The damage caused by floods is increasing worldwide, and if floods can be predicted, the economic and human losses from floods can be reduced. A key parameter of flooding is water level data, and this paper proposes a water level prediction model using long short-term memory (LSTM) and a gated recurrent unit (GRU). As variables used as input data, meteorological data, including upstream and downstream water level, temperature, humidity, and precipitation, were used. The best results were obtained when the LSTM–GRU-based model and the Automated Synoptic Observing System (ASOS) meteorological data were included in the input data when experiments were performed with various model structures and different input data formats. As a result of the experiment, the mean squared error (MSE) value was 3.92, the Nash–Sutcliffe coefficient of efficiency (NSE) value was 0.942, and the mean absolute error (MAE) value was 2.22, the highest result in all cases. In addition, the test data included the historical maximum water level of 3552.38 cm in the study area, and the maximum water level error was also recorded as 55.49, the lowest result. Through this paper, it was possible to confirm the performance difference according to the composition of the input data and the time series prediction model. In a future study, we plan to implement a flood risk management system that can use the predicted water level to determine the risk of flooding, and evacuate in advance.

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts.

Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. Firstly, the dataset is preprocessed, and an XGBoost cardiovascular disease prediction model is constructed for model training and compare it with other algorithms. Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. Finally, the improved particle swarm optimization algorithm is used to adjust the hyperparameters of the extreme gradient boosting tree algorithm, and selecting the optimal hyperparameter combination to construct the MFS-DLPSO-XGBoost model. The recall, precision, accuracy, F1 score, and area under the ROC curve (AUC) of the MFS-DLPSO-XGBoost model reached 71.4%, 76.3%, 74.7%, 73.6%, and 80.8%, respectively, which increased by 3.6%, 3.2%, 2.7%, 3.2%, and 2.3% compared to XGBoost. The results indicate that the model proposed in this article has good classification performance and can provide assistance for doctors and patients in predicting and preventing heart disease.

The optimization and control of traffic signals is very important for logistics transportation. It not only improves the operational efficiency and safety of road traffic, but also conforms to the direction of the intelligent, green, and sustainable development of modern cities. In order to improve the optimization effect of traffic signal control, this paper proposes a traffic signal optimization method based on deep reinforcement learning and Simulation of Urban Mobility (SUMO) software for urban traffic scenarios. The intersection training scenario was established using SUMO micro traffic simulation software, and the maximum vehicle queue length and vehicle queue time were selected as performance evaluation indicators. In order to be more relevant to the real environment, the experiment uses Weibull distribution to simulate vehicle generation. Since deep reinforcement learning takes into account both perceptual and decision-making capabilities, this study proposes a traffic signal optimization control model based on the deep reinforcement learning Deep Q Network (DQN) algorithm by considering the realism and complexity of traffic intersections, and first uses the DQN algorithm to train the model in a training scenario. After that, the G-DQN (Grouping-DQN) algorithm is proposed to address the problems that the definition of states in existing studies cannot accurately represent the traffic states and the slow convergence of neural networks. Finally, the performance of the G-DQN algorithm model was compared with the original DQN algorithm model and Advantage Actor-Critic (A2C) algorithm model. The experimental results show that the improved algorithm increased the main indicators in all aspects.

The rapid evolution of cyber threats poses significant challenges to the adaptability and performance of anomaly detection systems. This study presents an innovative hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models with a novel self-learning mechanism to enhance network traffic anomaly detection. Our key contributions include: (1) a synergistic two-stage model fusion architecture that captures both spatial and temporal traffic patterns; (2) an adaptive learning mechanism with multi-metric drift detection that autonomously responds to evolving threats; and (3) a knowledge preservation strategy that maintains detection capabilities while adapting to new attack patterns. The proposed CNN-LSTM model achieves F1-scores of 0.9778 and 0.9695 on the UNSW-NB15 and CICIDS2017 datasets respectively for binary classification of normal vs. anomalous traffic. The LSTM-Transformer model further classifies specific anomaly types with accuracies of 0.9632 and 0.9528 on these datasets, representing significant improvements over recent methods. Experiments demonstrate the framework’s robustness, maintaining an average accuracy of 0.955 ( σ ≈ 0.005) over a 15-day simulated period with multiple induced concept drifts. The self-learning mechanism, with multi-metric drift detection and an efficient model update strategy, enables the system to detect drifts and recover performance within 23.4 ± 0.20 hours post-drift, while achieving a 92.8% detection rate for zero-day attacks. The proposed framework offers a promising direction for developing efficient and autonomous cybersecurity systems capable of handling dynamic and evolving threat landscapes.

Network Intrusion Detection System (NIDS) detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments. To improve the detection capability of minority-class attacks... | Find, read and cite all the research you need on Tech Science Press