AIMC Topic: Neural Networks, Computer

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Optimal view detection for ultrasound-guided supraclavicular block using deep learning approaches.

Scientific reports
Successful ultrasound-guided supraclavicular block (SCB) requires the understanding of sonoanatomy and identification of the optimal view. Segmentation using a convolutional neural network (CNN) is limited in clearly determining the optimal view. The...

Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data.

Scientific reports
Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring ...

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.

Nature communications
Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the mult...

disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.

BMC bioinformatics
Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs ...

Advanced monitoring and numerical modelling of the stability, safety and reliability indicators of the earthen dam of Songloulou (Cameroon).

PloS one
For the determination of global stability after long term advanced monitoring, artificial intelligence have been used for the data analysis of water level and displacements of Songloulou earth dam at Cameroon. Measurements of safety and reliability i...

Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: EUS is a high-skill technique that requires numerous procedures to achieve competence. However, training facilities are limited worldwide. Convolutional neural network (CNN) models have been previously implemented for object dete...

A preliminary exploration into top-down and bottom-up deep-learning approaches to localising neuro-interventional point targets in volumetric MRI.

International journal of computer assisted radiology and surgery
PURPOSE: Point localisation is a critical aspect of many interventional planning procedures, specifically representing anatomical regions of interest or landmarks as individual points. This could be seen as analogous to the problem of visual search i...

Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels ...

Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition.

Sensors (Basel, Switzerland)
Sensor-based human activity recognition aims to classify human activities or behaviors according to the data from wearable or embedded sensors, leading to a new direction in the field of Artificial Intelligence. When the activities become high-level ...

KD-Net: Continuous-Keystroke-Dynamics-Based Human Identification from RGB-D Image Sequences.

Sensors (Basel, Switzerland)
Keystroke dynamics is a soft biometric based on the assumption that humans always type in uniquely characteristic manners. Previous works mainly focused on analyzing the key press or release events. Unlike these methods, we explored a novel visual mo...