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.

Ali Mehrizi | Machine Learning | Best Paper Award

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Dr. Ali Mehrizi | Machine Learning | Best Paper Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Ali Mehrizi is a distinguished researcher and lecturer in Artificial Intelligence (AI) and Machine Learning at Ferdowsi University of Mashhad (FUM), Iran. With a wealth of experience exceeding a decade, his expertise spans adaptive probabilistic models, distributed learning, multi-target tracking, time series forecasting, and Gaussian Mixture Probability Hypothesis Density (GMPHD) methods. Dr. Mehrizi has published multiple impactful articles in renowned journals such as Expert Systems with Applications and Fuzzy Sets and Systems. He is deeply committed to advancing the understanding and application of AI techniques and has successfully mentored numerous students in areas ranging from Data Mining to Advanced Operating Systems.

Profile

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Education

Dr. Mehrizi educational background is rooted in Artificial Intelligence. He is currently pursuing a Ph.D. in AI at Ferdowsi University of Mashhad (2017–2024), under the supervision of Professor H. Sadoghi Yazdi. His dissertation focuses on financial time series forecasting using experience-based adaptive learning, a project that has already produced several publications in top-tier journals. Previously, he earned an M.Sc. in AI from Azad University of Mashhad (2011–2013), where he worked on adaptive semi-supervised learning, optimizing self-organizing map models. His early academic journey began with a B.Sc. in Computer Engineering from the University of Birjand, later transferring to Azad University of Mashhad.

Experience

Dr. Mehrizi professional career spans various roles, beginning in 2001 when he became the IT & Network Manager at the Faculty of Engineering. In this capacity, he significantly improved the system performance and network management. Since 2011, he has been involved in research in AI and Machine Learning, contributing to the development of machine learning models and publishing his findings in high-impact journals. He has also served as a lecturer since 2013, teaching a variety of undergraduate and graduate courses, including Data Mining, Operating Systems, and Advanced Operating Systems. As a researcher, he has mentored students in their theses, particularly in machine learning and pattern recognition, fostering the next generation of AI experts.

Research Interests

Dr. Mehrizi  research interests are broad, focusing on several key areas within the domain of AI. His work on distributed adaptive learning, particularly through Diffusion LMS and Diffusion RLS, aims to optimize decentralized data processing for dynamic systems. In addition, he has contributed to probabilistic and hypothesis-based learning, exploring the use of Gaussian Mixture Probability Hypothesis Density (GMPHD) models for uncertainty-based learning and tracking. His research also delves into time series analysis and forecasting, with a particular focus on financial markets. Dr. Mehrizi’s interest in multi-target tracking extends to real-time tracking algorithms, emphasizing performance in noisy and incomplete data environments. He is also committed to semi-supervised learning, exploring hybrid methods that bridge supervised and unsupervised learning approaches in scenarios with limited labeled data.

Awards

Dr. Mehrizi contributions to the fields of AI and machine learning have earned him recognition in various academic and professional circles. He has been nominated for multiple awards for his research, particularly in adaptive learning and time series forecasting. His work is highly regarded in the academic community, and he continues to push the boundaries of AI research, especially in the areas of distributed learning and multi-target tracking.

Publications

Dr. Mehrizi has authored several articles in well-respected journals in AI and machine learning. His key publications include:

Mehrizi, A., & Yazdi, H. S. (2019). “Adaptive probabilistic methods for long-term financial time series forecasting.” Expert Systems with Applications.

Mehrizi, A., & Yazdi, H. S. (2020). “Semi-supervised learning using GSOM for adaptive classification.” Fuzzy Sets and Systems.

Mehrizi, A. (2022). “Distributed adaptive learning for dynamic systems using Diffusion LMS and RLS.” Emerging Markets Finance and Trade.

Mehrizi, A., & Yazdi, H. S. (2021). “Gaussian Mixture Probability Hypothesis Density for multi-target tracking.” Journal of Machine Learning Research.

These publications have been cited extensively by various researchers in the fields of machine learning, AI, and financial forecasting, underscoring Dr. Mehrizi’s significant impact on the academic community.

Conclusion

Dr. Ali Mehrizi is a leading researcher and educator in the field of Artificial Intelligence and Machine Learning, with a deep commitment to advancing these fields through his innovative research. His extensive academic background and his practical experience in both teaching and real-world applications have made him an invaluable asset to Ferdowsi University of Mashhad. With a strong focus on adaptive learning, probabilistic models, and time series forecasting, Dr. Mehrizi continues to contribute to the evolution of AI. His work not only shapes academic research but also provides vital insights into practical AI solutions for industries like finance and engineering. As a mentor and educator, he remains dedicated to shaping the future of AI professionals and researchers.

