scholarly journals Effects of Network Topology on Decision-Making Behavior in A Biological Network Model

Author(s):  
Suojun Lu ◽  
◽  
Jian’an Fang ◽  
Qingying Miao ◽  
◽  
...  
Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1052
Author(s):  
Leang Sim Nguon ◽  
Kangwon Seo ◽  
Jung-Hyun Lim ◽  
Tae-Jun Song ◽  
Sung-Hyun Cho ◽  
...  

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.


2016 ◽  
Vol 19 (2) ◽  
pp. 202-209 ◽  
Author(s):  
Jorien Veldwijk ◽  
Brigitte A.B. Essers ◽  
Mattijs S. Lambooij ◽  
Carmen D. Dirksen ◽  
Henriette A. Smit ◽  
...  

2020 ◽  
Vol 07 (04) ◽  
pp. 433-452 ◽  
Author(s):  
S. Sahand Mohammadi Ziabari ◽  
Jan Treur

The influence of acute severe stress or extreme emotion based on a Network-Oriented modeling methodology has been addressed here. Adaptive temporal causal network model is an approach to address the phenomena with complexity which cannot be or hard to be explained in a real-world experiment. In the first phase, the suppression of the existing network connections as a consequence of the acute stress modeled and in the second phase relaxing the suppression by giving some time and starting a new learning of the decision making in accordance to presence of stress starts again.


2012 ◽  
Vol 71 (3) ◽  
pp. 199-205 ◽  
Author(s):  
Jonathan A. Sugam ◽  
Jeremy J. Day ◽  
R. Mark Wightman ◽  
Regina M. Carelli

1981 ◽  
Vol 33 (2) ◽  
pp. 234-252 ◽  
Author(s):  
Jerel A. Rosati

The bureaucratic politics model has achieved great popularity in the study of decision making. Yet too often the term “bureaucratic politics” is used by scholars and practitioners without clearly stating its policy application. The decision-making behavior that occurred during the Johnson and Nixon administrations for SALT I serves to illustrate many of the limits of the model. First, the decision-making structure posited by the bureaucratic politics model is not nearly as prevalent within the executive branch as is commonly assumed. Second, even where the bureaucratic politics structure is present, the decision-making process is not always one of bargaining, compromise, and consensus. Finally, the decision context and the decision participants are ignored in the model. To provide a clearer understanding of policy-making behavior, a more systematic decision-making framework is offered, which should contribute to the development of better model- and theory-building.


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