Detekce výzkumných trendů
Research Trend Detection
Typ dokumentu
diplomová prácemaster thesis
Autor
Oleksii Shuhailo
Vedoucí práce
Mařík Radek
Oponent práce
Drchal Jan
Studijní obor
Cyber SecurityStudijní program
Open InformaticsInstituce přidělující hodnost
katedra počítačůPráva
A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.htmlVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html
Metadata
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The rapid growth of scientific knowledge and the ever-expanding volume of research publications pose significant challenges in identifying emerging trends and understanding the evolving research landscape. This diploma thesis investigates the field of research trend detection, aiming to explore existing technologies and methods, and develop a novel pipeline to detect and analyze research trends based on article titles. The thesis is organized into four main chapters. The first chapter provides an introduction to the research topic, highlighting the importance of trend detection in the context of scientific advancements and knowledge discovery. It also outlines the objectives and research questions that guide the investigation. The second and third chapters review methods and algorithms employed in research trend detection. Various techniques, such as clustering, topic modeling, and natural language processing, are discussed, along with their strengths and limitations. The third chapter goes into detail about articles and papers addressing challenges in trend detection within the research domain, providing valuable insights into the field's current state. The fourth chapter details the design and implementation of a novel pipeline for research trend detection. The pipeline leverages article titles as the primary input and incorporates various techniques, including topic extraction, document embedding, and clustering. The assembly of these components forms a cohesive framework capable of identifying and analyzing research trends. Additionally, the pipeline addresses challenges related to parameter selection and dataset variability. The fifth chapter showcases the results obtained from applying the developed pipeline to the arXiv dataset. It demonstrates the pipeline's capability to detect and visualize research trends over time. The rapid growth of scientific knowledge and the ever-expanding volume of research publications pose significant challenges in identifying emerging trends and understanding the evolving research landscape. This diploma thesis investigates the field of research trend detection, aiming to explore existing technologies and methods, and develop a novel pipeline to detect and analyze research trends based on article titles. The thesis is organized into four main chapters. The first chapter provides an introduction to the research topic, highlighting the importance of trend detection in the context of scientific advancements and knowledge discovery. It also outlines the objectives and research questions that guide the investigation. The second and third chapters review methods and algorithms employed in research trend detection. Various techniques, such as clustering, topic modeling, and natural language processing, are discussed, along with their strengths and limitations. The third chapter goes into detail about articles and papers addressing challenges in trend detection within the research domain, providing valuable insights into the field's current state. The fourth chapter details the design and implementation of a novel pipeline for research trend detection. The pipeline leverages article titles as the primary input and incorporates various techniques, including topic extraction, document embedding, and clustering. The assembly of these components forms a cohesive framework capable of identifying and analyzing research trends. Additionally, the pipeline addresses challenges related to parameter selection and dataset variability. The fifth chapter showcases the results obtained from applying the developed pipeline to the arXiv dataset. It demonstrates the pipeline's capability to detect and visualize research trends over time.
Kolekce
- Diplomové práce - 13136 [833]
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