Search Results (7,021)

Real-time seismic reservoir monitoring of geologic reservoirs requires both large-scale data management and efficient computational workflows. Addressing these challenges is facilitated by developing techniques capable of selectively capturing critical geologic features, thereby increasing computational efficiency and reducing data storage requirements. This paper proposes a double-compression framework integrating Haar wavelet transforms with machine learning (ML) for efficient multiparameter seismic inversion. First, Haar wavelet compression significantly reduces the dimensionality of the input elastic models, preserving essential geologic structures while limiting data volumes. Next, a convolutional neural network with the long short-term memory (CNN-LSTM) architecture, including dual encoders and multi-decoders, compresses seismic data into a latent space to generate a multi-scale P-wave velocity estimate. By leveraging transfer learning to speed up convergence and enhance prediction accuracy, we fine-tune the latent representation to estimate the P-to-S-wave velocity ratio and acoustic impedance at multiple resolution scales. Tests on the synthetic ${\mathrm{CO}}_2$-injection Kimberlina model show that wavelet-based compression—including detuning large-scale trends—minimizes artifacts in simulated wavefields and accelerates neural-network training. The results demonstrate that combining wavelet-based pre-compression for reservoir models with data-driven latent encodings for seismic data achieves high compression ratios, reduces computational costs, and maintains the fidelity of subsurface imaging. Compared with a redundant-decimation baseline, the proposed framework reduces network training time by approximately 70% and GPU memory usage by 33–73%, achieves a wavefield energy loss below 0.1% at a 16:1 model-dimension reduction, and produces multi-resolution predictions of $V_{\mathrm{P}}$, $V_{\mathrm{P}}/V_{\mathrm{S}}$, and acoustic impedance with normalized errors below 0.04 across all six wavelet decomposition levels. Thus, the double-compression framework enables robust and scalable seismic monitoring of elastic reservoir parameters. Full article

Fatigue driving causes 20–30% of global traffic accidents. To address limitations in feature fusion and real-time performance, this study proposes an improved You Only Look Once version 8 (YOLOv8)-Long Short-Term Memory (LSTM) model with symmetry-aware spatiotemporal feature learning. In the spatial phase, Group Shuffle Convolution (GSConv) and Slim Neck structures are introduced to enhance facial feature detection while reducing parameters by 32.3%. In the temporal phase, an improved Inverted Transformer(iTransformer) with differential attention is integrated with an LSTM-Feed-Forward Network (FFN) architecture, achieving a 90.1% prediction accuracy and an 84.6% noise suppression rate. A standardized dataset of 13,200 images was constructed using a four-level classification system. By implementing TensorRT acceleration and multi-process parallel frameworks, the system optimizes single-frame latency to 38 ms—a 9.5× efficiency gain—while maintaining an overall detection accuracy of 92.4%. These results demonstrate that the proposed framework effectively balances model lightweighting with high precision, providing a robust and efficient solution for real-time driver monitoring in complex driving scenarios. Full article

With the continuous advancement of industrial intelligence, the application of hydraulic robots is becoming increasingly widespread, and the demand for their health diagnosis and maintenance is becoming more urgent. By integrating digital twin (DT) and deep learning technologies, this paper presents an intelligent fault diagnosis method for hydraulic robots based on multi-source information fusion. Firstly, a fault diagnosis architecture and solution for hydraulic robots based on DT technology are proposed. Secondly, a DT model of the hydraulic robot, which incorporates a 3D model and an attribute model with virtual–physical synchronization capabilities, is established, and a calibration method for the twin model is explored. Next, for four typical faults—leakage in the hydraulic system, valve sticking, damping hole blockage, and filter blockage—fault mechanism analysis and evolution process simulation are conducted on the established DT model. A multi-source high-quality dataset, covering normal operating conditions and multiple fault scenarios, is constructed to drive the data twin model. Finally, a feature extraction method combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanisms is proposed. This is followed by using a Random Forest (RF) classifier to achieve accurate fault diagnosis for various hydraulic system failures. The experimental results validate the effectiveness and practicality of this method. Full article

