AIMC Topic: Neural Networks, Computer

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Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

PloS one
This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRC...

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

PloS one
Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcar...

Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.

PloS one
Globally, stroke is the third-leading cause of mortality and disability combined, and one of the costliest diseases in society. More accurate predictions of stroke outcomes can guide healthcare organizations in allocating appropriate resources to imp...

Frequency and phase correction of GABA-edited magnetic resonance spectroscopy using complex-valued convolutional neural networks.

Magnetic resonance imaging
PURPOSE: To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in convolutional neural networks (CNNs) for frequency and phase correction (FPC) of GABA-edite...

Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: The KRAS, NRAS, and BRAF genotypes are critical for selecting targeted therapies for patients with metastatic colorectal cancer (mCRC). Here, we aimed to develop a deep learning model that utilizes pathologic whole-slide images (WSIs) to ...

Image Fusion Method Based on Snake Visual Imaging Mechanism and PCNN.

Sensors (Basel, Switzerland)
The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared an...

Self-organising maps in the analysis of strains of human abdominal wall to identify areas of similar mechanical behaviour.

Journal of the mechanical behavior of biomedical materials
The study refers to the application of a type of artificial neural network called the Self-Organising Map (SOM) for the identification of areas of the human abdominal wall that behave in a similar mechanical way. The research is based on data acquire...

Automated identification of aquatic insects: A case study using deep learning and computer vision techniques.

The Science of the total environment
Deep learning techniques have recently found application in biodiversity research. Mayflies (Ephemeroptera), stoneflies (Plecoptera) and caddisflies (Trichoptera), often abbreviated as EPT, are frequently used for freshwater biomonitoring due to thei...

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinic...

Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification.

Waste management (New York, N.Y.)
This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environme...