Neural Network Expert System Research Articles

A neural network expert system that allows assessing the quality of the algorithm for choosing the parameters of optimal removal of low-frequency noise from seismograms using the fingerprint method

The article summarizes the results of research on the application of the fingerprint method in seismology. This method can be successfully used in solving various practical problems. We have used it as an effective tool for a thorough analysis of noisy seismograms of regional explosions and earthquakes in order to select the optimal filtering frequency. The cut-off frequency selected with the help of fingerprints will allow you to design an optimal filter that not only reliably suppresses low-frequency noise, but also carefully preserves the signal it hides. Moreover, the method works well when the noise intensity exceeds the signal intensity many times over and under conditions of partial overlap of their frequency ranges. Fundamental is the initial orientation of the method towards automatic implementation with minimal use of additional information. In conclusion, this article proposes to use a neural network expert system that allows you to evaluate the quality of the fingerprint algorithm. To create such a system, we needed to explore current trends in seismology regarding the use of artificial neural networks. Based on extensive worldwide experience, it has been shown that there is a clear renewed interest in the use of small, lowcost networks. The principle on which they are based lies in their perception as training and input data of compact, complexly structured parameters of a high degree of transformation of primary seismograms. It is these parameters that include the characteristics taken from fingerprints using the algorithms we propose. So our experience in creating a neural network expert system naturally turned out to be successful. The reliability of the estimates obtained turned out to be very close to the practical limit of the fingerprint method.

Human-Computer Collaborative Visual Design Creation Assisted by Artificial Intelligence

With the support and promotion of big data and cloud computing, AI has penetrated into every field of people's lives more and more deeply, with its characteristics of sustainable work, extremely fast computing speed, and a certain intelligence. This is an effective way to solve the general lack of demand and productivity of visual design, and relieve the pressure off designers to deal with relatively low-quality and high-demand designs. Therefore, the combination of design and artificial intelligence technology is a necessity. Research on the application of artificial intelligence technology for visual design is also in full swing at home and abroad However, at present, teams at home and abroad are in the exploratory stage. This paper considers whether it is possible to build an intelligent visual design and creation system by using artificial intelligence technology to help graphic communication designers achieve high-quality, high-efficiency, and high-quantity design output. Additionally, this paperexplores how to combine artificial intelligence technology with designers' design workflow so as to form a complementary human-computer cooperation mode. We will explore how to integrate AI technology with designers' design workflow and then create a human-machine collaboration model with complementary advantages to achieve the high quality, high efficiency, and high quantity of design output required by the intelligent visual design creation system being built. Finally, a basic framework of a generative smart human-computer collaborative visual design creation system based on a subset of neural network expert systems in multiple domains and an aggregate of different modules supported by the system is formed, and the working principle and usage process of the system are further elaborated with the example of packaging design.

A Proposed Waiting Time Algorithm for a Prediction and Prevention System of Traffic Accidents Using Smart Sensors

One of the applications of neural networks is to predict the fault section results of traffic utilizing the combined model estimation of the fault section and self-learning models with smart sensors. The prediction of the fault section can autonomously develop the internal model of the network to fit the pre-entered “traffic accident” section data and predict the occurrence of traffic accident sections. In this paper, we propose the results of waiting time for traffic accidents in case of traffic accidents by using a neural network and fuzzy expert system, in comparison with existing algorithms and algorithms for determining traffic accidents. It is used to estimate or predict traffic accident reliability as well. Typically, the type of fault data collected is the number of faults (the number of faults recorded during a given time interval) or the time of fault (the time-of-fault data recorded when each fault occurred), and this can be utilized only for group data types, rather than the time-of-fault data type.

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.

Design Hybrid Intelligent system for Cattle Diseases Diagnosis and Treatment

Cattle is one of the main concerns of people and companies in various countries of the world. This paper built the architecture of the cattle diseases by combining the characteristics of an expert system with a neural network Expert system focuses on diagnosing the possible diseases that affect the life of cattle through constructing knowledge bases. While the Radial base functions, a neural network is proposed for the cattle diseases treatments. A tightly coupled model is used to implement a hybrid intelligence technique for diagnosing and Treatment cattle diseases. The result generated by the system was enough to demonstrate its performance.

