Shaohuai Feng | Data Science | Best Researcher Award | 2350

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Dr. Shaohuai Feng | Data Science | Best Researcher Award

Student at Feng CGSS, China

Feng Shaohuai is a dedicated researcher focused on sustainable development 🌍 and carbon neutrality 🌱. His work explores how industrial systems, environmental policies, and technological innovation can drive the low-carbon transition, particularly in emerging economies. Through interdisciplinary approaches, Feng bridges academic research with practical solutions to address global climate challenges. His projects, such as sustainable industrial transformation and data-driven environmental policy models, offer actionable frameworks for achieving long-term sustainability goals 🌿. With a commitment to policy implementation, his research informs industry and government decision-making. Feng’s recent publications in top journals reflect his expertise in environmental governance and low-carbon industrial policies. Despite his impressive academic contributions, there is room for expansion in industry partnerships, patents, and editorial roles 📚. Feng is a rising star in his field, bringing impactful, real-world solutions to pressing global issues and advancing the sustainability agenda 🌟.

Professional Profile

Scopus Profile

Education 🎓

Feng Shaohuai’s academic journey is rooted in the study of sustainability and carbon neutrality 🌍. He holds degrees specializing in environmental governance and low-carbon technologies 🌱, which have shaped his expertise in addressing global climate issues. His education provides a deep understanding of sustainable development challenges, particularly in the context of developing economies 🌏. Feng’s studies have focused on green innovation, energy efficiency, and policy frameworks that support the transition to a low-carbon future 🔋. This educational background enables him to merge theory with practice, crafting real-world solutions that align with global climate goals 🌐. Through his learning, Feng has honed interdisciplinary skills to create strategies that foster sustainability across various sectors, positioning him as a key figure in the field of environmental research 🔬.

Professional Experience 💼

Feng Shaohuai currently serves as a researcher at Universiti Sains Malaysia, where he focuses on sustainable industrial transformation and energy efficiency ⚙️. His professional work bridges the gap between policy, data, and technology, creating practical solutions for sustainable development 🌍. Feng leads projects that develop low-carbon industrial policies and green innovation strategies, aiming to transform industries and promote sustainability in emerging economies 🌱. His role enables him to work with governments, academia, and industry leaders to drive global sustainability agendas. Through his interdisciplinary approach, Feng has made valuable contributions to carbon neutrality and energy efficiency, ensuring that his research delivers real-world impact in the fight against climate change 🌡️.

Research Interest 🔬

Feng Shaohuai’s research is dedicated to carbon neutrality, low-carbon industrial policies, and sustainable development 🌱. He explores the intersection of technological innovation, environmental governance, and market mechanisms to develop solutions that address global sustainability challenges 🌍. His focus is on how data-driven environmental policies can enhance decision-making and improve energy efficiency 🔋. Feng also investigates strategies for sustainable industrial transformation, particularly in developing economies, to foster green innovation and reduce carbon footprints 🌳. His interdisciplinary research combines policy, technology, and industry, aiming to create actionable solutions for global climate goals 🌏. Through this work, Feng seeks to accelerate the transition to a low-carbon future and promote environmental sustainability across sectors 🌐.

Awards and Honors 🏆

While Feng Shaohuai has yet to receive major formal awards, his research has earned recognition in the field of sustainability and carbon neutrality 🌿. His articles in respected journals like Resources Policy and Energy Strategy Reviews showcase his growing influence in the academic world 📚. Feng’s work on low-carbon industrial transformation and environmental policies has contributed to advancing sustainable development goals 🌏. Though not yet awarded, his published research and interdisciplinary approach are a testament to his expertise and potential for future recognition 🌟. As his work continues to influence the fields of green innovation and sustainable development, Feng is well-positioned to receive accolades for his contributions to the low-carbon transition and global climate solutions 🌱.

