By: A.Gaurav
Automated algorithms that learn from experience and data are known as machine learning (ML) algorithms. It’s considered a component of AI. To generate predictions or judgments without being explicitly programmed, machine learning algorithms create a model using sample data, known as training data [1]. Computer vision, email filtering, voice recognition, and other fields where traditional algorithms would be difficult or impossible to build rely on the versatility and adaptability offered by machine learning techniques. For the benefit of new scholars, this article provides a brief bibliometric overview of Machine learning research topics.
Hot Topics in Machine learning Research
The first and most important step in beginning a research project is determining the research subject. By analyzing several research papers published between 2010 to 2022 from the Dimensions database [2], we identified some important hot topics in machine learning, as represented in Figure 1. As seen in Figure 1, a large number of researchers are working in several fields of machine learning, including artificial intelligence, pattern recognition, feature selection, pharmaceuticals, and big data. This figure aids in the selection of study subjects for novice researchers.
Reserchers in the fiels of Machine learning Research
Following subject selection, the next stage is to identify the most renowned researchers in the area and learn from their work. To do this, we analyzed the writers who work in the area of machine learning. We evaluate writers based on their amount of citations, published papers, and collaborations.
We evaluate writers in our study who have published more than five research articles in reputable international journals or at reputable conferences. The author’s map according to collaboration (links reflect the collaboration network) and timeline are shown in Figure 2.
As seen in Figure 2, writers such as relgi luca, kernbach julius m, serra cario, staartjes victor e, exarchos themis, and xulei are actively publishing in the area of ML. Thus, fresh researches might continue their work on current developments.
Figure 3 depicts the researchers’ citation map; it quantitatively analyses the researchers’ performance. As seen in Figure 3, Ghassemi marzyeh, wang kai, lane thomas r, zorn kimberley m, ekins sean, and nielsen jens are among the most often mentioned writers. As a result, new researchers may refer to their work for in-depth understanding of ML themes.
Important Publishers in the Field of ML
The journal selection is also critical for the research since keeping up with the current issues and volumes of the journal provides the latest developments in the field of machine learning. As a result, we analyze in Figure 4 the publishers of high-quality research articles in the area of machine learning research.
From figure 4 it is clear that Sensors journal and plos one are publishing quality research papers in the field of ML research. Therefore, new researchers can follow these journals for the quality of information.
Highely Cited Papers in the Field of Machine learning Research
Selecting a high-quality article on machine learning follows a selection of the authors and publication sources. To do this, we provide 15 of the most highly referenced papers in the area of machine learning in table 1; this will aid students in comprehending the most recent research frameworks and algorithms in ML research.
Table 1: Highly cited papers
Note: Data is prepared by Dimensions database
References
- A. Khan, F. Colace (2021) Knowledge Graph: Applications with ML and AI and Open-Source Database Links in 2022, Insights2Techinfo. pp.1
- Diamentions databsed