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Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson's ratio of selected rock types.

Scientific reports
This research focuses on the predictive modeling between rocks' dynamic properties and the optimization of neural network models. For this purpose, the rocks' dynamic properties were measured in terms of quality factor (Q), resonance frequency (FR), ...

Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.

PLoS computational biology
Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygen...

Research on Intelligent English Education Based on the Short Video Recommendation Algorithm.

Computational intelligence and neuroscience
In order to solve the problems of English education in the form of a short video, a research method of English intelligent education based on a short video recommendation algorithm was proposed. The recommendation system is a branch of artificial int...

A survey on neural-symbolic learning systems.

Neural networks : the official journal of the International Neural Network Society
In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they have been found to lack effective reasoning and cognitive ability. On the other hand, symbolic systems exhibit exc...

Attribute Augmented Network Embedding Based on Generative Adversarial Nets.

IEEE transactions on neural networks and learning systems
Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both structure and attribute features have achieved in many real-world applica...

Looking at Boundary: Siamese Densely Cooperative Fusion for Salient Object Detection.

IEEE transactions on neural networks and learning systems
Though deep learning-based saliency detection methods have achieved gratifying performance recently, the predicted saliency maps still suffer from the boundary challenge. From the perspective of foreground-background separation, this article attempts...

Optimal Tracking in Switched Systems With Free Final Time and Fixed Mode Sequence Using Approximate Dynamic Programming.

IEEE transactions on neural networks and learning systems
Optimal tracking in switched systems with fixed mode sequence and free final time is studied in this article. In the optimal control problem formulation, the switching times and the final time are treated as parameters. For solving the optimal contro...

Multi-Constraint Latent Representation Learning for Prognosis Analysis Using Multi-Modal Data.

IEEE transactions on neural networks and learning systems
The Cox proportional hazard model has been widely applied to cancer prognosis prediction. Nowadays, multi-modal data, such as histopathological images and gene data, have advanced this field by providing histologic phenotype and genotype information....

A Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots.

IEEE transactions on neural networks and learning systems
The state-of-the-art reinforcement learning (RL) techniques have made innumerable advancements in robot control, especially in combination with deep neural networks (DNNs), known as deep reinforcement learning (DRL). In this article, instead of revie...

Spatial-Spectral Unified Adaptive Probability Graph Convolutional Networks for Hyperspectral Image Classification.

IEEE transactions on neural networks and learning systems
In hyperspectral image (HSI) classification task, semisupervised graph convolutional network (GCN)-based methods have received increasing attention. However, two problems still need to be addressed. The first is that the initial graph structure in th...