Conclusion 🌟

Feng Shaohuai is an emerging leader in sustainability research, focusing on carbon neutrality, green innovation, and low-carbon industrial policies 🌿. His academic background and professional expertise enable him to bridge the gap between policy, data, and technology, driving impactful solutions for global sustainability 🌍. Feng’s research on sustainable industrial transformation and energy efficiency plays a pivotal role in shaping strategies that support carbon neutrality and environmental governance 🌱. Through his interdisciplinary approach, Feng is actively contributing to the global shift toward low-carbon futures 🌏. As he continues to collaborate with governments, industries, and researchers, Feng’s work will accelerate climate goals and contribute to a sustainable and green world 🌳.

Publications Top Notes

Unlocking the potential of natural resources, fintech and fiscal policy for carbon neutrality; evidence from N-11 nations

  • Authors: Shaohuai Feng, Mohd Wira Mohd Shafiei, Theam Foo Ng, Jie Ren

  • Year: November 2024

  • Source: Resources Policy 🌍

The intersection of economic growth and environmental sustainability in China: Pathways to achieving SDG

  • Authors: Shaohuai Feng, Mohd Wira Mohd Shafiei, Theam Foo Ng, Yefeng Jiang

  • Year: September 2024

  • Citations: 9 📚

  • Source: Energy Strategy Reviews ⚡

 

Iliyas Khan | Statistics | Best Researcher Award

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Dr. Iliyas Khan | Statistics | Best Researcher Award

Teaching Assistance at Universiti Teknologi PETRONAS, Malaysia

Dr. Iliyas Karim Khan is a dedicated researcher specializing in machine learning, statistical modeling, forecasting, and big data analysis. He holds a Ph.D. from Universiti Teknologi PETRONAS (UTP), Malaysia, along with advanced degrees in statistics from Peshawar University, Pakistan. With a strong publication record in Q1 journals, his research focuses on optimizing clustering algorithms, particularly in K-means clustering and data science applications. His expertise extends to programming and statistical tools such as Python, SPSS, Stata, and EViews. Dr. Khan has gained extensive teaching experience, serving as a Teaching Assistant at UTP and a Subject Specialist in Pakistan. He was recognized with a Publication Recognition Achievement (2024) at UTP, underscoring his research contributions. His work is instrumental in improving data-driven decision-making and computational efficiency, making him a valuable asset in the academic and research community. His commitment to innovation and education positions him as a promising leader in his field.

Professional Profile 

Google Scholar
Scopus Profile

Education

Dr. Iliyas Karim Khan has a strong academic background in statistics and data science. He earned his Ph.D. in 2024 from Universiti Teknologi PETRONAS (UTP), Malaysia, focusing on optimizing machine learning algorithms. Prior to that, he completed an M.Phil. (2016) and M.Sc. (2014) in Statistics from Peshawar University, Pakistan, and a B.Sc. in Statistics (2012) from SBBU Sheringhal, Upper Dir, Pakistan. His foundational studies in science and engineering were completed at BISE Peshawar, along with a B.Ed. (2015) for pedagogical training. His educational journey reflects his commitment to advanced research in big data analysis, forecasting, and statistical modeling. Throughout his studies, he has demonstrated exceptional analytical skills, contributing to high-impact research in data clustering and computational efficiency. His academic achievements have positioned him as a promising researcher in the field of machine learning and statistical analytics, making significant contributions to data science methodologies.

Professional Experience

Dr. Iliyas Karim Khan has gained extensive teaching and research experience across multiple institutions. He worked as a Teaching Assistant at Universiti Teknologi PETRONAS (UTP), Malaysia, where he was actively involved in data science research and statistical analysis. In Pakistan, he served as a Subject Specialist at GHSS Bang Chitral for four years, strengthening his expertise in applied statistics and data analysis. His teaching career also includes one year at Abbottabad University of Science and Technology, where he mentored students in statistical modeling and machine learning techniques. Additionally, he completed an internship at the University of Peshawar and taught at Darban Degree College, Chitral. His experience encompasses curriculum development, student mentoring, and advanced research in statistical computing, making him an accomplished educator and researcher. His hands-on expertise in Python, SPSS, Stata, and EViews further enhances his ability to train future data scientists and statisticians.

