scholarly journals Prediction of PCR amplification from primer and template sequences using recurrent neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kotetsu Kayama ◽  
Miyuki Kanno ◽  
Naoto Chisaki ◽  
Misaki Tanaka ◽  
Reika Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Pcr Amplification ◽  
Nucleotide Sequences ◽  
Specific Primer ◽  
First Report ◽  
Novel Method ◽  
Primer Sequence ◽  
Dna Template ◽  
The Relationship

AbstractWe have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network, the usual double-stranded formation between the primer and template nucleotide sequences was herein expressed as a five-lettered word. The set of words (pseudo-sentences) was placed to indicate the success or failure of PCR targeted to learn recurrent neural network (RNN). After learning pseudo-sentences, RNN predicted PCR results from pseudo-sentences which were created by primer and template sequences with 70% accuracy. These results suggest that PCR results could be predicted using learned RNN and the trained RNN could be used as a replacement for preliminary PCR experimentation. This is the first report which utilized the application of neural network for primer design and prediction of PCR results.

scholarly journals Modeling Judges’ Scores in Artistic Gymnastics

2019 ◽  
Vol 12 (1) ◽  
pp. 1-9
Author(s):  
Melanie Mack ◽  
Maximilian Bryan ◽  
Gerhard Heyer ◽  
Thomas Heinen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Evaluation Process ◽  
Model Calculations ◽  
Kinematic Data ◽  
Complete Dataset ◽  
Exploratory Approach ◽  
Movement Kinematic ◽  
Alternative Approaches ◽  
The Relationship

Background: In artistic gymnastics, performance is observed and evaluated by judges based on criteria defined in the code of points. However, there is a manifold of influences discussed in the literature that could potentially bias the judges’ evaluations in artistic gymnastics. In this context, several authors claim the necessity for alternative approaches to judging gymnastics utilizing biomechanical methods. Objective: The aim of this study was to develop and evaluate a model-based approach to judge gymnastics performance based on quantitative kinematic data of the performed skills. Methods: Four different model variants based on kinematic similarity calculated by a multivariate exploratory approach and the Recurrent Neural Network method were used to evaluate the relationship between the movement kinematics and the judges’ scores. The complete dataset consisted of movement kinematic data and judgment scores of a total of N = 173 trials of three different skills and routines from women’s artistic gymnastics. Results: The results exhibit a significant relationship between the predicted score and the actual score for six of the twelve model calculations. The different model variants yielded a different prediction performance in general across all skills and also in terms of the different skills. In particular, only the Recurrent Neural Network model exhibited significant correlation values between the actual and the predicted scores for all three investigated skills. Conclusion: The results were discussed in terms of the differences of the models as well as the various factors that might play a role in the evaluation process.

Intelligent Identification of Flow Regime Based on a Novel Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1715-1718
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Flow Regime ◽  
Recurrent Neural Network ◽  
Pressure Difference ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Elman Neural Network ◽  
Flow Regime Identification ◽  
Novel Method

A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

scholarly journals RnRTD: Intelligent Approach Based on the Relationship-Driven Neural Network and Restricted Tensor Decomposition for Multiple Accusation Judgment in Legal Cases

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaoding Guo ◽  
Hongli Zhang ◽  
Lin Ye ◽  
Shang Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Recurrent Neural Network ◽  
Feature Space ◽  
Tensor Decomposition ◽  
Target Feature ◽  
Legal Cases ◽  
Legal Case ◽  
The Relationship

The use of intelligent judgment technology to assist in judgment is an inevitable trend in the development of judgment in contemporary social legal cases. Using big data and artificial intelligence technology to accurately determine multiple accusations involved in legal cases is an urgent problem to be solved in legal judgment. The key to solving these problems lies in two points, namely, (1) characterization of legal cases and (2) classification and prediction of legal case data. Traditional methods of entity characterization rely on feature extraction, which is often based on vocabulary and syntax information. Thus, traditional entity characterization often requires extensive energy and has poor generality, thus introducing a large amount of computation and limitation to subsequent classification algorithms. This study proposes an intelligent judgment approach called RnRTD, which is based on the relationship-driven recurrent neural network (rdRNN) and restricted tensor decomposition (RTD). We represent legal cases as tensors and propose an innovative RTD method. RTD has low dependence on vocabulary and syntax and extracts the feature structure that is most favorable for improving the accuracy of the subsequent classification algorithm. RTD maps the tensors, which represent legal cases, into a specific feature space and transforms the original tensor into a core tensor and its corresponding factor matrices. This study uses rdRNN to continuously update and optimize the constraints in RTD so that rdRNN can have the best legal case classification effect in the target feature space generated by RTD. Simultaneously, rdRNN sets up a new gate and a similar case list to represent the interaction between legal cases. In comparison with traditional feature extraction methods, our proposed RTD method is less expensive and more universal in the characterization of legal cases. Moreover, rdRNN with an RTD layer has a better effect than the recurrent neural network (RNN) only on the classification and prediction of multiple accusations in legal cases. Experiments show that compared with previous approaches, our method achieves higher accuracy in the classification and prediction of multiple accusations in legal cases, and our algorithm is more interpretable.

