Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

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Prof Dr. Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

Xin Su, PhD, is an Associate Professor at the School of Artificial Intelligence, Wuhan University. He supervises both masterโ€™s and PhD students. He earned his doctorate in Signal and Image Processing from Telecom ParisTech in 2015. He then worked as a postdoctoral researcher at INRIA, France, from 2015 to 2018. His research focuses on intelligent analysis of time-series images, spatiotemporal target recognition, and large-scale remote sensing models. He has led and participated in multiple national research projects, publishing extensively in top-tier journals such as IEEE TIP, IEEE TGRS, ISPRS, and JAG. ๐Ÿ“š๐ŸŽ“

Profile

Education ๐ŸŽ“

  • PhD (2015): Telecom ParisTech, France โ€“ Signal and Image Processing ๐ŸŽ“
  • Masterโ€™s Studies (2008-2011, uncompleted): Wuhan University โ€“ Signal and Information Processing ๐Ÿซ
  • Bachelorโ€™s (2004-2008): Wuhan University โ€“ Electronic Science and Technology (Engineering) ๐ŸŽ“

Experience ๐Ÿ‘จโ€๐Ÿซ

  • 2015-2018: Postdoctoral Researcher, INRIA, France ๐Ÿ‡ซ๐Ÿ‡ท
  • 2015: Postdoctoral Researcher, Telecom ParisTech, France ๐ŸŽ“

Research Interests ๐Ÿ”ฌ

Xin Su specializes in intelligent analysis of time-series remote sensing images, spatiotemporal object recognition, and large-scale AI models for remote sensing. His work spans geospatial applications, UAV-based surveillance, and hyperspectral data processing. He actively contributes to developing advanced AI techniques for satellite video analysis and infrastructure monitoring. ๐Ÿš€๐ŸŒ

Awards & Recognitions ๐Ÿ…

Xin Su has been recognized for his contributions to remote sensing and AI, receiving multiple national research grants and awards for excellence in scientific research and innovation. He has secured funding from National Natural Science Foundation projects and defense-related initiatives. His research has been featured in top IEEE and ISPRS journals, reinforcing his position as a leading researcher in the field. ๐ŸŒŸ๐Ÿ…

Publications ๐Ÿ“š

Md. Abdus Shabur | AI in Child Education | Young Scientist Award

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Md. Abdus Shabur | AI in Child Education | Young Scientist Award

Md. Abdus Shabur is a Lecturer at the Institute of Leather Engineering and Technology, University of Dhaka, specializing in Mechanical Engineering, Robotics, Sustainability, Industry 4.0, and Advanced Manufacturing. With a Masterโ€™s degree in Mechanical Engineering from Chittagong University of Engineering and Technology (CUET) and a BSc from BUET, he has a strong academic foundation. His research interests focus on the integration of Industry 4.0 in various sectors, sustainability, and the use of advanced manufacturing technologies. He is also an active educator, offering courses in mechanical engineering, robotics, and material science. Abdus Shabur has numerous research publications in prestigious journals and is involved in consultancy projects for technology upgradation in Bangladeshโ€™s footwear industry.

Profile

Education ๐ŸŽ“

  • MS in Mechanical Engineering (2022) โ€“ Chittagong University of Engineering & Technology, Bangladesh, CGPA: 4.00/4.00
  • BS in Mechanical Engineering (2018) โ€“ Bangladesh University of Engineering & Technology, Bangladesh, CGPA: 3.82/4.00, Merit position: 10/185
    He also holds various university scholarships, including the Deanโ€™s List and Merit Scholarships in 2015-2018.

Experience ๐Ÿ‘จโ€๐Ÿซ

  • Lecturer (Nov 2022 – Present) โ€“ Institute of Leather Engineering and Technology, University of Dhaka
  • Part-time Teacher (Jan 2023 – Present) โ€“ Department of Robotics and Mechatronics, University of Dhaka
  • Assistant Professor (June-Nov 2022) โ€“ CUET, Bangladesh
  • Lecturer (July 2019 – June 2022) โ€“ CUET and Bangladesh Army University of Science and Technology
    He has supervised undergrad thesis projects and coordinated courses across several universities, enhancing academic excellence.

