Abstract
The growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge, implying the complexity of each application. However, the entanglement of different modalities demands new and tailored refinements to enhance adaptability and accuracy in the field. This paper outlines the implementation of a Multimodal Artificial Intelligence methodology with Affective Computing to enhance consumer insights and marketing strategies. The application combines different data modalities, such as textual, visual, and audio inputs, to tackle complex issues in dealing with consumer sentiment. The proposed approach uses advanced preprocessing techniques, including word embeddings, neural networks, and recurrent models, to extract information from diverse modalities. Fusion strategies, such as attention-based and late fusion procedures, are utilized to combine knowledge, facilitating robust sentiment detection. The implementation includes the analysis of real-time customer feedback on social media and product assessments, demonstrating improvements in predicting engagement and shaping consumer behavior. The results underscore the practical viability of the suggested method, promoting progress in multimodal sentiment analysis to extract actionable consumer insights in marketing.

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.

Abstract
To maintain the performance of the latest generation of onshore and offshore wind turbine systems, a new methodology must be proposed to enhance the maintenance policy. In this context, this paper introduces an approach to designing a decision support tool that combines predictive capabilities with anomaly explanations for effective IoT predictive maintenance tasks. Essentially, the paper proposes an approach that integrates a predictive maintenance model with an explicative decision-making system. The key challenge is to detect anomalies and provide plausible explanations, enabling human operators to determine the necessary actions swiftly. To achieve this, the proposed approach identifies a minimal set of relevant features required to generate rules that explain the root causes of issues in the physical system. It estimates that certain features, such as the active power generator, blade pitch angle, and the average water temperature of the voltage circuit protection in the generator's sub-components, are particularly critical to monitor. Additionally, the approach simplifies the computation of an efficient predictive maintenance model. Compared to other deep learning models, the identified model provides up to 80% accuracy in anomaly detection and up to 96% for predicting the remaining useful life of the system under study. These performance metrics and indicators values are essential for enhancing the decision-making process. Moreover, the proposed decision support tool elucidates the onset of degradation and its dynamic evolution based on expert knowledge and data gathered through Internet of Things (IoT) technology and inspection reports. Thus, the developed approach should aid maintenance managers in making accurate decisions regarding inspection, replacement, and repair tasks. The methodology is demonstrated using a wind farm dataset provided by Energias De Portugal.

Abstract
PurposeThis study seeks to develop an Environmental Sustainability Balanced Scorecard (ESBSC) articulated through a strategic map for collaborative implementation by municipalities by municipalities. In addition, it aims to elucidate the architecture of this tool.Design/methodology/approachThe research uses qualitative methodology, initiating with document analysis, followed by municipal-level surveys and an interview with the Norte Portugal Regional Coordination and Development Commission (CCDR-N).FindingsThe study constructs an ESBSC that adopts an integrative approach to sustainability, focusing on municipal joint action. The tool fosters synergies and enhances cooperation. By incorporating a strategic mix, the tool contributes to improving the environmental management performance of the participating municipalities.Practical implicationsThis study introduces a tool designed for municipalities that aspire to incorporate environmental sustainability into their strategies. This tool facilitates the implementation and management of a long-term environmental strategy, with potential implications for organization and its culture. In addition, it highlights critical environmental factors that should serve as a starting point in future studies or applications of this tool.Social implicationsInvolving both an academic institution and multiple municipalities, this research identifies critical environmental factors that enhance environmental awareness within municipalities and designs a tool that, when consciously adopted, can influence the culture dynamics of the population involved. Furthermore, it proposes a structured and systematic research method for creating an ESBSC for joint municipal action.Originality/valueTo the best of authors' knowledge, this research constitutes the first exploratory attempt to devise an environmental strategy for joint municipal action. Although the tool emphasizes the environmental component, it promotes an integrated vision of sustainability. Despite the extensive application of balanced scorecards in various organizational contexts, their utilization in fostering environmental sustainability at a municipal level remains underexplored. This study addresses this gap by developing a tailored strategic tool that operationalizes environmental priorities within municipal governance frameworks.

Abstract
The domain of Human Activity Recognition (HAR) has undergone a remarkable evolution, driven by advancements in sensor technology, artificial intelligence (AI), and machine learning algorithms. The aim of this article consists of taking as a basis the previously obtained results to implement other techniques to analyze the same dataset and improve the results previously obtained in the different studies, such as neural networks with different configurations, random forest, support vector machine, CN2 rule inducer, Naive Bayes, and AdaBoost. The methodology consists of data collection from smartphone sensors, data cleaning and normalization, feature extraction techniques, and the implementation of various machine learning models. The study analyzed machine learning models for recognizing human activities using data from smartphone sensors. The results showed that the neural network and random forest models were highly effective across multiple metrics. The models achieved an area under the curve (AUC) of 98.42%, a classification accuracy of 90.14%, an F1-score of 90.13%, a precision of 90.18%, and a recall of 90.14%. With significantly reduced computational cost, our approach outperforms earlier models using the same dataset and achieves results comparable to those of contemporary deep learning-based approaches. Unlike prior studies, our work utilizes non-normalized data and integrates magnetometer signals to enhance performance, all while employing lightweight models within a reproducible visual workflow. This approach is novel, efficient, and deployable on mobile devices in real-time. This approach makes it an ideal fit for real-time mobile applications.

Abstract
Deep learning models have excelled in computer vision tasks in the past decade, but their lack of transparency raises ethical and legal concerns, especially in high-stakes areas such as surveillance and law enforcement. As such, regulations like the European Union's General Data Protection Regulation are now demanding interpretable Artificial Intelligence systems. This paper focuses on automatic face recognition, where existing systems lack interpretability and research into explainable alternatives is limited. To address this gap, we propose two interpretable facial verification models based on Siamese Networks that match and compare semantically-aligned local regions in the images. Experiments show these models rival and even outperform traditional baselines while offering clearer, more accountable explanations, advancing ethical and legally compliant facial recognition.