Search Results (5,961)

Land cover mapping is essential for territorial management due to its links with ecological, hydrological, climatic, and socioeconomic processes. Traditional methods use discrete classes per pixel, but this study proposes estimating cover fractions with Sentinel-2 imagery (20 m) and AI. We employed the French Land cover from Aerospace ImageRy (FLAIR) dataset (810 km² in France, 19 classes), with labels co-registered with Sentinel-2 to derive precise fractional proportions per pixel. From these references, we generated training sets combining spectral bands, derived indices, and auxiliary data (climatic and temporal variables). Various machine learning models—including XGBoost three deep neural network (DNN) architectures with different depths, and convolutional neural networks (CNNs)—were trained and evaluated to identify the optimal configuration for fractional cover estimation. Model validation on the test set employed RMSE, MAE, and R² metrics at both pixel level (20 m Sentinel-2) and scene level (100 m FLAIR). The training set integrating spectral bands, vegetation indices, and auxiliary variables yielded the best MAE and RMSE results. Among all models, DNN2 achieved the highest performance, with a pixel-level RMSE of 13.83 and MAE of 5.42, and a scene-level RMSE of 4.94 and MAE of 2.36. This fractional approach paves the way for advanced remote sensing applications, including continuous cover-change monitoring, carbon footprint estimation, and sustainability-oriented territorial planning. Full article

The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques. This paper presents an intelligent three-step procedure for optimally designing earthquake-resistant buildings based on the training (1st step) and successive inversion (2nd step) of Multi-Layer Perceptron Neural Networks. This involves solving the inverse problem of determining the optimal design parameters that meet pre-assigned, code-based performance targets, by means of a gradient-based optimization algorithm (3rd step). The effectiveness of the procedure was tested using an archetypal multistory, moment-resisting, concentrically braced steel frame with active tension diagonal bracing. The input dataset was obtained by varying four design parameters. The output dataset resulted from performance variables obtained through non-linear dynamic analyses carried out under three earthquakes consistent with the Chilean code spectrum, for all cases considered. Three spectrum-consistent records are sufficient for code-based seismic design, while each seismic excitation provides a wealth of information about the behavior of the structure, highlighting potential issues. For optimization purposes, only information relevant to critical sections was used as a performance indicator. Thus, the dataset for training consisted of pairs of design parameter sets and their corresponding performance indicator sets. A dedicated MLP was trained for each of the outputs over the entire dataset, which greatly reduced the total complexity of the problem without compromising the effectiveness of the solution. Due to the comparatively low number of cases considered, the leave-one-out method was adopted, which made the validation process more rigorous than usual since each case acted once as a validation set. The trained network was then inverted to find the input design search domain, where a cost-effective gradient-based algorithm determined the optimal design parameters. The feasibility of the solution was tested through numerical analyses, which proved the effectiveness of the proposed artificial intelligence-aided optimal seismic design procedure. Although the proposed methodology was tested on an archetypal building, the significance of the results highlights the effectiveness of the three-step procedure in solving complex optimization problems. This paves the way for its use in the design optimization of different kinds of earthquake-resistant buildings. Full article

Neural network training often suffers from spectral asymmetry, where gradient energy is disproportionately allocated to high-frequency components, leading to suboptimal convergence and reduced efficiency. This paper introduces Gradient Spectral Normalization (GSN), a novel optimization technique designed to restore spectral symmetry by dynamically reshaping gradient distributions in the frequency domain. GSN transforms gradients using FFT, applies layer-specific energy redistribution to enforce a symmetric balance between low- and high-frequency components, and reconstructs the gradients for parameter updates. By tailoring normalization schedules for attention and MLP layers, GSN enhances inference performance and improves model accuracy with minimal overhead. Our approach leverages the principle of symmetry to create more stable and efficient neural systems, offering a practical solution for resource-constrained environments. This frequency-domain paradigm, grounded in symmetry restoration, opens new directions for neural network optimization with broad implications for large-scale AI systems. Full article

Efficient routing optimization remains a pivotal challenge in Electronic Design Automation (EDA), as it profoundly influences circuit performance, power consumption, and manufacturing cost. The Rectilinear Steiner Minimum Tree (RSMT) problem plays a crucial role in this process by minimizing the routing length through the introduction of Steiner points. This paper proposes a reinforcement learning-driven RSMT construction model that incorporates a novel Selective Kernel Transformer Network (SKTNet) encoder to enhance feature representation. SKTNet integrates a Selective Kernel Convolution (SKConv) and an improved Macaron Transformer to improve multi-scale feature extraction and global topology modeling. Additionally, Self-Critical Sequence Training (SCST) is employed to optimize the policy by leveraging a greedy-decoded baseline sequence for the advantage computation. Experimental results demonstrate superior performance over state-of-the-art methods in wirelength optimization. Ablation studies further validate the contribution of this model, highlighting its effectiveness and scalability for routing. Full article

