Neural Networks for Astronomical Data Analysis and Bayesian Inference

In recent years, ICSs have become increasingly commonplace in virtually every industry. The abbreviation “ICSs” refers to industrial control systems. These are specially designed computers used for monitoring, managing, and controlling procedures and tasks across a wide range of industries and vital infrastructure sectors. Production, power, disinfection of water, transport, and other sectors all greatly benefit from ICS use. The authors of this paper aim to detect ICS cyber hazards in industry. This article is the result of the writers’ extensive research on ICS programs and the impact of cyberattacks on them as well. The study narrowed its attention to just three ICS applications because there are simply too many to count: power plants, water reservoirs, and gas pipelines. The present paper focuses on the development and evaluation of neural networks for use in cyberattacks. An early form of neural network, the residual system, came first in the field. When a breach is detected in the ICS, the neural network sorts it into one of several categories. The produced datasets must not compromise users’ privacy or cause harm to the relevant industry if they fall into the wrong hands. An encoding device, decoder, pseudo-encoder, and critical model neural networks work together to generate random data. Finally, a set of trials is conducted in which a residual neural network is utilized to classify cyberattacks based on both the created and original datasets. Results from a series of studies indicate that using the created dataset is an effective technique to train high-quality neural networks for use in cybersecurity on a large amount of data without sacrificing the accuracy of the models. The Kullback-Leibler and Jensen-Shannon divergences also serve as the theoretical foundation and technique, respectively. In particular, the paper recommends operational and maintenance cybersecurity standards for ICS. This entails such things as secure password practices, patch management, and anti-malware defense. Physical safeguards for ICS is another topic that is covered.

Traditional neural network training is usually based on the maximum likelihood to obtain the appropriate network parameters including weights and biases given the training data. However, if the available data are finite and noisy, the maximum likelihood-based network training can cause the neural network after being trained to overfit the noisy data. This problem has been overcome by using the Bayesian inference applied to the neural network training in various applications. The Bayesian inference can allow values of regularization parameters to be found using only the training data. In addition, the Bayesian approach also allows different models (e.g., neural networks with different numbers of hidden units to be compared using only the training data). Neural networks trained with Bayesian inference can be also known as Bayesian neural networks (BNNs). This chapter focuses on BNNs for classification problems with considerations of model complexity of BNNs conveniently handled by a method known as the evidence framework.

Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis

This paper presents a study of artificial neural networks for use in stock price prediction. The data from an emerging market, Tehran’s Stock Exchange (T.S.E), are applied as a case study. Based on the rescaled range (R/S) analysis, the behavior of stock price has been studied. R/S analysis is able to distinguish a random series from a non-random one. It is used to detect the long-memory effect in the TEPIX time series. It is shown that the behavior of stock price is non-random and short-term prediction of the TEPIX is possible, and modeling of stock price movements can be done. A multilayer perceptron (M.L.P) neural network model is used to determine and explore the relationship between some variables as independent factors and the level of stock price index as a dependent element in the stock market under study over time. The results show that the neural network models can get better outcomes compared with parametric models like regression and others traditional statistical techniques. Our test also shows that useful predictions can be made without the use of extensive market data or knowledge, and in the data mining process, neural networks can explore some orders which hide in the market structure.

The paper presents a solution to the problem of developing an artificial neural network for classifying electrocardiograms in order to identify a patient's cardiovascular diseases. A literary review of the use of artificial neural networks for use in medicine in the determination of various cardiac pathologies in the condition of patients has been carried out. The most significant six parameters of the heart rhythm have been determined, which can be used in neural network modeling of the state of the human cardiovascular system. An analysis of the capabilities of well-known commercial programs for the classification of electrocardiograms was carried out, their advantages and disadvantages were identified. The capabilities of modern devices for taking cardiograms are described with an indication of the accuracy of their measurements. A study of three different structures of the desired neural network was carried out and the best option was chosen. The software package is made in Python in the Pycharm 2023.1 environment. The network was trained on the basis of the error backpropagation algorithm, the Adam method served as an optimizer. The standard deviation of the network output signal from the standard was used as a criterion. The Softmax function used in the output layer is used to convert the output of neurons into probabilities. Thus, a neural mathematical model for detecting heart diseases has been developed, which has low system requirements for a computer, which can be used to develop software for wearable electronics. An artificial neural network has the following metric: accuracy - 90%, sensitivity - 90%, specificity - 10%.

Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis

Artificial neural networks (ANN) have been widely used in the field of data classification. Normally, training of neural network is applied with the traditional back propagation technique. As, this approach has various drawbacks, training of neural network is done with Particle Swarm Optimization (PSO). PSO has been widely used to solve the diverse kind of optimization problems. Population initialization performs a significant role in meta-heuristic algorithms. This paper describes a new initialization population approach Log Logistic termed as PSOLL-NN to create the initialization of the swarm. The proposed algorithm has been tested for weight optimization of feed forward neural network; and compared with back propagation Algorithm (BPA), standard PSO (PSO-NN), PSO initialized with Halton Sequence (PSOH-NN), Torus sequence (PSOT-NN) and Sobol sequence (PSOS-NN). The experimental results show that the proposed technique performed exceptionally better than the other traditional techniques. Moreover, the outcome of our work presents a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.

As the next generation of turbomachinery components becomes more sensitive to instrumentation intrusiveness, a reduction of the number of measurement devices required for the evaluation of performance is a possible and cost-effective way to mitigate the arising of non-mastered experimental errors. A hybrid methodology that couples experimental techniques with modeling techniques through a Bayesian data-driven framework is employed to reduce the instrumentation effort. A numerical model is employed to provide an initial belief of the flow, which is then updated based on undersampled experimental observations by a Bayesian inference algorithm. The goal of the present work is to showcase the proposed hybrid methodology and demonstrate its partial application through Gaussian Process regression in reducing the instrumentation effort and testing time at the outlet of a low aspect ratio axial compressor stage representative of the last stage of a high-pressure compressor. Preliminary results show an accurate reconstruction of the mean flow field with a direct uncertainty quantification provided by the Bayesian approach.

We use a neural network model and Atacama Large Millimeter/submillimeter Array (ALMA) observations of HCN and HNC to constrain the physical conditions, most notably the cosmic-ray ionization rate (CRIR, ζ), in the Central Molecular Zone (CMZ) of the starburst galaxy NGC 253. Using output from the chemical code UCLCHEM, we train a neural network model to emulate UCLCHEM and derive HCN and HNC molecular abundances from a given set of physical conditions. We combine the neural network with radiative transfer modeling to generate modeled integrated intensities, which we compare to measurements of HCN and HNC from the ALMA Large Program ALCHEMI. Using a Bayesian nested sampling framework, we constrain the CRIR, molecular gas volume and column densities, kinetic temperature, and beam-filling factor across NGC 253's CMZ. The neural network model successfully recovers UCLCHEM molecular abundances with ∼3% error and, when used with our Bayesian inference algorithm, increases the parameter-inference speed tenfold. We create images of these physical parameters across NGC 253's CMZ at 50 pc resolution and find that the CRIR, in addition to the other gas parameters, is spatially variable with ζ ∼ a few ×10−14 s−1 at r ≳ 100 pc from the nucleus, increasing to ζ > 10−13 s−1 at its center. These inferred CRIRs are consistent within 1 dex with theoretical predictions based on nonthermal emission. Additionally, the high CRIRs estimated in NGC 253's CMZ can be explained by the large number of cosmic-ray-producing sources as well as a potential suppression of cosmic-ray diffusion near their injection sites.

There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional features, are an integral part of uncertainty quantification in prediction and Bayesian inference. However, it is challenging to assess conditional density estimates and gain insight into modes of failure. While existing diagnostic tools can determine whether an approximated conditional density is compatible overall with a data sample, they lack a principled framework for identifying, locating, and interpreting the nature of statistically significant discrepancies over the entire feature space. In this paper, we present rigorous and easy-to-interpret diagnostics such as (i) the "Local Coverage Test" (LCT), which distinguishes an arbitrarily misspecified model from the true conditional density of the sample, and (ii) "Amortized Local P-P plots" (ALP) which can quickly provide interpretable graphical summaries of distributional differences at any location x in the feature space. Our validation procedures scale to high dimensions and can potentially adapt to any type of data at hand. We demonstrate the effectiveness of LCT and ALP through a simulated experiment and applications to prediction and parameter inference for image data.

