AI Medical Compendium Topic

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Generalization, Psychological

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Application of Adaptive Neural Network Algorithm Model in English Text Analysis.

Computational intelligence and neuroscience
Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be se...

Application Research for Fusion Model of Pseudolabel and Cross Network.

Computational intelligence and neuroscience
Datasets usually suffer from supervised information missing and weak generalization ability in deep convolution neural network. In this paper, pseudolabel (PL) of Weakly Supervised Learning (WSL) was used to address the problem of supervised informat...

Neural Network-Based Beam Pumper Model Optimization.

Computational intelligence and neuroscience
Beam pumper is the earliest and most popular rod pumper driven by surface dynamic transmission devices. Drawing on modern theories and methods of industrial model design, the model optimization of beam pumper could promote the diversity, serializatio...

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy.

Computational intelligence and neuroscience
Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehen...

Improving generalization of deep neural networks by leveraging margin distribution.

Neural networks : the official journal of the International Neural Network Society
Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass ...

Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy.

Neural networks : the official journal of the International Neural Network Society
Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as adversarial training) greatly hurt generalization performance on the un...

Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks.

Neural computation
Often in language and other areas of cognition, whether two components of an object are identical or not determines if it is well formed. We call such constraints identity effects. When developing a system to learn well-formedness from examples, it i...

Rutting prediction and analysis of influence factors based on multivariate transfer entropy and graph neural networks.

Neural networks : the official journal of the International Neural Network Society
The Rutting prediction model is an essential element of efficient pavement management systems. Accuracy of commonly used predictive model necessitates knowledge of the input parameters that was incorporated and local calibration of the model coeffici...

Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM.

Scientific reports
In this study, fault diagnosis method of bearing utilizing gray level co-occurrence matrix (GLCM) and multi-beetles antennae search algorithm (MBASA)-based kernel extreme learning machine (KELM) is presented. In the proposed method, feature extractio...

A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network.

IEEE transactions on neural networks and learning systems
The simultaneous-source technology for high-density seismic acquisition is a key solution to efficient seismic surveying. It is a cost-effective method when blended subsurface responses are recorded within a short time interval using multiple seismic...