Research Interests ๐Ÿ”ฌ

Abdus Shaburโ€™s research explores Industry 4.0 adoption, sustainability, and advanced manufacturing techniques. His work includes investigating the challenges and opportunities for implementing Industry 4.0 in Bangladeshโ€™s steel sector and the fertilizer industry. He also examines sustainable transportation modes, green supply chain management, and energy consumption in Bangladesh. He has authored several papers in top-tier journals such as Heliyon and Energy Informatics, contributing to academic and practical advancements in mechanical engineering and sustainabilit

Awards & Recognitions ๐Ÿ…

  • University Merit Scholarship (2016)
  • Deanโ€™s List Scholarship (2015, 2017, 2018) for high CGPA
  • Higher Secondary Scholarship by Rajshahi Education Board
  • Secondary Scholarship by Rajshahi Education Board
    These awards reflect his commitment to academic excellence and consistent performance throughout his education.

Publications ๐Ÿ“š

Pritpal Singh | Ambiguous set theory | Best Researcher Award

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Dr. Pritpal Singh | Ambiguous set theory | Best Researcher Award

Pritpal Singh is an Assistant Professor at the Department of Data Science and Analytics, Central University of Rajasthan, India. He earned his Ph.D. in Computer Science and Engineering from Tezpur (Central) University in 2015 and has held various academic and research positions in India, Taiwan, and Poland. His expertise includes soft computing, optimization algorithms, time series forecasting, image analysis, and machine learning. He has published extensively in high-impact journals like IEEE Transactions, Elsevier, and Springer. His research focuses on advanced computational techniques, including quantum-based optimization and fMRI data analysis. Dr. Singh has received prestigious research fellowships, including a Postdoctoral Fellowship from Taiwanโ€™s Ministry of Science and Technology and an International Visiting Research Fellowship from Polandโ€™s Foundation for Polish Science. His work significantly contributes to artificial intelligence, data science, and computational modeling, making him a key figure in these fields. ๐Ÿš€๐Ÿ“Š๐Ÿ“š

Profile

Education ๐ŸŽ“

Dr. Pritpal Singh obtained his Ph.D. in Computer Science and Engineering from Tezpur (Central) University, Assam, India, in 2015, specializing in soft computing applications for time series forecasting. He completed his Master in Computer Applications (MCA) from Dibrugarh University, Assam, in 2008, following a B.Sc. in Physics, Chemistry, and Mathematics from the same university in 2005. His academic journey began with Higher Secondary (2002) from the Assam Higher Secondary Education Council and HSLC (1999) from the Secondary Education Board of Assam. His doctoral dissertation focused on improving fuzzy time series forecasting models through hybridization with neural networks and optimization techniques like particle swarm optimization. His strong foundation in computational sciences, mathematics, and engineering has shaped his research in AI-driven predictive modeling, optimization, and data analytics. ๐ŸŽ“๐Ÿ“š๐Ÿ”ฌ

Experience ๐Ÿ‘จโ€๐Ÿซ

Dr. Singh has extensive academic and research experience. He is currently an Assistant Professor at the Central University of Rajasthan (since June 2022). Previously, he was an Assistant Professor at CHARUSAT University, Gujarat (2015-2019), and a Lecturer at Thapar University, Punjab (2013-2015). His research experience includes serving as an Adjunct Professor (Research) at Jagiellonian University, Poland (2020-2022) and a Postdoctoral Research Fellow at National Taipei University of Technology, Taiwan (2019-2020). Throughout his career, he has mentored students, led research projects, and contributed significantly to data science, artificial intelligence, and computational modeling. His global exposure has enriched his expertise in optimization, machine learning, and interdisciplinary AI applications. ๐ŸŒ๐Ÿ“Š

Research Interests ๐Ÿ”ฌ

Dr. Singhโ€™s research revolves around ambiguous set theory, optimization algorithms, time series forecasting, image analysis, and machine learning. He specializes in hybrid computational techniques, particularly quantum-based optimization and soft computing applications. His work extends to fMRI data analysis, mathematical modeling, and simulation. His research has been published in leading journals such as IEEE Transactions on Systems, Elsevierโ€™s Information Sciences, and Artificial Intelligence in Medicine. His focus on interdisciplinary AI applications, particularly in healthcare and data science, has positioned him as a key contributor to advancing machine learning methodologies. ๐Ÿง ๐Ÿ“Š๐Ÿค–Awards & Recognitions ๐Ÿ…

Dr. Singh has received multiple prestigious fellowships and recognitions. In 2019, he was awarded a Postdoctoral Research Fellowship by the Ministry of Science and Technology, Taiwan. In 2020, he received the International Visiting Research Fellowship from the Foundation for Polish Science, Poland. His contributions to artificial intelligence, optimization, and data science have been recognized globally through research grants, invited talks, and publications in top-tier journals. His work in soft computing and AI-driven predictive modeling continues to impact both academic and industrial research. ๐Ÿ…๐ŸŽ–๏ธ๐Ÿ“œ

