[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120061-en":3,"doc-seo-120061-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120061,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Trend Analysis in Machine Learning - Bachelor’s Thesis - Text Mining Methods","Rapid growth in machine learning has reshaped industries such as healthcare, finance, and autonomous systems, making trend understanding essential for guiding research and allocating resources. This bachelor’s thesis performs a comprehensive trend analysis of machine learning research from 2014 to 2024 using titles and abstracts of scientific articles. Descriptive qualifiers are extracted to classify research topics, analyze their evolution over time, study qualifier co-occurrence with association rules, and predict trends per topic. Results emphasize continuing dominance of artificial neural networks and deep learning while highlighting emergence of generative models, demonstrating the value of text mining for tracking and forecasting research directions.","TREND ANALYSIS IN MACHINE LEARNING  \nJUDITH DEVERS CANTERO  \nThesis supervisor: LUIS ANTONIO BELANCHE MUÑOZ (Department of Computer Science) Degree: Bachelor's Degree in Data Science and Engineering  \nBachelor's thesis  \nFacultat d'Informàtica de Barcelona (FIB)  \nEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB)  \nFacultat de Matemàtiques i Estadística (FME) Universitat Politècnica de Catalunya (UPC) -BarcelonaTech  \nUniversitat Polit`ecnica de Catalunya  \nFacultat d’Inform`atica de Barcelona  \nEscola T`ecnica Superior d’Enginyeria de Telecomunicaci´o de Barcelona  \nFacultat de Matem`atiques i Estad´ıstica  \nDegree in Data Science and Engineering  \nBachelor’s Degree Thesis  \nTrend Analysis in Machine Learning  \nJudith Devers Cantero  \nSupervised by Luis Antonio Belanche Mu˜noz Department of Computser Science  \nJuly, 2024  \nFirst and foremost, I would like to extend my deepest gratitude to Luis Antonio Belanche for his invaluable guidance and support throughout this project. His expertise and encouragement have been instrumental in the successful completion of this work.  \nI would also like to thank my classmates for making these past four years an incredible journey filled with hard work and wonderful moments.  \nLastly, I am deeply grateful to my friends and family for their unconditional support and encouragement throughout my studies. Their unwavering belief in me has been a constant source of motivation.  \nAbstract  \nThe rapid growth of machine learning has significantly transformed various industries, including healthcare, finance, and autonomous systems. Understanding trends in this dynamic field is crucial for guiding research, allocating resources, and anticipating future developments. This study addresses the need for a comprehensive trend analysis in machine learning research from 2014 to 2024 by examining the titles and abstracts of scientific articles. By extracting descriptive qualifiers, we classified articles into specific topics and analyzed their evolution over time. Our methodology includes a detailed study of qualifiers, the study of the co-occurrence of this qualifiers with association rules, topic classification of the articles, and trend prediction for each topic. Key findings highlight the continued prominence of topics such as ”Artificial Neural Networks and Deep Learning” and the emergence of new areas like ”Generative Models.” The analysis revealed significant shifts in research focus and identified consistent trends, providing valuable insights into the development of the field. This study demonstrates the effectiveness of text mining techniques in tracking and predicting research trends.  \nResumen  \nEl r´apido crecimiento del aprendizaje autom´atico ha transformado significativamente varias industrias, incluyendo la salud, las finanzas y los sistemas aut´onomos. Comprender las tendencias en este campodin´amico es crucial para guiar la investigaci´on, asignar recursos y anticipar desarrollos futuros. Este estudio aborda la necesidad de un an´alisis de tendencias exhaustivo en la investigaci´on de aprendizaje autom´aticodesde 2014 hasta 2024 mediante el examen de los t´ıtulos y res´umenes de art´ıculos cient´ıficos. Al extraer calificadores descriptivos, hemos clasificado los art´ıculos en ´areas espec´ıficas y hemos analizado su evoluci´on a lo largo del tiempo. Nuestra metodolog´ıa incluye un estudio detallado de calificadores, el estudio de la co-ocurrencia de los calificadores con reglas de asociaci´on, clasificaci´on de los art´ıculos en ´areas espec´ıficas y predicci´on de tendencias para cada ´area. Los hallazgos clave destacan la prominencia continua de temas como ”Redes Neuronales Artificiales y Aprendizaje Profundo” y la aparici´on de nuevas ´areas como los”Modelos Generativos”. El an´alisis ha identificado tendencias consistentes, proporcionando valiosas perspectivas sobre el desarrollo del campo. Este estudio demuestra la efectividad de las t´ecnicas de miner´ıa","cbCaio44yU0uggUR","https://ap.wps.com/l/cbCaio44yU0uggUR","pdf",3158143,1,53,"English","en",105,"# 1 Introduction\n## 1.1 Motivation\n## 1.2 Goals of the Project\n## 1.3 Structure of the Document\n# 2 Background\n## 2.1 Machine Learning\n## 2.2 Trend Analysis\n## 2.3 Text Mining\n# 3 Context on Trend Anal","[{\"question\":\"What time range and data sources does the thesis use for trend analysis?\",\"answer\":\"The study analyzes machine learning research from 2014 to 2024, using the titles and abstracts of scientific articles as the primary data sources.\"},{\"question\":\"How are research topics identified and tracked over time?\",\"answer\":\"Descriptive qualifiers are extracted from article text, used for topic classification, and then analyzed for how topics evolve across the years.\"},{\"question\":\"What role do association rules and co-occurrence play in the methodology?\",\"answer\":\"The methodology examines the co-occurrence of qualifiers and studies their relationships using association rules to better understand topic patterns.\"}]","Trend Analysis in Machine Learning - 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