To ensure a reliable electrical isolation point in power systems, the seismic performance assessment of disconnect switches is of critical importance for maintaining operational continuity under earthquake excitations. In this study, a hybrid method combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network is proposed for the seismic intelligent classification and response prediction of disconnect switches. Unlike conventional approaches that rely on finite element simulations or shake table tests with high computational costs, the proposed method learns directly from raw ground motion records. The CNN component is designed to capture local frequency characteristics of input ground motions, enabling automatic classification into low-, medium-, or high-frequency categories. Subsequently, category-specific LSTM models are established to map the ground motion time series to multi-dimensional performance indicators of the disconnect switch. These indicators include top absolute accelerations, bottom shear forces, and relative deformations of porcelain posts. A training set comprising 102 ground motion records is constructed based on numerical simulations of a validated simplified model, while another testing set comparing 21 ground motion records are employed to validate the performance of predicted models. Training and validation results demonstrate that the CNN achieves a great classification accuracy. The LSTM predictions show good agreement with the computed time-history responses, with errors of root-mean-square responses generally within 10%. The proposed method provides a rapid, data-driven alternative to traditional seismic analysis, significantly reducing computational time while preserving prediction fidelity. It also enables the parallel prediction of multiple coupled performance indicators, which is not readily achievable by existing single-output surrogate models. Full article

The transition to decentralized, clean energy systems is essential for sustainable development, particularly in rural South African communities where grid extension costs can exceed R300,000 per km. This paper presents a comprehensive analysis of Artificial Intelligence (AI) integration into hybrid solar-battery systems to address challenges of intermittency, load variability, and unreliable demand. We propose a model incorporating Long Short-Term Memory (LSTM) networks for energy forecasting and Reinforcement Learning (RL) for real-time optimization. Mathematical formulations for photovoltaic (PV) generation, battery state-of-charge dynamics, and a multi-objective cost function minimizing Levelized Cost of Energy (LCOE), carbon emissions, and reliability loss are derived with appropriate citations. A fairness metric is introduced as an operational constraint to mitigate algorithmic bias in energy allocation. Simulation results, calibrated with South African data, demonstrate a 20% improvement in forecasting accuracy (RMSE), a 30% reduction in diesel generator use, and a decrease in LCOE from R7.80 to R5.50/kWh. Furthermore, our fairness-constrained optimization reduced the Gini coefficient for load shedding from 0.38 to 0.19, ensuring more equitable access across households. This study concludes that AI-driven microgrids are technically viable, environmentally beneficial, and ethically sound for advancing equitable rural electrification in South Africa. Full article

Traffic flow forecasting remains challenging because raw traffic flow observations often contain mixed temporal patterns, including slowly varying trends and fast local fluctuations. To address this issue, this paper proposes a Multivariate Empirical Mode Decomposition (MEMD)-guided dual-branch recurrent framework for multistep point forecasting. Specifically, MEMD is used as an alignment-preserving multivariate decomposition mechanism to obtain frequency-aligned components, which are then reconstructed into low-frequency trend and high-frequency residual components. The trend component is modeled by a Long Short-Term Memory (LSTM) branch to capture smooth long-term evolution, while the residual component is learned by a Bidirectional Gated Recurrent Unit (Bi-GRU) branch to characterize short-term oscillatory dynamics. A lightweight fusion head is then used to integrate the two branch-specific representations for final prediction. Experiments on PeMS04 and PeMS08, two traffic datasets derived from the California Department of Transportation Performance Measurement System, show that the proposed method achieves competitive performance across mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), reaching 19.67/31.59/12.95% on PeMS04 and 15.51/24.43/9.86% on PeMS08. Compared with representative recent baselines, the proposed method achieves competitive results, with relative gains reaching 5.89% on PeMS04 and 5.35% on PeMS08 in selected metric-wise comparisons. These results indicate that MEMD-guided trend–residual representation learning can improve multistep traffic flow forecasting. Full article

Climate variability, environmental uncertainty, and carbon-market dynamics increasingly challenge agricultural investment and resource allocation decisions worldwide. This study proposes an integrated hybrid decision-support framework combining Long Short-Term Memory (LSTM) deep learning, Interval Type-2 Fuzzy Logic Systems, and Genetic Algorithms to support climate-resilient agricultural investment analysis under uncertainty. The framework integrates predictive modeling, uncertainty representation, and multi-objective optimization within a unified computational architecture. The empirical analysis was conducted using agricultural, climate, and carbon-market datasets covering Europe, Asia, and Africa over the 2010–2025 period. Agricultural productivity indicators, commodity price variables, climate-risk parameters, and carbon-market data were integrated into the modeling process. LSTM models were employed to analyze temporal agricultural and climate-related dynamics, while Interval Type-2 fuzzy systems were used to represent ambiguity associated with environmental and market uncertainty. Genetic Algorithms were subsequently applied to optimize investment allocation under conflicting objectives related to profitability, sustainability, and risk. The findings suggest that the proposed hybrid framework may improve adaptive investment evaluation and optimization performance under uncertain climate conditions relative to standalone computational approaches within the scope of the analyzed datasets. The results further highlight the importance of integrating predictive analytics, uncertainty modeling, and sustainability-oriented optimization within agricultural decision-support systems. However, the framework should be interpreted as a climate-resilient decision-support architecture rather than a universally deterministic forecasting mechanism. Overall, the study contributes to the emerging literature on agricultural sustainability and climate-resilient investment by presenting a transparent and uncertainty-aware computational framework under evolving environmental and carbon-market conditions. Full article

Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings. Full article

Due to low storage cost and fast query efficiency, cross-modal hashing has attracted considerable interest in multi-modal data retrieval. However, existing hashing methods face several challenges: one major challenge arises from the neglect of both local and non-local neighborhood structural relationships within multi-modal information, which makes it difficult to establish fine-grained semantic consistency associations between heterogeneous modalities. Additionally, the imbalance in the number of training samples limits the improvement of retrieval performance. To address these challenges, a Deep Neighborhood-similarity Preservation Hashing (DNsPH) method is proposed for cross-modal retrieval. To obtain the high-order statistical features of images, we first design a Context-aware Cross-layer Bilinear Fusion Network (C2BF-Net), which uses Long Short-Term Memory (LSTM) to model the context-dependent features of different convolutional layers. Furthermore, the image, text, and semantic labels information are fused through an adaptive weighting strategy to reconstruct the joint semantic similarity matrix to explore the fine-grained neighborhood structure between different modalities. Finally, we introduce a multi-similarity loss based on an adaptive margin to mining and weighting informative sample pairs, to alleviate the impact of sample imbalance on model training, and thereby generate more discriminative hash codes. Extensive experiments performed on the MIRFLICKR-25K and NUS-WIDE datasets demonstrate that DNsPH outperforms state-of-the-art cross-modal retrieval applications. Full article

This paper presents two forecast-driven energy trading methodologies for a grid-connected photovoltaic-battery system participating in the day-ahead electricity market. Both methodologies use bidirectional long short-term memory neural networks with attention to forecast electricity prices, but they differ in the way the resulting forecasts are converted into operational decisions. The first method uses 24- to 48 h-ahead price forecasts within a mixed-integer linear programming rolling-horizon optimizer to compute the revenue-maximizing schedule for the following day. The second method uses an online twin delayed deep deterministic policy gradient controller that outputs a complete 24 h charge–discharge schedule once per day, using state information that includes battery state, recent price history, forecast prices, and forecast photovoltaic production. The control models are trained using historical data from 2019 to 2022, validated chronologically on 2023 data, and tested on the 2024 annual horizon, while the price forecaster is trained and validated on non-2024 data and evaluated on the held-out 2024 test period. In the realistic execution setting, schedules are planned using forecast photovoltaic production and implemented against actual photovoltaic production, while the day-ahead omniscience benchmark uses actual next-day prices and actual PV production as ideal scheduling inputs. The BiLSTM-MILP framework achieves EUR 10,928.7 over the 2024 test horizon, corresponding to 82.67% of the day-ahead omniscience benchmark. The online BiLSTM-TD3 controller achieves EUR 10,884.9, corresponding to 82.34% of the same benchmark and 99.60% of the BiLSTM-MILP revenue, while outperforming a rule-based baseline by 34.9%. These results show that online deep reinforcement learning can approach the performance of explicit mathematical optimization in day-ahead PV-battery trading while substantially improving over simple rule-based operation. Overall, the results indicate that BiLSTM-based forecasts can support both optimization-based and reinforcement-learning-based day-ahead control for the examined PV-battery system. Full article

Smart microgrids depend on continuous communication between controllers, sensors, and actuators over industrial protocols like Modbus TCP, message queuing telemetry transport (MQTT), and distributed network protocol 3 (DNP3), which were designed without built-in security mechanisms. The gateway that aggregates this traffic represents a single point of failure and is vulnerable to distributed denial-of-service (DDoS) attacks. Most existing detection methods require labeled attack data for training, a condition rarely met in operational technology (OT) environments. This paper presents an unsupervised convolutional neural network–long short-term memory (CNN-LSTM) model trained exclusively on normal microgrid gateway traffic to predict the next traffic window; anomalies are flagged when the prediction error exceeds a threshold derived from the training distribution. A dual-branch architecture processes metric time-series through LSTM layers and flow aggregate features through CNN layers, fusing both representations for prediction. The model is evaluated against three protocol-specific DDoS attack scenarios—Modbus supervisory control and data acquisition (SCADA) flooding, MQTT publish storm, and DNP3 response flooding—none of which are seen during training. Compared against an isolation forest baseline and an autoencoder baseline under identical unsupervised conditions, the CNN-LSTM achieves higher precision and recall on all attack types. The framework is deployed within a web-based monitoring platform that supports real-time detection and anomaly logging. Full article

