AIMC Topic: Coloring Agents

Clear Filters Showing 1 to 10 of 98 articles

Subtype classification of gastric spindle cell tumors in whole slide images.

Computers in biology and medicine
AIMS: Accurate cancer subtype classification is critical due to variations in tumor progression and prognosis. Traditionally, pathologists classified subtypes manually by examining pathological slides under the microscope. To address increasing workl...

AI-driven neural time series network forecasting and cost analysis for dye removal prediction in packed bed adsorption using ultrasonic biomass composites for sustainable wastewater management.

Environmental research
The study investigates the application of Artificial Intelligence (AI) driven neural network time series (NNTS) model for the forecasting prediction of dye removal using ultrasonic activated mixed biomass. Surface and functional characterization of u...

Efficacious paper-based colorimetric detection of bacterial contamination in vegetables utilizing indicator dyes and machine learning.

Food chemistry
Food contamination from bacteria and resulting spoilage has been a persistent problem in the supply chain, leading to substantial waste and financial loss. Likewise, vegetables are prone to microbial contamination due to poor/unhygienic agricultural ...

A Machine Learning-Based Modeling Approach for Dye Removal Using Modified Natural Adsorbents.

Journal of chemical information and modeling
This study used machine learning models to investigate the potential of biosorbents derived from natural fruit seed waste (apricot, almond, and walnut) for removing a cationic dye. Levulinic acid (LA)-modified powders of almond shell (ASh), apricot k...

Novel PVDF mixed matrix membranes incorporated with green synthesized magnesium oxide nanoparticles for enhanced dye removal: Optimization using RSM, SOLVER, and ANN approach.

Environmental research
The application of nanofiltration membrane technology for removing pollutant dyes from industrial wastewater represents a significant advance in environmental remediation. This research explores the development and performance evaluation of a novel P...

DFT and machine learning integration to predict efficiency of modified metal-free dyes in DSSCs.

Journal of molecular graphics & modelling
Power conversion efficiency (PCE) prediction in dye-sensitized solar cells (DSSCs) increasingly relies on computation and machine learning, lowering experimental demands and accelerating materials discovery. In this work we incorporated quantum-chemi...

Machine learning-assisted prediction of engineered carbon systems' capacity to treat textile dyeing wastewater via adsorption technology.

Environmental monitoring and assessment
Dyes are widely used in industries like printing, cosmetics, paper, leather processing, textiles, and manufacturing to add color to products. However, improper disposal of dyes into wastewater has raised major concerns due to their harmful effects on...

An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.

Scientific reports
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inferen...

Deep eutectic solvent-modified polyvinyl alcohol/chitosan thin film membrane for dye adsorption: Machine learning modeling, experimental, and density functional theory calculations.

International journal of biological macromolecules
The polyvinyl alcohol/chitosan (PVA/CS) thin film membrane was modified using a deep eutectic solvent (DES) to enhance its adsorption capability and mechanical strength for the removal of brilliant green (BG) dye. Batch adsorption experiments, machin...

Development of Deep Learning-Based Virtual Lugol Chromoendoscopy for Superficial Esophageal Squamous Cell Carcinoma.

Journal of gastroenterology and hepatology
BACKGROUND: Lugol chromoendoscopy has been shown to increase the sensitivity of detection of esophageal squamous cell carcinoma (ESCC). We aimed to develop a deep learning-based virtual lugol chromoendoscopy (V-LCE) method.