Manjunath BR | Machine Learning | Best Researcher Award

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Prof. Dr. Manjunath BR | Machine Learning | Best Researcher Award

Professor | Tecnologico De Monterrey | Mexico

Prof. Dr. Manjunath BR is an accomplished academic leader and finance professional specializing in business analytics, financial modeling, econometrics, fintech, and artificial intelligence applications in finance. With extensive experience across academia and industry, he has contributed significantly to advancing data-driven financial education and research. His expertise spans financial analytics, investment management, corporate restructuring, and data visualization using advanced tools such as EViews, R, Python, Tableau, and Power BI. He has published extensively in ABDC, Scopus, UGC, and peer-reviewed journals, focusing on the intersection of finance, data science, and technology. As a researcher and educator, he integrates predictive analytics and machine learning into financial decision-making, contributing to the understanding of fintech adoption, banking innovations, and risk management. His academic leadership includes curriculum design, faculty development, and corporate collaborations to enhance experiential learning. He has served as a resource person for numerous international workshops and training programs on financial analytics, econometrics, and data visualization, empowering professionals and students with analytical and quantitative skills. Dr. Manjunath has authored and edited several books with leading global publishers, covering transformative areas such as AI in management education, blockchain economics, sustainable investment, and Quality 5.0 paradigms. He has also secured a patent for the application of AI in optimizing HR data management and authored a textbook on machine and deep learning. His professional journey embodies innovation, interdisciplinary scholarship, and a commitment to integrating technology with finance to foster global academic and industry excellence.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Raju, J. K., Manjunath, B. R., & Rehaman, M. (2018). An empirical study on the effect of gross domestic product on inflation: Evidence from Indian data. Academy of Accounting and Financial Studies Journal, 22(6), 1–11.

Raju, J. K., Manjunath, B. R., & Dhakal, M. H. (2015). Impact and challenges of merger and acquisition in Nepalese banking and financial institutions. Journal of Exclusive Management Science, 4(8), 25–33.

Raju, J. K., Manjunath, B. R., & G. M. M. N. (2015). Performance evaluation of Indian equity mutual fund schemes. Journal of Business Management & Social Sciences Research (JBM&SSR).

Manjunath, B. R., & Raju, J. K. (2020). Short-run performance evaluation of under-priced Indian IPOs. Law and Financial Markets Review.

Chaitra, R., Manjunath, B. R., & Rehaman, M. (2019). An analysis of pre and post-merger of Indian banks: An event analysis approach. International Journal for Research in Engineering Application & Management, 4.

Kehinde Akinwolere | Artificial intelligence | Best Researcher Award

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Mr. Kehinde Akinwolere | Artificial intelligence | Best Researcher Award

Ball State University, United States

Mr. Kehinde Akinwolere is a corporate lawyer and interdisciplinary researcher currently pursuing a Master’s degree in Information and Communication Sciences at Ball State University, USA, where he maintains a perfect GPA of 4.0. With a background in law (LL.B, Obafemi Awolowo University; BL, Nigerian Law School), he brings over six years of professional experience in legal advisory, corporate governance, and regulatory compliance.

Profile:

🎓 Academic Excellence:

  • 🎯 GPA: 4.0

  • 🧠 Currently pursuing a Master’s in Information and Communication Sciences at Ball State University (2024–2026)

  • ⚖️ Bachelor of Laws (LL.B) from Obafemi Awolowo University

  • 🎓 Licentiate Degree in Law (BL) from Nigerian Law School

🧩 Professional Experience Highlights:

  • 📢 Graduate Teaching Assistant – Ball State University (2024–Present)

  • ⚖️ Pre-Legal Counsel – A.P. Moller – Maersk, West Africa (2023–2024)

  • 🏛️ Corporate Governance Consultant – DCSL (formerly Deloitte Corporate Services Ltd) (2018–2023)

  • 🗣️ Corporate Communications Lead – DCSL (2018)

  • ⚖️ Legal Associate – Iyiola, Oyedepo & Co (2018)

📚 Research & Publication:

  • 🧾 MDPI Publication (2024):
    “Corporate Governance and Information Systems in a Data-Driven World”
    🔗 Read Article

💼 Core Skills:

