Manjunath BR | Machine Learning | Best Researcher Award

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.

Qili Chen | Artificial Neural Networks | Best Researcher Award

Ms. Qili Chen | Artificial Neural Networks | Best Researcher Award

Associate Professor Beijing Information Science and Technology University China

Dr. Qili Chen is an accomplished Associate Professor at Beijing Information Science and Technology University, specializing in artificial neural networks and intelligent systems. With a strong academic foundation and global collaboration experience, she has contributed significantly to the fields of deep learning and small object detection. Her academic journey reflects both international exposure and commitment to scientific excellence, having visited the University of Wisconsin, Milwaukee during her Ph.D. studies. Dr. Chen is a passionate researcher recognized for her innovative work in neural modeling and optimization.

Profile

Google Scholar

🎓 Education

Dr. Chen received both her Master’s (2010) and Ph.D. (2014) degrees in Pattern Recognition and Intelligent System from Beijing University of Technology. During her doctoral studies, she broadened her research perspective through a visiting scholar program (Sept 2012–Aug 2013) at the Department of Mathematical Sciences, University of Wisconsin, Milwaukee, USA.

💼 Experience

Dr. Qili Chen currently serves as an Associate Professor at Beijing Information Science and Technology University. She has led and participated in 14 research projects, collaborated with global researchers such as Doug Briggs and Yi Ming Zou, and contributed to both academia and industry through research consultancy. She also served as a Track TPC Member for the 2023 IEEE ICICN Conference. With memberships in prestigious AI and automation committees in China, her professional presence is robust and influential.

🔬 Research Interests

Her primary research interests include Artificial Neural Networks, Small Object Detection, Modelling, and Optimal Control. Dr. Chen focuses on improving aerial image analysis by enhancing deep learning strategies for detecting small objects—an area critical for applications in surveillance, environmental monitoring, and autonomous systems.

🏆 Awards

Dr. Chen has been nominated for the Best Researcher Award for her remarkable contributions to deep learning and remote sensing applications. Her research has high impact with 788 citations and an H-index of 10, signifying wide academic recognition. She has authored 1 book, published 3 patents, and contributed to 20 peer-reviewed journals, strengthening her candidacy as an innovative leader in AI.

📚 Publications Top Notes: 

Here are selected publications authored by Dr. Qili Chen, including publication years, journal details, and citation counts:

“A survey of small object detection in aerial images via deep learning”
Published in: Artificial Intelligence Review, 2025
🔗 Link to Publication
📝 Cited by: 5 articles

Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network

Research on an online self-organizing radial basis function neural network

Road safety performance function analysis with visual feature importance of deep neural nets

An adaptive hybrid attention based convolutional neural net for intelligent transportation object recognition

Accurate ovarian cyst classification with a lightweight deep learning model for ultrasound images

The Chemical Oxygen Demand Modeling Based on a Dynamic Structure Neural Network

An improved picture‐based prediction method of PM2. 5 concentration