Nested resolution mesh-graph CNN for automated extraction of liver surface anatomical landmarks.

Spatial-frequency domain aggregation upsampling for pan-sharpening.

Leaf Disease Detection based on Deep Learning Methods

Ranking Earthquake Prediction Algorithms: A Comprehensive Review of Machine Learning and Deep Learning Methods

NN-PCP: Screening phenotype-related core pathways to construct a prostate cancer metastasis prediction model based on multiple types of mutation data.

Physics-informed deep learning method for surface roughness prediction in milling process

A semi-supervised learning-based framework for quantifying litter fluxes in river systems.

Non-iterative and uncertainty-aware MRI-based liver fat estimation using an unsupervised deep learning method.

Feature extraction and intelligent diagnosis of ECG signals based on KANs and xLSTM.

LT-CNN: an integrated deep learning method for enhancing topic recognition in digital healthcare research trend discovering

Analysing user sentiments in social media: the supremacy of deep learning methods over traditional machine learning techniques

Doppler resilient complementary sequence set design via a model driven deep learning method

Discrete iterative deep learning method for solving neutron spatiotemporal dynamics

Multimodal self-supervised retinal vessel segmentation.

Forensic gender and stature identification from footprint images using machine learning.

A hybrid deep learning method for URL spoofing in websites

A survey of machine learning and deep learning methods for vibration-based Bearing fault diagnosis: The need, challenges, and potential future research directions

Semantic segmentation of remote sensing imagery (RSI) is a fundamental task that aims at assigning a category label to each pixel. To pursue precise segmentation with one or more fine-grained categories, semantic segmentation often requires holistic segmentation of whole-scene RSI (WRI), which is normally characterized by a large size. However, conventional deep learning methods struggle to handle holistic segmentation of WRI due to the memory limitations of the graphics processing unit (GPU), thus requiring to adopt suboptimal strategies such as cropping or fusion, which result in performance degradation. Here, we introduce the Robust End-to-end semantic Segmentation architecture for whole-scene remoTe sensing imagery (REST). REST is the first intrinsically endtoend framework for truly holistic segmentation of WRI, supporting a wide range of encoders and decoders in a plugandplay fashion. It enables seamless integration with mainstream semantic segmentation methods, and even more advanced foundation models. Specifically, we propose a novel spatial parallel interaction mechanism (SPIM) within REST to overcome GPU memory constraints and achieve global context awareness. Unlike traditional parallel methods, SPIM enables REST to process a WRI effectively and efficiently by combining parallel computation with a divideandconquer strategy. Both theoretical analysis and experiments demonstrate that REST attains nearlinear throughput scalability as additional GPUs are employed. Extensive experiments demonstrate that REST consistently outperforms existing cropping-based and fusion-based methods across a variety of scenarios, ranging from single-class to multi-class segmentation, from multispectral to hyperspectral imagery, and from satellite to drone platforms. The robustness and versatility of REST are expected to offer a promising solution for the holistic segmentation of WRI, with the potential for further extension to large-size medical imagery segmentation.

Unsupervised multimodal surface registration with geometric deep learning.

Predicting vessel Estimated Time of Arrival (ETA) with accuracy and consistency is integral to Intelligent Transportation Systems (ITS), enabling reduced delays, enhanced operational efficiency, and more sustainable maritime logistics. Existing ETA prediction models largely rely on Automatic Identification System (AIS) data but often overlook additional factors. This study introduces a deep learning-based Multi-Model learning approach that fuses multi-attribute data from multiple sources to enhance ETA prediction accuracy. The model integrates Convolutional Neural Networks (CNNs) to extract spatial features, Long Short-Term Memory (LSTM) networks to model sequential dependencies, Transformer-based attention mechanisms to dynamically weigh environmental factors, and a Multi-Layer Perceptron (MLP) for incorporating vessel-specific and other residual features. The approach is evaluated on a large-scale dataset from the Weser River, an inland waterway with multiple locks, and benchmarked against traditional and deep learning methods, including K-Nearest Neighbors (KNN), XGBoost, MLP, and LSTM. Results show the Multi-Model achieves a Mean Absolute Error (MAE) of 39.37 minutes and a Root Mean Square Error (RMSE) of 69.58 minutes, representing an 81.50% improvement over the baseline. Analysis indicates that upstream movements and shorter segments yield lower errors. The findings highlight the importance of integrating spatial, temporal, and environmental factors for reliable ETA prediction.