AIMC Topic: Neural Networks, Computer

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Hybrid multimodal fusion for graph learning in disease prediction.

Methods (San Diego, Calif.)
Graph neural networks (GNNs) have gained significant attention in disease prediction where the latent embeddings of patients are modeled as nodes and the similarities among patients are represented through edges. The graph structure, which determines...

Machine learning-based life cycle assessment for environmental sustainability optimization of a food supply chain.

Integrated environmental assessment and management
Effective resource allocation in the agri-food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted ...

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects.

Computers in biology and medicine
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual d...

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks.

Environmental science & technology
The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt de...

Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.

Sensors (Basel, Switzerland)
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) ...

Composite Graph Neural Networks for Molecular Property Prediction.

International journal of molecular sciences
Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of ...

A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.

BMC bioinformatics
BACKGROUND: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechn...

Prediction of COD in industrial wastewater treatment plant using an artificial neural network.

Scientific reports
In this investigation, the modeling of the Aksaray industrial wastewater treatment plant was performed using artificial neural networks with various architectures in the MATLAB software. The dataset utilized in this study was collected from the Aksar...

Sex classification of 3D skull images using deep neural networks.

Scientific reports
Determining the fundamental characteristics that define a face as "feminine" or "masculine" has long fascinated anatomists and plastic surgeons, particularly those involved in aesthetic and gender-affirming surgery. Previous studies in this area have...

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Journal of neural engineering
Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencep...