scholarly journals Using Artificial Neural Networks to Predict Local Disease Risk Indicators with Multi-Scale Weather, Land and Crop Data

2003 ◽  
Author(s):  
Donna M. Rizzo ◽  
Susanne Conklin ◽  
David E. Dougherty
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Disease Risk ◽  
Risk Indicators ◽  
Multi Scale ◽  
Local Disease ◽  
Artificial Neural

scholarly journals Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Turgay Ayer ◽  
Qiushi Chen ◽  
Elizabeth S. Burnside
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Disease Risk ◽  
Effective Means ◽  
Screening Mammography ◽  
Future Research ◽  
Diagnostic Decision ◽  
Artificial Neural ◽  
Diagnostic Decision Making

Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.

Technique for Intrusion Detection Based on NSST Domain Artificial Neural Networks

2015 ◽  
Vol 713-715 ◽  
pp. 2519-2522
Author(s):  
Yang Lei ◽  
Jing Ma
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Convergence Rates ◽  
Experimental Results ◽  
Shearlet Transform ◽  
Multi Scale ◽  
Novel Technique ◽  
Artificial Neural ◽  
Geometry Analysis

The issue of intrusion detection has been an active topic in both military and civilian areas, and a great many relevant algorithms and techniques have been developed accordingly. This paper addresses a novel technique based on non-subsampled shearlet transform (NSST) domain artificial neural networks (ANN) to solve the above problem, employing multi-scale geometry analysis (MGA) of NSST and the train characteristics of ANN together. Experimental results indicate that, compared with other existing conventional intrusion detection tools, the proposed one is superior to other current popular ones in both aspects of iteration numbers and convergence rates.

AC Loss Prediction Model of a 150 kJ HTS SMES Based on Multi-Scale Model and Artificial Neural Networks

2018 ◽  
Vol 54 (11) ◽  
pp. 1-5 ◽  
Author(s):  
Zhongping Zhang ◽  
Li Ren ◽  
Ying Xu ◽  
Zuoshuai Wang ◽  
Yajun Xia ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Ac Loss ◽  
Scale Model ◽  
Multi Scale ◽  
Loss Prediction ◽  
Artificial Neural ◽  
Hts Smes
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