Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

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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

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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.

Shih-Wen Hsiao | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Shih-Wen Hsiao | Artificial Intelligence | Best Researcher Award

Emeritus Professor at National Cheng Kung University, Taiwan

Dr. Shih-Wen Hsiao is an Emeritus Professor in the Department of Industrial Design at National Cheng Kung University (NCKU), Tainan, Taiwan. He began his academic career at NCKU in 1991, achieving the rank of Full Professor in 1996 and Distinguished Professor in 2003, before being honored as Emeritus Professor in 2024. Prior to his tenure at NCKU, Dr. Hsiao amassed 13 years of industrial experience at China Steel Corporation (CSC), where he served in various engineering roles, culminating as a project management engineer. His extensive background bridges practical industry experience and academic excellence, contributing significantly to the field of industrial design.

Profile

Scopus

Education

Dr. Hsiao earned his Ph.D. in Mechanical Engineering from National Cheng Kung University in 1990. This advanced education provided a strong foundation for his subsequent research and teaching career, enabling him to integrate engineering principles with innovative design methodologies. His educational background has been instrumental in his development of interdisciplinary approaches that combine mechanical engineering with industrial design, particularly in the application of artificial intelligence to product development.

Experience

Throughout his tenure at NCKU, Dr. Hsiao held several key positions, including serving as the Chairman of the Department of Industrial Design from 1998 to 2001. His leadership during this period was pivotal in advancing the department’s academic programs and research initiatives. Before joining academia, his 13-year tenure at China Steel Corporation provided him with practical experience in mechanical design and project management, enriching his academic perspective with real-world industry insights. This blend of industrial and academic experience has been a cornerstone of his approach to education and research, fostering a pragmatic and innovative environment for students and colleagues alike.

Research Interests

Dr. Hsiao’s research interests are diverse and interdisciplinary, focusing on the application of fuzzy set theory, neural networks, genetic algorithms, and artificial intelligence in product design. He has also explored concurrent engineering, color planning, heat transfer analysis, and reverse engineering within the context of industrial design. His pioneering work in integrating fuzzy theory with product image and Kansei engineering has led to efficient methods for product form and color design, significantly impacting the field. Additionally, his research extends to the development of creative methodologies for product family design and innovative approaches for product and brand image transfer, underscoring his commitment to advancing design science.

Awards

Dr. Hsiao’s contributions have been widely recognized. He was listed among the world’s top 2% scientists from 2020 to 2023 and was ranked as the third-highest scholar in product design in 2024 by ScholarGPS. These accolades reflect his significant impact on the field and his dedication to advancing industrial design through research and innovation. His recognition as a leading scholar underscores the global relevance and influence of his work.

Publications

Dr. Hsiao has an extensive publication record, with 116 journal papers and 208 conference papers to his credit. His recent works include:

“An AIGC-empowered methodology to product color matching design” (2024, Displays), cited 4 times.

“Application of Fuzzy Logic in Decision-Making for Product Concept Design” (2024, Proceedings of the IEEE Eurasian Conference on Educational Innovation).

“Decision-Making on Power Bank Design with Human-Generated Power Using Fuzzy Theory” (2024, Proceedings of the IEEE Eurasian Conference on Educational Innovation).

“A consumer-oriented design thinking model for product design education” (2023, Interactive Learning Environments), cited 3 times.

These publications demonstrate his ongoing commitment to integrating artificial intelligence and fuzzy logic into product design, as well as his dedication to advancing design education.

Conclusion

Dr. Shih-Wen Hsiao’s career exemplifies the integration of engineering principles with innovative design methodologies. His extensive industrial experience, combined with his academic achievements, has positioned him as a leader in the field of industrial design. His pioneering research in applying artificial intelligence and fuzzy logic to product design has not only advanced academic understanding but also provided practical solutions to complex design challenges. Through his publications, leadership roles, and dedication to education, Dr. Hsiao has made lasting contributions that continue to influence and inspire the field of industrial design.

