AIMC Topic: Deep Learning

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Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak.

Journal of environmental management
Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the ...

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

Acta radiologica (Stockholm, Sweden : 1987)
This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of phot...

Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques.

Movement disorders : official journal of the Movement Disorder Society
BACKGROUND: Tourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are ...

ProtEC: A Transformer Based Deep Learning System for Accurate Annotation of Enzyme Commission Numbers.

IEEE/ACM transactions on computational biology and bioinformatics
The advancements in next-generation sequencing technologies have given rise to large-scale, open-source protein databases consisting of hundreds of millions of sequences. However, to make these sequences useful in biomedical applications, they need t...

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs.

IEEE/ACM transactions on computational biology and bioinformatics
Predicting drug side effects before they occur is a critical task for keeping the number of drug-related hospitalizations low and for improving drug discovery processes. Automatic predictors of side-effects generally are not able to process the struc...

TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurat...

Characterization of lamb shashliks with different roasting methods by intelligent sensory technologies and GC-MS to simulate human muti-sensation: Based on multimodal deep learning.

Food chemistry
To simulate the functions of olfaction, gustation, vision, and oral touch, intelligent sensory technologies have been developed. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic noses (E-noses...

Automatic liver segmentation and assessment of liver fibrosis using deep learning with MR T1-weighted images in rats.

Magnetic resonance imaging
OBJECTIVES: To validate the performance of nnU-Net in segmentation and CNN in classification for liver fibrosis using T1-weighted images.

Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach.

The Science of the total environment
Recent advances in remote sensing techniques provide a new horizon for monitoring the spatiotemporal variations of harmful algal blooms (HABs) using hyperspectral data in inland water. In this study, a hierarchical concatenated variational autoencode...

Identification of pollution source and prediction of water quality based on deep learning techniques.

Journal of contaminant hydrology
Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely inform...