AIMC Topic: Deep Learning

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Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.

Journal of environmental management
The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting process...

Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics.

Theranostics
: Multimodal imaging provides important pharmacokinetic and dosimetry information during nanomedicine development and optimization. However, accurate quantitation is time-consuming, resource intensive, and requires anatomical expertise. : We present ...

Deep learning applications in protein crystallography.

Acta crystallographica. Section A, Foundations and advances
Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This f...

Deep learning to overcome Zernike phase-contrast nanoCT artifacts for automated micro-nano porosity segmentation in bone.

Journal of synchrotron radiation
Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacuna...

Predicting opinion using deep learning: From burning to sustainable management of organic waste in Indian State of Punjab.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
In winter season, the burning of crop residues for ease of sowing the next crop, along with industrial emissions and vehicular pollution leads to settling of a thick layer of smog in northern part of India. Therefore, to understand the opinion of far...

DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score.

Journal of structural biology
Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality ...

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant.

Water research
Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making supp...

The adsorption and release mechanism of different aged microplastics toward Hg(II) via batch experiment and the deep learning method.

Chemosphere
Aged microplastics are ubiquitous in the aquatic environment, which inevitably accumulate metals, and then alter their migration. Whereas, the synergistic behavior and effect of microplastics and Hg(II) were rarely reported. In this context, the adso...

Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study.

Journal of dentistry
OBJECTIVES: This study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dent...

Harnessing deep learning for enhanced ligand docking.

Trends in pharmacological sciences
Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how d...