Zihan Deng | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Dr. Zihan Deng | Artificial Intelligence | Best Researcher Award

Harbin Institute of Technology, China

Zihan Deng is a young and accomplished researcher in the field of imaging technology and computational tomography, with a strong foundation in deep learning and artificial intelligence. With a robust academic background and an array of interdisciplinary experiences, Deng has made significant contributions through high-impact publications, competitive grants, and patents. His expertise lies at the intersection of optical instrumentation and medical image analysis, and he continues to actively engage in scientific exploration with promising results.

Profile

Orcid

Education

Deng completed his undergraduate studies in Computer Science and Technology at Harbin Engineering University (2019–2023), ranking in the top 5% of his class. His academic curriculum included rigorous coursework in mathematics and computer science, scoring consistently above 90 in core subjects. He was subsequently recommended for direct admission into the graduate program at Harbin Institute of Technology, where he is currently pursuing his Master’s degree at the Institute of Ultra-Precision Optical Instrument Engineering under the mentorship of Professor Junning Cui and Academician Jiubin Tan. His research spans CT reconstruction, deep learning-based image enhancement, and X-ray detection technologies.

Experience

Deng has accumulated diverse experience through internships and collaborative projects. He served in leadership roles within student organizations and academic competitions, including receiving awards in national-level modeling and software contests. He undertook summer research at Tsinghua University’s IDG/McGovern Brain Research Institute and was later selected to join Germany’s PTB “Chief Engineer Class” as a visiting scholar. Professionally, he interned with Chengdu Shuzhilian Technology and Guangzhou CVTE, where he contributed to image processing and video enhancement projects. He has also played key roles in multimillion-yuan research collaborations with institutions like CGN Research Institute and GF High-End Semiconductor Imaging Systems.

Research Interest

Deng’s research interests revolve around imaging technology, deep learning, and CT reconstruction methods. He focuses on developing advanced algorithms for sparse-angle computed tomography, artifact reduction, and multi-view image correction using neural networks. His work integrates domain-specific knowledge from instrumentation science with state-of-the-art machine learning frameworks to improve image quality in both medical diagnostics and industrial inspection. He also investigates beam hardening correction and reconstruction under large field-of-view (FOV) conditions, addressing challenges in high-precision imaging systems.

Award

Over the course of his academic journey, Deng has received 11 scholarships and numerous accolades. These include five first-class and two second-class academic scholarships from Harbin Engineering University, the prestigious Xiaomi Scholarship, and the Outstanding Youth League Member Award. His undergraduate thesis on sparse-angle CT reconstruction was selected as an Excellent Graduation Project (top 2%). He has also won national-level awards in competitions such as the Mathematical Modeling Contest and the English Proficiency Championship.

Publication

Deng has authored or co-authored several influential papers in prestigious journals and conferences. His representative publications include:

  1. Deng Z., Wang Z., et al. (2024). “COO-DuDo: Computation Overhead Optimization Methods for Dual-Domain Sparse-View CT Reconstruction”, Expert Systems with Applications (JCR Q1, IF=7.5, in press) – cited in advanced CT algorithm research.

  2. Deng Z., Wang Z., Lin L., Wang S., Cui J. (2024). “Research on the Effectiveness of Multi-View Slice Correction Technology Based on Deep Learning in High-Pitch Spiral Scanning Reconstruction”, Journal of X-Ray Science and Technology (JCR Q2, IF=3.0) – applied in spiral CT systems.

  3. Wang Z.#, Deng Z.#, Liu F., et al. (2023). “OSNet & MNetO for Linear Computed Tomography in Multi-Scenarios”, IEEE Transactions on Instrumentation and Measurement (JCR Q1, IF=5.6) – widely cited in instrumentation imaging.

  4. Deng Z., Deng K., Wang Z., et al.. “Small Class Discussion-Based Teaching in Instrumentation Education”, The International Journal of Education – cited in engineering education reform discussions.

  5. Li Z., Li K., Deng Z., et al. (2024). “Assessment of Sheetlet Thickness in Human Left Ventricular Free Wall Using X-ray Phase-Contrast Microtomography”, Medical Image Analysis (JCR Q1, IF=10.9, accepted) – applied in cardiovascular research.

  6. Deng Z., Wang Z., Lin L., et al. (2025). “Computation Overhead Optimization Dual-Domain Network for Sparse-View CT Reconstruction”, ICASSP 2025 (CCF-B Conference) – in review, expected to support efficient CT image pipelines.

  7. Deng Z., Wang Z., Lin L., Wang S. “Hel-MUNet: Mamba-Unet with Helical Encoding for Clinical High Pitch Helical CT Reconstruction”, MICCAI 2025 (under review) – aligned with cutting-edge clinical imaging methods.

