Aizhen Ren | Machine learning | Excellence in Innovation Award

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Prof. Aizhen Ren | Machine learning
| Excellence in Innovation Award

College of Science, Inner Mongolia Agricultural University | China

Prof. Aizhen Ren research work centers on machine learning, deep learning, statistical inference, and their applications in economics, finance, and computational biology. A significant portion of the research contributes to the development and mathematical validation of advanced bootstrap techniques, including the speedy double bootstrap method, which enhances the statistical reliability of phylogenetic tree estimation and provides third-order accurate unbiased p-values. These methods have been applied to evolutionary analyses of horse breeds, supporting biological and genomic investigations with high-precision statistical tools. In the financial domain, the research explores machine-learning-based trend prediction models, such as multiscale bootstrap-corrected random forest voting systems used to forecast stock index movement with improved accuracy and inference reliability. Additional work includes the construction of financial risk early-warning models for listed companies using multiple machine learning approaches, reflecting an interdisciplinary blend of statistics, computing, and economics. Contributions also extend to consumption behavior analysis employing regression-based models, as well as deep learning ensemble frameworks integrating empirical mode decomposition and temporal convolutional networks for time-series prediction tasks. The released R package SDBP operationalizes the novel bootstrap methodology, enabling researchers to compute unbiased p-values efficiently. Overall, the research advances methodological innovation and practical applications across data-intensive scientific domains.

 Profile: Orcid

Featured Publications

Ren, A., Duan, Y., & Liu, J. (2025). Multiscale bootstrap correction for random forest voting: A statistical inference approach to stock index trend prediction. Mathematics, 13(22), 3601. https://doi.org/10.3390/math13223601

Ren, A., Ishida, T., & Akiyama, Y. (2020). Mathematical proof of the third order accuracy of the speedy double bootstrap method. Communications in Statistics – Theory and Methods, 49(16), 3950–3964. https://doi.org/10.1080/03610926.2019.1594295

Ren, A., Ishida, T., & Akiyama, Y. (2013). Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method. Molecular Phylogenetics and Evolution, 67(2), 429–435. https://doi.org/10.1016/j.ympev.2013.02.011

R Lakshman Naik |Computer Science and Engineering | Best Paper Award

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Mr.R Lakshman Naik|Computer Science and Engineering| Best Paper Award

Research Scholar at Indian Institute of Information Technology Sonepat , Haryana, India

Mr. R. Lakshman Naik is a Research Scholar at the Indian Institute of Information Technology (IIIT) Sonepat, Haryana, India. His research focuses on advanced topics in computer science, artificial intelligence, data science, or related fields. As a dedicated scholar, he actively contributes to academic research, publications, and innovative technological developments. IIIT Sonepat, recognized as an Institute of National Importance, provides a dynamic environment for cutting-edge research and interdisciplinary collaboration.

Publication Profile

Scopus

Education :

Lakshman Naik Ramavathu holds a Master of Technology (M.Tech) degree in Digital Communication from Kakatiya University, Warangal (2014-16), and another M.Tech in Computer Science and Engineering from JNT University, Hyderabad (2009-11). He completed his Bachelor of Technology (B.Tech) in Electronics and Communication Engineering from JNT University, Hyderabad (2001-05). His diverse educational background provides a strong foundation in computer science, digital communication, and information technology.

Experience :

Currently, he serves as an Assistant Professor (C) in the Department of Information Technology at KU College of Engineering & Technology, Warangal, where he has been teaching since 2016. His teaching expertise includes Operating Systems, Computer Architecture and Organization, Data Communication and Networking, Machine Learning, Python Programming, and Mobile Cloud Computing.

Prior to his academic career, he worked as a Part-time Lecturer in the Department of Computer Science at Kakatiya University (2012-2016), where he taught subjects like System Software, Cloud Computing, Mobile Communication, and Open-Source Software.

Research Focus :

Lakshman Naik Ramavathu’s research interests include Machine Learning, Cloud Computing, Data Mining, and Computer Networking. His work revolves around optimizing computational frameworks, developing intelligent predictive models, and improving networking protocols for enhanced system performance.

Skills:

Sun Certified System Administrator for Sun Solaris 9 (Part-I & II)Microsoft Certified Professional in Windows 2003 Enterprise Server Expertise in Cloud Computing, Machine Learning, Computer Networks, and Data Mining

Awards:

Recognized for impactful research contributions in cloud computing and machine learning Multiple research papers published in high-impact international journals Significant contributions to academia and industry in system administration and computing

 

Publication :

  • Comparison of Data Mining Versus Traditional Analysis in Textile Business”

    • Publication: IFRSA International Journal of Data Warehousing & Mining
    • ISSN (Online): 2249–2186
    • ISSN (Print): 2249–7161
    • Volume: 1, Issue 1

  • “DFFS: Detecting Fraud in Finance Sector”

    • Authors: R. Lakshman Naik, Dr. Manjula Bairam
    • Publication: International Journal of Advanced Engineering Sciences and Technologies
    • ISSN: 2230-7818
    • Volume: 9, Issue 2

  • “Study of Trends in Higher Education”

    • Publication: International Journal of Computer Trends and Technology
    • ISSN: 2231-2803
    • Volume: 1, Issue 1

  • “Stock Prediction using Neural Network”

    • Publication: International Journal of Advanced Engineering Sciences and Technologies
    • ISSN: 2230-7818
    • Volume: 10, Issue 1

  • “Session Data Protection Using Tree-Based Dependency”

    • Publication: International Journal of Advances in Engineering & Technology
    • ISSN: 2231-1963
    • Volume: 2, No. 1

  • “Secure Authentication Scheme for Mobile Ad Hoc Networks”

    • Publication: International Journal of Mobile & Adhoc Network
    • ISSN (Online): 2231-6825
    • ISSN (Print): 2249-202X
    • Volume: 2, Issue 1

  • “Secure Scheme of Data Protection in Cloud Computing”

    • Publication: International Journal of Computer Science and Technology
    • ISSN: 0976-8491
    • ISSN: 2229-4333
    • Volume: 3, No. 1

  • “Cloud Computing: Research Issues and Implications”

    • Publication: International Journal of Cloud Computing and Services Science
    • ISSN: 2089-3337
    • Volume: 2, No. 2

  • “Prediction of BSE Stock Data using MapReduce K-Mean Cluster Algorithm”

    • Publication: International Journal of Current Engineering and Technology, INPRESSCO
    • E-ISSN: 2277–4106
    • P-ISSN: 2347–5161
    • Volume: 5, No. 3

  • “Current Apprises of Opinion Mining Methods”

    • Publication: International Journal of Engineering and Advanced Technology (IJEAT)
    • ISSN: 2249–8958
    • Volume: 9, Issue 2

 

 Conclusion

Based on his research achievements, Lakshman Naik Ramavathu is well-suited for a Best Paper Award, provided the submission is among his most impactful and high-quality research works. Enhancing recent publications, collaborations, and practical implementations will further solidify his standing in the academic and research community.