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

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.

Zahid Sarwar | Big Data Analytics | Best Researcher Award

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Dr. Zahid Sarwar | Big Data Analytics | Best Researcher Award

Postdoctoral Researcher | Shanxi University | China 

Dr. Zahid Sarwar is a dedicated academic and researcher in the field of Business Management, currently serving as a postdoctoral researcher at Shanxi University, China. He earned his Ph.D. in Business Management from Dongbei University of Finance and Economics, Dalian, China, where his dissertation focused on enhancing innovativeness through digital capabilities. Dr. Sarwar’s research spans various topics, including strategic management, technology management, digitalization, and sustainable business strategies, all of which are critical to modern organizational success. With a strong background in both theoretical knowledge and practical application, he continues to contribute valuable insights to the academic community, with a focus on the intersection of technology and strategy.

Profile

Scholar

Education

Dr. Sarwar’s academic journey is marked by a robust foundation in Business Management and related fields. He completed his Ph.D. at Dongbei University of Finance and Economics in 2023, where his research examined the role of digital capabilities in enhancing innovation within organizations. Prior to his doctoral studies, he earned a Master’s in Project Management from COMSATS Institute of Information Technology, Abbottabad, Pakistan, where he achieved a CGPA of 3.37/4.0. His undergraduate studies in Business Administration were completed at Gomal University, Pakistan, in 2014, where he graduated with a CGPA of 2970/4200. These academic credentials, along with his diverse experiences, have equipped him with a deep understanding of business management and research methodologies.

Experience

Dr. Sarwar’s professional experience is a combination of research and practical roles. As a postdoctoral researcher at Shanxi University, his current work explores key topics such as information technology, sustainable strategies, and digitalization in management. Before this, he worked as a full-time research assistant at Iqra National University, Pakistan, where he assisted Dr. Ali with various research projects. Additionally, he gained industry experience as an administrative assistant at Lucky Cement Factory Ltd., assisting in routine administrative and procurement activities. These roles have helped Dr. Sarwar develop not only his research skills but also a strong ability to bridge the gap between academia and industry.

Research Interest

Dr. Sarwar’s research interests lie at the intersection of strategic management, digital transformation, and innovation. He is particularly interested in understanding how digital platforms, big data analytics, and technological capabilities can enhance organizational innovation and performance. His work also delves into sustainable business practices, including the role of organizational culture and strategic alignment in fostering long-term business success. Dr. Sarwar is passionate about exploring the dynamic capabilities that organizations can leverage to remain competitive in an increasingly digitalized business environment. His research has profound implications for managers and policymakers seeking to drive innovation through technology and sustainability.

Awards

Dr. Sarwar’s research excellence has been recognized by numerous academic institutions. He received an Honorary Credential Certificate from Dongbei University of Finance and Economics, highlighting his exceptional contributions to the field. He was also awarded the First Prize for Outstanding Paper in 2023 and the Second Prize for Outstanding Paper in 2022, both by Dongbei University of Finance and Economics. These awards reflect his ability to produce high-quality, impactful research that resonates within the academic community. His work continues to be acknowledged for its rigor and relevance in advancing the field of Business Management.

Publications

Dr. Sarwar’s publication record reflects his expertise in strategic management, digitalization, and sustainability. Notable publications include:

Sarwar, Z., Gao, J., & Khan, A. (2024). Nexus of digital platforms, innovation capability, and strategic alignment to enhance innovation performance in the Asia Pacific region: A dynamic capability perspective. Asia Pacific Journal of Management, 41(2), 867-901.

Sarwar, Z., Song, Z. H., Ali, S. T., Khan, M. A., & Ali, F. (2025). Unveiling the path to innovation: Exploring the roles of big data analytics management capabilities, strategic agility, and strategic alignment. Journal of Innovation & Knowledge, 10(1), 100643.

Sarwar, Z., & Song, Z. (2024). How to improve sustainable business performance: Examining the roles of organizational rationale for sustainability and green organizational culture. Sustainability Accounting, Management and Policy Journal. (Publishing soon)

Gao, J., & Sarwar, Z. (2024). How do firms create business value and dynamic capabilities by leveraging big data analytics management capability? Information Technology and Management, 25(3), 283-304.

Sarwar, Z., & Song, Z. (2023). Machiavellianism and affective commitment as predictors of unethical pro-organization behavior: exploring the moderating role of moral disengagement. Kybernetes.

Sarwar, Z., Khan, M. A., Yang, Z., Khan, A., Haseeb, M., & Sarwar, A. (2021). An Investigation of Entrepreneurial SMEs’ Network Capability and Social Capital to Accomplish Innovativeness: A Dynamic Capability Perspective. SAGE Open, 11(3).

Sarwar, Z., Khan, M. A., & Sarwar, A. (2021). The strategic management model for COVID-19: a race against time: evidence from People’s Republic of China. Gomal University Journal of Research, 37(3), 278-286.

Conclusion

Dr. Zahid Sarwar’s career trajectory showcases a profound commitment to advancing the field of Business Management through rigorous research and scholarship. His focus on digital transformation, sustainable strategies, and innovation reflects the growing demands of today’s global business environment. With numerous publications in top-tier journals and several prestigious awards, Dr. Sarwar has established himself as a significant contributor to both academic knowledge and practical business strategies. His continued research endeavors at Shanxi University promise to further advance our understanding of the complex relationships between digital capabilities, innovation, and sustainability, positioning him as a future leader in his field.

