AIMC Topic: Algorithms

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Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.

Tomography (Ann Arbor, Mich.)
The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated ...

Light-Weight Deep Learning Techniques with Advanced Processing for Real-Time Hand Gesture Recognition.

Sensors (Basel, Switzerland)
In the discipline of hand gesture and dynamic sign language recognition, deep learning approaches with high computational complexity and a wide range of parameters have been an extremely remarkable success. However, the implementation of sign languag...

Machine learning prediction of academic collaboration networks.

Scientific reports
We investigate the different roles played by nodes' network and non-network attributes in explaining the formation of European university collaborations from 2011 to 2016, in three European Research Council (ERC) domains: Social Sciences and Humaniti...

Game theoretical trajectory planning enhances social acceptability of robots by humans.

Scientific reports
Since humans and robots are increasingly sharing portions of their operational spaces, experimental evidence is needed to ascertain the safety and social acceptability of robots in human-populated environments. Although several studies have aimed at ...

Improved visualization of high-dimensional data using the distance-of-distance transformation.

PLoS computational biology
Dimensionality reduction tools like t-SNE and UMAP are widely used for high-dimensional data analysis. For instance, these tools are applied in biology to describe spiking patterns of neuronal populations or the genetic profiles of different cell typ...

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.

Magnetic resonance in medicine
PURPOSE: The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy an...

The design of compounds with desirable properties - The anti-HIV case study.

Journal of computational chemistry
Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identif...

DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning.

Acta neurochirurgica
PURPOSE: Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative mag...

A soft sensing method of billet surface temperature based on ILGSSA-LSSVM.

Scientific reports
It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimi...

ANIMAL-SPOT enables animal-independent signal detection and classification using deep learning.

Scientific reports
Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensi...