Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Database. The caloric content of foods was estimated using the nutritional composition (i.e.

The Impacts of Bio-Based and Synthetic Hydrogels on Soil Hydraulic Properties: A Review.

Soil hydraulic properties are important for the movement and distribution of water in agricultural soils. The ability of plants to easily extract water from soil can be limited by the texture and structure of the soil, and types of soil amendments applied to the soil. Superabsorbent polymers (hydrogels) have been researched as potential soil amendments that could help improve soil hydraulic properties and make water more available to crops, especially in their critical growing stages.

Word2vec neural model-based technique to generate protein vectors for combating COVID-19: a machine learning approach.

The world was ambushed in 2019 by the COVID-19 virus which affected the health, economy, and lifestyle of individuals worldwide. One way of combating such a public health concern is by using appropriate, rapid, and unbiased diagnostic tools for quick detection of infected people. However, a current dearth of bioinformatics tools necessitates modeling studies to help diagnose COVID-19 cases.