| Title: | Open-source transformer-based information retrieval system for energy efficient robotics related literature |
|---|
| Authors: | ID Bertoncel, Tine (Author) |
|---|
| Files: |  https://sciendo.com/article/10.2478/orga-2025-0012
 RAZ_Bertoncel_Tine_2025.pdf (1,62 MB)
MD5: 425B19FEB8B731EB9FC63D5AE2216524
|
|---|
| Language: | English |
|---|
| Work type: | Unknown |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FOV - Faculty of Organizational Sciences in Kranj
|
|---|
| Abstract: | Background and Purpose: This article employs the Hugging Face keyphrase-extraction-kbir-inspec machine learning model to analyze 654 abstracts on the topic of energy efficiency in systems and control, computer science and robotics. Methods: This study targeted specific arXiv categories related to energy efficiency, scraping and processing ab - stracts with a state-of-the-art Transformer-based Hugging Face AI model to extract keyphrases, thereby enabling the creation of related keyphrase networks and the retrieval of relevant scientific preprints. Results: The results demonstrate that state-of-the-art open-source machine learning models can extract valuable information from unstructured data, revealing prominent topics in the evolving field of energy-efficiency. Conclusion: This showcases the current landscape and highlights the capability of such information systems to pinpoint both well researched and less researched areas, potentially serving as an information retrieval system or early warning system for emerging technologies that promote environmental sustainability and cost efficiency. |
|---|
| Keywords: | energy efficiency, keyphase extraction, early warning system, information system, semantic network, transformer models, industry 4.0 |
|---|
| Publication date: | 01.05.2025 |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 196-208 |
|---|
| Numbering: | Vol. 58, ǂiss.ǂ2 |
|---|
| PID: | 20.500.12556/DKUM-94191  |
|---|
| UDC: | 334.72(497.4) |
|---|
| ISSN on article: | 1318-5454 |
|---|
| COBISS.SI-ID: | 237913859 |
|---|
| DOI: | 10.2478/orga-2025-0012 |
|---|
| Publication date in DKUM: | 07.08.2025 |
|---|
| Views: | 177 |
|---|
| Downloads: | 7 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
