Search Results (54)

Background: Electroencephalography (EEG) signals play a crucial role in diagnosing epilepsy by reflecting distinct patterns associated with normal brain activity, ictal (seizure) states, and interictal (between-seizure) periods. However, the manual classification of these patterns is labor-intensive, time-consuming, and depends heavily on specialized expertise. While deep learning methods have shown promise, many current models suffer from limitations such as excessive complexity, high computational demands, and insufficient generalizability. Developing lightweight and accurate models for real-time epilepsy detection remains a key challenge. Methods: This study proposes a novel dual-channel deep learning model to classify epileptic EEG signals into three categories: normal, ictal, and interictal states. Channel 1 integrates a bidirectional long short-term memory (BiLSTM) network with a Squeeze-and-Excitation (SE) ResNet attention module to dynamically emphasize critical feature channels. Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. The model’s performance was evaluated on the publicly available Bonn EEG dataset. Results: The proposed model achieved an outstanding accuracy of 98.57%. The dual-channel structure improved specificity to 99.43%, while the dual-branch CNN boosted sensitivity by 5.12%. Components such as SE-ResNet attention modules contributed 4.29% to the accuracy improvement, and BiLSTM further enhanced specificity by 1.62%. Ablation studies validated the significance of each module. Conclusions: By leveraging a lightweight design and attention-based mechanisms, the dual-channel model offers high diagnostic precision while maintaining computational efficiency. Its applicability to real-time automated diagnosis positions it as a promising tool for clinical deployment across diverse patient populations. Full article

This study investigates the global asymptotic stability of hybrid bidirectional associative memory (BAM) complex-valued neural networks (CVNNs) with time-varying delays and uncertain parameters, where the system matrices are assumed to be symmetric. By constructing an appropriate Lyapunov–Krasovskii functional (LKF), new sufficient conditions are derived to guarantee the existence and uniqueness of equilibrium points, as well as to establish the global asymptotic stability of the proposed symmetric hybrid BAM CVNNs. The validity and effectiveness of the theoretical results are further demonstrated through detailed numerical examples. Full article

This paper addresses the semi-global polynomial synchronization (SGPS) problem for a class of high-order bidirectional associative memory neural networks (HOBAMNNs) with multiple proportional delays. The time-delay-dependent semi-global polynomial stability criterion for error systems was established via a direct approach. The derived stability conditions are formulated as several simple inequalities that are readily solvable, facilitating direct verification using standard computational tools (e.g., YALMIP). Notably, this method can be applied to many system models with proportional delays after minor modifications. Finally, a numerical example is provided to validate the effectiveness of the theoretical results. Full article

This study investigates extremal solutions for fractional-order delayed difference equations, utilizing the Caputo nabla operator to establish mild lower and upper approximations via discrete fractional calculus. A new approach is employed to demonstrate the uniform convergence of the sequences of lower and upper approximations within the monotone iterative scheme using the summation representation of the solutions, which serves as a discrete analogue to Volterra integral equations. This research highlights practical applications through numerical simulations in discrete bidirectional associative memory neural networks. Full article

With the rapid development of new energy vehicle technologies, higher demands have been placed on fault diagnosis for automotive transmission gearboxes. To address the poor adaptability of traditional methods under complex operating conditions, this paper proposes a sensor data-driven fault diagnosis method based on improved ensemble empirical mode decomposition (EEMD) combined with convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The method incorporates a dynamic noise adjustment mechanism, allowing the noise amplitude to adapt to the characteristics of the signal. This improves the stability and accuracy of signal decomposition, effectively reducing the instability and error accumulation associated with fixed-amplitude white noise in traditional EEMD. By combining the CNN and BiLSTM modules, the approach achieves efficient feature extraction and dynamic modeling. First, vibration signals of the transmission gearbox under different operating states are collected via sensors, and an improved EEMD method is employed to decompose the signals, removing background noise and nonstationary components to extract diagnostically significant intrinsic mode functions (IMFs). Then, the CNN is utilized to extract features from the IMFs, deeply mining their spatiotemporal characteristics, while the BiLSTM captures the temporal sequence dependencies of the signals, enhancing the comprehensive modeling of nonlinear and dynamic fault features. The combination of these two networks enables efficient adaptation to complex conditions, achieving accurate classification and identification of multiple gearbox fault modes. Results indicate that the proposed approach is highly accurate and robust for identifying gearbox fault modes, significantly exceeding the performance of conventional methods and isolated network models. This provides an efficient and intelligent solution for fault diagnosis of automotive transmission gearboxes. Full article