Conclusion

Zihan Deng exemplifies the next generation of research professionals driving innovation in imaging and artificial intelligence. Through a blend of strong theoretical foundation, hands-on project experience, and impactful publications, he has demonstrated exceptional capability in solving complex technical problems. With continued guidance under leading scholars and global exposure, Deng is well-positioned to become a prominent figure in the advancement of smart medical imaging and intelligent instrumentation.

Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

Posted on by sciencefather

Dr. Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

Associate Professor at University of Guilan, Rasht, Iran

Dr. Hemad Zareiforoush is an Assistant Professor at the Department of Biosystems Engineering, University of Guilan, Rasht, Iran, where he has been contributing to both academic and practical advancements in biosystems engineering since 2015. With a focus on agricultural machinery, automation, and quality inspection systems, his work bridges engineering and food science, particularly in areas like computer vision, image processing, and renewable energy applications. His research is highly interdisciplinary, combining mechanical engineering principles with computational intelligence for improving the agricultural industry’s efficiency.

Profile

Google Scholar

Education

Dr. Zareiforoush’s educational background is robust, with a PhD in Mechanical and Biosystems Engineering from Tarbiat Modares University in Tehran, Iran, completed in 2014. His academic excellence is evident in his GPA of 17.84 out of 20. He earned his MSc in Mechanical Engineering of Agricultural Machinery at Urmia University in 2010, where he graduated with a remarkable GPA of 19.29 out of 20. Earlier, Dr. Zareiforoush obtained his BSc in the same field from Urmia University in 2007, graduating with a GPA of 15.75 out of 20. He also attended a specialized governmental high school for excellent pupils, where he focused on mathematics and physics, graduating with a GPA of 18.71 out of 20.

Experience

Since joining the University of Guilan in 2015, Dr. Zareiforoush has been teaching various courses, including Engineering Properties of Food and Agricultural Products, Renewable Energy, and Measurement and Instrumentation Principles. His practical experience spans various engineering disciplines, with a particular emphasis on instrumentation, automation in agriculture, and food quality monitoring. Notably, his research has led to the development of innovative systems for rice quality inspection using computer vision and fuzzy logic. Additionally, he has been involved in numerous projects related to agricultural machinery, renewable energy, and automation for optimizing food production processes.

Research Interests

Dr. Zareiforoush’s research interests lie at the intersection of biosystems engineering, computational intelligence, and food science. He is particularly interested in computer vision applications for food quality inspection, using advanced image processing techniques to enhance product quality and safety. His work also explores hyperspectral imaging and spectroscopy for monitoring the quality of food materials. Another key area of his research is the application of machine learning algorithms for modeling and classifying food products based on their quality attributes. Additionally, he is involved in renewable energy applications in agriculture, focusing on solar-assisted drying systems and energy-efficient food processing methods.

Awards

Dr. Zareiforoush has received several prestigious awards throughout his academic career. He was honored with the Iran Ministry of Science, Research, and Technology Scholarship in 2012 and the National Elite Scholarship by the Iran National Foundation for Elites (INFE) in 2011. His exceptional academic performance earned him the title of “Best Student” at Urmia University in 2009. Additionally, he has been recognized as a “Talented Student” at Tarbiat Modares University and ranked 1st among MSc students in his department.

Publications

Dr. Zareiforoush has published several influential papers in high-impact journals. Some of his notable publications include:

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. Journal of Food Measurement and Characterization, 14: 1402–1416, Cited by: 43.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features. Plant Methods, 16:153, Cited by: 25.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2021). Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer. Food Science & Nutrition (JCR), 9(1), 532-543, Cited by: 12.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2016). Design, Development, and Performance Evaluation of an Automatic Control System for Rice Whitening Machine Based on Computer Vision and Fuzzy Logic. Computers and Electronics in Agriculture, 124: 14-22, Cited by: 67.

Soodmand-Moghaddam, S., Sharifi, M., Zareiforoush, H. (2020). Mathematical modeling of lemon verbena leaves drying in a continuous flow dryer equipped with a solar pre-heating system. Quality Assurance and Safety of Crops & Foods, 12(1): 57-66, Cited by: 30.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2015). Qualitative Classification of Milled Rice Grains Using Computer Vision and Metaheuristic Techniques. Journal of Food Science and Technology (Springer), 53(1): 118-131, Cited by: 45.

Zareiforoush, H., Komarizadeh, M.H., Alizadeh, M.R. (2010). Effects of crop-screw parameters on rough rice grain damage in handling with a horizontal screw auger. Journal of Food, Agriculture and Environment, 8(3): 132-138, Cited by: 19.

Conclusion

Dr. Hemad Zareiforoush’s academic and professional contributions significantly impact the fields of biosystems engineering, food science, and agricultural machinery. His work in developing intelligent systems for quality inspection and automation has improved agricultural productivity and food safety. His expertise in computational techniques, including fuzzy logic and machine learning, continues to shape the future of smart farming and food processing. With numerous awards, highly cited publications, and a track record of excellence, Dr. Zareiforoush is a leading figure in his field.

