scholarly journals ENNGene: an Easy Neural Network model building tool for Genomics

2021 ◽  
Author(s):  
Eliska Chalupova ◽  
Ondrej Vaculik ◽  
Filip Jozefov ◽  
Jakub Polacek ◽  
Tomas Majtner ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Model Building ◽  
Evolutionary Conservation ◽  
Learning Methods

Background: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. Results: Here we present ENNGene - Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. Conclusions: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

scholarly journals Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

2021 ◽  
Vol 72 (1) ◽  
pp. 11-20
Author(s):  
Mingtao He ◽  
Wenying Li ◽  
Brian K. Via ◽  
Yaoqi Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Price Volatility ◽  
Google Trends ◽  
Learning Models ◽  
Fresh Perspective ◽  
Futures Price

Abstract Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

scholarly journals Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jian Liu ◽  
Xin Gu ◽  
Chao Shang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Fraud Detection ◽  
Quantitative Detection ◽  
Financial Fraud

At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the features of the behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms.

scholarly journals Neural Reversible Steganography with Long Short-Term Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ching-Chun Chang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Low Level ◽  
Long Short Term Memory

Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks in reversible steganography, a class of steganographic methods that permits the distortion caused by message embedding to be removed. The underdevelopment of the field of reversible steganography with deep learning can be attributed to the perception that perfect reversal of steganographic distortion seems scarcely achievable, due to the lack of transparency and interpretability of neural networks. Rather than employing neural networks in the coding module of a reversible steganographic scheme, we instead apply them to an analytics module that exploits data redundancy to maximise steganographic capacity. State-of-the-art reversible steganographic schemes for digital images are based primarily on a histogram-shifting method in which the analytics module is often modelled as a pixel intensity predictor. In this paper, we propose to refine the prior estimation from a conventional linear predictor through a neural network model. The refinement can be to some extent viewed as a low-level vision task (e.g., noise reduction and super-resolution imaging). In this way, we explore a leading-edge neuroscience-inspired low-level vision model based on long short-term memory with a brief discussion of its biological plausibility. Experimental results demonstrated a significant boost contributed by the neural network model in terms of prediction accuracy and steganographic rate-distortion performance.

scholarly journals On increasing the productive time of drilling oil and gas wells using machine learning methods

Georesursy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 79-85
Author(s):  
Anatoliy N. Dmitrievsky ◽  
Alexander G. Sboev ◽  
Nikolai A. Eremin ◽  
Alexander D. Chernikov ◽  
Aleksandr V. Naumov ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drilling Process ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods

The article is devoted to the development of a hybrid method for predicting and preventing the development of troubles in the process of drilling wells based on machine learning methods and modern neural network models. Troubles during the drilling process, such as filtrate leakoff; gas, oil and water shows and sticking, lead to an increase in unproductive time, i.e. time that is not technically necessary for well construction and is caused by various violations of the production process. Several different approaches have been considered, including based on the regression model for predicting the indicator function, which reflects an approach to a developing trouble, as well as anomaly extraction models built both on basic machine learning algorithms and using the neural network model of deep learning. Showing visualized examples of the work of the developed methods on simulation and real data. Intelligent analysis of Big Geodata from geological and technological measurement stations is based on well-proven machine learning algorithms. Based on these data, a neural network model was proposed to prevent troubles and emergencies during the construction of wells. The use of this method will minimize unproductive drilling time.

scholarly journals Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples

2018 ◽  
Vol 8 (9) ◽  
pp. 1573 ◽  
Author(s):  
Vladimir Kulyukin ◽  
Sarbajit Mukherjee ◽  
Prakhar Amlathe
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Feature Engineering ◽  
Learning Methods ◽  
Machine Learning Methods

Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing samples were separated from the validation samples by beehive and location, a shallower raw audio convolutional neural network with a custom layer outperformed three deeper raw audio convolutional neural networks without custom layers and performed on par with the four machine learning methods trained to classify feature vectors extracted from raw audio samples. On a more challenging dataset of 12,914 audio samples where the training and testing samples were separated from the validation samples by beehive, location, time, and bee race, all raw audio convolutional neural networks performed better than the four machine learning methods and a convolutional neural network trained to classify spectrogram images of audio samples. A trained raw audio convolutional neural network was successfully tested in situ on a low voltage Raspberry Pi computer, which indicates that convolutional neural networks can be added to a repertoire of in situ audio classification algorithms for electronic beehive monitoring. The main trade-off between deep learning and standard machine learning is between feature engineering and training time: while the convolutional neural networks required no feature engineering and generalized better on the second, more challenging dataset, they took considerably more time to train than the machine learning methods. To ensure the replicability of our findings and to provide performance benchmarks for interested research and citizen science communities, we have made public our source code and our curated datasets.

scholarly journals ALGORITHMIC STABILITY OF DEEP LEARNING NEURAL NETWORKS IN RECOGNIZING THE MICROSTRUCTURE OF MATERIALS

2021 ◽  
Vol 21 (1) ◽  
pp. 159-166
Author(s):  
R.A. Klestov ◽  
◽  
A.V. Klyuev ◽  
V.Yu. Stolbov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Process ◽  
Functional Materials ◽  
Quality Of Data ◽  
Algorithmic Stability ◽  
The Stability

The division of data for training a neural network into training and test data in various proportions to each other is investigated. The question is raised about how the quality of data distribution and their correct annotation can affect the final result of constructing a neural network model. The paper investigates the algorithmic stability of training a deep neural network in problems of recognition of the microstructure of materials. The study of the stability of the learning process makes it possible to estimate the performance of a neural network model on incomplete data distorted by up to 10%. Purpose. Research of the stability of the learning process of a neural network in the classification of microstructures of functional materials. Materials and methods. Artificial neural network is the main instrument on the basis of which produced the study. Different subtypes of deep convolutional networks are used such as VGG and ResNet. Neural networks are trained using an improved backpropagation method. The studied model is the frozen state of the neural network after a certain number of learning epochs. The amount of data excluded from the study was randomly distributed for each class in five different distributions. Results. Investigated neural network learning process. Results of experiments conducted computing training with gradual decrease in the number of input data. Distortions of calculation results when changing data with a step of 2 percent are investigated. The percentage of deviation was revealed, equal to 10, at which the trained neural network model loses its stability. Conclusion. The results obtained mean that with an established quantitative or qualitative deviation in the training or test set, the results obtained by training the network can hardly be trusted. Although the results of this study are applicable to a particular case, i.e., microstructure recognition problems using ResNet-152, the authors propose a simpler technique for studying the stability of deep learning neural networks based on the analysis of a test, not a training set.

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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