Research Interest

Dr. Iliyas Karim Khan’s research revolves around machine learning, big data analytics, statistical modeling, and forecasting. His primary focus is on clustering algorithms, particularly enhancing the K-means clustering method for improved efficiency and accuracy. His work addresses key challenges in data science, such as non-spherical data, outlier handling, and optimal cluster selection, which have significant implications for AI-driven decision-making. Additionally, he explores computational time optimization and statistical accuracy in clustering techniques, contributing to the advancement of data-driven methodologies. His studies extend to forecasting models, applied statistical techniques, and real-world big data applications. Through numerous Q1 journal publications, he has proposed innovative solutions for refining data science models. His research is highly relevant in industries reliant on machine learning, artificial intelligence, and predictive analytics, positioning him as a key contributor to the evolution of data-centric technologies and computational intelligence.

Awards and Honors

Dr. Iliyas Karim Khan’s academic excellence and research contributions have been recognized through prestigious accolades. In 2024, he received the “Publication Recognition Achievement” award from Universiti Teknologi PETRONAS (UTP), Malaysia, honoring his impactful research in machine learning and statistical analysis. His Q1 journal publications in high-impact journals demonstrate his ability to produce innovative and widely acknowledged research. Additionally, his contributions to data clustering and computational efficiency have been well-received within the academic and research communities. His work on addressing K-means clustering limitations and enhancing algorithmic efficiency has been cited extensively, highlighting its significance in data science and artificial intelligence. His growing recognition in machine learning and big data analytics marks him as a promising scholar in his field. Through his research, he continues to make valuable contributions that shape the future of data-driven decision-making and statistical computing.

Conclusion

Dr. Iliyas Karim Khan is an exceptional researcher, educator, and innovator in the field of machine learning, statistical modeling, and big data analysis. His strong academic background, extensive teaching experience, and high-impact research make him a significant contributor to computational intelligence and data science advancements. His studies on enhancing K-means clustering efficiency, outlier handling, and computational time optimization have added valuable insights to the field. Through prestigious publications and academic honors, he has demonstrated his expertise in refining statistical methodologies for real-world applications. His proficiency in Python, SPSS, Stata, and advanced statistical techniques enables him to bridge the gap between theoretical advancements and practical applications in data science. As he continues to explore new frontiers in machine learning and predictive analytics, his work is expected to have a lasting impact on data-driven industries and AI-based decision-making systems.

Publications Top Noted

  • Title: Determining the Optimal Number of Clusters by Enhanced Gap Statistic in K-Mean Algorithm
    Authors: Iliyas Karim Khan, HB Daud, NB Zainuddin, R Sokkalingam, M Farooq, ME Baig, …
    Year: 2024
    Citations: 7
    Source: Egyptian Informatics Journal, Volume 27, Article 100504

  • Title: Numerical Solution of Heat Equation Using Modified Cubic B-Spline Collocation Method
    Authors: M Iqbal, N Zainuddin, H Daud, R Kanan, R Jusoh, A Ullah, Iliyas Karim Khan
    Year: 2024
    Citations: 3
    Source: Journal of Advanced Research in Numerical Heat Transfer, Volume 20, Pages 23-35

  • Title: Numerical Solution by Kernelized Rank Order Distance (KROD) for Non-Spherical Data Conversion to Spherical Data
    Authors: Iliyas Karim Khan, HB Daud, R Sokkalingam, NB Zainuddin, A Abdussamad, …
    Year: 2024
    Citations: 2
    Source: AIP Conference Proceedings, Volume 3123 (1)