Jaya Raju G | Machine Learning | Best Researcher Award

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Mr. Jaya Raju G | Machine Learning | Best Researcher Award

Assistant Professor at Aditya University, India

G. Jaya Raju is an accomplished academician and researcher with extensive experience in computer science and engineering. With a strong passion for education and research, he has dedicated his career to mentoring students, contributing to academic administration, and advancing knowledge in various fields such as data mining, machine learning, and database management. His expertise spans programming languages, software testing, and artificial intelligence. Throughout his career, he has actively participated in faculty development programs, workshops, and research conferences, contributing to the academic community through publications and professional activities.

Profile

Scopus

Education

G. Jaya Raju is currently pursuing a Ph.D. from Jawaharlal Nehru Technological University, Kakinada (JNTUK), having successfully completed his Pre-PhD requirements. He obtained his M.Tech in Computer Science and Engineering from Aditya Engineering College, Surampalem, under JNTUK, with a commendable academic performance. Additionally, he holds an M.Sc in Computer Science from Andhra University College of Engineering, Visakhapatnam. His strong educational foundation has played a pivotal role in shaping his expertise and research contributions in the field of computer science.

Experience

With over a decade of experience in academia, G. Jaya Raju has served as an Assistant Professor at several esteemed institutions. Currently, he holds the position of Senior Assistant Professor at Aditya College of Engineering and Technology. Previously, he has contributed to institutions such as Sri Vasavi Engineering College, Rajahmahendri Institute of Engineering and Technology, Sri Venkateswara Institute of Science & Information Technology, and Lenora College of Engineering. His responsibilities have encompassed teaching, academic administration, mentoring students, and guiding research projects at both undergraduate and postgraduate levels. Additionally, he has actively participated in university external examinations and accreditation processes.

Research Interests

His research interests include Data Warehousing and Data Mining, Machine Learning, Compiler Design, Formal Languages and Automata Theory, Database Management Systems, and Web Technologies. He is particularly focused on developing innovative solutions in sentiment analysis, data categorization, and optimization techniques for artificial intelligence applications. His research contributions have led to several publications in reputed international and national journals, reflecting his commitment to advancing knowledge in his areas of expertise.

Awards and Recognitions

G. Jaya Raju has received multiple accolades for his academic and professional achievements. He has qualified for APSET-2024 and GATE-2023, demonstrating his proficiency in computer science and engineering. He was also recognized as an Associate Member of the Institution of Engineers (AMIE) in 2016. Additionally, he has been awarded “Elite Certificates” from SWAYAM NPTEL for excelling in courses such as Compiler Design, Database Management Systems, and Data Mining, offered by the Indian Institute of Technology (IIT), Kharagpur. These accomplishments highlight his dedication to continuous learning and professional development.

Publications

“Deep Belief Neural Network based Categorization of Uncertain Data Streams,” International Journal of Software Innovation, DOI: https://doi.org/10.4018/IJSI.312262, cited by multiple research articles.

“Classical Software Testing Using Semi-Proving,” IJCST Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), cited in numerous studies related to software testing methodologies.

“Implementation of Skyline Sweeping Algorithm,” International Journal of Computer Science and Technology (IJCST) Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), referenced in data structure optimization research.

“Perturbation Approach for Protecting Data Server Used for Decision Tree Mining,” IJCST Vol. 3, Issue 4, Oct-Dec 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), widely cited in data security studies.

Conclusion

G. Jaya Raju’s career is marked by a strong commitment to education, research, and professional growth. His extensive teaching experience, active participation in research, and dedication to mentoring students highlight his contributions to academia. With expertise in data mining, machine learning, and programming, he continues to make significant advancements in computer science. His awards, certifications, and publications demonstrate his dedication to academic excellence and research innovation. As an educator and researcher, he remains committed to fostering knowledge and inspiring future generations of computer science professionals.

Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

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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.