Emotional information is present in every spoken audio event that individuals frequently hear. As a result, Speech Emotion Recognition (SER) has gained widespread recognition. Over the past ten years, this has grown into a significant research topic. Through the use of human voices or everyday conversation, SER can detect people's emotional states. It is essential for developing Human-Computer Interaction (HCI) and signal processing systems. Emotions in humans also evolve with time. Therefore, to comprehend the dependencies in the speech sign over time, a strong model is required. In this work, Transformer-based method using the CNN model in parallel (TASER-Net) method is used for SER. With the use of a parallel CNN model, our novel approach to temporal emotion modelling for SER overcomes information loss from noise and bi-directional propagation while creating multi-scale contextual emotional representations across a range of time frames.
The increasing prevalence of malware propagation in IoT networks requires the development of efficient and adaptive mitigation strategies. Classical approaches based on optimal control theory, such as Pontryagin's Maximum Principle (PMP), provide mathematically optimal solutions, but require solving a complex two-point boundary value problem. Recent advances in machine learning have introduced Neural Ordinary Differential Equations (ODEs) as a new alternative, allowing learning of non-linear control policies through gradient-based optimization. In this work, we apply neural ODEs to solve an optimal control problem in a modified SIR model of malware propagation, incorporating two control functions: u(1)(t), which reduces transmission, and u(2)(t), which enhances recovery. We compare this approach with a PMP-based control solution obtained using the shooting method. Our results show that both methods effectively reduce the infection peak, but exhibit different behaviours in the timing and magnitude of interventions. This study highlights the advantages and limitations of both approaches, and provides a foundation for hybrid control strategies that combine machine learning with traditional optimal control techniques for malware mitigation.
Digital TV broadcasting networks rely on alarm systems to monitor equipment and service health. However, the large volume of alarms, many of which are non-critical, makes it difficult for Network Operation Center (NOC) operators to identify early signs of service-affecting incidents. This paper presents an automated incident prediction system based on machine learning techniques, developed with a Spanish digital TV operator, to address this challenge. The system is tested under three synthetic scenarios-Baseline, Degraded, and Alarm-storm-that simulate increasing levels of network degradation. Alarm data are encoded under four contextual settings: (i) without additional information; (ii) including pattern-based features to capture interactions with other network elements; (iii) incorporating time-based statistical features extracted from alarm activity; and (iv) combining both types of contextual information. Results show that incorporating temporal context significantly improves detection, especially in highly imbalanced conditions where incidents are rare. LightGBM stands out among the evaluated classifiers, achieving the highest balanced accuracy when time-based statistical features are used.
Incremental feature learning (IFL) is a supervised learning paradigm for neural networks (NNs), where the input layer is incrementally expanded over time. NNs dynamically expand the input layer with new features, while also reducing overfitting and model complexity. Feature ranking based on feature importance determines the order of feature integration. The incremental nature of IFL results in a dynamic optimization problem (DOP), where both the search space and its dimensionality changes over time. Particle swarm optimisation (PSO) has been extended to dynamic environments. This study adapts various dynamic PSO variants to train incrementally constructed NNs (INNs). The performance of INNs is compared to fully constructed NNs (FNNs) trained with BP and standard PSO on seven regression tasks. Results demonstrate that IFL effectively allows NNs to incorporate new features dynamically and acts as a regularisation technique.
The safe and efficient operation of hydrogen refueling stations is essential to support the global transition towards low-carbon energy systems. However, the scarcity of real-world operational data remains a major obstacle for advanced monitoring and anomaly detection. This study proposes a deep learning framework that combines LSTM-based synthetic data generation with unsupervised anomaly detection. A generative LSTM was used to simulate 756 realistic hydrogen refueling scenarios enriched with physically plausible anomalies. An LSTM-Autoencoder was subsequently trained to detect deviations in key process variables, achieving 92