  • 📊 Corporate Law & Governance

  • 📄 Legal Drafting & Research

  • 🔍 Risk Identification & Policy Development

  • 🗣️ Communication & Negotiation

  • 🧪 Data Analysis & Regulatory Strategy

🏅 Notable Attributes:

  • 🌐 Interdisciplinary thinker in law, technology, and communication

  • 🏆 Recognized for practical impact in legal consulting and governance reform

  • 📈 Strong academic and research promise in a data-driven regulatory landscape

Publication:

  • Text Classification: How Machine Learning Is Revolutionizing Text Categorization

Pakezhamu Nuradili | Computer Science and Artificial Intelligence | Best Researcher Award

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Dr. Pakezhamu Nuradili| Computer Science and Artificial Intelligence | Best Researcher Award

PhD candidate University of Electronic Science and Technology of China

Pakezhamu Nuradili, a native of China, is a Ph.D. student specializing in Information and Communication Engineering. She is currently enrolled in a joint Ph.D. program between the University of Electronic Science and Technology of China (UESTC) and the University of Trento, Italy. Her expertise spans deep learning-based image processing, semantic segmentation, and thermal infrared imaging. Known for her attention to detail and excellent communication skills in multiple languages, she excels in both technical and interpersonal domains.

Profile

Orcid

Education 🎓

  • High School: Jiangpu Senior High School, Jiangsu Province, China (2010–2013)
  • Bachelor’s Degree: Electronics and Information Engineering, Hebei University of Science and Technology (2013–2017)
  • Master’s Degree: Radio Physics, Yili Normal University, China, focusing on face recognition algorithms (2017–2020)
  • Ph.D.: Information and Communication Engineering, UESTC, with a joint program at the University of Trento, Italy (2021–Present)

Work Experience 💼

  • Teaching:
    • Substitute Teacher, Basic Computer Applications, Silk Road College of Ili (2017–2018)
    • Graduate Assistant, Basic Computer Applications, Yili Normal University (2018–2019)
    • Substitute Teacher, Advanced and Intermediate Mathematics, Ili Vocational and Technical College (2020–2021)
    • Graduate Teaching Assistant, Principles of Remote Sensing, UESTC (2022)
  • Volunteering: Marathon Distance Race Volunteer, Trento, Italy (2024)

Research Interests 🔬

Pakezhamu’s research focuses on:

  • Deep learning-based image processing and semantic segmentation.
  • Thermal infrared and multispectral imaging for UAV applications.
  • Wetland segmentation using advanced models like SegFormer.

Awards 🏆

  • Hebei Provincial Inspiration Scholarship (2016)
  • Outstanding Graduation Design Award, Hebei University of Science and Technology (2017)
  • Graduate Student Scholarship, Yili Normal University (2018)
  • Xinjiang Autonomous Region Postgraduate Scholarship (2019)
  • UESTC Academic Scholarships (2022, 2023, 2024)
  • Outstanding Teaching Assistant Award, UESTC (2022)

Publications Top Notes: 📚

P. Nuradili et al., “UAV Remote-Sensing Image Semantic Segmentation Strategy Based on Thermal Infrared and Multispectral Image Features,” IEEE Journal on Miniaturization for Air and Space Systems, 4(3): 311-319, Sept. 2023. Cited by: 5

Nuradili, P. et al., “Semantic segmentation for UAV low-light scenes based on deep learning and thermal infrared image features,” International Journal of Remote Sensing, 45(12): 4160–4177, 2024. Cited by: 8

Nuradili, P. et al., “Wetland Segmentation Method for UAV Multispectral Remote Sensing Images Based on SegFormer,” IGARSS 2024 IEEE Symposium, 2024. Cited by: 3

Nuradili, P. et al., “Deep Learning Method for Wetland Segmentation in Unmanned Aerial Vehicle Multispectral Imagery,” Remote Sensing, 16(24): 4777, 2024. Cited by: 6

Nuradili, P. et al., “Fire Detection Based on Deep Learning Segmentation Methods,” Journal TBD, 2024 (Under Process).

Wang, Z. et al., “Removing temperature drift and temporal variation in thermal infrared images of a UAV uncooled thermal infrared imager,” ISPRS Journal of Photogrammetry and Remote Sensing, 203: 392-411, 2023. Cited by: 12