In smart manufacturing, the automation of anomaly detection is essential for increasing productivity. Timeseries data from production processes are often complex sequences and their assessment involves many variables. Thus, anomaly detection with deep learning approaches is considered as an efficient and effective methodology. In this work, anomaly detection with deep autoencoders is examined. Three autoencoders are employed to analyze an industrial dataset and their performance is assessed. Autoencoders based on long short-term memory and convolutional neural networks appear to be the most promising.

The demand for interpretable and accurate machine learning models continues to grow, especially in critical domains. The data-driven Fuzzy Cognitive Map (FCM) classifier is an interpretable and transparent decision-making method. Its core element, the weight matrix, is derived using predominantly population-based supervised learning methods which often suffer from degraded performance. Recent research has adopted gradient-based learning techniques to compete with the predictive performance of black-box models. Nonetheless, such methods modify foundational principles and compromise interpretability, highlighting the necessity to improve existing approaches. In this work, we introduce a novel learning and structural modeling method, termed Neural-FCM, which leverages deep neural networks and gradient descent to enhance the accuracy and robustness of FCM learning. Neural-FCM employs a hybrid network comprising both dense and convolutional layers and is trained using a categorical cross-entropy loss function specifically aligned with FCM reasoning. This hybrid model is trained to output instance-specific weight matrices for effective and targeted FCM inference, introducing structural adaptability, a feature not supported by previous static or globally optimized approaches. Focusing on generalization across domains, the Neural-FCM approach is evaluated on different classification tasks across six widely used public datasets and one proprietary medical dataset, consistently showing improved predictive performance. Notably, the comparative analysis against standard population-based FCM learning methods reveals consistent accuracy improvements, with gains of up to 34%. While less transparent gradient-based methods also yield improved accuracy, Neural-FCM demonstrates competitive or superior performance in most cases, with accuracy improvements ranging from 1 to 6% across different domains, while preserving the underlying interpretability. The performance enhancement and the use of instance-specific matrices contribute to the broader goal of developing gradient-based models that balance computational efficiency with the intrinsic FCM interpretability.

In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inheriting the limitations of human-performed procedures. To address this, we propose an automatic optical inspection framework that utilizes convolutional neural networks (CNNs) and post-hoc explainable methods. Our methodology handles glue quality inspection as a three-fold procedure. Initially, a detection system based on CenterNet MobileNetV2 is developed to localize PCBs, thus, offering a flexible lightweight tool for targeting and cropping regions of interest. Consequently, a CNN is proposed to classify PCB images into three classes based on the placed glue volume achieving 92.2% accuracy. This classification step ensures that varying glue volumes are accurately assessed, addressing potential quality issues that appear early in the production process. Finally, the Deep SHAP and Grad-CAM methods are applied to the CNN classifier to produce explanations of the decision making and further increase the interpretability of the proposed approach, targeting human-centered artificial intelligence. These post-hoc explainable methods provide visual explanations of the model’s decision-making process, offering insights into which features and regions contribute to each classification decision. The proposed method is validated with real industrial data, demonstrating its practical applicability and robustness. The evaluation procedure indicates that the proposed framework offers increased accuracy, low latency, and high-quality visual explanations, thereby strengthening quality assurance in PCB manufacturing.

Fuzzy cognitive maps (FCMs) are widely used for modeling complex causal systems under uncertainty. However, the FCM development lifecycle, encompassing data preprocessing, model construction, weight optimization, and scenario analysis, is typically fragmented across heterogeneous tools. Such fragmentation limits reproducibility and increases the technical burden. This paper presents FuzzyMind, an open‐source, web‐based platform that provides an integrated environment for the end‐to‐end FCM workflow. Built using the Streamlit framework, FuzzyMind combines data processing, interactive visualization, FCM modeling, and weight matrix optimization within a unified platform‐agnostic interface. The system supports both numerical and linguistic inputs and incorporates knowledge aggregation mechanisms to preserve expert‐driven modeling. In addition, it integrates a no‐code Neural‐FCM optimization pipeline, enabling automated model calibration without requiring programming expertise. A case study in health informatics demonstrates the applicability of the platform and highlights the seamless integration of optimization components. FuzzyMind lowers the barrier to entry for non‐technical users while providing a flexible foundation for further research in computational intelligence and complex systems modeling. The development code of the project is publicly available to promote scalability and adoption by the research community.