AIMC Topic: Neural Networks, Computer

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Automated deep learning model for estimating intraoperative blood loss using gauze images.

Scientific reports
The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL predic...

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat.

Journal of computer-aided molecular design
An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candi...

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method.

PloS one
Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic sig...

Assessment of brain tumor detection techniques and recommendation of neural network.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures...

OPG-based dental age estimation using a data-technical exploration of deep learning techniques.

Journal of forensic sciences
Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges lik...

Predicting disease-gene associations through self-supervised mutual infomax graph convolution network.

Computers in biology and medicine
Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contribute to treatments, but a large number of associations remained unexplored. To identify novel disease-gene associations, many computational m...

Controlling human causal inference through in silico task design.

Cell reports
Learning causal relationships is crucial for survival. The human brain's functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal infer...

Diagnosing oral and maxillofacial diseases using deep learning.

Scientific reports
The classification and localization of odontogenic lesions from panoramic radiographs is a challenging task due to the positional biases and class imbalances of the lesions. To address these challenges, a novel neural network, DOLNet, is proposed tha...

A neural network model for the evolution of learning in changing environments.

PLoS computational biology
Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex ne...

Reservoir parameters prediction based on spatially transferred long short-term memory network.

PloS one
Reservoir reconstruction, where parameter prediction plays a key role, constitutes an extremely important part in oil and gas reservoir exploration. With the mature development of artificial intelligence, parameter prediction methods are gradually sh...