A Clinical Diagnostic Model Based on Supervised Learning

2019 ◽  
Vol 8 (3) ◽  
pp. 949-953
Author(s):  
Oguntimilehin A ◽  
Keyword(s):  
Supervised Learning ◽  
Diagnostic Model ◽  
Model Based ◽  
Clinical Diagnostic

scholarly journals A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

2021 ◽  
Vol 9 (5) ◽  
pp. 409-409
Author(s):  
Xiaohan Tang ◽  
Rui Tang ◽  
Xingzhi Sun ◽  
Xiang Yan ◽  
Gan Huang ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Gradient Boosting ◽  
Diagnostic Model ◽  
Model Based ◽  
Clinical Diagnostic ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

CONSTRUCTION OF THE DIAGNOSTIC MODEL BASED ON COMBINING SPECTRAL CHARACTERISTICS OF NONLINEAR DYNAMIC OBJECTS

2020 ◽  
Vol 3 (1) ◽  
pp. 431-442
Author(s):  
Hanna Fedorova ◽  
Oleksandr Fomin ◽  
Oleksandr Ruban ◽  
Vitaliy Pavlenko
Keyword(s):  
Nonlinear Dynamic ◽  
Spectral Characteristics ◽  
Diagnostic Model ◽  
Model Based ◽  
Dynamic Objects

Automated Diagnostic Model Based on Isoline Map Analysis of Myocardial Tissue Structure

2019 ◽  
pp. 221-238
Author(s):  
Olga V. Senyukova ◽  
Danuta S. Brotikovskaya ◽  
Svetlana G. Gorokhova ◽  
Ekaterina S. Tebenkova
Keyword(s):  
Tissue Structure ◽  
Diagnostic Model ◽  
Myocardial Tissue ◽  
Model Based ◽  
Map Analysis

scholarly journals A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer Models

2008 ◽  
Vol 18 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Antoni Ligęza ◽  
Jan Kościelny
Keyword(s):  
Fault Diagnosis ◽  
Simultaneous Occurrence ◽  
Diagnostic Model ◽  
Rule Based ◽  
New Approach ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Model Based ◽  
Multiple Fault Diagnosis ◽  
System Variables

A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer ModelsThe diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.

A cost-sensitive semi-supervised learning model based on uncertainty

2017 ◽  
Vol 251 ◽  
pp. 106-114 ◽  
Author(s):  
Hongyu Zhu ◽  
Xizhao Wang
Keyword(s):  
Supervised Learning ◽  
Learning Model ◽  
Model Based

scholarly journals Digital reality: a model-based approach to supervised learning from synthetic data

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Tim Dahmen ◽  
Patrick Trampert ◽  
Faysal Boughorbel ◽  
Janis Sprenger ◽  
Matthias Klusch ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Synthetic Data ◽  
Model Based

scholarly journals Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1350 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Wu ◽  
Xiong ◽  
Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Early Warning ◽  
Flight Control ◽  
Data Driven ◽  
Diagnostic Model ◽  
Flight Control System ◽  
Model Based ◽  
On Line

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

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