A Novel Method for Generating Benchmark Functions Using Recurrent Neural Network

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.

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

Scientific Reports ◽

10.1038/s41598-021-86357-1 ◽

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.

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Estimating normal moveout velocity using the recurrent neural network

Interpretation ◽

10.1190/int-2018-0243.1 ◽

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.

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

Remote Sensing ◽

10.3390/rs13214271 ◽

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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A Hybrid Intelligent Method of Predicting Stock Returns

Advances in Artificial Neural Systems ◽

10.1155/2014/246487 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Akhter Mohiuddin Rather

Keyword(s):

Neural Network ◽

Stock Returns ◽

Recurrent Neural Network ◽

Stock Exchange ◽

Summation Method ◽

Prediction Errors ◽

Proposed Model ◽

Nonlinear Processing ◽

Novel Method ◽

Hybrid Intelligent Method

This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements.

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A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network

European Neurology ◽

10.1159/000511306 ◽

2020 ◽

Vol 83 (5) ◽

pp. 468-486

Author(s):

Foad Moradi ◽

Hiwa Mohammadi ◽

Mohammad Rezaei ◽

Payam Sariaslani ◽

Nazanin Razazian ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Sleep Stage ◽

Sleep Stages ◽

Discrete Wavelet ◽

Eeg Signals ◽

Proposed Model ◽

Novel Method

Introduction: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN). Methods: Two RNNs were designed and trained separately based on a database of classical guitar pieces and Kurdish tanbur Makams using a long short-term memory model. Moreover, discrete wavelet transform and wavelet packet decomposition were used to determine the association between the EEG signals and musical pitches. Continuous wavelet transform was applied to extract musical beat-based features from the EEG. Then, the pretrained RNN was used to generate music. To test the proposed model, 11 sleep EEGs were mapped onto the guitar and tanbur frequency intervals and presented to the pretrained RNN. Next, the generated music was randomly presented to 2 neurologists. Results: The proposed model classified the sleep stages with an accuracy of >81% for tanbur and more than 93% for guitar musical pieces. The inter-rater reliability measured by Cohen’s kappa coefficient (κ) revealed good reliability for both tanbur (κ = 0.64, p < 0.001) and guitar musical pieces (κ = 0.85, p < 0.001). Conclusion: The present EEG sonification method leads to valid sleep staging by clinicians. The method could be used on various EEG databases for classification, differentiation, diagnosis, and treatment purposes. Real-time EEG sonification can be used as a feedback tool for replanning of neurophysiological functions for the management of many neurological and psychiatric disorders in the future.

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Automated detection of mouse scratching behaviour using convolutional recurrent neural network

Scientific Reports ◽

10.1038/s41598-020-79965-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Koji Kobayashi ◽

Seiji Matsushita ◽

Naoyuki Shimizu ◽

Sakura Masuko ◽

Masahito Yamamoto ◽

...

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Lysophosphatidic Acid ◽

Deep Neural Network ◽

Detection Method ◽

Automated Detection ◽

Experimental Animals ◽

Novel Method ◽

Predictive Rate ◽

Fully Connected

AbstractScratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The predicted number and durations of scratching events correlated with those of the human observation. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8%; positive predictive rate, 82.1%). In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models.

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Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189290 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8927-8935

Author(s):

Bing Zheng ◽

Dawei Yun ◽

Yan Liang

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Behavior Pattern ◽

Rapid Development ◽

Video Data ◽

Support Vector ◽

Behavior Recognition ◽

Learning Methods ◽

The Impact ◽

Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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