scholarly journals Automatic lumen detection and magnetic alignment control for magnetic-assisted capsule colonoscope system optimization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sheng-Yang Yen ◽  
Hao-En Huang ◽  
Gi-Shih Lien ◽  
Chih-Wen Liu ◽  
Chia-Feng Chu ◽  
...  
Keyword(s):  
High Performance ◽  
Learning Algorithm ◽  
Network Models ◽  
Predictive Performance ◽  
System Optimization ◽  
Intubation Time ◽  
Neural Network Models ◽  
Cecal Intubation ◽  
Control Time ◽  
Cecal Intubation Time

AbstractWe developed a magnetic-assisted capsule colonoscope system with integration of computer vision-based object detection and an alignment control scheme. Two convolutional neural network models A and B for lumen identification were trained on an endoscopic dataset of 9080 images. In the lumen alignment experiment, models C and D used a simulated dataset of 8414 images. The models were evaluated using validation indexes for recall (R), precision (P), mean average precision (mAP), and F1 score. Predictive performance was evaluated with the area under the P-R curve. Adjustments of pitch and yaw angles and alignment control time were analyzed in the alignment experiment. Model D had the best predictive performance. Its R, P, mAP, and F1 score were 0.964, 0.961, 0.961, and 0.963, respectively, when the area of overlap/area of union was at 0.3. In the lumen alignment experiment, the mean degrees of adjustment for yaw and pitch in 160 trials were 21.70° and 13.78°, respectively. Mean alignment control time was 0.902 s. Finally, we compared the cecal intubation time between semi-automated and manual navigation in 20 trials. The average cecal intubation time of manual navigation and semi-automated navigation were 9 min 28.41 s and 7 min 23.61 s, respectively. The automatic lumen detection model, which was trained using a deep learning algorithm, demonstrated high performance in each validation index.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models

2005 ◽  
Vol 128 (3) ◽  
pp. 444-454 ◽  
Author(s):  
M. Venturini
Keyword(s):  
Measurement Errors ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Network Models ◽  
Transient Behavior ◽  
Optimal Number ◽  
Measured Data ◽  
Outlet Temperature ◽  
Neural Network Models ◽  
Hidden Layer

In the paper, self-adapting models capable of reproducing time-dependent data with high computational speed are investigated. The considered models are recurrent feed-forward neural networks (RNNs) with one feedback loop in a recursive computational structure, trained by using a back-propagation learning algorithm. The data used for both training and testing the RNNs have been generated by means of a nonlinear physics-based model for compressor dynamic simulation, which was calibrated on a multistage axial-centrifugal small size compressor. The first step of the analysis is the selection of the compressor maneuver to be used for optimizing RNN training. The subsequent step consists in evaluating the most appropriate RNN structure (optimal number of neurons in the hidden layer and number of outputs) and RNN proper delay time. Then, the robustness of the model response towards measurement uncertainty is ascertained, by comparing the performance of RNNs trained on data uncorrupted or corrupted with measurement errors with respect to the simulation of data corrupted with measurement errors. Finally, the best RNN model is tested on field data taken on the axial-centrifugal compressor on which the physics-based model was calibrated, by comparing physics-based model and RNN predictions against measured data. The comparison between RNN predictions and measured data shows that the agreement can be considered acceptable for inlet pressure, outlet pressure and outlet temperature, while errors are significant for inlet mass flow rate.

Risk factors associated with longer cecal intubation time: a systematic review and meta-analysis

2018 ◽  
Vol 33 (4) ◽  
pp. 359-365 ◽  
Author(s):  
Veeravich Jaruvongvanich ◽  
Tomoki Sempokuya ◽  
Passisd Laoveeravat ◽  
Patompong Ungprasert
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Meta Analysis ◽  
Intubation Time ◽  
Cecal Intubation ◽  
Factors Associated ◽  
Cecal Intubation Time

scholarly journals SOM-based aggregation for graph convolutional neural networks

2020 ◽  
Author(s):  
Luca Pasa ◽  
Nicolò Navarin ◽  
Alessandro Sperduti
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Predictive Performance ◽  
Aggregation Operator ◽  
Graph Representation ◽  
Approximation Properties ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Node Level ◽  
Real World Datasets

