Applying Artificial Intelligence in Malaria Mosquito Research: A Bibliometric Study on Species Identification and Automated Detection

This study presents a bibliometric analysis on the integration of Artificial Intelligence in identifying and automatically detecting malaria mosquito species. The research explores trends and patterns in malaria research literature using a bibliometric data analysis approach. Data from 321 malaria research publications were collected and analyzed to identify frequently occurring keywords, authors, institutions, and collaborations among researchers. The analysis process involved sorting 1304 keywords by frequency, with "malaria," "artificial intelligence," and "machine learning" emerging as dominant keywords. Visualization techniques such as bar charts, word clouds, and network graphs were employed to understand keyword distribution, relationships, and evolution over time. The study highlights the interdisciplinary approach in current malaria research, combining medical, biological, and computational sciences to address this global health issue. By utilizing Python programming for analysis and visualization, this research demonstrates the effective processing and visualization of bibliometric data, providing deep insights into the patterns and trends in malaria research.