Publications ๐Ÿ“š

  • Scopus 1-2023: P. Singh, An investigation of ambiguous sets and their application to
    decision-making from partial order to lattice ambiguous sets. Decision Analytics
    Journal (Elsevier), 08, 100286, 2023.
  • Scopus 2-2023: P. Singh, A general model of ambiguous sets to a single-valued ambiguous numberswith aggregation operators. Decision Analytics Journal (Elsevier), 08,
    100260, 2023.
  • Scopus 3-2023: P. Singh, Ambiguous set theory: A new approach to deal with unconsciousness and ambiguousness of human perception. Journal of Neutrosophic and
    Fuzzy Systems (American Scientific Publishing Group), 05(01), 52โ€“58, 2023.
  • Scopus 4-2022: P. Singh, Marcin W ห›atorek, Anna Ceglarek, Magdalena F ห›afrowicz, and
    Paweล‚ Oยดswiห›ecimka, Analysis of fMRI Time Series: Neutrosophic-Entropy Based
    Clustering Algorithm. Journal of Advances in Information Technology, 13(3), 224โ€“
    229, 2022.

Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

 

Profile

Education

She holds a Ph.D. in Econometrics and Machine Learning from the University of Orleans (2018โ€“2022), where she investigated new machine learning approaches for financial fraud detection and survival analysis in the insurance industry under the supervision of S. Tokpavi. In addition, she earned a Data Science Certificate (Executive) from the Institute of Risk Management (IRM) in 2016โ€“2017. Her academic background also includes a Masterโ€™s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004โ€“2007) and a Masterโ€™s degree in Mathematics from the University of Marmara in Istanbul (1995โ€“1999). Since September 2022, she has been an Assistant Professor of Data Science at IPAG Business School in Nice and Paris. With extensive experience in the insurance sector, she integrates her professional insights into the classroom, emphasizing practical AI applications. Her curriculum reflects the latest trends in data science, fostering a dynamic learning environment tailored to students’ needs. She adapts resources and pedagogical methods to specific course objectives, utilizing tools such as Tableau for data visualization and exploring real-world business applications, including Netflix, Uber, ChatGPT, Gemini, and facial recognition technologies.

 

Work experience

She has held various academic and professional roles, combining her expertise in data science, machine learning, and business analytics. From September 2022 to January 2023, she was an adjunct faculty member at the International University of Monaco, where she taught Mathematics for Business. Prior to that, from September 2021 to August 2022, she served as an adjunct faculty member at IPAG Business School (Nice), teaching courses such as โ€œData Analysis for Business Managementโ€ (BBA3), โ€œData Processingโ€ (MSc, e-learning), โ€œDigital and Salesโ€ (GEP 5th year), and โ€œIntroduction to Statisticsโ€ (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught โ€œQuantitative Data Analytics โ€“ SPSSโ€ (GEP 4th year, hybrid learning) and supervised masterโ€™s theses for GEP 5th-year students.

In addition to her academic roles, she has extensive experience in the consulting and insurance sectors. From March to November 2020, she worked as a Senior Consultant at Fraeris (Paris), supporting clients in project development and providing technical solutions. She collaborated with the โ€œCaisse de Prรฉvoyance Socialeโ€ (CPS) of French Polynesia, modeling healthcare expenditures using machine learning techniques. She developed predictive models to analyze healthcare costs from both the insured’s and CPSโ€™s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019โ€“2020, she was a Senior Manager in Pricing & Data P&C at Addactis (Paris), where she supported clients in project development, innovation, and strategic planning. As an expert referent for ADDACTISยฎ Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

โ€ข Membership of the American Risk and Insurance Association (ARIA)
โ€ข Membership of the academic association AFSE.
โ€ข Member of the RED Flag Project of the University of Orlรฉans in cooperation with CRJPothier.
โ€ข Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
โ€ข Virtual Incubator Tailored to All Entrepreneurs (VITAE).
โ€ข Artificial Intelligence in high Education (PRAIME),

Research topics:

Studies focus on the application of data science techniques to business issues, particularly in the insurance
sector, and on climate change. Another topic of study is the relationship between AI and education.

