2025
Autores
Bruno Veloso; Hugo Amorim Neto; Fernando Buarque; João Gama;
Publicação
Data Mining and Knowledge Discovery
Abstract
2025
Autores
Dintén, R; Zorrilla, M; Veloso, B; Gama, J;
Publicação
Information Fusion
Abstract
2025
Autores
Pires, PB; Santos, JD; de Brito, PQ; Delgado, C;
Publicação
Smart Innovation, Systems and Technologies
Abstract
The advent of new technologies has led to significant changes in the field of marketing, demanding a rethinking of existing knowledge and skills. This research proposes a set of transversal curricula in digital marketing. The methodology employed included an exploratory analysis of digital marketing courses offered at universities and major online platforms, focus groups, and interviews, conducted in four countries. The countries included in the study were Finland, Poland, the Netherlands, and Portugal. The findings indicated that an introductory course and specialization blocks would be beneficial. Social media, analytics, digital advertising, search engine optimization (SEO), digital marketing strategies, web content, e-mail marketing, customer experience, landing pages, user experience, leads, conversion rate optimization, and E-commerce were identified as the key subjects of study for the introductory course in digital marketing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
2025
Autores
Ana Nogueira; Conceição Rocha; Pedro Campos;
Publicação
Machine Learning Perspectives of Agent-Based Models
Abstract
2025
Autores
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Litvak, M; Cordeiro, JP; Rocha, C; Sousa, HO; Cunha, LF; Mansouri, B;
Publicação
SIGIR Forum
Abstract
2025
Autores
Carvalhido, F; Cardoso, HL; Cerqueira, V;
Publicação
THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 28
Abstract
Multimodal models, namely vision-language models, present unique possibilities through the seamless integration of different information mediums for data generation. These models mostly act as a black-box, making them lack transparency and explicability. Reliable results require accountable and trustworthy Artificial Intelligence (AI), namely when in use for critical tasks, such as the automatic generation of medical imaging reports for healthcare diagnosis. By exploring stresstesting techniques, multimodal generative models can become more transparent by disclosing their shortcomings, further supporting their responsible usage in the medical field.
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