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In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.

To address the challenges of sample utilization efficiency and managing temporal dependencies, this paper proposes an efficient path planning method for mobile robot in dynamic environments based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed method, named PL-TD3, integrates prioritized experience replay (PER) and long short-term memory (LSTM) neural networks, which enhance both sample efficiency and the ability to handle time-series data. To verify the effectiveness of the proposed method, simulation and practical experiments were designed and conducted. In the simulation experiments, both static and dynamic obstacles were included in the test environment, along with experiments to assess generalization capabilities. The algorithm demonstrated superior performance in terms of both execution time and path efficiency. The practical experiments, based on the assumptions from the simulation tests, further confirmed that PL-TD3 has improved the effectiveness and robustness of path planning for mobile robot in dynamic environments.

Extreme water levels (EWLs) resulting from cyclones pose significant flood hazards and risks to coastal communities and interconnected ecosystems. To date, physically based models have enabled accurate prediction of EWLs despite their inherent high computational cost. However, the applicability of these models is limited to data‐rich sites with diverse characteristics. The dependence on high quality spatiotemporal data, which is often computationally expensive, hinders the applicability of these models to regions of either limited or data‐scarce conditions. To address this challenge, we present a Long Short‐Term Memory (LSTM) network framework to predict the evolution of EWLs beyond site‐specific training stations. The framework, named LSTM‐Station Approximated Models (LSTM‐SAM), consists of a collection of bidirectional LSTM models enhanced with a custom attention mechanism layer embedded in the architecture. LSTM‐SAM incorporates a transfer learning approach applicable to target (tide‐gage) stations along the U.S. Atlantic Coast. Importantly, LSTM‐SAM helps analyze: (a) the underlying limitations associated with transfer learning, (b) evaluate EWL predictions beyond training domains, and (c) capture the evolution of EWL caused by tropical and extratropical cyclones. The framework demonstrates satisfactory performance with “transferable” models achieving Kling‐Gupta Efficiency (KGE), Nash‐Sutcliffe Efficiency (NSE), and Root‐Mean Square Error (RMSE) ranging from 0.78 to 0.92, 0.90 to 0.97, and 0.09–0.18 m at the target stations, respectively. We show that LSTM‐SAM can accurately predict not only EWLs but also their evolution over time, that is, onset, peak, and dissipation, which could assist in operational flood forecasting in regions with limited resources to set up physically based models. Plain Language Summary Water levels in rivers, estuaries, and bays rise significantly during hurricanes, leading to severe flood risks and hazards in low‐lying areas and interconnected ecosystems. With climate change increasing the frequency of extreme events, it has become crucial to develop models that can accurately simulate extreme water levels in a short time frame and support emergency management for future events. Conventional modeling approaches that help us predict extreme water levels rely on either physically based or data‐driven models. Unlike state‐of‐the‐art data‐driven models such as deep learning, the former models are site‐specific and cannot be applied or transferred to other regions. In this study, we propose a framework that leverages a model trained on extreme water levels from one region to accurately predict those of neighboring regions through a technique known as “transfer learning”. We address the limitations associated with this technique, including the inability of transferable models to accurately generalize new input data from those neighboring regions and examine how changes in model parameters influence the development of efficient transferable models. We show that these models can effectively capture the magnitude and timing of extreme water levels, making this framework suitable for early and operational warning systems. Key Points We present a deep learning framework that accurately predicts the evolution of cyclone‐induced water levels across multiple domains An attention mechanism enhances the framework's recognition of extreme water level patterns within and beyond training locations It effectively identifies unseen water level patterns, different from those in training; thus enhancing model's transfer learning capability