Estimating normal moveout velocity using the recurrent neural network

2019 ◽  
Vol 7 (4) ◽  
pp. T819-T827
Author(s):  
Reetam Biswas ◽  
Anthony Vassiliou ◽  
Rodney Stromberg ◽  
Mrinal K. Sen
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Mean Squared Error ◽  
Correction Term ◽  
Computation Time ◽  
Velocity Estimation ◽  
Data Set ◽  
Arrival Times ◽  
Nmo Correction ◽  
Novel Method

Machine learning (ML) has recently gained immense popularity because of its successful application in complex problems. It develops an abstract relation between the input and output. We have evaluated the application of ML to the most basic seismic processing of normal moveout (NMO) correction. The arrival times of reflection events in a common midpoint (CMP) gather follow a hyperbolic trajectory; thus, they require a correction term to flatten the CMP gather before stacking. This correction term depends on an rms velocity, also referred to as the NMO velocity. In general, NMO velocity is estimated using the semblance measures and picking the peaks in the velocity panel. This process requires a lot of human intervention and computation time. We have developed a novel method using one of the tools based on an ML- approach and applied to the NMO velocity estimation problem. We use the recurrent neural network (RNN) to estimate the NMO velocity directly from the seismic data. The input to the network is a seismic gather and corresponding precalculated NMO velocity (as prelabeled data set) to flatten the gather. We first train the network to develop a relationship between the input gathers (before NMO correction) and the corresponding NMO velocities for a few CMPs as a supervised learning process. Adam optimization algorithm is used to train the RNN. The output from the network is then compared against the correct NMO velocity. The error between the two velocities is then used to update the weight of the neurons and to minimize the mean-squared error between the two velocities. After the network is trained, it can be used to calculate the NMO velocity for the rest of the seismic gathers. We evaluate our method on a noisy data set from Poland. We used only 10% of the CMPs to train the network, and then we used the trained network to predict NMO velocity for the remaining CMP locations. The stack section obtained by using RNN-generated NMO velocities is nearly identical to that obtained by the conventional semblance method.

scholarly journals Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information

2019 ◽  
Vol 9 (10) ◽  
pp. 2170 ◽  
Author(s):  
Changgyun Kim ◽  
Youngdoo Son ◽  
Sekyoung Youm
Keyword(s):  
Neural Network ◽  
Chronic Diseases ◽  
Recurrent Neural Network ◽  
Multilayer Perceptron ◽  
Missing Values ◽  
Health And Nutrition ◽  
Partially Observed ◽  
Data Points ◽  
Multilayer Perceptron Network ◽  
The Relationship

The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information.

A Novel Method of Stereo Camera Calibration Using BP Neural Network

2010 ◽  
Vol 29-32 ◽  
pp. 2692-2697
Author(s):  
Jiu Long Xiong ◽  
Jun Ying Xia ◽  
Xian Quan Xu ◽  
Zhen Tian
Keyword(s):  
Neural Network ◽  
Linear Relationship ◽  
Camera Calibration ◽  
Bp Neural Network ◽  
Calibration Method ◽  
Two Stage ◽  
Non Linear ◽  
Novel Method ◽  
Calibration Accuracy ◽  
The Relationship

Camera calibration establishes the relationship between 2D coordinates in the image and 3D coordinates in the 3D world. BP neural network can model non-linear relationship, and therefore was used for calibrating camera by avoiding the non-linear factors of the camera in this paper. The calibration results are compared with the results of Tsai’s two stage method. The comparison show that calibration method based BP neural network improved the calibration accuracy.

A new car-following model considering recurrent neural network

2019 ◽  
Vol 33 (26) ◽  
pp. 1950304 ◽  
Author(s):  
Chen Hua
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Relative Velocity ◽  
Road Traffic ◽  
Safe Distance ◽  
Velocity Difference ◽  
Car Following ◽  
Depth Analysis ◽  
Car Following Model ◽  
The Relationship

A new car-following model is proposed based on recurrent neural network (RNN) to effectively describe the state change and road traffic congestion while the vehicle is moving. The model firstly gives a full velocity difference car-following model according to the driver’s reaction sensitivity and relative velocity, and then takes the vehicle position and velocity as the input parameters to optimize the safe distance between the front and rear vehicles in the car-following model based on RNN model. Finally, the effectiveness of the above model is validated by building a simulation experiment platform, and an in-depth analysis is conducted on the relationship among influencing factors, e.g., relative velocity, reaction sensitivity, headway, etc. The results reveal that, compared with traditional car-following models, the model can quickly analyze the relationship between initial position and velocity of the vehicle in a shorter time and thus obtain a smaller safe distance. In the case of small velocity difference between the front and rear vehicles, the running velocity of the front and rear vehicles is relatively stable, which is conducive to maintaining the headway.

scholarly journals Sequentially Delineation of Rooftops with Holes from VHR Aerial Images Using a Convolutional Recurrent Neural Network

2021 ◽  
Vol 13 (21) ◽  
pp. 4271
Author(s):  
Wei Huang ◽  
Zeping Liu ◽  
Hong Tang ◽  
Jiayi Ge
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Irregular Shapes ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Novel Method ◽  
Statistical Indicators ◽  
Instance Segmentation

Semantic and instance segmentation methods are commonly used to build extraction from high-resolution images. The semantic segmentation method involves assigning a class label to each pixel in the image, thus ignoring the geometry of the building rooftop, which results in irregular shapes of the rooftop edges. As for instance segmentation, there is a strong assumption within this method that there exists only one outline polygon along the rooftop boundary. In this paper, we present a novel method to sequentially delineate exterior and interior contours of rooftops with holes from VHR aerial images, where most of the buildings have holes, by integrating semantic segmentation and polygon delineation. Specifically, semantic segmentation from the Mask R-CNN is used as a prior for hole detection. Then, the holes are used as objects for generating the internal contours of the rooftop. The external and internal contours of the rooftop are inferred separately using a convolutional recurrent neural network. Experimental results showed that the proposed method can effectively delineate the rooftops with both one and multiple polygons and outperform state-of-the-art methods in terms of the visual results and six statistical indicators, including IoU, OA, F1, BoundF, RE and Hd.

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