Publication

  • Yankol-Schalck, M. (2023). Auto Insurance Fraud Detection: Leveraging Cost Sensitive and Insensitive
    Algorithms for comprehensive Analysis, Insurance: Mathematics and Economics.(
    (https://www.sciencedirect.com/science/article/abs/pii/S0167668725000216)
    Banulescuโ€Radu, D., & Yankolโ€Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud
    in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867-
    913.
    Yankol-Schalck, M. (2022). A Fraud Score for the Automobile Insurance Using Machine Learning and
    Cross-Data set Analysis, Research in International Business and Finance, Volume 63, 101769.
    Schalck, C., Yankol-Schalck, M. (2021). Failure Prediction for SME in France: New evidence from
    machine learning techniques, Applied Economics, 53(51), 5948-5963.
    On- going research:
    Yankol-Schalck (2025). Auto Insurance Fraud Detection: Machine Learning and Deep Learning
    Applications, submitted in Journal of Risk and Insurance.
    Schalck, C., Yankol-Schalck, M. (2024). Churn prediction in the French insurance sector using Grabit
    model, revision in Journal of Forecasting.
    Schalck, C., Seungho, L., Yankol-Schalck, M. (2024). Characteristics of firms and climate risk
    management: a machine learning approach. Work in progress for The Journal of Financial Economics.
    Yankol-Schalck M.and Chabert Delio C., (2024). The application of machine learning to analyse changes in
    consumer behaviour in a major crisis. Work in progress.
    Yankol-Schalck M. and Nasseri A. (2024).An investigation into the integration of artificial intelligence in
    education: Implications for teaching and learning methods. Work in progress.

Zihao Li | Digital Economy | Best Researcher Award

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Prof. Zihao Li | Digital Economy | Best Researcher Award

 

Profile

Education

He obtained his PhD in Applied Economics from Hunan University, where he studied from September 2010 to June 2014. Prior to that, he completed his master’s degree in International Trade at Jiangnan University between September 2006 and July 2008. His academic journey began at Henan Normal University, where he earned his undergraduate degree in Economics from September 2002 to July 2006.

Work experience

Since September 2022, he has been serving as an Associate Professor, Professor, and Master Supervisor at the Business School of Nanjing University of Information Science and Technology. Prior to this, from June 2014 to August 2022, he worked at the International Business School of Henan University of Economics and Law as a Lecturer, Associate Professor, and Master Supervisor.

 

Achievement

He has authored several influential books, including Research on the Impact of Foreign Direct Investment on China’s Carbon Emissions (Economic Science Press, 2016), Foreign Direct Investment, Economic and Social Transformation and Environmental Pollution (China Financial and Economic Publishing House, 2017), and Economic and Social Transformation and Improvement of Local Government’s Environmental Governance (China Economic Publishing House, 2020).

His academic contributions have been recognized with multiple awards. He received the Third Prize of the Henan Social Science Outstanding Achievement Award (Provincial and Ministerial Level) in 2020 for his research on Environmental Governance of Local Governments with Spatial Relevance and Threshold Effect from the perspective of integrity. In 2019, he was awarded another Third Prize for his study on Local Government Tax Competition, Industrial Restructuring, and China’s Regional Green Development. Additionally, in 2016, his work on China’s Opening Up, Economic Transformation, and Low Carbon Economic Development earned him the Third Prize of the Hunan Provincial Social Science Outstanding Achievement Award.

Scientific Research Project

He has led several research projects funded by national and provincial institutions. Currently, he is hosting a General Program of the National Philosophy and Social Science Foundation (21BJY114), focusing on the Mechanism, Effect, and Policy of Digital Economy Promoting China’s Collaborative Governance of Carbon Smog (2021/9โ€“2024/9).

Previously, he successfully completed a Youth Program of the National Philosophy and Social Science Foundation (15CGL042), which examined Anti-Corruption and Environmental Governance Improvement of Local Governments (2015/6โ€“2019/12). He also led two provincial-level Soft Science Projects funded by the Henan Provincial Department of Science and Technology: one on Industrial Transfer and Green Development of Henan’s Industry (162400410201, 2016/6โ€“2017/9) and another on Enterprise Green Technology Innovation and Haze Pollution Control in Henan (202400410061, 2020/1โ€“2021/6).

