lstm

KNOU-Flood extends the open AIFL recipe for global streamflow forecasting by adding lead-aware inputs, GraphCast forcing, longer input windows, and a probabilistic latent-noise ensemble, achieving significant KGE′ and CRPS gains over 2,099 test basins and showing much of the reported lead-time decay is an input-protocol effect.

A time-series conditioned diffusion framework for MIMO channel estimation that uses an LSTM to encode temporal dynamics and an SNR-gated late-fusion shortcut, with SNR-adaptive DDIM denoising for low-latency inference in 5G/6G systems.

This paper presents the first neural benchmark for Sinhala Sandhi splitting using character-level seq2seq models on the SandhiLex dataset, with a bidirectional LSTM encoder reaching 68.40% exact-match accuracy on hard lexicalized/derivational Sandhi versus 94% on regular affixational cases.

A matched-protocol comparison of synchronous ring gossip, FedAvg, local and centralized training for LSTM failure detectors on the NASA C-MAPSS turbofan benchmark, showing gossip as a practical serverless alternative to federated averaging that matches FedAvg performance without a coordinator.

This paper combines transfer learning with conformalized quantile regression to improve solar PV forecasting accuracy and uncertainty quantification under data scarcity caused by load-shedding in Bangladesh, demonstrating significant performance gains over baseline methods.

This paper proposes a method using Gaussian Process decorrelation to remove spatial correlations before training an LSTM for predicting snow water equivalent, improving predictive accuracy and incorporating conformal prediction for uncertainty quantification.

ResNLS is a hybrid neural network model combining ResNet and LSTM that improves stock price forecasting by emphasizing dependencies between stock prices, achieving at least 20% improvement over baselines and demonstrating practical trading applications.

A comparative study of temporal deep learning architectures for physiological emotion recognition using multimodal wearable datasets, evaluating LSTM, TCN, and Transformer models under different sensing configurations.

The paper introduces a Hegselmann-Krause opinion dynamics approach for coalition formation in federated learning to address data heterogeneity in IoT systems, achieving significant error reduction in water consumption forecasting.

A Unified Particle Filter LSTM framework is proposed for data-driven process simulation, maintaining weighted recurrent-state hypotheses to handle partial observations in event logs, and outperforming baselines in reproducing routing and duration behaviors.

This preprint compares XGBoost and LSTM for forecasting transmitted heat energy in District Heating Systems, finding that XGBoost consistently outperforms LSTM while offering lower computational cost and environmental impact.

This paper proposes a multi-representation deep learning framework combining CNNs, LSTMs, and recurrence plot analysis to characterize alpha and gamma EEG biomarkers in Fragile X Syndrome, showing improved classification over single-modality baselines.

The paper proposes MambaLSTM, a framework combining Mamba state-space models and LSTM for spatio-temporal traffic accident risk prediction, addressing noise in feature fusion and global spatial correlation.

This paper proposes a dual-domain fused LSTM (DDF-LSTM) model for time-dependent reliability analysis that integrates time-independent random variables and stochastic processes to efficiently estimate failure probabilities via Monte Carlo simulation.

This paper proposes PTTSD, a probabilistic framework for depression severity detection from clinical interview transcripts that models uncertainty and provides temporal interpretability, achieving competitive performance on benchmark datasets.

This paper presents a comparative study of LSTM and traditional models for sentiment analysis of public opinion.

This paper proposes INTENT, an LSTM framework for predicting vehicle intentions (straight, left turn, right turn) at intersections 2 seconds ahead, achieving 99.71% accuracy on the InD dataset.

This paper evaluates the effect of frame sampling rate on sequence-based classification of autism-related self-stimulatory hand behaviors using LSTM and GRU models, achieving up to 98.75% accuracy at a 15-frame interval, and analyzes data augmentation strategies for small behavioral video datasets.

Proposes an uncertainty-aware reinforcement learning framework for algorithmic trading that integrates distributional, epistemic, and aleatoric uncertainty using SHAP-weighted reconstruction, MC Dropout, and LSTM consensus. Outperforms traditional models on five major US stock indices.

This paper proposes a data-driven surrogate modeling framework using a hybrid Graph Neural Network-Long Short-Term Memory architecture to predict the static response of additively manufactured short-fiber thermoplastics, achieving high accuracy (R²≈0.98) and two orders of magnitude speedup over finite element simulations.