Abstract Violence recognition is challenging because it can be presented in very different forms. For example, it can be present in an image by a person hitting another person or present in audio by a person being rude to another. Thus, audio and video are essential features to be analysed. In the audio approach, speech processing, music, and ambient sound are some of the main points of this problem since finding similarities and differences between these domains is necessary. Human activity can be classified into four different categories in the video approach, depending on the complexity and the number of body parts involved in the action. Examples of Human activity categories are considered: gestures, actions, interactions and activities. Recognizing human actions in the video becomes a challenge with this varied set of human activities. Furthermore, in the last years, the growth of deep learning techniques applied to this area has been enormous, and the reason is that their results surpass traditional signal processing on a large scale. This article is based on audio and video signals inside a vehicle to detect violence. Furthermore, the architecture used was ResNet model with Mel‐spectrogram methodology for audio signals. The proposed method for video signal representation was RGB, which applied four different models: C2D, I3D, X3D, and Flow‐Gated. Finally, multimodal fusion was applied at the end of the process.

Abstract In recent years, we have seen a growing need for energy, which has had environmental consequences through the use of fossil fuels. Some of the sectors of our society make intensive use of energy, as is the case with wastewater treatment plants (WWTPs). Through anaerobic digestion, these infrastructures can produce energy, therefore improve energy efficiency and decrease the environmental footprint. This study aims to design, tune and evaluate a hybrid deep learning (DL) model, called incremental dual path network (IDPN), to forecast energy production in an anaerobic reactor for the next two days. The hybrid model’s performance was compared against five DL models conceived: long short-term memory (LSTMs), multi-layer perception (MLP), gated recurrent units (GRUs), Transformers and convolutional neural networks (CNNs). Furthermore, two data processing strategies were applied due to system failures and missing values. Four model evaluation metrics and the obtained results show that the hybrid model, which combines LSTMs and CNNs, presented the best performance in both approaches of data processing, with the best candidate model presenting a mean absolute error (MAE) of 312.1 kWh, root mean squared error (RMSE) of 341.6 kWh, mean absolute percentage error (MAPE) of 15.9% and R $$^{2}$$ 2 of 0.95. Following this, an ablation study was conducted, demonstrating that across several variations, the baseline IDPN consistently achieved the best results. Moreover, in both approaches, the removal or modification of the CNN led to a severe decline in performance, surpassing the impact of altering the LSTM, reinforcing its importance in the model’s architecture.