Balaji Dhamodharan | AI Expert | Lifetime achievement Award

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Mr Balaji Dhamodharan | AI Expert | Lifetime achievement Award

Global Data Science Leader at  NXP Semiconductors,  United States

Balaji Dhamodharan is an award-winning AI and data science visionary with over 15 years of experience driving innovation, building high-performing teams, and delivering transformative AI/ML solutions across industries such as Oil & Gas, Manufacturing, and Retail. Recognized among the Top 40 Under 40 Data Scientists and a recipient of the AI 100 Award, he excels at integrating cutting-edge technologies to optimize processes, foster business growth, and address complex challenges.

Profile:

Leadership & Impact:

  • Global Data Science Leader, NXP Semiconductors
    • Established a Center of Excellence (CoE) for Data Intelligence, delivering advanced AI solutions that saved $10M annually.
    • Led cross-functional teams to implement generative AI and machine learning strategies, achieving 30% efficiency improvements.
    • Designed and executed the Data Science Roadmap, a visionary framework for governance and innovation.
  • Technology Advisor: Consistently integrates emerging AI/ML technologies, enabling data-driven decision-making for enterprises.
  • Scaling Expertise: Built and nurtured high-performing data science teams, fostering a culture of innovation and collaboration.

Key Technical Skills:

  • AI & ML Expertise: Generative AI, LLMs, Deep Learning, MLOps, and Natural Language Processing (NLP).
  • Data Solutions: Proficient in Python, PySpark, SQL, Snowflake, and DataRobot.
  • Visualization & Cloud: Tableau, Power BI, AWS, Azure, and Databricks.

Professional Timeline:

  • NXP Semiconductors (2022 – Present): Global Data Science Leader
  • DataRobot (2021 – 2022): Lead Data Scientist
  • Yum Brands (2021): Sr. Manager, Data Science
  • Dell Technologies (2019 – 2021): Consultant, Data Science
  • Honeywell Process Solutions (2012 – 2019): Sr. Data Scientist

Accomplishments:

  • Co-inventor of a patent-pending NLP-based contract analysis algorithm.
  • Published author of the technical book “Applied Data Science using PySpark” (Apress).
  • Editorial Board Member for leading AI journals.
  • Recognized as a Global Thought Leader in Manufacturing (2024) and Generative AI Leader of the Year.
  • Forbes Technology Council Member and speaker on AI’s transformative role in digital economies.

Thought Leadership & Advocacy

  • Active contributor to advancing responsible AI practices aligned with the United Nations Sustainable Development Goals (SDGs).
  • Advisory roles at Harvard, Oklahoma State University, and Gartner’s Evanta CDAO community.
  • Advocate for ethical AI through memberships in AI 2030 Responsible AI and 3AI Leadership Council.

Publication Top Notes:

  1. Optimizing Industrial Operations: A Data-Driven Approach to Predictive Maintenance through Machine Learning
    B. Dhamodharan
    International Journal of Machine Learning for Sustainable Development, 3(1), 2021.
  2. Beyond Traditional Methods: A Novel Approach to Anomaly Detection and Classification Using AI Techniques
    B. Dhamodharan
    Transactions on Latest Trends in Artificial Intelligence, 3(3), 2022.
  3. AI-Infused Quantum Machine Learning for Enhanced Supply Chain Forecasting
    L.M. Gutta, B. Dhamodharan, P.K. Dutta, P. Whig
    Quantum Computing and Supply Chain Management: A New Era of Optimization, 48–63, 2024.
  4. Empowering Enterprise Intelligence: The Transformative Influence of AutoML and Feature Engineering
    B. Dhamodharan
    International Journal of Creative Research in Computer Technology and Design, 2023.
  5. Driving Business Value with AI: A Framework for MLOps-Driven Enterprise Adoption
    B. Dhamodharan
    International Journal of Sustainable Development in Computing Science, 5(4), 2023.
  6. Harnessing Disaster Tweets: A Deep Dive into Disaster Tweets with EDA, Cleaning, and BERT-Based NLP
    B. Dhamodharan
    International Transactions in Artificial Intelligence, 6(6), 1–14, 2022.
  7. Applied Data Science Using PySpark: Learn the End-to-End Predictive Model-Building Cycle
    R. Kakarla, S. Krishnan, V. Gunnu, B. Dhamodharan
    Apress, 2024.
  8. Quantum Computing Applications in Real-Time Route Optimization for Supply Chains
    R.K. Vaddy, B. Dhamodharan, A. Jain
    Quantum Computing and Supply Chain Management: A New Era of Optimization, 2024.
  9. Multilingual Tokenization Efficiency in Large Language Models: A Study on Indian Languages
    B.D. Mohamed Azharudeen M
    Lattice – The Machine Learning Journal, 5(2), 2024.