AIMC Topic: Algorithms

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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa).

Environment international
Quantitative structure-activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application...

MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation.

Sensors (Basel, Switzerland)
Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. Transformer...

Improved facial emotion recognition model based on a novel deep convolutional structure.

Scientific reports
Facial Emotion Recognition (FER) is a very challenging task due to the varying nature of facial expressions, occlusions, illumination, pose variations, cultural and gender differences, and many other aspects that cause a drastic degradation in qualit...

Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation.

Scientific reports
Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve patient prognosis and inform treatment decisions. This development in the form of an early detection ...

IHGNN: Iterative Interpretable HyperGraph Neural Network for semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
Learning on hypergraphs has garnered significant attention recently due to their ability to effectively represent complex higher-order interactions among multiple entities compared to conventional graphs. Nevertheless, the majority of existing method...

Graph Batch Coarsening framework for scalable graph neural networks.

Neural networks : the official journal of the International Neural Network Society
Due to the neighborhood explosion phenomenon, scaling up graph neural networks to large graphs remains a huge challenge. Various sampling-based mini-batch approaches, such as node-wise, layer-wise, and subgraph sampling, have been proposed to allevia...

Deep graph clustering via aligning representation learning.

Neural networks : the official journal of the International Neural Network Society
Deep graph clustering is a fundamental yet challenging task for graph data analysis. Recent efforts have witnessed significant success in combining autoencoder and graph convolutional network to explore graph-structured data. However, we observe that...

Finite-time optimal control for MMCPS via a novel preassigned-time performance approach.

Neural networks : the official journal of the International Neural Network Society
This paper studies the finite-time optimal stabilization problem of the macro-micro composite positioning stage (MMCPS). The dynamic model of the MMCPS is established as an interconnected system according to the Newton's second law. Different from ex...

Multi-task magnetic resonance imaging reconstruction using meta-learning.

Magnetic resonance imaging
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity ...

A deep neural network improves endoscopic detection of laterally spreading tumors.

Surgical endoscopy
BACKGROUND: Colorectal cancer (CRC) is the malignant tumor of the digestive system with the highest incidence and mortality rate worldwide. Laterally spreading tumors (LSTs) of the large intestine have unique morphological characteristics, special gr...