 

Publication

  • (1) Zihao Li, Yue Wang, Tingting Bai. International digital trade and synergetic control of pollution and carbon emissions: Theory and evidence based on a nonlinear framework[J]. Journal of Environmental Management,2025, 376(3):124450.๏ผˆSCI, JCR Q1๏ผ‰

    (2) Zihao Li, Bingbing Yuan, Yue Wang, Jingwen Qian, Haitao Wu. The role of digital finance on the synergistic governance of pollution & carbon: Evidence from Chinese cities[J]. Sustainable Cities and Society,2024, 115(1):105812.๏ผˆSCI, JCR Q1๏ผ‰

    (3)ๆŽๅญ่ฑช,็Ž‹ๆ‚ฆ.ๆ•ฐๅญ—่ดธๆ˜“ๅฏนๅŸŽๅธ‚ๅ‡ๆฑก้™็ขณๅๅŒๅ‘ๅฑ•็š„ๅฝฑๅ“โ€”โ€”ๅŸบไบŽไบงไธš้›†่šไธŽ่ฆ็ด ้…็ฝฎ่ง†่ง’[J].็ปๆตŽ็ป็บฌ,2025,(1):67-79. (CSSCIๆฃ€็ดข)๏ผ›

    Zihao Li, Yue Wang. The impact of digital trade on the coordinated development of urban pollution reduction and carbon reduction: based on the perspective of industrial agglomeration and factor allocation[J] Economic longitude and latitude, 2025, (1):67-79. (CSSCI)

    (4) Zihao Li, Bai Tingting, Wang Yue, Wu Haitao. The Impact of Digital Government on Corporate Green Innovation: Evidence from China[J]. Technological Forecasting and Social Change๏ผˆSSCI, JCR Q1๏ผ‰

    (5) Tingting Bai, Yong Qi, Zihao Li, Dong Xu. Digital economy, industrial transformation and upgrading, and spatial transfer of carbon emissions: The paths for low-carbon transformation of Chinese cities[J]. Journal of Environmental Management, 2023, 344: 118528. (SCI, JCR Q1๏ผŒESI );

    (6) Bai Tingting, Qi Yong, Li Zihao*, Xu Dong. Will carbon emission trading policy improve the synergistic reduction efficiency of pollution and carbon? Evidence from216 Chinese cities[J]. Managerial and Decision Economics,2023,(8):1-24. (SSCI, JCR Q1, corresponding author)๏ผ›

    (7) Zihao Li, Bingbing Yuan, Tingting Bai, Dong Xu, Haitao Wu. Shooting two hawks with one arrow: The role of digitization on the coordinated development of resources and environment [J].โ€ฏ Resources Policy, 2024, 90(3):104827. (SSCI, JCR Q1)

    (8) Zihao Li, Xihang Xie, Xinyue Yan, Tingting Bai, Dong Xu*. Impact of Chinaโ€™s Rural Land Marketization on Ecological Environment Quality Based on Remote Sensing[J]. Int. J. Environ. Res. Public Health 2022, 19, 12619

    (SSCI, JCR Q1)

    (9) Zihao Li, Bai Tingting*, Tang Chang. How does the low-carbon city pilot policy affect the synergistic governance efficiency of carbon and smog? Quasi-experimental evidence from China[J]. Journal of Cleaner Production,2022(8): 133809 (SCI, JCR Q1)

    (10)ๆŽๅญ่ฑช,่ตตๅ…ƒ,ๅคๅญ่ฐฆ.็Žฏไฟ็ฃๆ”ฟไธŽๅœฐๆ–นๆ”ฟๅบœ็ขณ้œพๅๅŒๆฒป็†็ปฉๆ•ˆๆๅ‡โ€”โ€”ๅŸบไบŽ็Žฏไฟ็บฆ่ฐˆ็š„ๅ‡†่‡ช็„ถๅฎž้ชŒไผฐ่ฎก[J].ไธญๅ›ฝ่ฝฏ็ง‘ๅญฆ, 2023,(12):192-201. (CSSCIๆฃ€็ดข);

    Zihao Li, Yuan Zhao, Ziqian Xia. Performance improvement of coordinated governance of environmental protection supervision and local government carbon haze: quasi natural experimental estimation based on environmental interviews[J]. China Soft Science, 2023, (12): 192-201. (CSSCI)

    (11)ๆŽๅญ่ฑช,็Ž‹ๅ€ฉๅ€ฉ.ๆ•ฐๅญ—็ปๆตŽๅ‘ๅฑ•่ƒฝๅฆๆ”นๅ–„ๅœฐๅŒบ้“ถ่กŒไธš้ฃŽ้™ฉ๏ผŸ-ๅŸบไบŽๅŸŽๅธ‚ๅ•†ไธš้“ถ่กŒ็š„่€ƒๅฏŸ[J].่ดข็ป่ฎบไธ›,2023,(12):47-57.๏ผˆCSSCI๏ผ‰๏ผ›