Jafar keighobadi | Automated Machine Learning (AutoML) | Best Researcher Award

Posted on by sciencefather

Prof. Dr. Jafar keighobadi | Automated Machine Learning (AutoML) | Best Researcher Award

Professor at Tabriz university, Iran

Dr. Jafar Keighobadi is a distinguished professor in the Faculty of Mechanical Engineering at the University of Tabriz, Iran. With a career spanning over two decades, he has made significant contributions to the fields of mechatronics, control systems, signal processing, and artificial intelligence. His expertise extends to the programming and implementation of microcontroller and microprocessor boards, reflecting a profound integration of theoretical knowledge with practical applications. Throughout his tenure, Dr. Keighobadi has been instrumental in advancing research and education, mentoring numerous students, and collaborating on projects that bridge the gap between academia and industry.

Profile

Scopus

Education

Dr. Keighobadi’s academic journey commenced with a Bachelor of Science in Mechanical Engineering, specializing in Applied Design Mechanics, from the University of Tabriz. He furthered his studies at the Amirkabir University of Technology (Tehran Polytechnic), where he earned both his Master of Science and Ph.D. in Mechanical Engineering. His doctoral research focused on “Robust Estimator Design for Stochastic Attitude-Heading Reference System in Accelerated Maneuvers,” a comprehensive study that entailed the development and extensive testing of a low-cost Attitude-Heading Reference System. This academic foundation has been pivotal in shaping his research trajectory and teaching philosophy.

Experience

Dr. Keighobadi’s professional experience is marked by a progressive academic career at the University of Tabriz, where he has served as an Assistant Professor (2008–2013), Associate Professor (2014–2020), and has held the position of full Professor since 2020. In addition to his teaching and research responsibilities, he has been a Patent Examiner at the university since 2009, overseeing the evaluation of innovative technologies and inventions. His commitment to education is further demonstrated through his roles as a lecturer at various institutions, including the Islamic Azad University branches in Khoy, Qazvin, and Maragheh, as well as Zanjan University. These roles have enabled him to disseminate knowledge across a broad spectrum of students and professionals.

Research Interests

Dr. Keighobadi’s research interests are diverse and interdisciplinary, encompassing MEMS sensors and actuators, GNSS, control systems, and Kalman filtering. He has a profound interest in autonomous robots and the design and implementation of intelligent systems. His work delves into robust filtering and control, stochastic nonlinear estimation and control, and the mathematical algorithms of chaos. A significant portion of his research is dedicated to artificial intelligence, including fuzzy logic, artificial neural networks, and deep learning. Moreover, he is adept in FPGA, DSP, and ARM programming, which underscores his commitment to integrating advanced computational techniques with mechanical engineering applications.

Awards

Throughout his illustrious career, Dr. Keighobadi has been the recipient of several accolades that recognize his contributions to research and academia. Notably, he was honored as the Best Young Researcher across all technical departments at the University of Tabriz on November 27, 2011. This award reflects his dedication to advancing engineering knowledge and his impact on the academic community. Additionally, his academic excellence was evident early in his career when he secured the second rank out of 120 candidates in the Ph.D. entrance exam at Amirkabir University of Technology on June 18, 2001. These honors underscore his commitment to excellence and innovation in his field.

Publications

Dr. Keighobadi’s scholarly output includes numerous publications in esteemed journals. A selection of his notable works includes:

“Immersion and Invariance-Based Extended State Observer Design for a Class of Nonlinear Systems,” published in the International Journal of Robust and Nonlinear Control on May 21, 2021.

“Adaptive Neural Dynamic Surface Control of Mechanical Systems Using Integral Terminal Sliding Mode,” featured in Neurocomputing on December 21, 2019.

“Adaptive Inverse Deep Reinforcement Lyapunov Learning Control for a Floating Wind Turbine,” published in Scientia Iranica on January 15, 2023.

“Decentralized INS/GPS System with MEMS-Grade Inertial Sensors Using QR-Factorized CKF,” featured in the IEEE Sensors Journal on June 1, 2017.

“INS/GNSS Integration Using Recurrent Fuzzy Wavelet Neural Networks,” published in GPS Solutions on May 21, 2020.

“Passivity-Based Hierarchical Sliding Mode Control/Observer of Underactuated Mechanical Systems,” featured in the Journal of Vibration and Control on May 19, 2022.

“Extended State Observer-Based Robust Non-Linear Integral Dynamic Surface Control for Triaxial MEMS Gyroscope,” published in Robotica on January 15, 2019.

These publications highlight Dr. Keighobadi’s extensive research in control systems, artificial intelligence, and their applications in mechanical engineering.

