AIMC Topic: Neural Networks, Computer

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Probabilistic Motion Prediction and Skill Learning for Human-to-Cobot Dual-Arm Handover Control.

IEEE transactions on neural networks and learning systems
In this article, we focus on human-to-cobot dual-arm handover operations for large box-type objects. The efficiency of handover operations should be ensured and the naturalness as if the handover is going on between two humans. First of all, we study...

The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: To summarize the use of deep learning in the detection of voice disorders using acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy, and assess their effectiveness compared to expert clinical visual exam...

TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images.

Computers in biology and medicine
Deep learning architectures based on convolutional neural network (CNN) and Transformer have achieved great success in medical image segmentation. Models based on the encoder-decoder framework like U-Net have been successfully employed in many realis...

DL-SPhos: Prediction of serine phosphorylation sites using transformer language model.

Computers in biology and medicine
Serine phosphorylation plays a pivotal role in the pathogenesis of various cellular processes and diseases. Roughly 81% of human diseases have links to phosphorylation, and an overwhelming 86.4% of protein phosphorylation takes place at serine residu...

ML interpretability: Simple isn't easy.

Studies in history and philosophy of science
The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to ...

Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.

Nature neuroscience
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as 'credit assignment'. It has long been assumed that c...

Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming.

Scientific reports
This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 45...

Deep learning system for true- and pseudo-invasion in colorectal polyps.

Scientific reports
Over 15 million colonoscopies were performed yearly in North America, during which biopsies were taken for pathological examination to identify abnormalities. Distinguishing between true- and pseudo-invasion in colon polyps is critical in treatment p...

Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.

Scientific reports
The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, whi...

Deep causal speech enhancement and recognition using efficient long-short term memory Recurrent Neural Network.

PloS one
Long short-term memory (LSTM) has been effectively used to represent sequential data in recent years. However, LSTM still struggles with capturing the long-term temporal dependencies. In this paper, we propose an hourglass-shaped LSTM that is able to...