Phishing and malicious websites are a serious threat on the internet. Attackers use fake links to trick users and steal their private information. Detecting these links is difficult because attackers change their tricks often. Many old methods cannot detect new or hidden threats. Some recent models use deep learning (DL), but they are large, slow, and hard to use in real-time systems. In this paper, we present a lightweight and accurate model called MiniLM-CNNLSTM. It combines a small transformer model (MiniLM) with a hybrid DL network using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers. The transformer learns the meaning of URLs. The CNN finds important patterns. The LSTM captures the order of characters. We also add handcrafted features that help the model detect tricky URLs. We test our method on two public datasets: the Phishing Site URLs dataset and the Malicious URLs dataset from Kaggle. We use 3-fold cross-validation and early stopping to ensure fair and stable results. The MiniLM-CNN-LSTM model outperformed previous benchmarks by achieving an average three-fold cross-validation accuracy of 98.98%, a precision of 98.63%, a recall of 98.29%, an F1-score of 98.46%, and a false positive rate of 0.68%. The proposed model has a higher accuracy, precision, recall, F1-score and a lower false positive rate, which enhances the accuracy by 1.88, precision by 3.77, recall by 4.17 and decreases the false positive rate by 61.58% compared with the strongest baseline (Distil BERT + CNN-LSTM), showing significant practical improvements. The results show that our approach is fast, small, and highly effective. It can detect phishing and malicious links with high accuracy. This makes it a good choice for real-time security systems like browsers, email filters, or firewalls. Full article

This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forecasting frameworks that integrate three signal decomposition techniques—Wavelet Decomposition (WD), Variational Mode Decomposition (VMD), and Empirical Mode Decomposition (EMD)—with a Long Short-Term Memory network enhanced by an attention mechanism (LSTM–Attention). The decomposition methods are first applied to extract multiple frequency components from the original time series, allowing the forecasting model to capture both short-term fluctuations and long-term dynamics inherent in this specific digital asset. Each decomposed component is then modeled using the LSTM–Attention architecture, and the forecasts are aggregated to produce the final prediction. The predictive performance of the proposed models is evaluated using MAE, MSE, RMSE, and MAPE, and the results are compared with benchmark models including ARIMA-GARCH and standard LSTM–Attention. Forecast accuracy is assessed through out-of-sample one-step-ahead predictions, and robustness is ensured by averaging results across 10 independent runs. The empirical results demonstrate that incorporating decomposition techniques substantially improves forecasting accuracy. Among the tested models, the EMD–LSTM–Attention framework achieves the best performance, producing the lowest forecasting errors. While focused on the Ethereum market, these findings highlight the effectiveness of combining signal decomposition and attention-based deep learning architectures to enhance predictive performance in high-volatility cryptocurrency environments. Full article

Aspect-Based Sentiment Analysis explores sentiment trends related to specific opinion aspects and holds significant commercial potential for monitoring brand reputation, understanding customer satisfaction, and personalizing recommendations. However, traditional methods rely exclusively on textual input and often struggle when the target aspect is not mentioned in the sentence. Multimodal Aspect-Based Sentiment Analysis addresses this limitation by incorporating both textual and visual modalities to enable more comprehensive sentiment understanding. Despite advancements in deep learning and transformer-based architectures, existing models often suffer from suboptimal modality fusion and weak aspect grounding, limiting their classification accuracy. To overcome these challenges, we propose an Attention-Driven Feature Fusion (ADFF) approach based on a three-stage hierarchical attention mechanism. First, it only fuses text and image embeddings. Second, it incorporates aspect-level features. Third, a multi-head attention layer further enhances cross-modal dependencies. The resulting representation is passed to a Long Short-Term Memory (LSTM) classifier for sentiment polarity prediction. We evaluate our model on three benchmark datasets, namely Twitter-2015, Twitter-2017, and MASAD. The experimental results demonstrate that the proposed model substantially outperforms state-of-the-art multimodal and unimodal baselines, improves both accuracy and F1-score, achieving 82.55% accuracy and 81.05% F1-score on Twitter-2015, 77.07% accuracy and 77.15% F1-score on Twitter-2017, and up to 99.67% accuracy and F1-score in the Plant domain of MASAD, where we observe consistent improvements across all seven domains. These results highlight the effectiveness and scalability of the hierarchical attention-based fusion strategy for real-world aspect-based sentiment analysis tasks. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article