ESTiG - Resumos em Proceedings Não Indexados à WoS/Scopus
URI permanente para esta coleção:
Navegar
Entradas recentes
- A mind map of factors influencing the choice of eco-friendly and Sustainable products: insights from neuromarketingPublication . Martins, Oliva M.D.; Silva, Natacha Jesus; Menshikova, Maria; Dumančić, KosjenkaNeuromarketing evaluates human expressions to understand people's behavior, and marketing can exploit the main factors associated with each behavior to help individuals make more conscious and environmentally friendly decisions. The decision making process is complex, but through emotional responses to experiences (products or advertisements), communication can be more assertive if it considers consumer perception. Attractive ecofriendly packaging and messaging, innovative design, and consumer-centred marketing strategies can influence the decision to purchase eco-friendly and sustainable products, especially when they take into account the level of awareness of perceived value among consumers and buyers. Despite considerations (rational, emotional, and associated risks), can people see the same product differently? To answer this question, the objective of this research was defined: to develop a comprehensive understanding of the factors that influence consumer decision-making. On November 27, 2025, this research conducted a literature review in the Web of Science Core Collection, searching for articles related to Neuromarketing to understand consumer decision-making processes, which can be applied to an eco-friendly and sustainable products campaign. The results structured the factors into five dimensions, and a mind map emerges from it.
- A draft-proposal for a focus group about awareness of anthelminthic resistance and its residues environmental impactPublication . Mateus, Teresa L.; Martins, Oliva M.D.; Velde, Fiona Vande; Flannery, Sinéad; Claerebout, Edwin; Keane, OrlaA focus group is a qualitative research methodology that utilizes group interaction to discuss a specific topic and is particularly useful for gaining a deeper understanding of the context surrounding a problem or specific phenomenon. Through in-depth interviews conducted with small and homogeneous groups (4 to 12), the moderator facilitates the discussion. The group should feel free to express their perspective. A proposal to conduct a focus group targeting ruminant veterinarians is presented, aiming to identify potential barriers, uncover hidden challenges, and explore opportunities and motivations for implementing sustainable practices. Veterinarians would be selected through convenience sampling. To explain the implementation, this research developed a guide based on a theoretical framework. The guide helps to implement the group interview. Finally, the data should be analysed. This phase involves transcription and categorization of data. The guide is based on the COM-B Model (Capability, Opportunity, Motivation led to Behaviour change). The variables (and respective indicators) would be Capability/Knowledge (knowledge, its source, and recommendations), local perception (perception, its source, and advisors), opportunity (support, training, and animal/environmental health), and motivation (willingness to change, enablers, and barriers). Some questions to be included would be: what are the main challenges you face in your daily professional life? Or how do you currently suggest farmers control helminths? Do you diagnose it in your daily practice? What do you know about the presence of anthelmintic drugs in the environment and their ecological impact? Hopefully, we'll get more information to set up focus groups with the producers.
- Assessing the reliability of AI-based angle detection for shoulder and elbow rehabilitationPublication . Klein, Luan; Chellal, Arezki Abderrahim; Grilo, Vinicius; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, JoséAngle assessment is crucial in rehabilitation and significantly influences physiotherapistsŠ decisionmaking. Although visual inspection is commonly used, it is known to be approximate. This preliminary study aims to integrate and evaluate AI image-based approaches for assessing upper-limb angles. The study involved 28 participants performing four different rotational joints movement in the shoulder and elbow complex. Two AI algorithms, utilizing MediaPipe Holistic and Yolo v7, were employed for angle estimation. The accuracy of the estimations was evaluated against a wall-mounted compass, considering the ground truth. The results showed that the AI image-based algorithms displayed promising capabilities in assessing the exercises. Yolo v7 achieved the highest quality of estimations, with MAE equal to or less than 5ž, while MediaPipe, despite producing poorer results, where the MAE reaches values of 17ž, offered more features and required lower computational power than Yolo v7. However, it is worth noting that Yolo v7 was limited to exercises in 2D and did not estimate the position of key body points in 3D. Nevertheless, Yolo v7 would provide a cost-effective and easily implementable solution for measuring angles in rehabilitation activities for 1 Degree of Freedom (DOF) exercises. Overall, this study demonstrates the great promise of angle estimation for rehabilitation purposes of the AI approach.
- An evaluation of image preprocessing in skin lesions detectionPublication . Silva, Giuliana; Lazzaretti, André; Monteiro, Fernando C.This study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Network (CNNs) in the task of skin lesion classification. The study is made on the ISIC 2017 dataset, a widely used resource in skin cancer diagnosis research. Thirteen popular CNN models were trained using transfer learning. An ensemble strategy was also employed to generate a final diagnosis based on the classifications of different models. The results indicate that image preprocessing can significantly enhance the performance of CNN models in skin lesion classification tasks. Our best model obtained a balanced accuracy of 0.7879.
- A comparative analysis of MATLAB and Python neural networks for diabetes predictionPublication . Pimentel, G.O.; Dessanti, A.L.; Teixeira, João Paulo
- Automatic speech recognition for Portuguese: a comparative studyPublication . Borghi, P.H.; Teixeira, João Paulo; Freitas, D.
- Deep learning and machine learning techniques applied to speaker identification on small datasetsPublication . Manfron, E.; Teixeira, João Paulo; Minetto, R.
- Comparative analysis of windows for speech emotion recognition using CNNPublication . Teixeira, Felipe; Soares, S.; Abreu, J.L.; Oliveira, P.; Teixeira, João Paulo
- ECG and sEMG conditioning and wireless transmission with a biosignal acquisition boardPublication . Luiz, Luiz; Teixeira, João Paulo; Coutinho, F.
- Forecasting COVID-19 in european countries using long short-term memoryPublication . Carvalho, Kathleen; Teixeira, Rita; Reis, Luis Paulo; Teixeira, João PauloEffective time series forecasts are increasingly important in supporting judgment in various decisions. Various prediction models are available to support these projections based on how each area provides a diverse set of data with variable behavior. Artificial neural networks (ANNs) significantly contribute to medical research since using predictive ideas allows for the study of disease progression in the future, as well as the behavior of other variables. This study implemented the proposed model based on Long Short-Term Memory (LSTM) to forecast COVID-19 daily new cases, deaths, and ICU patients. The methodology uses quantitative and qualitative data from six European countries: Austria, France, Germany, Italy, Portugal, and Spain to predict the last 242 days of the COVID-19 pandemic. The dataset uses the healthcare parameters of the number of daily new cases, deaths, ICU patients, and mitigation procedures, such as the percentage of the population fully vaccinated, the mandatory use of masks, and the lockdown. Two approaches were used to evaluate the model’s performance: the mean absolute error (MAE) and the mean square error (MSE). The results demonstrate that the LSTM model efficiently captures general trends in COVID-19 metrics but shows limitations when predicting data with low values or high variability, such as daily deaths. The model reported the lowest errors for Spain and Portugal, while France and Germany exhibited higher error rates due to differences in data reporting and pandemic dynamics. These findings highlight the importance of contextualizing predictive models based on specific regional characteristics.