Conclusion

Dr. Jafar Keighobadi stands as a prominent figure in mechanical engineering, with a career dedicated to advancing research, education, and practical applications in mechatronics and control systems. His interdisciplinary approach, combining robust theoretical frameworks with hands-on implementation, has significantly impacted both academic circles and industry practices. As a mentor, researcher, and educator, Dr. Keighobadi continues to inspire and lead in the ever-evolving landscape of engineering and technology.

Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Assoc. Prof. Dr. Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

Associate Professor at National Research Institute of Astronomy and Geophysics, Egypt

Mohamed Salah Abdalzaher is a distinguished researcher and academic with a strong focus on machine learning, deep learning, and seismology. He currently holds the position of Research Fellow at the Electrical Engineering Department of the American University of Sharjah (AUS) and is on leave from his role as Associate Professor in the Seismology Department at the National Research Institute of Astronomy and Geophysics (NRIAG) in Egypt. Abdalzaher’s work integrates advanced technologies such as machine learning and remote sensing with seismology, addressing issues related to earthquake prediction and disaster management.

Profile

Scopus

Education

Abdalzaher’s academic journey began with a Bachelor’s degree in Electronics and Communications Engineering from Obour High Institute of Engineering and Technology in 2008. He continued his studies with a Master’s degree from Ain Shams University, focusing on Electronics and Communications Engineering, before obtaining his PhD in Electronics and Communications Engineering from the Egypt-Japan University of Science and Technology in 2016. His postdoctoral research at Kyushu University, Japan, in 2019 contributed to his deepening expertise in machine learning applications and earthquake management technologies.

Experience

Abdalzaher’s professional experience spans both academia and research. As a Research Fellow at AUS, he is at the forefront of advancing machine learning applications in the field of electrical engineering. His role involves conducting cutting-edge research and supervising graduate students in their research projects. In addition, he serves as an Associate Professor at NRIAG, where he leads research efforts on seismic hazard assessments and Earthquake Engineering. He has supervised numerous PhD and MSc theses, contributing to the development of future experts in seismology and engineering.

Research Interest

Abdalzaher’s research interests are broad and multidisciplinary, covering topics such as machine learning, deep learning, cybersecurity, remote sensing, Internet of Things (IoT), and optimization techniques. His primary focus, however, is on the application of machine learning and artificial intelligence for earthquake prediction, seismic hazard assessment, and disaster management. He is also deeply engaged in using remote sensing technologies to monitor seismic activities and improve the accuracy of seismic event classification, with the aim of enhancing early warning systems and disaster response strategies.

Awards

Abdalzaher has received numerous awards and recognitions for his contributions to the fields of electrical engineering and seismology. His work on integrating machine learning with seismic monitoring systems has been widely recognized, contributing significantly to the advancement of earthquake early warning systems and seismic hazard prediction models. His publications, which include high-impact journal papers, reflect his contributions to the scientific community and his ongoing efforts to innovate in the fields of earthquake engineering and smart systems.

Publications

Sharshir, S.W., Joseph, A., Abdalzaher, M.S., et al. (2024). “Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit.” Desalination and Water Treatment.

Etman, A., Abdalzaher, M. S., et al. (2024). “A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks.” IEEE ACCESS.

Habbak E. L., Abdalzaher, M. S., et al. (2024). “Enhancing the Classification of Seismic Events With Supervised Machine Learning and Feature Importance.” Scientific Report.

Abdalzaher, M. S., Soliman, M. S., & Fouda, M. M. (2024). “Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System.” IEEE Transactions on Geoscience and Remote Sensing.

Krichen, M., Abdalzaher, M. S., et al. (2024). “Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions.” Progress in Disaster Science.

Abdalzaher, M. S., Moustafa, S. R., & Yassien, M. (2024). “Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region.” Natural Hazards.

Moustafa, S. S., Mohamed, G. E. A., Elhadidy, M. S., & Abdalzaher, M. S. (2023). “Machine learning regression implementation for high-frequency seismic wave attenuation estimation in the Aswan Reservoir area, Egypt.” Environmental Earth Sciences.

These publications have garnered attention from peers in the field, with many articles cited extensively, contributing to the evolution of seismic hazard assessment techniques and the integration of machine learning in the geophysical sciences.

Conclusion

Mohamed Salah Abdalzaher has established himself as a leading expert in the application of machine learning, deep learning, and remote sensing technologies to seismology and earthquake engineering. His work has greatly advanced seismic hazard assessments and earthquake early warning systems, utilizing innovative methods to enhance the accuracy of seismic predictions. Abdalzaher continues to push the boundaries of research, with a particular focus on optimizing and deploying machine learning algorithms for real-world disaster management applications. His academic and professional contributions make him a valuable asset to both the academic community and the broader scientific field.