Diet & Nutrition

Image registration of 2D optical thin sections in a 3D porous medium: Application to a Berea sandstone digital rock image

This study proposes a systematic image registration approach to align 2D optical thin-section images within a 3D digital rock volume. Using template image matching with differential evolution optimization, we identify the most similar 2D plane in 3D. The method is validated on a synthetic porous medium, achieving exact registration, and applied to Berea sandstone, where it achieves a structural ...

Predicting Soil Macronutrient Levels: A Machine Learning Approach Models Trained on pH, Conductivity, and Average Power of Acid-Base Solutions

Soil macronutrients, particularly potassium ions (K$^+$), are indispensable for plant health, underpinning various physiological and biological processes, and facilitating the management of both biotic and abiotic stresses. Deficient macronutrient content results in stunted growth, delayed maturation, and increased vulnerability to environmental stressors, thereby accentuating the imperative for...

Multimodal Reference Visual Grounding

Visual grounding focuses on detecting objects from images based on language expressions. Recent Large Vision-Language Models (LVLMs) have significan...

Machine Learning in Automated Food Processing: A Mini Review.

Industrial food processing is rapidly transforming into automation and digitalization. Automated food processing systems adapt to variations in raw ma...

DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting

Reconstructing sharp 3D representations from blurry multi-view images are long-standing problem in computer vision. Recent works attempt to enhance ...

Improving Diseases Predictions Utilizing External Bio-Banks

Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is o...

A Scalable Framework for Evaluating Health Language Models

Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate u...

Using Machine Learning for Lunar Mineralogy-I: Hyperspectral Imaging of Volcanic Samples

This study examines the mineral composition of volcanic samples similar to lunar materials, focusing on olivine and pyroxene. Using hyperspectral im...

Personalized Class Incremental Context-Aware Food Classification for Food Intake Monitoring Systems

Accurate food intake monitoring is crucial for maintaining a healthy diet and preventing nutrition-related diseases. With the diverse range of foods...

MarsLGPR: Mars Rover Localization with Ground Penetrating Radar

In this work, we propose the use of Ground Penetrating Radar (GPR) for rover localization on Mars. Precise pose estimation is an important task for ...

A Protocol to Exposure Path Analysis for Multiple Stressors Associated with Cardiovascular Disease Risk: A Novel Approach Using NHANES Data

Background: Multiple medical and non-medical stressors, along with the complicity of their exposure pathways, have posted significant challenges to ...

GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after meals, is a critical indicator of progression toward ...

Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks

We propose a novel Graph Neural Network-based method for segmentation based on data fusion of multimodal Scanning Electron Microscope (SEM) images. ...

Estimation of total body fat using symbolic regression and evolutionary algorithms

Body fat percentage is an increasingly popular alternative to Body Mass Index to measure overweight and obesity, offering a more accurate representa...

Image-based food groups and portion prediction by using deep learning.

Chronic diseases such as obesity and hypertension due to malnutrition can be prevented by following the appropriate diet, correct diet intake with cor...

A Pilot Study on Using an Artificial Intelligence Algorithm to Identify Urolith Composition through Abdominal Radiographs in the Dog.

In small animal practice, patients often present with urinary lithiasis, and prediction of urolith composition is essential to determine the appropria...

A Deep Learning Model for Three-Dimensional Determination of Whole Thoracic Vertebral Bone Mineral Density from Noncontrast Chest CT: The Multi-Ethnic Study of Atherosclerosis.

Background Recent studies have investigated how deep learning (DL) algorithms applied to CT using two-dimensional (2D) segmentation (sagittal or axial...

NutriGen: Personalized Meal Plan Generator Leveraging Large Language Models to Enhance Dietary and Nutritional Adherence

Maintaining a balanced diet is essential for overall health, yet many individuals struggle with meal planning due to nutritional complexity, time co...

Challenges for Predictive Modeling With Neural Network Techniques Using Error-Prone Dietary Intake Data.

Dietary intake data are routinely drawn upon to explore diet-health relationships, and inform clinical practice and public health. However, these data...

Convolutional neural networks for mineral prospecting through alteration mapping with remote sensing data

Traditional geological mapping, based on field observations and rock sample analysis, is inefficient for continuous spatial mapping of features like...