Comparison of simple feedforward neural network, recurrent neural network and ensemble neural networks in phishing detection

This paper describes a method to diagnose the most frequent faults of a screw compressor and assess magnitude of these faults by tracking changes in compressor’s dynamics. To determine the condition of the compressor, a feedforward neural network model is first employed to identify the dynamics of the compressor. A recurrent neural network is then used to classify the model into one of the three conditions including baseline, gaterotor wear and excessive friction. Finally, another recurrent neural network estimates the magnitude of a fault from the model. The method’s ability to generalize was evaluated. Experimental validation of the method was also performed. The results show significant improvement over the previous method which used only feedforward neural networks.

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Operational Determination of the Activated Sludge Process Using Neural Networks

Water Science & Technology ◽

10.2166/wst.1992.0762 ◽

1992 ◽

Vol 26 (9-11) ◽

pp. 2461-2464 ◽

Cited By ~ 2

Author(s):

R. D. Tyagi ◽

Y. G. Du

Keyword(s):

Neural Network ◽

Neural Networks ◽

Steady State ◽

Activated Sludge ◽

Feedforward Neural Network ◽

Training Data ◽

Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS

Vestnik komp iuternykh i informatsionnykh tekhnologii ◽

10.14489/vkit.2021.01.pp.044-051 ◽

2021 ◽

pp. 44-51

Author(s):

E. Yu. Shchetinin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Deep Neural Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Audio Recordings ◽

Computer Studies

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

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Artificial Intelligence Techniques for Phishing Detection

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i8499.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 2330-2335

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Neural Systems ◽

Element Determination ◽

Artificial Intelligence Techniques ◽

The Neural Network ◽

Proper Number ◽

Phishing Detection

The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.

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Prospects for recurrent neural network models to learn RNA biophysics from high-throughput data

10.1101/227611 ◽

2017 ◽

Author(s):

Michelle J Wu ◽

Johan OL Andreasson ◽

Wipapat Kladwang ◽

William J Greenleaf ◽

Rhiju Das ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Free Energy ◽

Recurrent Neural Network ◽

Neural Network Model ◽

Data Augmentation ◽

Rna Folding ◽

Large Datasets ◽

Turner Model ◽

Rna Complexes

AbstractRNA is a functionally versatile molecule that plays key roles in genetic regulation and in emerging technologies to control biological processes. Computational models of RNA secondary structure are well-developed but often fall short in making quantitative predictions of the behavior of multi-RNA complexes. Recently, large datasets characterizing hundreds of thousands of individual RNA complexes have emerged as rich sources of information about RNA energetics. Meanwhile, advances in machine learning have enabled the training of complex neural networks from large datasets. Here, we assess whether a recurrent neural network model, Ribonet, can learn from high-throughput binding data, using simulation and experimental studies to test model accuracy but also determine if they learned meaningful information about the biophysics of RNA folding. We began by evaluating the model on energetic values predicted by the Turner model to assess whether the neural network could learn a representation that recovered known biophysical principles. First, we trained Ribonet to predict the simulated free energy of an RNA in complex with multiple input RNAs. Our model accurately predicts free energies of new sequences but also shows evidence of having learned base pairing information, as assessed by in silico double mutant analysis. Next, we extended this model to predict the simulated affinity between an arbitrary RNA sequence and a reporter RNA. While these more indirect measurements precluded the learning of basic principles of RNA biophysics, the resulting model achieved sub-kcal/mol accuracy and enabled design of simple RNA input responsive riboswitches with high activation ratios predicted by the Turner model from which the training data were generated. Finally, we compiled and trained on an experimental dataset comprising over 600,000 experimental affinity measurements published on the Eterna open laboratory. Though our tests revealed that the model likely did not learn a physically realistic representation of RNA interactions, it nevertheless achieved good performance of 0.76 kcal/mol on test sets with the application of transfer learning and novel sequence-specific data augmentation strategies. These results suggest that recurrent neural network architectures, despite being naïve to the physics of RNA folding, have the potential to capture complex biophysical information. However, more diverse datasets, ideally involving more direct free energy measurements, may be necessary to train de novo predictive models that are consistent with the fundamentals of RNA biophysics.Author SummaryThe precise design of RNA interactions is essential to gaining greater control over RNA-based biotechnology tools, including designer riboswitches and CRISPR-Cas9 gene editing. However, the classic model for energetics governing these interactions fails to quantitatively predict the behavior of RNA molecules. We developed a recurrent neural network model, Ribonet, to quantitatively predict these values from sequence alone. Using simulated data, we show that this model is able to learn simple base pairing rules, despite having no a priori knowledge about RNA folding encoded in the network architecture. This model also enables design of new switching RNAs that are predicted to be effective by the “ground truth” simulated model. We applied transfer learning to retrain Ribonet using hundreds of thousands of RNA-RNA affinity measurements and demonstrate simple data augmentation techniques that improve model performance. At the same time, data diversity currently available set limits on Ribonet’s accuracy. Recurrent neural networks are a promising tool for modeling nucleic acid biophysics and may enable design of complex RNAs for novel applications.