    Zihao Li, Qianqian Wang. Can the development of digital economy improve regional banking risks- Based on the Investigation of Urban Commercial Banks [J]. Collected Essays on Finance and Economics, 2023,(12):47-57.๏ผˆCSSCI๏ผ‰

    (12)ๆŽๅญ่ฑช,็™ฝๅฉทๅฉท.ๆ”ฟๅบœ็Žฏไฟๆ”ฏๅ‡บใ€็ปฟ่‰ฒๆŠ€ๆœฏๅˆ›ๆ–ฐไธŽ้›พ้œพๆฑกๆŸ“[J].็ง‘็ ”็ฎก็†,2021,(2):52-63. (CSSCIๆฃ€็ดข, ๅ›ฝๅฎถ่‡ช็ง‘ๅŸบ้‡‘ๅง”็ฎก็†็ฑปA็บง้‡่ฆๆœŸๅˆŠ๏ผŒใ€Šๆ–ฐๅŽๆ–‡ๆ‘˜ใ€‹ๅ…จๆ–‡่ฝฌ่ฝฝ)๏ผ›

    Zihao Li, Bai Tingting. Government environmental protection expenditure, green technology innovation and smog pollution[J]. Science Research Management, 2021, (2):52-63. (CSSCI)

    (13)ๆŽๅญ่ฑช,่ขไธ™ๅ…ต.ๅœฐๆ–นๆ”ฟๅบœ็š„้›พ้œพๆฒป็†ๆ”ฟ็ญ–ไฝœ็”จๆœบๅˆถ: ๆ”ฟ็ญ–ๅทฅๅ…ทใ€็ฉบ้—ดๅ…ณ่”ๅ’Œ้—จๆง›ๆ•ˆๅบ”[J].่ต„ๆบ็ง‘ๅญฆ๏ผŒ 2021,(1):40-56. (CSSCIๆฃ€็ดข)๏ผ›

    Zihao Li, Bingbing Yuan. Local government’s policy mechanism for haze control: policy tools, spatial correlation and threshold effect [J]. Resource science, 2021,(1):40-56. (CSSCI);

    (14)ๆŽๅญ่ฑช,่ขไธ™ๅ…ต.็ฉบ้—ดๅ…ณ่”ๅ’Œ้—จๆง›ๆ•ˆๅบ”็š„ๅœฐๆ–นๆ”ฟๅบœ็Žฏๅขƒๆฒป็†็ ”็ฉถ-ๅŸบไบŽๅป‰ๆดๅบฆ่ง†่ง’็š„่€ƒๅฏŸ[J].ไธญๅ›ฝ่ฝฏ็ง‘ๅญฆ, 2019,(10):61-69. (CSSCIๆฃ€็ดข,ๅ›ฝๅฎถ่‡ช็ง‘ๅŸบ้‡‘ๅง”็ฎก็†็ฑปA็บง้‡่ฆๆœŸๅˆŠ)๏ผ›

    Zihao Li, Bingbing Yuan. A Study on Environmental Governance of Local Government Based on Spatial Correlation and Threshold Effect: An Investigation from the Perspective of Integrity. [J]. China Soft Science, 2019, (10):61-69. (CSSCI)

    (15) Zihao Li, Mao Jun. Local Governmentsโ€™ Tax Competition, Industrial Structure Adjustment and Regional Green Development in China[J]. China Finance and Economic Review,2019(1): 93-111. (ESCI)๏ผ›

Vikas Palekar | Machine Leaning | Best Researcher Award

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Mr. Vikas Palekar | Machine Leaning | Best Researcher Award

 

Profile

Education

He is currently pursuing a Ph.D. in Computer Science and Engineering at Vellore Institute of Technology, Bhopal, Madhya Pradesh, since December 2018. His research focuses on developing an Adaptive Optimized Residual Convolutional Image Annotation Model with a Bionic Feature Selection Strategy. He holds a Master of Engineering (M.E.) in Information Technology from Prof. Ram Meghe College of Engineering Technology and Research, Badnera (SGBAU Amravati), which he completed in December 2012 with an impressive 88.00%, securing the first merit position in the university for the summer 2012 examination. Prior to that, he earned a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering Technology and Research, Nanded (SRTMNU, Nanded), in June 2007, achieving a commendable 74.40%.

Work experience

He is currently working as an Assistant Professor in the Department of Computer Engineering at Bajaj Institute of Technology, Wardha, since July 31, 2023. In addition to his teaching responsibilities, he serves as the Academic Coordinator of the department and has worked as a Senior Supervisor for the DBATY Winter-23 Exam at Government College of Engineering, Yavatmal.

