In this article, a multivariate statistical process control (MSPC) strategy, devoted to bias identification and estimation for processes operating under st... MC Sánchez,CR Alvarez,A Brandolin - 《Aiche Journal》 被引量: 7发表: 2010年 NEURAL NETWORK INTELLIGENT SYSTEM FOR THE ON-LINE OPTIMIZATI...
The best result was obtained using an ANN composed of two hidden layer (10 nodes in each layer), radial bias as transfer function and gradient descent backpropagation algorithm for training. Using this architecture, the regression coefficient was 0.9265. Even with many variables affecting the ...
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A convolutional neural network, a deep learning approach, was applied to approximately 150 000 high-resolution satellite images from Google Static Maps API (application programing interface) to extract features of the built environment in Los Angeles, California; Memphis, Tennessee; San Antonio, ...
Describe methods or analyses included to address and manage AI-related methodologic bias and inaccuracy of AI-generated content. Indicate, when appropriate, if sensitivity analyses were performed to explore the performance of the AI model in vulnerable or underrepresented subgroups. ...
Methods for controlling the bias/variance tradeoff typically assume that overfitting or overtraining is a global phenomenon. For multi-layer perceptron (ML... S Lawrence,CL Giles - IEEE-INNS-ENNS International Joint Conference on Neural Networks 被引量: 270发表: 2000年 Improving generalization of ML...
domains were considered of moderate risk, or if two or more domains were flagged as high risk. The Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) [52] tool assessed risk of bias in non-randomized studies (DBS Studies); all extracted DBS studies were included in this ...
In the past two decades, over-prescription of opioids for pain management has driven a steep increase in opioid use disorder (OUD) and death by overdose, exerting a dramatic toll on western countries. OUD is a chronic relapsing disease associated with a
Given that large lesions can bias the lesion volume size predictions without affecting clinical importance, we also analyzed lesion volume predictions in cases with lesions smaller than 100 mL separately.Two data analysis steps were performed (Figure 1). First, the models were tested on all ...
Even after you set the network initial state to compensate for the initial condition of the test signal, a small offset is still visible in the predicted response. This is because of the incorrect bias term that the LSTM network learned during training. You can fix the offset by using the ...