AIMC Topic: Neural Networks, Computer

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Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation.

International journal of neural systems
Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effec...

Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks.

International journal of neural systems
Meaning Representation parsing aims to represent a sentence as a structured, Directed, Acyclic Graph (DAG), in an attempt to extract meaning from text. This paper extends an existing 2-stage pipeline AMR parser with state-of-the-art techniques in dep...

Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.

Orthodontics & craniofacial research
BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric ...

Observer-based state estimation for discrete-time semi-Markovian jump neural networks with round-robin protocol against cyber attacks.

Neural networks : the official journal of the International Neural Network Society
This paper investigates an observer-based state estimation issue for discrete-time semi-Markovian jump neural networks with Round-Robin protocol and cyber attacks. In order to avoid the network congestion and save the communication resources, the Rou...

Deep Learning for Identifying Promising Drug Candidates in Drug-Phospholipid Complexes.

Molecules (Basel, Switzerland)
Drug-phospholipid complexing is a promising formulation technology for improving the low bioavailability of active pharmaceutical ingredients (APIs). However, identifying whether phospholipid and candidate drug can form a complex through in vitro tes...

Deep learning for diffusion in porous media.

Scientific reports
We adopt convolutional neural networks (CNN) to predict the basic properties of the porous media. Two different media types are considered: one mimics the sand packings, and the other mimics the systems derived from the extracellular space of biologi...

Stochastic representation of many-body quantum states.

Nature communications
The quantum many-body problem is ultimately a curse of dimensionality: the state of a system with many particles is determined by a function with many dimensions, which rapidly becomes difficult to efficiently store, evaluate and manipulate numerical...

Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis.

Journal of neural engineering
The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process,...

Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach.

Journal of neural engineering
. Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an a...

Identifying Young Adults at High Risk for Weight Gain Using Machine Learning.

The Journal of surgical research
INTRODUCTION: Weight gain among young adults continues to increase. Identifying adults at high risk for weight gain and intervening before they gain weight could have a major public health impact. Our objective was to develop and test electronic heal...