Previously, he worked as an Assistant Professor (UGC Approved, RTMNU, Nagpur) in the Department of Computer Science and Engineering at Datta Meghe Institute of Engineering, Technology & Research, Wardha, from June 14, 2011, to June 30, 2023. During this tenure, he held the position of Head of the Department from April 21, 2016, to June 30, 2023. He taught various subjects, including Distributed Operating Systems, TCP/IP, System Programming, Data Warehousing and Mining, Artificial Intelligence, and Computer Architecture and Organization. Additionally, he contributed to university examinations as the Chief Supervisor in the Winter-2015 Examination and a committee member for the Summer-2013, Summer-2015, and Summer-2018 Examinations. He also played a key role in institutional development as a member of the Admission Committee, NBA & NAAC core committees at the department level, and as the convener of the National Level Technical Symposium “POCKET 16” organized by the CSE Department on March 16, 2016.

Earlier in his career, he served as an Assistant Professor in the Department of Computer Engineering at Bapurao Deshmukh College of Engineering, Wardha, from November 26, 2008, to April 30, 2011. He taught subjects such as Unix and Shell Programming, Object-Oriented Programming, and Operating Systems while also serving as a Department Exam Committee Member.

Achievement

He was the first university topper (merit) in M.Tech (Information Technology) and received the Best Paper Award at the 2021 International Conference on Computational Performance Evaluation (ComPE), organized by the Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India, from December 1st to 3rd, 2023. He has actively participated in various conferences, including presenting the paper “Label Dependency Classifier using Multi-Feature Graph Convolution Networks for Automatic Image Annotation” at ComPE 2021 in Shillong, India. He also presented his research on “Visual-Based Page Segmentation for Deep Web Data Extraction” at the International Conference on Soft Computing for Problem Solving (SocProS 2011) held from December 20-22, 2011. Additionally, he contributed to the Computer Science & Engineering Department at Sardar Vallabhbhai National Institute of Technology, Surat, by presenting “A Critical Analysis of Learning Approaches for Image Annotation Based on Semantic Correlation” from December 13-15, 2022. His work on “A Survey on Assisting Document Annotation” was featured at the 19th International Conference on Hybrid Intelligent Systems (HIS) at VIT Bhopal University, India, from December 10-12, 2022. Furthermore, he co-authored a study titled “Review on Improving Lifetime of Network Using Energy and Density Control Cluster Algorithm,” which was presented at the 2018 IEEE International Students’ Conference on Electrical, Electronics, and Computer Science (SCEECS) in Bhopal, India.

 

Publication

Kaveri Hatti | Engineering| Women Researcher Award

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Mrs. Kaveri Hatti | Engineering| Women Researcher Award

 

 

Profile

Education

averi Hatti is a dedicated researcher and educator in the field of VLSI Design, Embedded Systems, and Hardware Security. She is currently pursuing a Ph.D. at Amrita School of Engineering, Bangalore, focusing on FPGA-based security architectures. With a strong academic background, she holds an M.Tech in VLSI Design and Embedded Systems from VTU Regional Office, Gulbarga, and a B.Tech in Electronics and Communication Engineering from SLN College of Engineering, Raichur.

Kaveri has extensive teaching experience, having served as a Lecturer at Tagore Memorial Polytechnic College and Government Polytechnic College in Raichur before joining Amrita School of Engineering, Bangalore, as a Teaching Assistant in 2022. Her expertise lies in FPGA design, Verilog, RTL design, and hardware security implementations, utilizing tools like Xilinx ISE, VIVADO, ModelSim, and Cadence.

 

Work experience

Kaveri Hatti has a strong background in academia, with extensive teaching experience spanning several years. She began her career as a Lecturer at Tagore Memorial Polytechnic College, Raichur, from August 2009 to December 2012, where she played a key role in instructing and mentoring students in electronics and communication engineering. Simultaneously, she also served as a Lecturer at Government Polytechnic College, Raichur, from June 2009 to June 2011. Currently, she is working as a Teaching Assistant at Amrita School of Engineering, Bangalore, since February 2022, contributing to research and assisting in the academic development of students in the field of VLSI Design and Embedded Systems.