  • Title: A Modified Basis of Cubic B-Spline with Free Parameter for Linear Second Order Boundary Value Problems: Application to Engineering Problems
    Authors: M Iqbal, N Zainuddin, H Daud, R Kanan, H Soomro, R Jusoh, A Ullah, …
    Year: 2024
    Citations: 1
    Source: Journal of King Saud University-Science, Volume 36 (9), Article 103397

  • Title: Standardizing Reference Data in Gap Statistic for Selection of Optimal Number of Clusters in K-Means Algorithm
    Authors: Iliyas Karim Khan, H Daud, N Zainuddin, R Sokkalingam
    Year: 2025
    Source: Alexandria Engineering Journal, Volume 118, Pages 246-260

  • Title: A Hybrid Stacked Sparse Autoencoder (HSSAE) Model for Predicting Type 2 Diabetes
    Authors: A Abdussamad, H Daud, R Sokkalingam, M Zubair, Iliyas Karim Khan, Z Mahmood
    Year: 2025
    Source: To be published

  • Title: A Mini Review of the State-of-the-Art Development in Oil Recovery Under the Influence of Geometries in Nanoflood
    Authors: M Zafar, H Sakidin, A Hussain, M Sheremet, I Dzulkarnain, R Safdar, …
    Year: 2024
    Source: Journal of Advanced Research in Micro and Nano Engineering, Volume 26 (1), Pages 83-101

  • Title: Exploring K-Means Clustering Efficiency: Accuracy and Computational Time Across Multiple Datasets
    Authors: Iliyas Karim Khan, H Daud, N Zainuddin, R Sokkalingam, A Abdussamad, AS Azad, …
    Year: 2024
    Source: Journal of Advanced Research in Applied Sciences and Engineering Technology

  • Title: Forecasting the Southeast Asian Currencies Against the British Pound Sterling Using Probability Distributions
    Authors: Iliyas Karim Khan, Ahmad Abubakar Suleiman, Hanita Daud, Mahmod Othman, Abdullah …
    Year: 2023
    Source: Data Science Insights, Volume 1 (1), Pages 31-51

  • Title: Addressing Limitations of the K-Means Clustering Algorithm: Outliers, Non-Spherical Data, and Optimal Cluster Selection
    Authors: Iliyas Karim Khan, Abdussamad, Abdul Museeb, Inayat Agha
    Year: 2024
    Citations: 4
    Source: AIMS Mathematics, Volume 9, Pages 25070-25097

 

 

Meichen Feng | Data Science | Best Researcher Award

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Prof. Meichen Feng | Data Science | Best Researcher Award

Professor at Shanxi Agricultural University, China.

Feng Meichen is a distinguished professor at Shanxi Agricultural University, specializing in crop ecology, precision agriculture, and agricultural information technology. As the Deputy Dean of the College of Agriculture, she has led 22 research projects, authored 82 SCI/Scopus-indexed papers, and secured 18 patents, demonstrating a strong commitment to advancing sustainable agriculture. With 988 citations and an H-index of 19, her work has significantly impacted agricultural innovation and technology. She has published 5 books, contributed to multiple academic committees, and serves on the editorial boards of leading agricultural journals. Her research focuses on improving crop yield, resource efficiency, and environmental sustainability, benefiting both academia and local farming communities. While her expertise is well-recognized in China, expanding global collaborations could further enhance her research impact. With a remarkable career in agricultural research and innovation, Feng Meichen is an outstanding candidate for the Best Researcher Award.

Professional Profile 

Scopus Profile
ORCID Profile

Education

Feng Meichen holds an advanced degree in agriculture and crop ecology, equipping her with a deep understanding of agricultural information technology, precision farming, and ecological sustainability. Her academic journey has been dedicated to exploring innovative agricultural techniques that improve productivity while ensuring environmental sustainability. Through extensive research and continuous professional development, she has gained expertise in 3S technology (GIS, GPS, and remote sensing) and its application in modern agriculture. Her education has provided a strong foundation for her contributions to precision agriculture, crop management, and smart farming technologies. With a commitment to advancing agricultural science, she has successfully integrated academic knowledge with practical applications, benefiting both researchers and farming communities. Her ability to translate theoretical concepts into real-world solutions has made her a recognized leader in the field of crop science and agricultural technology.

