AIMC Topic: Algorithms

Clear Filters Showing 3641 to 3650 of 28713 articles

Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.

PloS one
CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure...

The artificial intelligence-based agricultural field irrigation warning system using GA-BP neural network under smart agriculture.

PloS one
This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural...

Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning.

PloS one
Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are ...

Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack.

PloS one
In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and netw...

Advanced deep learning algorithms in oral cancer detection: Techniques and applications.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis
As the 16 most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of...

An efficient framework based on local multi-representatives and noise-robust synthetic example generation for self-labeled semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
While self-labeled methods can exploit unlabeled and labeled instances to train classifiers, they are also restricted by the labeled instance number and distribution. SEG-SSC, k-means-SSC, LC-SSC, and LCSEG-SSC are sophisticated solutions for overcom...

Simplified self-supervised learning for hybrid propagation graph-based recommendation.

Neural networks : the official journal of the International Neural Network Society
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenge...

Physics-informed Neural Implicit Flow neural network for parametric PDEs.

Neural networks : the official journal of the International Neural Network Society
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric...

GAN-based data reconstruction attacks in split learning.

Neural networks : the official journal of the International Neural Network Society
Due to the distinctive distributed privacy-preserving architecture, split learning has found widespread application in scenarios where computational resources on the client side are limited. Unlike clients in federated learning retaining the whole mo...

DVPT: Dynamic Visual Prompt Tuning of large pre-trained models for medical image analysis.

Neural networks : the official journal of the International Neural Network Society
Pre-training and fine-tuning have become popular due to the rich representations embedded in large pre-trained models, which can be leveraged for downstream medical tasks. However, existing methods typically either fine-tune all parameters or only ta...