Development and Fault Prediction of a New Operating Mechanism of HTPPM

In this article, we take a 126 kV single-break vacuum circuit breaker as the research object and study the application of high-energy-density PM motor in the high-voltage circuit breaker for the first time. The PM motor maintains maximum power density and torque density during the start-up phase. Note that most of the faults of high-voltage circuit breakers are mechanical faults. We designed a set of mechanical fault prediction systems for high-voltage circuit breakers. We present the prediction method of the opening and closing action curve of the high-voltage circuit breaker. It is inspired by Chaos Ant Colony Algorithm (CAS) and an optimized Long- and Short-Term Memory (LSTM) cycle neural network. We constructed the main structure of the neural network expert system and established the fault prediction model of the high-voltage circuit breaker, based on the LSTM cycle neural network, optimized by CAS. We used the improved least-square method to achieve the operation accuracy of the phase control switch. Finally, we completed the development and experiment of the prototype.

Применение нейросетевых экспертных систем для контроля, диагностики и прогнозирования технического состояния авиационных двигателей

Рассматривается применение нейросетевых экспертных систем в области контроля, диагностики и прогнозирования технического состояния авиационных ГТД на основе нечеткой логики. Показана методика для решения таких задач в области технической эксплуатации авиационной техники совместно с использованием фаззи-интерференсной системы программы MATLAB. Используя статистические данные о работе двигателя формируется экспертная система на основе нейронной сети позволяющая осуществлять контроль и диагностику ГТД, а также прогнозировать дальнейшее техническое состояния анализируемого двигателя. The application of neural network expert systems in the field of monitoring, diagnostics and forecasting of the technical condition of aviation gas turbine engines based on fuzzy logic is considered. The technique for solving such problems in the field of technical operation of aircraft and using the fuzzy-interference system of the MATLAB program is shown. Using statistical data on the operation of the engine, an expert system is based on the fundamental of a neural network that provide monitoring and diagnostics of gas turbine engines, as well as predicting the further technical condition of the analyzed engine.

Prediction of load-displacement curve in a complex structure using artificial neural networks: A study on a long bone

Long bones are composite materials possessing nonhomogeneous and anisotropic properties. They repair themselves (self-repairing) and adapt to changing mechanical demands by altering their shape and mechanical properties (self-adapting). Such exceptional features make long bones intriguing materials to comprehend properly. This also expands our knowledge of engineering materials and motivates researchers to employ novel techniques where conventional approaches may present limitations. This paper delves into the use of artificial neural network (ANN) expert systems to predict load-displacement curves of a long bone. Thirteen hydrated third metacarpal (MC3) bones from thoroughbred horses aged from twelve hours to three years were loaded in compression in an MTS machine. Strain readings from one three-gauge rosette and three distinct single-element gauges at different locations on the MC3 midshaft, displacement, load, load exposure time, horse age and bone side (left or right limb) were recorded for each bone. This information shaped ANNs input variables. Two in-series feedforward back-propagation ANNs were employed where the experimental recordings except for load were fed into the first ANN to predict load. Then, the predicted load along with the rest of experimental recordings were fed into the second ANN to predict displacement. Cyclic load-displacement and stiffness predicted by ANNs were plotted versus experimental counterparts. ANNs regression analyses showed R > 0.95 for training and testing datasets. To confirm their accuracy, ANNs were used to predict responses of specific bone samples that were not used in ANNs training. The ANNs trained using 17,718 experimental data points from twelve bones predicted the load (R = 0.997, RMSE = 2.44 kN), displacement (R = 0.948, RMSE = 0.321 mm), and stiffness (R = 0.982, RMSE = 1.197 kN/mm) of the thirteenth bone. The encouraging outcomes exhibit the exceptional ability of artificial neural networks in capturing the mechanical characteristics of complex structures.

DeNNeS: deep embedded neural network expert system for detecting cyber attacks

With the advances in computing powers and increasing volumes of data, deep learning’s emergence has helped revitalize artificial intelligence research. There is a growing trend of applying deep learning techniques to image processing, speech recognition, self-driving cars, and even health-care. Recently, several deep learning models have been employed to detect a cyber threat such as network attack, malware infiltration, or phishing website; nevertheless, they suffer from not being explainable to security experts. Security experts not only do need to detect the incoming threat but also need to know the incorporating features that cause that particular security incident. To address this issue, in this paper, we propose a deep embedded neural network expert system (DeNNeS) that extracts refined rules from a trained deep neural network (DNN) architecture to substitute the knowledge base of an expert system. The knowledge base later is used to classify an unseen security incident and inform the final user of the corresponding rule that made that inference. We consider different rule extraction scenarios, and to prove the robustness of DeNNeS, we evaluate it on two cybersecurity datasets including UCI phishing websites dataset and Android malware dataset comprising more than 4000 Android APKs from several sources. The comparison results of DeNNeS with standalone DNN, JRip, and common machine learning algorithms show that DeNNeS with the retraining uncovered samples scenario outperforms other algorithms on both datasets. Furthermore, the extracted rules approximately reproduce the accuracy of the neural network from which they are derived. DeNNeS achieves an outstanding accuracy of $$97.5\%$$ and a negligible false positive rate of $$1.8\%$$ about $$2.4\%$$ higher and $$3.5\%$$ lower than the rule learner JRip on the phishing dataset. Moreover, DeNNeS outperforms random forest (RF), which produces the highest results among decision tree (DT), support vector machine, k-nearest neighbor, and Gaussian naive Bayes. Despite smaller training data in the malware dataset, DeNNeS achieves an accuracy of $$95.8\%$$ and $${F_{1}\,score}$$ of $$91.1\%$$ , much higher than JRip and RF.