Professional Experience

As a Professor and Deputy Dean at the College of Agriculture, Shanxi Agricultural University, Feng Meichen has established herself as a leader in agricultural research and education. She has successfully led 22 major research projects, contributing to advancements in crop ecology, precision farming, and smart agriculture. Her expertise extends beyond academia, as she actively collaborates with government agencies, research institutions, and industry leaders to develop sustainable farming practices. She has authored 82 peer-reviewed journal articles, secured 18 patents, and published 5 books, showcasing her multidisciplinary expertise. Additionally, she serves on the editorial boards of prestigious agricultural journals, including the Journal of Smart Agriculture and Shanxi Agricultural Sciences. She is also a member of multiple professional committees, influencing agricultural policies and research directions in China. Her extensive academic, research, and administrative experience highlights her dedication to advancing agricultural science and technology for long-term sustainability.

Research Interests

Feng Meichen’s research focuses on crop ecology, precision agriculture, and agricultural information technology, with an emphasis on sustainable and smart farming solutions. She integrates 3S technology (GIS, GPS, remote sensing) with crop production models to enhance agricultural efficiency, resource management, and environmental conservation. Her work aims to optimize crop yield, reduce environmental impact, and improve agricultural decision-making processes. She is particularly interested in applying artificial intelligence and big data analytics to develop predictive models for crop health monitoring and precision irrigation systems. Her research extends to organic dryland agriculture, where she explores climate-resilient farming techniques. By collaborating with industry experts, policymakers, and farmers, she ensures that her research findings have practical applications that benefit the agricultural sector. Her commitment to advancing smart agriculture technologies positions her as a pioneering researcher in the field of modern agriculture.

Awards and Honors

Feng Meichen has received multiple awards and recognitions for her outstanding contributions to agricultural science and research. As a Deputy Chief Expert in the Shanxi Modern Agricultural Specialty Grain Industry Technology System, she has played a pivotal role in shaping agricultural policies and technologies. Her patents and scientific contributions have earned her recognition at national and provincial levels. She has been honored by Shanxi Agricultural University and various academic organizations for her contributions to precision farming, agricultural technology development, and ecological sustainability. She is a member of several prestigious agricultural committees, including the China Modern Agriculture Graduate School and the Chinese Society of Crops. Through her active involvement in academic and industry collaborations, she continues to make a lasting impact on the agricultural sector. Her dedication to agricultural innovation and sustainability has established her as a leading researcher and academician.

Conclusion

Feng Meichen is a highly accomplished researcher, academic leader, and innovator in agricultural science. With extensive research contributions, patents, and leadership roles, she has significantly advanced the fields of crop ecology, precision agriculture, and smart farming technologies. Her work has not only improved crop productivity and resource efficiency but also contributed to sustainable farming practices that benefit both academic research and practical applications. While her impact is widely recognized in China, expanding international collaborations and industry partnerships could further elevate her global research influence. Her dedication to scientific excellence, innovation, and sustainability makes her an outstanding candidate for the Best Researcher Award.

Publications Top Noted

2025 Publications

🔹 Evaluating the Potential of Airborne Hyperspectral Imagery in Monitoring Common Beans with Common Bacterial Blight at Different Infection Stages

  • Authors: Binghan Jing, Jiachen Wang, Xin Zhang, Xiaoxiang Hou, Kunming Huang, Qianyu Wang, Yiwei Wang, Yaoxuan Jia, Meichen Feng, Wude Yang et al.
  • Year: 2025
  • DOI: 10.1016/j.biosystemseng.2025.02.002
  • Source: Biosystems Engineering (Crossref)

🔹 Potential Impacts of Climate Change on the Spatial Distribution Pattern of Naked Oats in China

