AIMC Topic: Algorithms

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A deep-learning framework for multi-level peptide-protein interaction prediction.

Nature communications
Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide...

Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection.

IEEE transactions on cybernetics
Band selection has been widely utilized in hyperspectral image (HSI) classification to reduce the dimensionality of HSIs. Recently, deep-learning-based band selection has become of great interest. However, existing deep-learning-based methods usually...

Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis.

Computational intelligence and neuroscience
Incidence rate of mental illness is increasing year by year with the development of city. The amount of modern medical data is huge and complex. In many cases, it is difficult to realize the rational allocation of resources, which puts forward an urg...

An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.

Nutrients
Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making ...

ECG data dependency for atrial fibrillation detection based on residual networks.

Scientific reports
Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with l...

Vision-Based Intelligent Perceiving and Planning System of a 7-DoF Collaborative Robot.

Computational intelligence and neuroscience
In this paper, an intelligent perceiving and planning system based on deep learning is proposed for a collaborative robot consisting of a 7-DoF (7-degree-of-freedom) manipulator, a three-finger robot hand, and a vision system, known as IPPS (intellig...

Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the rel...

Estimating PM concentration using the machine learning GA-SVM method to improve the land use regression model in Shaanxi, China.

Ecotoxicology and environmental safety
With rapid economic growth, urbanization and industrialization, fine particulate matter with aerodynamic diameters ≤ 2.5 µm (PM) has become a major pollutant and shows adverse effects on both human health and the atmospheric environment. Many studies...

Artificial intelligence for solid tumour diagnosis in digital pathology.

British journal of pharmacology
Tumour diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunit...

Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

Medical physics
BACKGROUND: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showi...