Predictive Maintenance (PdM) is crucial in manufacturing, enabling early detection of equipment failures and optimized maintenance to improve reliability and reduce costs. Yet, extracting accurate and explainable predictions from complex time-series data remains challenging. Deep learning models such as Long Short-Term Memory (LSTM) networks offer strong predictive power but act as ‘black boxes,’ while fuzzy systems provide interpretability but lack robustness in dynamic industrial settings. To address these limitations, recent studies have integrated fuzzy inference with LSTMs. In this paper, we propose FiLSTM (Fuzzy Rule Induction for LSTM), a hybrid model that leverages Decision Trees to generate fuzzy rules and membership functions directly from data. This approach enhances both interpretability and prediction, achieving higher accuracy, faster computation, and lower execution time compared to FLSTM. FiLSTM advances PdM systems toward real-time, scalable, and transparent industrial applications.

Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. While the majority of prior research has centered on employing purely time series-based methodologies for solar forecasting, only a limited number of studies have taken into account factors such as cloud cover or the surrounding physical context. In this paper, we put forth a deep learning architecture designed to harness spatio-temporal context using satellite data, to attain highly accurate day-ahead time-series forecasting for any given station, with a particular emphasis on forecasting Global Horizontal Irradiance (GHI). We also suggest a methodology to extract a distribution for each time step prediction, which can serve as a very valuable measure of uncertainty attached to the forecast. When evaluating models, we propose a testing scheme in which we separate particularly difficult examples from easy ones, in order to capture the model performances in crucial situations, which in the case of this study are the days suffering from varying cloudy conditions. Furthermore, we present a new multi-modal dataset gathering satellite imagery over a large zone and time series for solar irradiance and other related physical variables from multiple geographically diverse solar stations. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective integration of solar power into the grid.

The computational complexity of classical numerical methods for solving Partial Differential Equations (PDE) scales significantly as the resolution increases. As an important example, climate predictions require fine spatio-temporal resolutions to resolve all turbulent scales in the fluid simulations. This makes the task of accurately resolving these scales computationally out of reach even with modern supercomputers. As a result, current numerical modelers solve PDEs on grids that are too coarse (3km to 200km on each side), which hinders the accuracy and usefulness of the predictions. In this paper, we leverage the recent advances in Implicit Neural Representations (INR) to design a novel architecture that predicts the spatially continuous solution of a PDE given a spatial position query. By augmenting coordinate-based architectures with Graph Neural Networks (GNN), we enable zero-shot generalization to new non-uniform meshes and long-term predictions up to 250 frames ahead that are physically consistent. Our Mesh Agnostic Neural PDE Solver (MAgNet) is able to make accurate predictions across a variety of PDE simulation datasets and compares favorably with existing baselines. Moreover, our model generalizes well to different meshes and resolutions up to four times those trained on.

Clustering is a widely used technique in data mining applications for discovering patterns in underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, data sets with mixed types of attributes are common in real life data mining applications. In this paper, we introduce a new framework for clustering mixed data which is based on Random Forest dissimilarity and PAM clustering. Then we apply this framework to segment market of services stations in Morocco to identify features that most influence on profit of each service station.