EMGraph: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnect Using Graph Convolution Networks

Training neural network by spline weight function (SWF) has overcomed many defects of traditional neural networks (such as local minima, slow convergence and so on). It becomes more important because of its simply topological structure, fast learning speed and high accuracy. To generalize the SWF algorithm, this paper introduces a kind of rational spline weight function neural network and analyzes the performance of approximation for this neural network.

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Fast learning and efficient memory utilization with a prototype based neural classifier

Pattern Recognition ◽

10.1016/0031-3203(94)00114-2 ◽

1995 ◽

Vol 28 (4) ◽

pp. 581-593 ◽

Cited By ~ 6

Author(s):

M.A. Abou-nasr ◽

M.A. Sid-Ahmed

Keyword(s):

Fast Learning ◽

Neural Classifier ◽

Efficient Memory ◽

Memory Utilization

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Fast learning of relational kernels

Machine Learning ◽

10.1007/s10994-009-5163-1 ◽

2010 ◽

Vol 78 (3) ◽

pp. 305-342 ◽

Cited By ~ 15

Author(s):

Niels Landwehr ◽

Andrea Passerini ◽

Luc De Raedt ◽

Paolo Frasconi

Keyword(s):

Fast Learning

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Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System

Applied Sciences ◽

10.3390/app11010203 ◽

2020 ◽

Vol 11 (1) ◽

pp. 203

Author(s):

Primož Jelušič ◽

Andrej Ivanič ◽

Samo Lubej

Keyword(s):

Fuzzy Inference System ◽

Goodness Of Fit ◽

Mean Squared Error ◽

Fuzzy Inference ◽

Ground Vibration ◽

Residential Building ◽

Ground Vibrations ◽

Adaptive Network ◽

Inference System ◽

Fast Learning

Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The efficiency criteria chosen for the comparison between the predicted and actual data were the sum of squares due to error (SSE), the root mean squared error (RMSE), and the goodness of fit (R-squared and adjusted R-squared). The results show that the ANFIS prediction model performs better than the commonly used predictors.

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Molecular Breast Imaging: A Scientific Review

Journal of Breast Imaging ◽

10.1093/jbi/wbab039 ◽

2021 ◽

Author(s):

Katie N Hunt

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Breast Mri ◽

Cancer Staging ◽

Scientific Review ◽

Molecular Breast Imaging ◽

Breast Cancer Staging ◽

Fast Learning ◽

Technical Advances ◽

History Of

Abstract Molecular breast imaging (MBI) is a nuclear medicine technique that has evolved considerably over the past two decades. Technical advances have allowed reductions in administered doses to the point that they are now acceptable for screening. The most common radiotracer used in MBI, 99mTc-sestamibi, has a long history of safe use. Biopsy capability has become available in recent years, with early clinical experience demonstrating technically successful biopsies of MBI-detected lesions. MBI has been shown to be an effective supplemental screening tool in women with dense breasts and is also utilized for breast cancer staging, assessment of response to neoadjuvant chemotherapy, problem solving, and as an alternative to breast MRI in women who have a contraindication to MRI. The degree of background parenchymal uptake on MBI shows promise as a tool for breast cancer risk stratification. Radiologist interpretation is guided by a validated MBI lexicon that mirrors the BI-RADS lexicon. With short interpretation times, a fast learning curve for radiologists, and a substantially lower cost than breast MRI, MBI provides many benefits in the practices in which it is utilized. This review will discuss the current state of MBI technology, clinical applications of MBI, MBI interpretation, radiation dose associated with MBI, and the future of MBI.

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