AbstractGraph property prediction is becoming more and more popular due to the increasing availability of scientific and social data naturally represented in a graph form. Because of that, many researchers are focusing on the development of improved graph neural network models. One of the main components of a graph neural network is the aggregation operator, needed to generate a graph-level representation from a set of node-level embeddings. The aggregation operator is critical since it should, in principle, provide a representation of the graph that is isomorphism invariant, i.e. the graph representation should be a function of graph nodes treated as a set. DeepSets (in: Advances in neural information processing systems, pp 3391–3401, 2017) provides a framework to construct a set-aggregation operator with universal approximation properties. In this paper, we propose a DeepSets aggregation operator, based on Self-Organizing Maps (SOM), to transform a set of node-level representations into a single graph-level one. The adoption of SOMs allows to compute node representations that embed the information about their mutual similarity. Experimental results on several real-world datasets show that our proposed approach achieves improved predictive performance compared to the commonly adopted sum aggregation and many state-of-the-art graph neural network architectures in the literature.

Cecal Intubation Time: No Difference Based on Age, Gender, BMI, Type of Sedation, or Time of Colonoscopy

2011 ◽  
Vol 106 ◽  
pp. S524
Author(s):  
Andy Thanjan ◽  
Clyde Collins ◽  
Wallace Wang ◽  
Soham Shah ◽  
Seth Richter
Keyword(s):  
Intubation Time ◽  
Cecal Intubation ◽  
Cecal Intubation Time

Colonoscopy with magnetic control system to navigate the forepart of colonoscope shortens the cecal intubation time

2014 ◽  
Vol 28 (8) ◽  
pp. 2480-2483
Author(s):  
Chung-Sheng Yang ◽  
Fat-Moon Suk ◽  
Chun-Nan Chen ◽  
Cheng-Long Chuang ◽  
Joe-Air Jiang ◽  
...  
Keyword(s):  
Control System ◽  
Magnetic Control ◽  
Intubation Time ◽  
Cecal Intubation ◽  
Cecal Intubation Time

STUDY ON CHEMOTAXIS BEHAVIORS OF C. ELEGANS USING DYNAMIC NEURAL NETWORK MODELS: FROM ARTIFICIAL TO BIOLOGICAL MODEL

2010 ◽  
Vol 18 (spec01) ◽  
pp. 3-33 ◽  
Author(s):  
JIAN-XIN XU ◽  
XIN DENG
Keyword(s):  
Neural Network ◽  
Sensory Neurons ◽  
Motor Neurons ◽  
Learning Algorithm ◽  
Network Models ◽  
Biological Models ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
C Elegans ◽  
Logical Functions

With the anatomical understanding of the neural connection of the nematode Caenorhabditis elegans (C. elegans), its chemotaxis behaviors are investigated in this paper through the association with the biological nerve connections. The chemotaxis behaviors include food attraction, toxin avoidance and mixed-behaviors (finding food and avoiding toxin concurrently). Eight dynamic neural network (DNN) models, two artifical models and six biological models, are used to learn and implement the chemotaxis behaviors of C. elegans. The eight DNN models are classified into two classes with either single sensory neuron or dual sensory neurons. The DNN models are trained to learn certain switching logics according to different chemotaxis behaviors using real time recurrent learning algorithm (RTRL). First we show the good performance of the two artifical models in food attraction, toxin avoidance and the mixed-behaviors. Next, six neural wire diagrams from sensory neurons to motor neurons are extracted from the anatomical nerve connection of C. elegans. Then the extracted biological wire diagrams are trained using RTRL directly, which is the first time in this field of research by associating chemotaxis behaviors with biological neural models. An interesting discovery is the need for a memory neuron when single-sensory models are used, which is consistent with the anatomical understanding on a specific neuron that functions as a memory. In the simulations, the chemotaxis behaviors of C. elegans can be depicted by several switch logical functions which can be learned by RTRL for both artifical and biological models.

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