Results 151 to 160 of about 970,151 (199)

Artificial Intelligence in Climate and Sustainable Finance: A Blessing or a Curse?

Journal of Economic Surveys, Volume 40, Issue 4, Page 1854-1873, September 2026.
ABSTRACT While there are concerns regarding the sustainability of artificial intelligence (AI), it is a potential ally in the transition toward a greener future. It offers advanced tools for data analysis; risk modeling; and environmental, social, and governance (ESG) assessment.
Filippo di Pietro, Pilar Giráldez‐Puig, Pedro R. Palos‐Sánchez, Zbigniew Korzeb   +3 more
wiley   +1 more source

Mapping the Landscape of the Economics of AI Literature: Gaps and Opportunities for Research and Policy

Journal of Economic Surveys, Volume 40, Issue 4, Page 2111-2128, September 2026.
ABSTRACT This paper investigates the evolving landscape of research on the economics of artificial intelligence (AI). Using topic modeling on a corpus of over 4,600 academic papers, we identify the dominant themes that have shaped the literature to date and highlight areas that remain relatively underexplored. In addition, we assess the extent to which
Lucy Hampton, John Lourenze Poquiz
wiley   +1 more source

Female Budgerigars Attend Equally to Rhythmic Beat or Repetition, While Humans Prioritize Their Combination

Annals of the New York Academy of Sciences, Volume 1563, Issue 1, September 2026.
We investigated preferences of budgerigars and humans for rhythmic and arrhythmic sounds, specifically beat‐based regularity and repetition, using a place‐preference paradigm. We found that female budgerigars prefer regular and repetitive sounds equally, while humans prefer sounds containing a regular beat over repetition.
Felix Haiduk, Daniel L. Bowling, Shahrzad Afroozeh, Jan Kopač, Mahin Azarian, Marisa Hoeschele   +5 more
wiley   +1 more source

Personnel Psychology's 40 Questions Series: Artificial Intelligence

Personnel Psychology, Volume 79, Issue 3, Page 353-369, Autumn (Fall) 2026.
ABSTRACT In this article, we present a curated set of 40 questions on Artificial Intelligence (AI) to address its rapidly evolving role in Industrial/Organizational (I/O) Psychology, Human Resources (HR), and Organizational Behavior (OB) research and practice. We solicited questions from our professional networks and organized the responses into themes:
Emily D. Campion, Scott Tonidandel
wiley   +1 more source

DETEKSI TOPIK FASHION PADA TWITTER DENGAN LATENT DIRICHLET ALLOCATION

, 2017
Bagi orang-orang yang bergerak di bidang fashion mengetahui tren fashion adalah hal yang penting. Salah satu cara untuk mengetahui tren adalah dengan mendeteksi topik mengenai fashion yang dibicarakan di media sosial.
Yupa Umigi Al-khairi, Budi Laksono Putro, Yudi Wibisono   +2 more
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Latent Dirichlet Allocation

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 2012
openaire   +1 more source

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Distributed Latent Dirichlet Allocation on Streams

ACM Transactions on Knowledge Discovery from Data, 2021
Latent Dirichlet Allocation (LDA) has been widely used for topic modeling, with applications spanning various areas such as natural language processing and information retrieval. While LDA on small and static datasets has been extensively studied, several real-world challenges are posed in practical scenarios where datasets are often huge ...
Yunyan Guo, Jianzhong Li 0001
openaire   +1 more source

Authorship Attribution with Latent Dirichlet Allocation

2022
The problem of authorship attribution – attributing texts to their original authors – has been an active research area since the end of the 19th century, attracting increased interest in the last decade. Most of the work on authorship attribution focuses on scenarios with only a few candidate authors, but recently considered cases with tens to ...
Yanir Seroussi, Ingrid Zukerman, Fabian Bohnert   +2 more
openaire   +2 more sources

Latent Dirichlet Allocation

2002
Summary: We describe Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics.
David M. Blei, Andrew Y. Ng, Michael I. Jordan   +2 more
openaire   +3 more sources

Multi-dependent Latent Dirichlet Allocation

2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2017
Latent Dirichlet Allocation (LDA) is an attractive topic model research because LDA is so flexible for solving different problems. Because of its different core dependencies, it can be applied to many topics, such as emotion detection, information systems or image clustering.
Wei-Cheng Hsin, Jen-Wei Huang
openaire   +2 more sources