The condition monitoring of mooring equipment is an important engineering reliability issue during the operation of a floating production storage and offloading unit (FPSO). The chain jack (CJ) is the key equipment for powering the mooring chain in a spread mooring system. Under complex and dynamic marine operating conditions, different severity faults in the CJ hydraulic system display distinct time-scale characteristics. Hence, this paper proposes a real-time fault diagnosis method of the CJ hydraulic system based on multi-scale feature fusion. Firstly, the model incorporates a convolutional neural network (CNN) layer to extract localized spatial features from multivariate time-series data, effectively identifying fault patterns over the associated short intervals. Subsequently, the bidirectional long short-term memory (BiLSTM) layer is introduced to construct a dynamic temporal model to comprehensively capture the evolution of the fault severity. Finally, a multi-scale global attention mechanism (GAM) emphasizes persistent fault behaviors across time scales, dynamically prioritizing relevant features to improve diagnostic accuracy and model interpretability. The study results indicate that the proposed model’s accuracy improves by 7.36% over the CNN-GAM for 11 failure modes, up to 99.34%. This study contributes to the safe operation of an FPSO by guiding monitoring CJ operations under different load conditions. Full article

Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter optimization, using data from Open-Seneca sensors along the Nairobi Expressway, combined with meteorological and traffic data. First, analysis showed that the PBT-tuned ResNet34 was the most effective model, achieving a precision (0.988), recall (0.971), F1-Score (0.979), Matthews Correlation Coefficient (MCC) of 0.904, Geometric Mean (G-Mean) of 0.962, and Balanced Accuracy (BA) of 0.962, outperforming alternative models, including ResNet18, ResNet34, and baseline approaches such as Feedforward Neural Networks (FNN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Gene Expression Programming (GEP). Subsequent feature importance analysis using a permutation-based strategy, along with SHAP analysis, revealed that humidity and hourly traffic volume were the most influential features. The findings indicated that medium to high humidity values were associated with an increased likelihood of HE, while medium to high traffic volumes similarly contributed to the occurrence of HE. Full article

In this paper, we focus on h-manifolds related to impulsive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays. By constructing a new Lyapunov-type function and a comparison principle, sufficient conditions that guarantee the global practical exponential stability of specific states are established. The states of interest are determined by the so-called h-manifolds, i.e., manifolds defined by a specific function h, which is essential for various applied problems in imposing constraints on their dynamics. The established criteria are less restrictive for the variable domain and diffusion coefficients. The effect of some uncertain parameters on the stability behavior is also considered and a robust practical stability analysis is proposed. In addition, the obtained h-manifolds’ practical stability results are applied to a bidirectional associative memory (BAM) neural network model with impulsive perturbations and time-varying delays. Appropriate examples are discussed. Full article

This study delves into the synchronization issues of the impulsive fractional-order, mainly the Caputo derivative of the order between 0 and 1, bidirectional associative memory (BAM) neural networks incorporating the diffusion term at a fixed time (FXT) and a predefined time (PDT). Initially, this study presents certain characteristics of fractional-order calculus and several lemmas pertaining to the stability of general impulsive nonlinear systems, specifically focusing on FXT and PDT stability. Subsequently, we utilize a novel controller and Lyapunov functions to establish new sufficient criteria for achieving FXT and PDT synchronizations. Finally, a numerical simulation is presented to ascertain the theoretical dependency. Full article

The present research investigates the global asymptotic stability of bidirectional associative memory (BAM) neural networks using distinct sufficient conditions. The primary objective of this study is to establish new generalized criteria for the global asymptotic robust stability of time-delayed BAM neural networks at the equilibrium point, utilizing the Frobenius norm and the positive symmetrical approach. The new sufficient conditions are derived with the help of the Lyapunov–Krasovskii functional and the Frobenius norm, which are important in deep learning for a variety of reasons. The derived conditions are not influenced by the system parameter delays of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed conclusions regarding network parameters. Full article

Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaints remains a difficult problem. Current multi-label classification methods applied to complaint texts have not been able to fully utilize complaint information, and little research has been performed on complaint classification in the aviation industry. Therefore, to solve the problems of insufficient text feature extraction and the insufficient learning of inter-feature relationships, we constructed a multi-label classification model (MAG, or multi-feature attention gradient boosting decision tree classifier) for civil aviation service quality complaint texts. This model incorporates multiple features and attention mechanisms to improve the classification accuracy. First, the BERT (Bidirectional Encoder Representations from Transformers) model and attention mechanisms are used to represent the semantic and label features of the text. Then, the Text-CNN (a convolutional neural network) and BiLSTM (bidirectional long short-term memory) multi-channel feature extraction networks are used to extract the local and global features of the complaint text, respectively. Subsequently, a co-attention mechanism is used to learn the relationship between the local and global features. Finally, the travelers’ complaint texts are accurately classified by integrating the base classifiers. The results show that our proposed model improves the multi-label classification accuracy, outperforming other modern algorithms. We demonstrate how the label feature representation based on association rules and the multi-channel feature extraction network can enrich textual information and more fully extract features. Overall, the co-attention mechanism can effectively learn the relationships between text features, thereby improving the classification accuracy of the model and enabling better identification of travelers’ complaints. This study not only effectively extracted text features by integrating multiple features and attention mechanisms, but also constructed a targeted feature word set for complaint texts based on the domain-specific characteristics of the civil aviation industry. Furthermore, by iterating the basic classifier using a multi-label classification model, a classifier with higher accuracy was successfully obtained, providing strong technical support and new practical paths for improving the civil aviation service quality and complaint management. Full article

Accurate electricity consumption forecasting is essential for power scheduling. In short-term forecasting, electricity consumption data exhibit periodic patterns, as well as fluctuations associated with production events. Traditional forecasting methods typically focus on sequential features of the data, which may lead to an over-smoothing issue for the fluctuations. In practice, the fluctuations of electricity consumption associated with these events tend to follow recognizable patterns. By emphasizing the impact of these experiential electricity consumption fluctuations on the current prediction process, we can capture the volatility variations to alleviate the over-smoothing problem. To this end, we propose an encoding decomposition-based multi-scale graph neural network (CMNN). The CMNN starts by decomposing the electricity data into various components. For the high-order components that exhibit approximate periodic behavior, the CMNN designs a Multi-scale Bi-directional Long Short-Term Memory (MBLSTM) network for fitting and prediction. For the low-order components that exhibit fluctuations, the CMNN transforms these components from one-dimensional time series into a two-dimensional low-order component graph to model the volatility of the low-order components, and proposes a Gaussian Graph Auto-Encoder to forecast the low-order components. Finally, the CMNN combines the predicted components to produce the final electricity consumption prediction. Experiments demonstrate that the CMNN enhances the accuracy of electricity consumption predictions. Full article

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis. Full article

A four-dimensional (4D) trajectory is a multi-dimensional time series that embodies rich spatiotemporal features. However, its high complexity and inherent uncertainty pose significant challenges for accurate prediction. In this paper, we present a novel 4D trajectory prediction model that integrates convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and multi-head attention mechanisms. This model effectively addresses the characteristics of aircraft flight trajectories and the difficulties associated with simultaneously extracting spatiotemporal features using existing prediction methods. Specifically, we leverage the local feature extraction capabilities of CNNs to extract key spatial and temporal features from the original trajectory data, such as geometric shape information and dynamic change patterns. The BiLSTM network is employed to consider both forward and backward temporal orders in the trajectory data, allowing for a more comprehensive capture of long-term dependencies. Furthermore, we introduce a multi-head attention mechanism that enhances the model’s ability to accurately identify key information in the trajectory data while minimizing the interference of redundant information. We validated our approach through experiments conducted on a real ADS-B trajectory dataset. The experimental results demonstrate that the proposed method significantly outperforms comparative approaches in terms of trajectory estimation accuracy. Full article

The Internet’s development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public’s demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian–Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network—Bidirectional Long Short-Term Memory (CNN-BiLSTM) model’s hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters. Full article