  • Authors: Zhenwei Yang, Xujing Yang, Yuheng Huang, Yalin Zhang, Yao Guo, Meichen Feng, Mingxing Qin, Ning Jin, Muhammad Amjad, Chao Wang et al.
  • Year: 2025
  • DOI: 10.3390/agronomy15020362
  • Source: Agronomy (Crossref)

2024 Publications

🔹 A Model for the Detection of β-Glucan Content in Oat Grain Based on Near Infrared Spectroscopy

  • Authors: Yang Z., Cheng Z., Su P., Wang C., Qin M., Song X., Xiao L., Yang W., Feng M., Zhang M.
  • Year: 2024
  • DOI: 10.1016/j.jfca.2024.106105
  • Source: Journal of Food Composition and Analysis (Scopus – Elsevier)

🔹 Combined Use of Spectral Resampling and Machine Learning Algorithms to Estimate Soybean Leaf Chlorophyll

  • Authors: Gao C., Li H., Wang J., Zhang X., Huang K., Song X., Yang W., Feng M., Xiao L., Zhao Y. et al.
  • Year: 2024
  • DOI: 10.1016/j.compag.2024.108675
  • Source: Computers and Electronics in Agriculture (Scopus – Elsevier)

🔹 Efficient Prediction of SOC and Aggregate OC Components by Continuous Wavelet Transform Spectra Under Different Feature Selection Methods

  • Authors: Yang S., Wang Z., Ji C., Hao Y., Liang Z., Yan X., Qiao X., Feng M., Xiao L., Song X. et al.
  • Year: 2024
  • DOI: 10.1016/j.compag.2023.108550
  • Source: Computers and Electronics in Agriculture (Scopus – Elsevier)

🔹 Prediction of the Potential Distribution and Analysis of the Freezing Injury Risk of Winter Wheat on the Loess Plateau Under Climate Change

  • Authors: Qing Liang, Xujing Yang, Yuheng Huang, Zhenwei Yang, Meichen Feng, Mingxing Qing, Chao Wang, Wude Yang, Zhigang Wang, Meijun Zhang et al.
  • Year: 2024
  • Source: Journal of Integrative Agriculture

2023 Publications

🔹 AMF Inoculation Positively Regulates Soil Microbial Activity and Drought Tolerance of Oat

  • Authors: Li Y., Li L., Zhang B., Lü Y.-F., Feng M.-C., Wang C., Song X.-Y., Yang W.-D., Zhang M.-J.
  • Year: 2023
  • DOI: 10.11674/zwyf.2022561
  • Source: Journal of Plant Nutrition and Fertilizers (Scopus – Elsevier)

🔹 Analyzing Protein Concentration from Intact Wheat Caryopsis Using Hyperspectral Reflectance

  • Authors: Zhang X., Hou X., Su Y., Yan X., Qiao X., Yang W., Feng M., Kong H., Zhang Z., Shafiq F. et al.
  • Year: 2023
  • DOI: 10.21203/rs.3.rs-2887647/v1
  • Source: Research Square

🔹 Hyperspectral Monitoring of Growth and Physiology Parameters of Winter Wheat Based on Different Quantification Methods

  • Authors: Wang Z.-G., Yang S., Feng M.-C., Yang W.-D., Liang Q., Yang X.-J., Yan X.-B., Sun X.-K., Qin M.-X., Wang C. et al.
  • Year: 2023
  • DOI: 10.2139/ssrn.4535833
  • Source: SSRN

🔹 Identification of Structural Variations Related to Drought Tolerance in Wheat (Triticum aestivum L.)

  • Authors: Zhao J., Li X., Qiao L., Zheng X., Wu B., Guo M., Feng M., Qi Z., Yang W., Zheng J.
  • Year: 2023
  • DOI: 10.1007/s00122-023-04283-4
  • Source: Theoretical and Applied Genetics (Scopus – Elsevier)