AIMC Topic: Neural Networks, Computer

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High-throughput, rapid, and non-destructive detection of common foodborne pathogens via hyperspectral imaging coupled with deep neural networks and support vector machines.

Food research international (Ottawa, Ont.)
Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspec...

CLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction.

Aquatic toxicology (Amsterdam, Netherlands)
As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of di...

MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia.

Brain research bulletin
The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. In t...

Applying MLP-Mixer and gMLP to Human Activity Recognition.

Sensors (Basel, Switzerland)
The development of deep learning has led to the proposal of various models for human activity recognition (HAR). Convolutional neural networks (CNNs), initially proposed for computer vision tasks, are examples of models applied to sensor data. Recent...

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.

Sensors (Basel, Switzerland)
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visi...

SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence.

Scientific reports
Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and fu...

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

Scientific reports
In the present scenario, cancerous tumours are common in humans due to major changes in nearby environments. Skin cancer is a considerable disease detected among people. This cancer is the uncontrolled evolution of atypical skin cells. It occurs when...

Spatio-temporal analysis of litterfall load in the lower reaches of Qarqan and Tarim rivers using BP neural networks.

Scientific reports
Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Li...

Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning.

Scientific reports
BACKGROUND AND OBJECTIVES: For the planning of surgical procedures involving the bony reconstruction of the mandible, the autologous iliac crest graft, along with the fibula graft, has become established as a preferred donor region. While computer-as...

Liver fibrosis stage classification in stacked microvascular images based on deep learning.

BMC medical imaging
BACKGROUND: Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fib...