AIMC Topic: Algorithms

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An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning.

Computational intelligence and neuroscience
Aiming at some existing issues in the sports industry, the existing model is optimized by deep learning and time series theory based on the relevant algorithm, and the scale of the sports industry is analyzed and predicted by the model. The results s...

A study on a vehicle semi-active suspension control system based on road elevation identification.

PloS one
A semi-active suspension system can effectively improve vehicle ride comfort and handling stability, and the active detection of road information is key to achieving semi-active suspension. To improve the road elevation perception ability of vehicles...

Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Molecular diversity
Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a trai...

Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review.

Advances in wound care
As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at prov...

Unsupervised learning methods for efficient geographic clustering and identification of disease disparities with applications to county-level colorectal cancer incidence in California.

Health care management science
Many public health policymaking questions involve data subsets representing application-specific attributes and geographic location. We develop and evaluate standard and tailored techniques for clustering via unsupervised learning (UL) algorithms on ...

Metamodeling for Policy Simulations with Multivariate Outcomes.

Medical decision making : an international journal of the Society for Medical Decision Making
PURPOSE: Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We devel...

Cardinality-constrained portfolio selection via two-timescale duplex neurodynamic optimization.

Neural networks : the official journal of the International Neural Network Society
This paper addresses portfolio selection based on neurodynamic optimization. The portfolio selection problem is formulated as a biconvex optimization problem with a variable weight in the Markowitz risk-return framework. In addition, the cardinality-...

A novel machine learning approach to shorten depression risk assessment for convenient uses.

Journal of affective disorders
BACKGROUND: Depression is a mental disorder affecting many people worldwide which has been exacerbated by the current pandemic. There is an urgent need for a reliable yet short scale for individuals to self-assess the risk of depression conveniently ...