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A Cooperative Recurrent Neural Network for Solving L1 Estimation Problems with General Linear Constraints

Neural Computation ◽

10.1162/neco.2007.10-06-376 ◽

2008 ◽

Vol 20 (3) ◽

pp. 844-872 ◽

Cited By ~ 11

Author(s):

Youshen Xia ◽

Mohamed S. Kamel

Keyword(s):

Neural Network ◽

Neural Networks ◽

Recurrent Neural Network ◽

Parallel Implementation ◽

Linear Constraints ◽

Estimation Problem ◽

Least Square ◽

General Linear ◽

Attractive Alternative ◽

Estimation Problems

The constrained L1 estimation is an attractive alternative to both the unconstrained L1 estimation and the least square estimation. In this letter, we propose a cooperative recurrent neural network (CRNN) for solving L1 estimation problems with general linear constraints. The proposed CRNN model combines four individual neural network models automatically and is suitable for parallel implementation. As a special case, the proposed CRNN includes two existing neural networks for solving unconstrained and constrained L1 estimation problems, respectively. Unlike existing neural networks, with penalty parameters, for solving the constrained L1 estimation problem, the proposed CRNN is guaranteed to converge globally to the exact optimal solution without any additional condition. Compared with conventional numerical algorithms, the proposed CRNN has a low computational complexity and can deal with the L1 estimation problem with degeneracy. Several applied examples show that the proposed CRNN can obtain more accurate estimates than several existing algorithms.

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Phishing Detection Research Based on LSTM Recurrent Neural Network

Communications in Computer and Information Science - Data Science ◽

10.1007/978-981-13-2203-7_52 ◽

2018 ◽

pp. 638-645 ◽

Cited By ~ 2

Author(s):

Wenwu Chen ◽

Wei Zhang ◽

Yang Su

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Phishing Detection

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Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

10.21203/rs.3.rs-22343/v1 ◽

2020 ◽

Author(s):

Rahil Sarikhani ◽

Farshid Keynia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Cognitive Radio ◽

Spectrum Sensing ◽

Recurrent Neural Network ◽

Cooperative Spectrum Sensing ◽

Primary User ◽

Secondary Users ◽

Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

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Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽

10.3390/healthcare8020181 ◽

2020 ◽

Vol 8 (2) ◽

pp. 181 ◽

Cited By ~ 5

Author(s):

Patricia Melin ◽

Julio Cesar Monica ◽

Daniela Sanchez ◽

Oscar Castillo

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Fuzzy Logic ◽

Network Models ◽

Neural Network Models ◽

Validation Data ◽

Data Set ◽

Ensemble Neural Networks ◽

Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

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Wave physics as an analog recurrent neural network

Science Advances ◽

10.1126/sciadv.aay6946 ◽

2019 ◽

Vol 5 (12) ◽

pp. eaay6946 ◽

Cited By ~ 26

Author(s):

Tyler W. Hughes ◽

Ian A. D. Williamson ◽

Momchil Minkov ◽

Shanhui Fan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Recurrent Neural Network ◽

Audio Signals ◽

Digital Implementation ◽

Analog Machine ◽

Learning Platforms ◽

Complex Features ◽

Hardware Platforms

Analog machine learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here, we identify a mapping between the dynamics of wave physics and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.

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