AIMC Topic: Coloring Agents

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DFT-assisted machine learning for polyester membrane design in textile wastewater recovery applications.

Water research
Resource recovery from textile wastewater has attracted increasing interest because it simultaneously addresses wastewater treatment and maximizes the utilization of the residual dyes. Although polyester membranes have demonstrated great potential fo...

Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation.

Journal of environmental management
In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of...

Chitosan-based adsorbents for remediation of toxic dyes from wastewater: A review on adsorption mechanism, reusability, machine learning based modeling and future perspectives.

International journal of biological macromolecules
The disposal of recalcitrant dyes in aquatic environments from various industrial sectors is a threat to both the plant and animal kingdom. The presence of dyes in various water bodies undermines the availability of uncontaminated drinking water and ...

AUGUR-AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer.

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland
AIM: Recent randomized controlled trials and meta-analyses have demonstrated a reduction in the anastomotic leak rate when indocyanine green fluorescence angiography (ICGFA) is used versus when it is not in colorectal resections. We have previously d...

Rethinking ImageNet Pre-training for Computational Histopathology.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models on a Histopathology dataset. However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on ...

Automated deep learning in ophthalmology: AI that can build AI.

Current opinion in ophthalmology
PURPOSE OF REVIEW: The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demons...

Multistain segmentation of renal histology: first steps toward artificial intelligence-augmented digital nephropathology.

Kidney international
Artificial intelligence (AI), and particularly deep learning (DL), are showing great potential in improving pathology diagnostics in many aspects, 1 of which is the segmentation of histology into (diagnostically) relevant compartments. Although most ...