Abstract
Predictive maintenance methods play a crucial role in the early detection of failures and errors in machinery, preventing them from reaching critical stages. This paper presents a comprehensive study on a real-world dataset called MetroPT3, with data from a Metro do Porto train's air production unit (APU) system. The dataset comprises data collected from various analogue and digital sensors installed on the APU system, enabling the analysis of behavioural changes and deviations from normal patterns. We propose a data-driven predictive maintenance framework based on a Long Short-Term Memory Autoencoder (LSTM-AE) network. The LSTM-AE efficiently identifies abnormal data instances, leading to a reduction in false alarm rates. We also implement a Sparse Autoencoder (SAE) approach for comparative analysis. The experimental results demonstrate that the LSTM-AE outperforms the SAE regarding F1 Score, Recall, and Precision. Furthermore, to gain insights into the reasons for anomaly detection, we apply the Shap method to determine the importance of features in the predictive maintenance model. This approach enhances the interpretability of the model to support the decision-making process better.

Abstract

Abstract
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning.

Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black -box models are popular approaches based on deep -learning techniques due to their predictive accuracy. This paper proposes a neural -symbolic architecture that uses an online rule -learning algorithm to explain when the black -box model predicts failures. The proposed system solves two problems in parallel: (i) anomaly detection and (ii) explanation of the anomaly. For the first problem, we use an unsupervised state-of-the-art autoencoder. For the second problem, we train a rule learning system that learns a mapping from the input features to the autoencoder's reconstruction error. Both systems run online and in parallel. The autoencoder signals an alarm for the examples with a reconstruction error that exceeds a threshold. The causes of the signal alarm are hard for humans to understand because they result from a non-linear combination of sensor data. The rule that triggers that example describes the relationship between the input features and the autoencoder's reconstruction error. The rule explains the failure signal by indicating which sensors contribute to the alarm and allowing the identification of the component involved in the failure. The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure. We evaluate the proposed system in a real -world case study of Metro do Porto and provide explanations that illustrate its benefits.

Abstract
Artificial intelligence (AI) is a strategy for global economic development due to its economic potential. However, the need for more transparency in AI applications generates mistrust because of the complexity of the algorithms. AI has transformed the service industry along with the development and challenge of human-AI interactions. This interaction can elicit negative feelings in consumers, creating communities to voice their disapproval and hatred of brands. Research in this area needs to be improved, and this study aims to understand the negative feelings that result from human-AI interaction in online communities (Reddit). Using sentiment analysis techniques and a qualitative approach, we aimed to identify the predominant negative emotions generated by this interaction. This study also hopes to understand the emotional effects of this interaction better, thus filling in a gap in the literature. The insights obtained can help develop more effective interaction strategies between humans and AI that can benefit brands and society. The results show a sizable presence of negative feelings such as hate anger and frustration. It is, therefore, essential to understand the negative interactions between consumers, brands and AI and the need to develop strategies to mitigate these feelings. Contributions from the academic and corporate fields emphasise the importance of monitoring feelings and promoting more positive interactions between brands and consumers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Integrating emerging technologies, such as AI, the Metaverse, and IoT, revolutionizes management and brand practices. Brands can create captivating virtual experiences within the metaverse, including virtual storefronts and interactive events. Scientific data on brand management in the metaverse must be improved due to the concept’s early-stage development. While virtual environments exist, they do not fully encompass the metaverse’s scope. So, this research bridges this gap by exploring the relationship between brand management and the metaverse, focusing on consumer perceptions and their contribution to brand equity in this virtual realm. Netnography with a data mining approach was the methodology followed in this paper. Data were extracted by a metaverse community on the Reddit platform and, in total, 696 posts and comments were analyzed from June 2022 until May 2023. The results highlighted a positive and favorable consumer perception of brand management in the metaverse reality. This research contributes to the emerging field of metaverse brand management, investigating the impact of consumer perceptions on brand equity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.