In intensive broiler production, rapid environmental fluctuations can induce heat stress, adversely affecting flock welfare and productivity. Apparent temperature (AT), integrating temperature, humidity, and wind speed, provides a comprehensive thermal index, guiding predictive climate control. This study develops a multi-step AT forecasting model based on a hybrid SE-TCN–Transformer architecture enhanced with Kalman filtering. The temporal convolutional network with SE attention extracts short-term local trends, the Transformer captures long-range dependencies, and Kalman smoothing reduces prediction noise, collectively improving robustness and accuracy. The model was trained on multi-source time-series data from a commercial broiler house and evaluated for 5, 15, and 30 min horizons against LSTM, GRU, Autoformer, and Informer benchmarks. Results indicate that the proposed model achieves substantially lower prediction errors and higher determination coefficients. By combining multi-variable feature integration, local–global temporal modeling, and dynamic smoothing, the model offers a precise and reliable tool for intelligent ventilation control and heat stress management. These findings provide both scientific insight into multi-step thermal environment prediction and practical guidance for optimizing broiler welfare and production performance. Full article

Tracing the source of air pollution presents a significant challenge, especially in densely populated urban areas, because of the unpredictable and complex nature of aerodynamics. To address this issue, intelligent lamp posts have been developed with smart sensors and edge computing capabilities. These lamp posts serve as nodes in the EIPA (Edge Intelligent Perception Agent) network within urban campuses. These lamp posts aim to track air pollutants by employing a tracking algorithm that utilizes big data learning and Gaussian diffusion models. This approach focuses on monitoring the quality of urban air and identifying pollution sources, rather than relying solely on traditional CFD simulations for air pollution dispersion. The algorithm comprises three primary components: (1) the Federated Learning framework built on the EIPA system; (2) the LSTM model implemented on the edge nodes of the EIPA system; and (3) a genetic algorithm utilized for optimizing the model parameters. By using CFD simulations in a simulated city park, training data on air dynamic movements is gathered. The usefulness of the method for tracing air pollutants based on federated learning of edge intelligent perception agents is demonstrated by the outcomes of algorithm training. Experimental results show that, compared to the traditional genetic algorithm (GA) and LSTM + genetic algorithm, the proposed FL + LSTM + GA method significantly improves the pollution source positioning accuracy to 99.5% and reduces the average absolute error (MAE) of Gaussian model parameter estimation to 0.20. Full article

With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system. Full article

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

Astrocytoma is the most common type of brain glioma and is classified by the World Health Organization into four grades, providing prognostic insights and guiding treatment decisions. The accurate determination of astrocytoma grade is critical for patient management, especially in high-malignancy-grade cases. This study proposes a fully optimized Convolutional Neural Network (CNN) for the classification of astrocytoma MRI slices across the three malignant grades (G2–4). The training dataset consisted of 1284 pre-operative axial 2D MRI slices from T1-weighted contrast-enhanced and FLAIR sequences derived from 69 patients. To provide the best possible model performance, an extensive hyperparameter tuning was carried out through the Hyperband method, a variant of Successive Halving. Training was conducted using Repeated Hold-Out Validation across four randomized data splits, achieving a mean classification accuracy of 98.05%, low loss values, and an AUC of 0.997. Comparative evaluation against state-of-the-art pre-trained models using transfer learning demonstrated superior performance. For validation purposes, the proposed CNN trained on an altered version of the training set yielded 93.34% accuracy on unmodified slices, which confirms the model’s robustness and potential use for clinical deployment. Model interpretability was ensured through the application of two Explainable AI (XAI) techniques, SHAP and LIME, which highlighted the regions of the slices contributing to the decision-making process. Full article

With the rapid evolution of artificial intelligence technologies in power systems, data-driven time-series forecasting has become instrumental in enhancing the stability and reliability of power systems, allowing operators to anticipate demand fluctuations and optimize energy distribution. Despite the notable progress made by current methods, they are still hindered by two major limitations: most existing models are relatively small in architecture, failing to fully leverage the potential of large-scale models, and they are based on fixed nonlinear mapping functions that cannot adequately capture complex patterns, leading to information loss. To this end, an LLM-Empowered Kolmogorov–Arnold frequency learning (LKFL) is proposed for time series forecasting in power systems, which consists of LLM-based prompt representation learning, KAN-based frequency representation learning, and entropy-oriented cross-modal fusion. Specifically, LKFL first transforms multivariable time-series data into text prompts and leverages a pre-trained LLM to extract semantic-rich prompt representations. It then applies Fast Fourier Transform to convert the time-series data into the frequency domain and employs Kolmogorov–Arnold networks (KAN) to capture multi-scale periodic structures and complex frequency characteristics. Finally, LKFL integrates the prompt and frequency representations through an entropy-oriented cross-modal fusion strategy, which minimizes the semantic gap between different modalities and ensures full integration of complementary information. This comprehensive approach enables LKFL to achieve superior forecasting performance in power systems. Extensive evaluations on five benchmarks verify that LKFL sets a new standard for time-series forecasting in power systems compared with baseline methods. Full article

