ESE - Artigos em Revistas Indexados à WoS/Scopus
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Browsing ESE - Artigos em Revistas Indexados à WoS/Scopus by Sustainable Development Goals (SDG) "09:Indústria, Inovação e Infraestruturas"
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- Innovative Applications of Artificial Neural Networks in Tax ForecastingPublication . Rodolfo, Bruno Couto de Abreu; Gonçalves, Bruno F.The importance of forecasting tax revenues is vital for economic planning and financial sustainability in Mozambique. The study addresses this topic by exploring the potential of Artificial Neural Networks (ANNs) to improve such predictions. The central problem is the limitation of conventional methods in capturing the complexity of fiscal data. The objective is to develop an ANN model that incorporates historical data and economic factors, providing a mixed methodology that enriches the analysis with qualitative and quantitative data. The rationale for adopting ANNs lies in their superior modeling and prediction capabilities in large and complex data environments. The results achieved demonstrate that ANNs can predict tax revenues with greater accuracy, surpassing traditional models. The conclusion points to the ANN as a valuable tool for tax authorities, enhancing efficiency in collection and contributing to the country's fiscal stability.
- Technology-Mediated Education: impact of AI on the main distance learning modalitiesPublication . Morgado, Elsa; Silva, Leonido Levi; Pereira, Antonino; Gouveia, Luís BorgesArtificial Intelligence (AI) and distance learning (EaD) have profoundly transformed education, redefining teaching methodologies and learning dynamics. When integrated into educational environments, these technologies facilitate equitable access to knowledge, enable personalized learning, and enhance pedagogical flexibility. E-learning, blended learning (b-learning), and mobile learning (m-learning) exemplify technology-mediated instructional modalities that empower learners and educators to engage in innovative knowledge construction, transcending geographical barriers. This study critically examines the impact of AI-driven digital platforms on distance learning and their implications for pedagogical efficacy. A quantitative research design was employed to evaluate the benefits and challenges associated with AI-integrated digital platforms in distance education. Data were collected through an online survey administered to education professionals from three Portuguese-speaking regions with significant engagement in digital learning: Portugal (Europe), Brazil (Latin America), and Angola (Africa). The survey assessed perceptions regarding the extent to which AI-enhanced platforms influence pedagogical practices and learning outcomes. Analysis of 230 validated responses revealed substantial insights into the role of AI-driven platforms in education. Findings indicate that these technologies foster accessibility, support individualized learning pathways, and enhance instructional adaptability. Additionally, the study identifies key areas for improvement in the integration of AI-driven platforms within pedagogical frameworks to optimize educational efficacy. The findings highlight the transformative potential of AI and digital platforms in distance education, emphasizing their capacity to enhance learning experiences and promote knowledge democratization. Nevertheless, the study underscores the necessity for ongoing optimization of these technologies to fully harness their pedagogical benefits and address existing limitations.