Alexander Bakumenko- MSc in Data Science, MSc in Biomedical Data Science and Informatics
- Doctorate Research Assistant at Clemson University
Alexander Bakumenko
- MSc in Data Science, MSc in Biomedical Data Science and Informatics
- Doctorate Research Assistant at Clemson University
Data Scientist, Researcher, Ph.D. candidate
About
5
Publications
3,667
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
70
Citations
Introduction
Alexander Bakumenko has extensive cross-industry experience in data science and AI. He holds an M.Sc. with a perfect GPA in Biomedical Data Science from Clemson University, USA, and an M.Sc. in Data Science from Luleå University of Technology, Sweden. His research interests include NLP, LLMs, machine learning-based automation, and data-informed decision-making, focusing on explainable and ethical AI. He researches anomaly detection and predictive modeling techniques in healthcare and finance.
Current institution
Publications
Publications (5)
Objectives
This study aims to summarize the usage of large language models (LLMs) in the process of creating a scientific review by looking at the methodological papers that describe the use of LLMs in review automation and the review papers that mention they were made with the support of LLMs.
Materials and Methods
The search was conducted in Jun...
Objective: This study aims to summarize the usage of Large Language Models (LLMs) in the process of creating a scientific review. We look at the range of stages in a review that can be automated and assess the current state-of-the-art research projects in the field. Materials and Methods: The search was conducted in June 2024 in PubMed, Scopus, Dim...
Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potentially fraudulent journal entries, each characterized by a varying number of transactions. In machine learning, heterogeneity in featu...
Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully...
In countries where groundwater is being used for drinking water and other clean water needs, the protection of the resources is crucial. Therefore, there is an increasingly urgent focus on the presence of pesticides in groundwater in countries where pesticides are or have been used for agricultural and other purposes, including Denmark. Previously...
