Pain Management

Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks

Automation in agriculture plays a vital role in addressing challenges related to crop monitoring and disease management, particularly through early detection systems. This study investigates the effectiveness of combining multimodal Large Language Models (LLMs), specifically GPT-4o, with Convolutional Neural Networks (CNNs) for automated plant disease classification using leaf imagery. Leveragin...

Learning to fuse: dynamic integration of multi-source data for accurate battery lifespan prediction

Accurate prediction of lithium-ion battery lifespan is vital for ensuring operational reliability and reducing maintenance costs in applications like electric vehicles and smart grids. This study presents a hybrid learning framework for precise battery lifespan prediction, integrating dynamic multi-source data fusion with a stacked ensemble (SE) modeling approach. By leveraging heterogeneous dat...

Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency

Recent advancements in Large Vision-Language Models (LVLMs) have significantly enhanced their ability to integrate visual and linguistic information...

Deciphering the unique dynamic activation pathway in a G protein-coupled receptor enables unveiling biased signaling and identifying cryptic allosteric sites in conformational intermediates

Neurotensin receptor 1 (NTSR1), a member of the Class A G protein-coupled receptor superfamily, plays an important role in modulating dopaminergic n...

A Machine Learning-Based Risk Assessment Model for Poor Postoperative Pain Outcome.

Postoperative pain is a relevant and unresolved problem in clinical practice. In order to reduce the occurrence of severe postoperative pain, preventi...

Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction

Accurately forecasting sea ice concentration (SIC) in the Arctic is critical to global ecosystem health and navigation safety. However, current meth...

Cyberoception: Finding a Painlessly-Measurable New Sense in the Cyberworld Towards Emotion-Awareness in Computing

In Affective computing, recognizing users' emotions accurately is the basis of affective human-computer interaction. Understanding users' interocept...

Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record

Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains...

Adaptation Method for Misinformation Identification

Multimodal fake news detection plays a crucial role in combating online misinformation. Unfortunately, effective detection methods rely on annotated...

A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement

Accurate retinal vessel segmentation provides essential structural information for ophthalmic image analysis. However, existing methods struggle wit...

Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis

Multimodal Aspect-Based Sentiment Analysis (MABSA) seeks to extract fine-grained information from image-text pairs to identify aspect terms and dete...

Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification

Background: Deep learning has significantly advanced medical image analysis, with Vision Transformers (ViTs) offering a powerful alternative to conv...

Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution

Convolutional neural networks and Transformer have made significant progresses in multi-modality medical image super-resolution. However, these meth...

DTFSal: Audio-Visual Dynamic Token Fusion for Video Saliency Prediction

Audio-visual saliency prediction aims to mimic human visual attention by identifying salient regions in videos through the integration of both visua...

Mavors: Multi-granularity Video Representation for Multimodal Large Language Model

Long-context video understanding in multimodal large language models (MLLMs) faces a critical challenge: balancing computational efficiency with the...

GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals

Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropa...

Slow Thinking for Sequential Recommendation

To develop effective sequential recommender systems, numerous methods have been proposed to model historical user behaviors. Despite the effectivene...

Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit

Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health. Due to a variety of reaso...

HGMamba: Enhancing 3D Human Pose Estimation with a HyperGCN-Mamba Network

3D human pose lifting is a promising research area that leverages estimated and ground-truth 2D human pose data for training. While existing approac...

Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities

Widespread stigma, both in the offline and online spaces, acts as a barrier to harm reduction efforts in the context of opioid use disorder (OUD). T...