AIMC Topic: Algorithms

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Hybrid Approach Named HUAPO Technique to Guide the Lander Based on the Landing Trajectory Generation for Unmanned Lunar Mission.

Computational intelligence and neuroscience
This manuscript proposes a hybrid method for landing trajectory generation of unmanned lunar mission. The proposed hybrid control scheme is the joint execution of the human urbanization algorithm (HUA) and political optimizer (PO) with radial basis f...

The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement.

Computational and mathematical methods in medicine
Nonlinear friction could affect the high-precision motion system, resulting in poor tracking accuracy in the end. This is due to the fact that the Lugre friction model's parameter identification process comprises both static and dynamic parameter ide...

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach.

BioMed research international
There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classificatio...

A supervised deep neural network approach with standardized targets for enhanced accuracy of IVIM parameter estimation from multi-SNR images.

NMR in biomedicine
Extraction of intravoxel incoherent motion (IVIM) parameters from noisy diffusion-weighted (DW) images using a biexponential fitting model is computationally challenging, and the reliability of the estimated perfusion-related quantities represents a ...

A novel method for feature selection based on molecular interactive effect network.

Journal of pharmaceutical and biomedical analysis
Analyzing the biological data by considering the molecule interactions may induce a more accurate identification of disease-related biomarkers. In this study, a novel feature selection method based on molecule (feature) interactive effect network is ...

A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction.

Computers in biology and medicine
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging. However, the pure data-driven nature of deep learning models may limit the model generalizability and application scope. Here we establish a geometry-inf...

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications.

Environmental science & technology
Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computati...

Towards Convolutional Neural Network Acceleration and Compression Based on -Means.

Sensors (Basel, Switzerland)
Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often use...

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm.

Scanning
Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemic...

Analysis of Traditional Cultural Acceptance Based on Deep Learning.

Computational intelligence and neuroscience
Technological development has resulted in the utilisation of advanced technology search using deep learning technology in all industries. Artificial intelligence is set to be filled with machines that can perform tasks that need human intelligence. T...