Publication

Dingming Wu | Computer Science | Best Researcher Award

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Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Masterโ€™s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelorโ€™s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wangโˆ—, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wangโˆ—, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

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Mr. Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Lahijan Azad ,Iran

He understands the growing need for Machine Learning and has a keen interest in the field, which he considers a blessing. Recognizing the importance of managing large and complex computations to control various aspects of the human environment has led him into this vast world. He is particularly fascinated by machine learning, especially reinforcement learning, supervised learning, semi-supervised learning, outliers, and basic data challenges. Furthermore, optimization, an area of artificial intelligence that requires fundamental studies and a change in approach, is another of his key research interests.

Profile

Education

He holds a Masterโ€™s degree in Artificial Intelligence Engineering from the University of Shafagh, completed in 2020. His thesis was titled “Trees Social Relations Optimization Algorithm: A New Swarm-Based Metaheuristic Technique to Solve Continuous and Discrete Optimization Problems.” He also earned a Bachelorโ€™s degree in Software Engineering from Azad Lahijan University, which he attended from 2007 to 2011.

Research Interests

Theory: Reinforcement Learning (high-dimensional problems, regularized algorithms, model
learning,
representation learning and deep RL, learning from demonstration, inverse optimal control, deep
Reinforcement Learning); Machine Learning (statistical learning theory, nonparametric
algorithms, time series. processes, manifold learning, online learning); Large-scale Optimization;
Evolutionary Computation, Metaheuristic Algorithm, Deep Learning, Healthcare Machine
learning, Big Data, Data Problems (Imbalanced), Signal Analysis
Applications: Automated control, space affairs, robotic control, medicine and health, asymmetric
data, data science, scheduling, proposing systems, self-enhancing systems

Work Experience

He is a freelance programmer with expertise in various operating systems, including Microsoft Windows and Linux (Arch, Ubuntu, Fedora). He is proficient in software tools such as Microsoft Office, Anaconda, Jupyter, PyCharm, Visual Studio, Tableau, RapidMiner, MATLAB, and Visual Studio. His programming skills include Matlab, Python, C++, Scala, Java, and Julia, with a focus on data mining, data science, computer vision, and machine learning. He is experienced with Python libraries like Pandas, Numpy, Matplotlib, Seaborn, PyCV, TensorFlow, Time Series Analysis, Spark, Hadoop, and Cassandra. Additionally, he is skilled in using Github, Docker, and MySQL. His expertise spans machine learning, deep learning, imbalanced data, missing data, semi-supervised learning, healthcare machine learning, algorithm design, and metaheuristic algorithms. He is fluent in English and Persian.

Publications

Nuo Yu | Radiomics | Best Researcher Award

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Ms. Nuo Yu | Radiomics | Best Researcher Award

Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College ,China

Nuo Yu is a Ph.D. candidate at the Cancer Institute and Hospital of the Chinese Academy of Medical Sciences, specializing in radiation oncology with a focus on esophageal squamous cell carcinoma (ESCC). His research primarily explores innovative chemoradiotherapy regimens to improve treatment outcomes for patients with locally advanced ESCC.

Yu has contributed to several peer-reviewed publications in SCI-indexed journals. Notably, he co-authored a study titled “Conversion Chemoradiotherapy Combined with Nab-Paclitaxel Plus Cisplatin in Patients with Locally Advanced Borderline-Resectable or Unresectable Esophageal Squamous Cell Carcinoma: A Phase I/II Prospective Cohort Study,” published in Strahlentherapie und Onkologie in August 2024. This research evaluated the efficacy and safety of a novel chemoradiotherapy regimen, demonstrating promising results in locoregional control and overall survival rates.

In March 2023, Yu co-authored another significant study, “Efficacy and Safety of Concurrent Chemoradiotherapy Combined with Nimotuzumab in Elderly Patients with Esophageal Squamous Cell Carcinoma: A Prospective Real-world Pragmatic Study,” published in Current Cancer Drug Targets. This research focused on treatment strategies for elderly patients with ESCC, highlighting the potential benefits of combining chemoradiotherapy with nimotuzumab.

Yu’s work has been recognized at international conferences, including presentations at the American Society for Radiation Oncology (ASTRO), the Federation of Asian Organizations for Radiation Oncology (FARO), and the Korean Society for Radiation Oncology (KOSRO). These engagements underscore his active participation in the global radiation oncology community and his commitment to advancing cancer treatment research.

While still in the early stages of his career, Yu’s focused research on ESCC and his contributions to the field of radiation oncology position him as a promising candidate for the Best Researcher Award. Continued efforts to expand his research scope, increase publication impact, and assume leadership roles in larger-scale studies will further strengthen his candidacy.

Profile

Scientific Publications