INTRODUCTION Leveraging routinely collected electronic health records (EHRs) from multiple health‐care institutions, this approach aims to assess the feasibility of using federated learning (FL) to predict the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS We analyzed EHR data from the OneFlorida+ consortium, simulating six sites, and used a long short‐term memory (LSTM) model with a federated averaging (FedAvg) algorithm. A personalized FL approach was used to address between‐site heterogeneity. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and feature importance techniques. RESULTS Of 44,899 MCI patients, 6391 progressed to AD. FL models achieved a 6% improvement in AUC compared to local models. Key predictive features included body mass index, vitamin B12, blood pressure, and others. DISCUSSION FL showed promise in predicting AD progression by integrating heterogeneous data across multiple institutions while preserving privacy. Despite limitations, it offers potential for future clinical applications. Highlights We applied long short‐term memory and federated learning (FL) to predict mild cognitive impairment to Alzheimer's disease progression using electronic health record data from multiple institutions. FL improved prediction performance, with a 6% increase in area under the receiver operating characteristic curve compared to local models. We identified key predictive features, such as body mass index, vitamin B12, and blood pressure. FL shows effectiveness in handling data heterogeneity across multiple sites while ensuring data privacy. Personalized and pooled FL models generally performed better than global and local models.

Personalized blood glucose (BG) prediction in Type 1 Diabetes (T1D) is challenged by significant inter-patient heterogeneity. To address this, we propose BiT-MAML, a hybrid model combining a Bidirectional LSTM-Transformer with Model-Agnostic Meta-Learning. We evaluated our model using a rigorous Leave-One-Patient-Out Cross-Validation (LOPO-CV) on the OhioT1DM dataset, ensuring a fair comparison against re-implemented LSTM and Edge-LSTM baselines. The results show our model achieved a mean RMSE of 24.89 mg/dL for the 30 min prediction horizon, marking a substantial improvement of 19.3% over the standard LSTM and 14.2% over the Edge-LSTM. Notably, our model also achieved the lowest standard deviation (±4.60 mg/dL), indicating more consistent and generalizable performance across the patient cohort. A key finding of our study is the confirmation of significant performance variability across individuals, a known clinical challenge. This was evident as our model’s 30 min RMSE ranged from an excellent 19.64 mg/dL to a more challenging 30.57 mg/dL, reflecting the inherent difficulty of personalizing predictions rather than model instability. From a clinical safety perspective, Clarke Error Grid Analysis confirmed the model’s robustness, with over 92% of predictions falling within the clinically acceptable Zones A and B. This study concludes that the development of effective personalized BG prediction requires not only advanced model architectures but also robust evaluation methods that transparently report the full spectrum of performance, providing a realistic pathway toward reliable clinical tools.

The accuracy of peak streamflow simulation is often lower than that of normal streamflow simulation, posing a significant challenge. This study introduces stochastic resonance (SR) to enhance simulation accuracy, utilizing its ability to leverage noise energy to improve correlations between streamflow and meteorological factors. The proposed SR‐LSTM model, validated across major Chinese basins, demonstrates that SR effectively enhances the accuracy of streamflow simulations. By using SR, the Nash‐Sutcliffe efficiency increased from 0.70 to 0.79, and the kling‐gupta efficiency improved from 0.69 to 0.82. Furthermore, this study utilizes the global Caravan streamflow data set (including CAMELES, CAMELESBR, CAMELESAUS, and LamaH) comprising 1,244 station data points to validate the applicability of SR‐LSTM. Results indicate that SR improves accuracy at approximately 70% of 1,244 stations, particularly in regions with high‐quality data. Comparative analysis shows that incorporating SR enhances the performance of deep learning models, highlighting its potential for improving both global and peak streamflow simulation accuracy. These findings underscore the effectiveness of SR in enhancing streamflow simulation accuracy. Key Points This study introduces stochastic resonance (SR) to enhance hydrological signal features and improve streamflow simulation accuracy By integrating SR with the long short‐term memory (LSTM) neural network, a novel streamflow simulation model, SR‐LSTM, is proposed The SR‐LSTM model has strong capability in capturing peak flow and has better simulation accuracy compared to other models

Absolute dynamic topography (ADT) obtained from satellite altimeter data mapping is widely used in marine environment monitoring and research. Traditional numerical ADT prediction models exhibit high computational demands and low operational efficiency. This study proposes a deep learning framework integrating U‐Net architecture with a self‐attention convolutional long short‐term memory network (SA‐ConvLSTM) to develop a high‐precision ADT forecasting model for the South China Sea. The approach utilizes 0.08° high resolution multi‐source satellite data. Training optimization incorporating teacher forcing and scheduled sampling enhanced model capability in representing complex ocean dynamics. The SA‐ConvLSTM is shown to outperform the traditional ConvLSTM model and several existing models in terms of both forecast accuracy and computational efficiency. This framework is demonstrated significant potential for high‐resolution marine forecasting and disaster early warning systems, offering an efficient alternative to traditional numerical models for regional ocean dynamic monitoring.

Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues. The human motion intention prediction mechanism plays an essential role in various applications, such as assistive and rehabilitation robots, that execute specific tasks among elders and physically impaired individuals. However, more complications are increased in the human–machine-based interaction techniques, creating more scope for personalized assistance for the human motion intention prediction system. Therefore, in this paper, an Adaptive Hybrid Network (AHN) is implemented for effective human motion intention prediction. Initially, multimodal data like electroencephalogram (EEG)/Electromyography (EMG) signals and sensor measures data are collected from the available data resource. The gathered EEG/EMG signals are then converted into spectrogram images and sent to AH-CNN-LSTM, which is the integration of an Adaptive Hybrid Convolution Neural Network (AH-CNN) with a Long Short-Term Memory (LSTM) network. Similarly, the data details of sensor measures are directly subjected to AH-CNN-Res-LSTM, which is the combination of Adaptive Hybrid CNN with Residual Network and LSTM (Res-LSTM) to get the predictive result. Further, to enhance the prediction, the parameters in both the AH-CNN-LSTM and AH-CNN-Res-LSTM techniques are optimized using the Improved Yellow Saddle Goatfish Algorithm (IYSGA). The efficiency of the implemented model is computed by conducting the comparison experiment of the proposed technique with other standard models. The performance outcome of the developed method outperformed the other traditional methods.

Sarcasm is a form of irony that expresses negative opinions. Sarcasm poses a linguistic problem owing to its symbolic nature, where deliberate meaning challenges correct understanding. Sarcasm is more common on social media and in day-to-day life. Sarcasm detection in written text is a challenge that has attracted the attention of several researchers. Therefore, sarcasm is an essential process in natural language processing (NLP). This study discusses the ideas of sarcasm and its significance in present sarcasm research. The automated model of sarcasm recognition includes selecting appropriate approaches, selecting a dataset, and preprocessing steps involving Transformer architectures, a rule-based approach, deep learning (DL), and machine learning (ML) models. This manuscript proposes a Sarcasm Classification and Detection using NLP on Social Media Platforms (SDCNLP-SM) technique. The objective of the SDCNLP-SM technique is to effectively and automatically recognise sarcastic text. To accomplish this, the SDCNLP-SM technique performs data preprocessing and a Word2Vec-based word-embedding step. Finally, the Self-Attention with Bidirectional Long Short-Term Memory (SA-BLSTM) model is employed for sarcasm classification. The comparison analysis of the SDCNLP-SM model showed a superior accuracy of 94.45% compared to existing models on the headline dataset.

Wildfires not only severely damage the natural environment and global ecological balance but also cause substantial losses to global forest resources and human lives and property. Unprecedented fire events such as Australia's bushfires have alerted us to the fact that wildfire prediction is a critical scientific problem for fire management. Therefore, robust, long-lead models and dynamic predictions of wildfire are valuable for global fire prevention. However, despite decades of effort, the dynamic, effective, and accurate prediction of wildfire remains problematic. There is great uncertainty in predicting the future based on historical and existing spatiotemporal sequence data, but with advances in deep learning algorithms, solutions to prediction problems are being developed. Here, we present a dynamic prediction model of global burned area of wildfire employing a deep neural network (DNN) approach that produces effective wildfire forecasts based on historical time series predictors and satellite-based burned area products. A hybrid DNN that combines long short-term memory and a two-dimensional convolutional neural network (CNN2D-LSTM) was proposed, and CNN2D-LSTM model candidates with four different architectures were designed and compared to construct the optimal architecture for fire prediction. The proposed model was also shown to outperform convolutional neural networks (CNNs) and the fully connected long short-term memory (FcLSTM) approach using the refined index of agreement and evaluation metrics. We produced monthly global burned area spatiotemporal prediction maps and adequately reflected the seasonal peak in fire activity and highly fire-prone areas. Our combined CNN2D-LSTM approach can effectively predict the global burned area of wildfires 1 month in advance and can be generalized to provide seasonal estimates of global fire risk.