in companies such as LG Madiran, Military Industries, and the Defense Industrials, where he was involved in the design and development of complex systems such as missile tracking, electronic fault finding, and smart systems for medical and security applications
Research Interests🔬
Dr. Yazdi’s research interests encompass a broad range of topics, including:
- Pattern Recognition
- Machine Learning
- Machine Vision
- Signal Processing
His work focuses on developing innovative solutions in these areas, with applications ranging from industrial automation and medical diagnostics to smart systems and security technologies.
Author Metrics and Achievements
Dr. Yazdi has authored and co-authored numerous research papers and holds several patents in the fields of artificial intelligence and electronics. Some of his key patents include the development of smart systems for applications such as fire detection, facial recognition, and traffic light control. His academic contributions, particularly in pattern recognition and machine learning, have been pivotal in shaping modern approaches to these fields. He has worked on over 40 research projects, both in academia and industry, demonstrating his leadership and impact on technological development.
Publications Top Notes 📄
1.Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise
- Authors: R Izanloo, SA Fakoorian, HS Yazdi, D Simon
- Published: 2016 Annual Conference on Information Science and Systems (CISS), pp. 500-505
- Year: 2016
- Citations: 243
- Summary: This paper introduces a Kalman filter that utilizes the maximum correntropy criterion (MCC) to handle non-Gaussian noise in dynamic systems, providing a more robust estimation framework for real-time filtering in challenging environments.
2. ECG arrhythmia classification with support vector machines and genetic algorithm
- Authors: JA Nasiri, M Naghibzadeh, HS Yazdi, B Naghibzadeh
- Published: 2009 Third UKSim European Symposium on Computer Modeling and Simulation, pp. 187-192
- Year: 2009
- Citations: 171
- Summary: This work explores the classification of ECG arrhythmias using support vector machines (SVM) optimized by a genetic algorithm (GA), demonstrating how this combined approach enhances the accuracy of detecting different types of arrhythmias.
3. An eigenspace-based approach for human fall detection using integrated time motion image and neural network
- Authors: H Foroughi, A Naseri, A Saberi, HS Yazdi
- Published: 2008 9th International Conference on Signal Processing, pp. 1499-1503
- Year: 2008
- Citations: 127
- Summary: This paper proposes an eigenspace-based method for human fall detection by integrating time-motion images with a neural network. The approach enhances detection accuracy, providing a reliable system for fall detection in various applications.
4. Probabilistic Kalman filter for moving object tracking
- Authors: F Farahi, HS Yazdi
- Published: Signal Processing: Image Communication 82, 115751
- Year: 2020
- Citations: 101
- Summary: This research introduces a probabilistic Kalman filter designed for tracking moving objects. The proposed method enhances the ability of Kalman filters to track objects in uncertain environments, improving real-time tracking applications in various domains.
5. IRAHC: Instance reduction algorithm using hyperrectangle clustering
- Authors: J Hamidzadeh, R Monsefi, HS Yazdi
- Published: Pattern Recognition, 48(5), pp. 1878-1889
- Year: 2015
- Citations: 90
- Summary: This paper presents an instance reduction algorithm (IRAHC) that utilizes hyperrectangle clustering to improve the efficiency and effectiveness of machine learning algorithms, particularly for large datasets. The proposed method enhances the performance of classifiers by reducing the number of instances required for training.
Conclusion
Prof. Hadi Sadoghi Yazdi is a deserving candidate for the Best Researcher Award, owing to his significant contributions to the fields of pattern recognition, machine learning, and signal processing. His innovative solutions and patents, particularly in AI and electronics, have far-reaching implications for industries such as healthcare, security, and defense. As an academic leader, Prof. Yazdi has not only advanced theoretical research but also bridged the gap between academia and industry, shaping modern technological landscapes. With continued interdisciplinary collaboration and a focus on solving global challenges, his impact on the world of engineering and technology will undoubtedly continue to grow. His leadership in both research and education makes him a standout figure worthy of the Best Researcher Award.