Shaojin Ma | Predictive Analytics | Best Researcher Award

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Dr. Shaojin Ma | Predictive Analytics | Best Researcher Award

China Agriculture University | China

Dr. Shaojin Ma is a prominent researcher at China Agricultural University, specializing in food quality, safety, and non-destructive testing technologies. With a focus on innovative techniques like spectroscopy and laser-induced fluorescence, Ma has significantly contributed to the field of agricultural engineering. His work aims to improve food safety, quality monitoring, and processing, with particular attention to non-invasive analysis methods for food products such as grains, legumes, and peppers. He has published extensively in top-tier journals, establishing himself as a key figure in food science and agricultural engineering.

Profile

Scopus

Education

Shaojin Ma completed his higher education at China Agricultural University, where he earned his degrees in agricultural engineering. His academic background laid a solid foundation for his career in food quality control and non-destructive testing. During his studies, he developed a strong interest in the application of optical and imaging technologies for food safety and quality monitoring, which has been the core of his subsequent research and academic contributions.

Experience

Dr. Ma’s professional career includes significant research work in agricultural engineering and food science. He is currently affiliated with China Agricultural University, where he collaborates with various academic and industry experts to advance food safety technologies. Over the years, he has worked on multiple projects focused on food quality, precision agriculture, and the development of portable devices for food testing. His research has led to the development of innovative non-invasive techniques for assessing food quality, particularly in the processing and storage of fruits, vegetables, and grains.

Research Interests

Shaojin Ma’s research interests primarily revolve around the application of advanced optical and imaging technologies in the food industry. He is particularly focused on non-destructive testing methods such as LED and laser-induced fluorescence for quality control in agricultural products. Ma’s work also explores the use of spectroscopy, computer vision, and deep learning to monitor food safety and detect contaminants. His research extends to improving the efficiency and accuracy of food analysis techniques, offering practical solutions for the food processing industry.

Awards

Shaojin Ma has been recognized for his contributions to food science and engineering, receiving several awards for his innovative research. His work in non-destructive testing and food quality monitoring has earned him acclaim in both academic and industrial circles. Although specific awards are not listed, his research excellence and influential publications have positioned him as a leader in his field.

Publications

Shaojin Ma has published a selection of impactful articles in prestigious journals related to food science and agricultural engineering. His notable publications include:

Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers

    • Authors: Ma, S., Li, Y., Peng, Y., Wang, W., Zhang, Y.
    • Publication Year: 2024
    • Citations: 0

A portable dual-gear device for non-destructive testing on multi-quality of citrus

    • Authors: Li, Y., Wu, J., Wang, W., Ma, S.
    • Publication Year: 2023
    • Citations: 3

Rapid detection of lactic acid bacteria in yogurt based on laser-induced fluorescence

    • Authors: Ma, S., Li, Y., Peng, Y., Wang, Q.
    • Publication Year: 2023
    • Citations: 0

Toward commercial applications of LED and laser-induced fluorescence techniques for food identity, quality, and safety monitoring: A review

    • Authors: Ma, S., Li, Y., Peng, Y., Wang, W.
    • Publication Year: 2023
    • Citations: 8

Spectroscopy and computer vision techniques for noninvasive analysis of legumes: A review

    • Authors: Ma, S., Li, Y., Peng, Y.
    • Publication Year: 2023
    • Citations: 15

Design and Experiment of a Handheld Multi-Channel Discrete Spectrum Detection Device for Potato Processing Quality

    • Authors: Wang, W., Li, Y.-Y., Peng, Y.-K., Yan, S., Ma, S.-J.
    • Publication Year: 2022
    • Citations: 1

Research Progress of Rapid Optical Detection Technology and Equipment for Grain Quality

    • Authors: Nie, S., Ma, S., Peng, Y., Wang, W., Li, Y.
    • Publication Year: 2022
    • Citations: 3

Predicting ASTA color values of peppers via LED-induced fluorescence

    • Authors: Ma, S., Li, Y., Peng, Y., Yan, S., Wang, W.
    • Publication Year: 2022
    • Citations: 8

Detection of nitrofurans residues in honey using surface-enhanced Raman spectroscopy

    • Authors: Yan, S., Li, Y., Peng, Y., Ma, S., Han, D.
    • Publication Year: 2022
    • Citations: 14

An intelligent and vision-based system for Baijiu brewing-sorghum discrimination

    • Authors: Ma, S., Li, Y., Peng, Y., Yan, S., Zhao, X.
    • Publication Year: 2022
    • Citations: 8

These publications have been cited by numerous articles, reflecting their impact in the scientific community.

Conclusion

Shaojin Ma has established himself as a leading researcher in the field of agricultural engineering and food science. His work on non-destructive testing techniques has enhanced the monitoring and improvement of food quality and safety. Through his publications, he has made significant strides in the application of advanced technologies for food analysis, benefiting both the scientific community and the food industry. With a career focused on innovation and practical solutions, Dr. Ma continues to contribute to the advancement of food safety technologies, setting a high standard for future research in this domain.