Alternative Medicine

Explainable Artificial Intelligence techniques for interpretation of food datasets: a review

Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer science, including Food Engineering, where there is a growing demand for accurate and trustworthy predictions to meet stringent food quality standards. However, this requires increasingly complex AI models, raising reli...

Accurate Diagnosis of Respiratory Viruses Using an Explainable Machine Learning with Mid-Infrared Biomolecular Fingerprinting of Nasopharyngeal Secretions

Accurate identification of respiratory viruses (RVs) is critical for outbreak control and public health. This study presents a diagnostic system that combines Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) from nasopharyngeal secretions with an explainable Rotary Position Embedding-Sparse Attention Transformer (RoPE-SAT) model to accurately identify multiple RVs ...

Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly

Vision Language Models (VLMs) demonstrate promising chart comprehension capabilities. Yet, prior explorations of their visualization literacy have b...

Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection

Alzheimer's disease (AD) leads to progressive cognitive decline, making early detection crucial for effective intervention. While deep learning mode...

Analytical Discovery of Manifold with Machine Learning

Understanding low-dimensional structures within high-dimensional data is crucial for visualization, interpretation, and denoising in complex dataset...

New opportunities and challenges for conservation evidence synthesis from advances in natural language processing.

Addressing global environmental conservation problems requires rapidly translating natural and conservation social science evidence to policy-relevant...

Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). R...

Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification

Multi-label classification is crucial for comprehensive image understanding, yet acquiring accurate annotations is challenging and costly. To addres...

Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information

We present a novel framework for enhancing the visual fidelity and consistency of text-guided 3D Gaussian Splatting (3DGS) editing. Existing editing...

A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets

Precise elevation perception in binaural audio remains a challenge, despite extensive research on head-related transfer functions (HRTFs) and spectr...

Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size

Vector Quantization (VQ) techniques face significant challenges in codebook utilization, limiting reconstruction fidelity in image modeling. We intr...

Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification

Few-shot image classification has become a popular research topic for its wide application in real-world scenarios, however the problem of supervisi...

GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images

While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, they still face two key limitations: (1) insuffici...

MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU Patients: Development and Validation of an Acute Brain Dysfunction Prediction Model

Acute brain dysfunction (ABD) is a common, severe ICU complication, presenting as delirium or coma and leading to prolonged stays, increased mortali...

RGB-Thermal Infrared Fusion for Robust Depth Estimation in Complex Environments

Depth estimation in complex real-world scenarios is a challenging task, especially when relying solely on a single modality such as visible light or...

Through the Static: Demystifying Malware Visualization via Explainability

Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effecti...

COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation

Accurate segmentation of 3D vascular structures is essential for various medical imaging applications. The dispersed nature of vascular structures l...

Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction.

The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a bett...

MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.

DNA methylation plays a crucial role in human diseases pathogenesis. Substantial experimental evidence from clinical and biological studies has confir...

Revolutionizing Brain Tumor Detection Using Explainable AI in MRI Images.

Due to the complex structure of the brain, variations in tumor shapes and sizes, and the resemblance between tumor and healthy tissues, the reliable a...