CNN Intrusion Detection for Threat Analysis of a Network

The technological advancement realized in the discovery and embrace of both IoT and IIoT is totally indispensable. Many systems and subsystems both robust and miniaturized have made their existence into the technical arena due to IoT. It goes without saying that IoT has brought into light very diverse benefits that cut across universal applications.However, the pre-requisite of a network channel existence for an IoT operation to be successful is the only pitfall that this essentially unique system possesses. There is a significant amount of danger associated with transmission networks. They have very substantial susceptibility to both online and offline threats by malicious cyber intentions.This paper focuses on the analyses of the threats posed to these IoT networks through Artificial Neural Networks. Specifically, a model is trained through recurrent and convolutional neural network to do intensive analysis on the threat intensity, type and threat source for data logging purposes. The Intruder detection system (IDS) explored in this paper registers a success rate of 99% based on the empirical data posed to the model.

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Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190114151255 ◽

2020 ◽

Vol 16 (5) ◽

pp. 584-591 ◽

Cited By ~ 1

Author(s):

Muhammad Hanif Ahmad Nizar ◽

Chow Khuen Chan ◽

Azira Khalil ◽

Ahmad Khairuddin Mohamed Yusof ◽

Khin Wee Lai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Heart Disease ◽

Aortic Valve ◽

Real Time ◽

Convolutional Neural Networks ◽

Valvular Heart Disease ◽

Detection System ◽

Processing Unit ◽

Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽

10.3390/s21072538 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2538

Author(s):

Shuang Zhang ◽

Feng Liu ◽

Yuang Huang ◽

Xuedong Meng

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Spread Spectrum ◽

High Efficiency ◽

Detection System ◽

Direct Sequence Spread Spectrum ◽

Detection Performance ◽

Detection Methods ◽

Direct Sequence ◽

Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement

Applied Sciences ◽

10.3390/app11083429 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3429

Author(s):

Željka Beljkaš ◽

Nikola Baša

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Experimental Research ◽

Fiber Reinforced Polymer ◽

Glass Fiber Reinforced Polymer ◽

Reinforced Polymer ◽

Continuous Beams ◽

Glass Fiber Reinforced ◽

Gfrp Reinforcement

Deflections on continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement are calculated in accordance with the appropriate standards (ACI 440.1R-15, CSA S806-12). However, experimental research provides results which differ from the values calculated pursuant to the standards, particularly when it comes to continuous beams. Machine learning methods can be applied for predicting a deflection level on continuous beams with GFRP (glass fiber-reinforced polymer) reinforcement and loaded with a concentrated load. This paper presents research on using artificial neural networks for deflection estimation and an optimal prediction model choice. It was necessary to first develop a database, in order to train the neural network. The database was formed based on the results of the experimental research on continuous beams with GFRP reinforcement. Using the best trained neural network model, high accuracy was obtained in estimating deflection, expressed over the mean absolute percentage error, 9.0%. This result indicates a high level of reliability in the prediction of deflection with the help of artificial neural networks.

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The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽

10.3390/biom11040500 ◽

2021 ◽

Vol 11 (4) ◽

pp. 500

Author(s):

László Keresztes ◽

Evelin Szögi ◽

Bálint Varga ◽

Viktor Farkas ◽

András Perczel ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector ◽

User Needs ◽

Computational Technique ◽

Linear Support Vector Machine ◽

Β Sheets ◽

Artificial Neural ◽

Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

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Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽

10.3390/met11010018 ◽

2020 ◽

Vol 11 (1) ◽

pp. 18

Author(s):

Rahel Jedamski ◽

Jérémy Epp

Keyword(s):

Neural Network ◽

Neural Networks ◽

Regression Analysis ◽

Artificial Neural Networks ◽

Linear Regression ◽

Linear Regression Analysis ◽

Case Hardening ◽

Artificial Neural ◽

Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽

10.3390/s21010047 ◽

2020 ◽

Vol 21 (1) ◽

pp. 47

Author(s):

Vasyl Teslyuk ◽

Artem Kazarian ◽

Natalia Kryvinska ◽

Ivan Tsmots

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Input Data ◽

Optimization Criterion ◽

Fuzzy Input ◽

Different Types ◽

Smart House ◽

Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

International Astronomical Union Colloquium ◽

10.1017/s0252921100057419 ◽

2000 ◽

Vol 176 ◽

pp. 135-136

Author(s):

Toshiki Aikawa

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Hybrid System ◽

Nonlinear Time Series ◽

Photometric Data ◽

Nonlinear Time Series Analysis ◽

Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

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IMPROVED NEURAL NETWORKS FOR LINEAR AND NONLINEAR PROGRAMMING

International Journal of Neural Systems ◽

10.1142/s0129065791000303 ◽

1991 ◽

Vol 02 (04) ◽

pp. 331-339 ◽

Cited By ~ 10

Author(s):

Jiahan Chen ◽

Michael A. Shanblatt ◽

Chia-Yiu Maa

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Nonlinear Programming ◽

Penalty Function ◽

New Combination ◽

Optimal Point ◽

Linear And Nonlinear Programming ◽

Linear And Nonlinear ◽

Simulation Results

A method for improving the performance of artificial neural networks for linear and nonlinear programming is presented. By analyzing the behavior of the conventional penalty function, the reason for the inherent degenerating accuracy is discovered. Based on this, a new combination penalty function is proposed which can ensure that the equilibrium point is acceptably close to the optimal point. A known neural network model has been modified by using the new penalty function and the corresponding circuit scheme is given. Simulation results show that the relative error for linear and nonlinear programming is substantially reduced by the new method.

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Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

Volume 6B: Energy ◽

10.1115/imece2015-51723 ◽

2015 ◽

Cited By ~ 1

Author(s):

M. A. Rafe Biswas ◽

Melvin D. Robinson

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Fuel Cell ◽

Direct Methanol Fuel Cell ◽

Cell Voltage ◽

Operating Conditions ◽

Direct Methanol ◽

Artificial Neural ◽

Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

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Generalization of Patterns by Identification with Polynomial Neural Network

Journal of Electrical Engineering ◽

10.2478/v10187-010-0017-4 ◽

2010 ◽

Vol 61 (2) ◽

pp. 120-124 ◽

Cited By ~ 3

Author(s):

Ladislav Zjavka

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Functional Dependence ◽

Polynomial Neural Network ◽

New Type ◽

Input Variables ◽

The Brain ◽

Functional Output ◽

Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

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