BACKGROUND: Currently, artificial intelligence in the form of artificial neural networks is being actively implemented in a number of areas of our lives, including medicine. In particular, in otorhinolaryngology, artificial neural networks are used to analyze images obtained during endoscopic examinations of patients (e.g., videolaryngoscopy) [1–3]. The interpretation of laryngoscopic images often presents significant difficulties for practicing physicians, which reduces the frequency of detection of precancerous laryngeal diseases and contributes to the increase in the number of patients with stage III–IV laryngeal cancer [4, 5]. This underscores the significance of prompt performance and accurate interpretation of the findings of endoscopic examinations of patients with laryngeal disorders. Artificial neural networks can be employed to analyze the results of videolaryngoscopy, furnishing the physician with supplementary information that can enhance diagnostic accuracy and diminish the probability of error [6, 7]. AIM: The study aims to develop and train an artificial neural network for recognizing characteristic features of laryngeal neoplasms and variants of laryngeal normality. MATERIALS AND METHODS: The study was conducted under the grant of the Moscow Center for Innovative Technologies in Healthcare (grant No. 2112-1/22) entitled “Using Neural Networks (Artificial Intelligence Algorithms) for Control and Improving the Quality of Diagnosis and Treatment of Diseases of Laryngeal and Ear Structures through Digital Technologies”.The following methods were used during the course of the study: data collection for the creation of a photobank (dataset) of medical images obtained during videolaryngoscopy; data partitioning for the formation of datasets for individual nosologies and groups of diseases; the method of consilium; analysis of the accuracy of recognition and classification of digital endoscopic images; and training of classification neural networks. Consequently, a dataset comprising 1,471 laryngeal images in digital formats (JPEG, BMP) was assembled, labelled, and uploaded for the purpose of training the artificial neural network. Of the total number of images, 410 were classified as pertaining to laryngeal formation, while 1061 were classified as variants of normality. Subsequently, the neural network was trained and tested to identify the signs of normal and laryngeal masses. RESULTS: The results of the testing of the artificial neural network indicated the formation of an inaccuracy matrix, the calculation of the value of recognition accuracy, the calculation of the quality indicators of the model performance, and the construction of the ROC curve. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing the signs of laryngeal masses and norms. CONCLUSIONS: This study demonstrates that a trained artificial neural network can successfully distinguish between signs of normal and laryngeal masses in endoscopic photographs. With further training of the neural network and achievement of high accuracy, this technology can be used in clinical practice as an assistant in the interpretation of laryngoscopic images and early diagnosis of laryngeal masses. It can also be employed to control and improve the quality of diagnosis and treatment of diseases of the throat, nose, and ears by primary care physicians.

The Sixth International Symposium on Neural Networks (ISNN 2009)

ABSTRACTA back‐propagation neural network was applied to predicting the KIC values using tensile material data and investigating the effects of crack plane orientation and temperature. The 595 KIC data of structural steels were used for training and testing the neural network model. In the trained neural network model, yield stress has relatively the most effect on KIC value among tensile material properties and KIC value was more sensitive to KIC test temperature than to crack plane orientation valid in the range of material data covered in this study. The performance of the trained artificial neural network (ANN) was evaluated by comparing output of the ANN with results of a conventional least squares fit to an assumed shape. The conventional linear or nonlinear least squares fitting methods gave very poor fitting results but the results predicted by the trained neural network were considerably satisfactory. This study shows that the neural network can be a good tool to predict KIC values according to the variation of the temperature and the crack plane orientation using tensile test results.

Measurement of global solar radiation is particularly required for proper design of solar energy conversion systems. This study investigates the use of software tools like neural networks and fuzzy inference systems for modelling so as to predict global solar radiation using different input parameters based on available weather data. Advantages include simplicity, speed and efficiency, to make short term predictions of global solar radiation at different locations in India, Germany and United Kingdom. It helps in estimation of effectiveness of the applied model which matches solar radiation and other meteorological parameters which are in a non-linear relationship. Bayesian Inference algorithm is used for the current study in estimation of global solar radiation.

We present a hybrid Bayesian network (HBN) framework to analyse dynamic fault trees. By incorporating a new approximate inference algorithm for HBNs involving dynamically discretising the domain of all continuous variables, accurate approximations for the failure distribution of both static and dynamic fault tree constructs are obtained. Unlike in other approaches no numerical integration techniques or simulation methods are required. Moreover, no exact expression for the posterior marginal is needed and no conditional probability tables need to be completed. Sensitivity analysis, uncertainty, diagnosis, common cause failure analysis, can all be easily performed within this framework. Posterior estimates of parameterised marginal failure distributions can also be obtained using available raw failure data together with prior information from expert judgement.