In recent years, research in sequential recommendation has primarily refined user intent by constructing sequence-level contrastive learning tasks through data augmentation or by extracting preference information from the latent space of user behavior sequences. However, existing methods suffer from two critical limitations. Firstly, they fail to account for how random data augmentation may introduce unreasonable item associations in contrastive learning samples, thereby perturbing sequential semantic relationships. Secondly, the neglect of temporal dependencies may prevent models from effectively distinguishing between incidental behaviors and stable intentions, ultimately impairing the learning of user intent representations. To address these limitations, we propose TCLRec, a novel temporal-aware and intent contrastive learning framework for sequential recommendation, incorporating symmetry into its architecture. During the data augmentation phase, the model employs a symmetrical contrastive learning architecture and incorporates semantic enhancement operators to integrate user preferences. By introducing user rating information into both branches of the contrastive learning framework, this approach effectively enhances the semantic relevance between positive sample pairs. Furthermore, in the intent contrastive learning phase, TCLRec adaptively attenuates noise information in the frequency domain through learnable filters, while in the pre-training phase of sequence-level contrastive learning, it introduces a temporal-aware network that utilizes additional self-supervised signals to assist the model in capturing both long-term dependencies and short-term interests from user behavior sequences. The model employs a multi-task training strategy that alternately performs intent contrastive learning and sequential recommendation tasks to jointly optimize user intent representations. Comprehensive experiments conducted on the Beauty, Sports, and LastFM datasets demonstrate the soundness and effectiveness of TCLRec, where the incorporation of symmetry enhances the model’s capability to represent user intentions. Full article

We propose an edge-enhanced federated learning framework for real-time itinerary optimization in elderly oriented adventure tourism, addressing the critical need for adaptive scheduling that balances activity intensity with health constraints. The system integrates lightweight convolutional neural networks with a priority-based scheduling algorithm, processing participant profiles and real-time biometric data through a decentralized computation model to enable dynamic adjustments. A modified Hungarian algorithm incorporates physical exertion scores, temporal proximity weights, and health risk factors, then optimizes activity assignments while respecting physiological recovery requirements. The federated learning architecture operates across distributed edge nodes, preserving data privacy through localized model training and periodic global aggregation. Furthermore, the framework interfaces with transportation systems and medical monitoring infrastructure, automatically triggering itinerary modifications when vital sign anomalies exceed adaptive thresholds. Implemented on NVIDIA Jetson AGX Orin modules, the system achieves 300 ms end-to-end latency for real-time schedule updates, meeting stringent safety requirements for elderly participants. The proposed method demonstrates significant improvements over conventional itinerary planners through its edge computing efficiency and personalized adaptation capabilities, particularly in handling the latency-sensitive demands of intensive tourism scenarios. Experimental results show robust performance across diverse participant profiles and activity types, confirming the system’s practical viability for real-world deployment in elderly adventure tourism operations. Full article

Accurate brain tumor classification from MRI is often constrained by limited labeled data. We systematically compare conventional machine learning, deep learning, and few-shot learning (FSL) for four classes (glioma, meningioma, pituitary, no tumor) using a standardized pipeline. Models are trained on the Kaggle Brain Tumor MRI Dataset and evaluated across dataset regimes (100%→10%). We further test generalization on BraTS and quantify robustness to resolution changes, acquisition noise, and modality shift (T1→FLAIR). To support clinical trust, we add visual explanations (Grad-CAM/saliency) and report per-class results (confusion matrices). A fairness-aligned protocol (shared splits, optimizer, early stopping) and a complexity analysis (parameters/FLOPs) enable balanced comparison. With full data, Convolutional Neural Networks (CNNs)/Residual Networks (ResNets) perform strongly but degrade with 10% data; Model-Agnostic Meta-Learning (MAML) retains competitive performance (AUC-ROC ≥ 0.9595 at 10%). Under cross-dataset validation (BraTS), FSL—particularly MAML—shows smaller performance drops than CNN/ResNet. Variability tests reveal FSL’s relative robustness to down-resolution and noise, although modality shift remains challenging for all models. Interpretability maps confirm correct activations on tumor regions in true positives and explain systematic errors (e.g., “no tumor”→pituitary). Conclusion: FSL provides accurate, data-efficient, and comparatively robust tumor classification under distribution shift. The added per-class analysis, interpretability, and complexity metrics strengthen clinical relevance and transparency. Full article