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.

Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

Mr. Ali Raza | Computer Science and Artificial Intelligence | Young Scientist Award

lecturer The University Of Lahore Pakistan

Ali Raza is a passionate researcher, educator, and developer specializing in computer science. With a strong academic background and extensive experience in machine learning, deep learning, and computer vision, he has contributed significantly to cutting-edge research. Currently serving as a Lecturer at the University of Lahore, Ali has also worked as a Visiting Lecturer at KFUEIT and a Full Stack Python Developer in the software industry. His expertise lies in AI-driven solutions, research writing, and technological advancements in artificial intelligence.

Profile

Google Scholar

Education 🎓

  • MS Computer Science (2021-2023) | Khwaja Fareed University of Engineering and Information Technology (KFUEIT), CGPA: 3.93
  • BS Computer Science (2017-2021) | KFUEIT, CGPA: 3.47

Professional Experience 💼

  • Lecturer | University of Lahore (2024 – Present)
  • Visiting Lecturer | KFUEIT (2022 – 2023)
  • Full Stack Python Developer | BuiltinSoft Software Industry (2020 – 2021)

Research Interests 📈

Ali Raza’s research focuses on artificial intelligence, machine learning, deep learning, and computer vision. He is particularly interested in developing AI-driven solutions for medical imaging, agricultural applications, and energy consumption prediction. His contributions span multiple domains, showcasing his ability to integrate AI with real-world challenges.

Awards & Certifications 🏆

  • Best Researcher Award | ScienceFather (26/06/2024)
  • Use of Generative AI in Higher Education | Punjab Higher Education Commission
  • Machine Learning with Python (ML0101EN) | IBM Developer Skills Network

Publications Top Notes: 📚

Ali Raza has authored 61 research publications in reputed journals with high impact factors. Below are some of his recent publications:

“Novel Transfer Learning Approach for Hand Drawn Mathematical Geometric Shapes Classification” (2025) PeerJ Computer Science (IF: 3.8)

“Citrus Diseases Detection Using Innovative Deep Learning Approach and Hybrid Meta-Heuristic” (2025) PLOS ONE (IF: 2.9)

“Novel Deep Neural Network Architecture Fusion for Energy Consumption Prediction” (2025) PLOS ONE (IF: 2.9)

“Novel Transfer Learning Based Bone Fracture Detection Using Radiographic Images” (2025) BMC Medical Imaging (IF: 2.9)

“Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crops” (2025) Food Science & Nutrition (IF: 3.5)

“BdSentiLLM: A Novel LLM Approach to Sentiment Analysis of Product Reviews” (2024) IEEE Access (IF: 3.4)

“An Innovative Artificial Neural Network Model for Smart Crop Prediction” (2024) PeerJ Computer Science (IF: 3.8)

“Enhanced Interpretable Thyroid Disease Diagnosis Using Synthetic Oversampling and Machine Learning” (2024) BMC Medical Informatics (IF: 3.3)

“Diagnosing Epileptic Seizures Using EEG Data and Independent Components” (2024) Digital Health (IF: 3.7)

“A Novel Meta Learning Based Approach for Thyroid Syndrome Diagnosis” (2024) PLOS ONE (IF: 2.9)