STRUKTURA OBSŁUGOWA OBIEKTU TECHNICZNEGO

W pracy zaprezentowano nowe podejście do problemy eksploatacji obiektów technicznych. W tym celu przedstawiono schemat automatycznego układu regulacji zasobu użytkowania obiektów technicznych. W zaprezentowanym układzie wykorzystywane są nowoczesne inteligentne systemy: diagnostyczny i obsługowy. W skład proponowanego systemu wchodzi sztuczna sieć neuronową SSN i obsługowy system ekspertowy. Artykuł przedstawia problematykę przetwarzania informacji diagnostycznej do postaci zbioru informacji obsługowej. Zaprezentowano schemat inteligentnego systemu obsługiwania. Opisano możliwość oceny jakości odnawiania obiektu w systemie obsługiwania złożonego obiektu technicznego.

Prediction of load in a long bone using an artificial neural network prediction algorithm

The hierarchical nature of bone makes it a difficult material to fully comprehend. The equine third metacarpal (MC3) bone experiences nonuniform surface strains, which are a measure of displacement induced by loads. This paper investigates the use of an artificial neural network expert system to quantify MC3 bone loading. Previous studies focused on determining the response of bone using load, bone geometry, mechanical properties, and constraints as input parameters. This is referred to as a forward problem and is generally solved using numerical techniques such as finite element analysis (FEA). Conversely, an inverse problem has to be solved to quantify load from the measurements of strain and displacement. Commercially available FEA packages, without manipulating their underlying algebraic formulae, are incapable of completing a solution to the inverse problem. In this study, an artificial neural network (ANN) was employed to quantify the load required to produce the MC3 displacement and surface strains determined experimentally. Nine hydrated MC3 bones from thoroughbred horses were loaded in compression in an MTS machine. Ex-vivo experiments measured strain readings from one three-gauge rosette and three distinct single-element gauges at different locations on the MC3 midshaft, associated displacement, and load exposure time. Horse age and bone side (left or right limb) were also recorded for each MC3 bone. This information was used to construct input variables for the ANN model. The ability of this expert system to predict the MC3 loading was investigated. The ANN prediction offered excellent reliability for the prediction of load in the MC3 bones investigated, i.e. R2 ≥ 0.98.

An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings

In this study, a new hybrid model, namely the Electromagnetism-based Firefly Algorithm - Artificial Neural Network (EFA-ANN), is proposed to forecast the energy consumption in buildings. The model is applied to evaluate the heating load (HL) and cooling load (CL) using two given datasets. Each dataset was obtained by monitoring the effect of the façade system and dimensions of the building, respectively, on energy consumption. The performance of EFA-ANN is validated by comparing the obtained results with other methods. It is shown that EFA-ANN provides a faster and more accurate prediction of HL and CL. A sensitivity analysis is performed to identify the impact of each input on the energy performance of the building. From the results of this study, it is evident that EFA-ANN can assist civil engineers and construction managers in the early designs of energy-efficient buildings.

Rapid measurement of the fourth-order texture coefficient by laser ultrasonic surface acoustic waves based on a neural network expert system.

In anisotropic materials, texture components and orientation density directly affect the surface acoustic waves' (SAW) velocity dispersion and SAW velocity variation. In this paper, a texture feature recognition and analysis system for a neural network is constructed based on the corresponding characteristics of texture components and orientation density and SAW velocity dispersion and variation by combining laser ultrasound technology with the partial texture analysis method, which is used for the identification and analysis of texture type and feature. At the same time, based on the relationship between surface wave velocity and the fourth-order texture coefficient, an expert system for accurate prediction of the fourth-order texture coefficient is constructed. Then, the fourth-order texture coefficients predicted by the neural network expert system (NNES) are compared with the texture coefficients measured by electron backscattered diffraction. The results show that the NNES can not only quickly identify and analyze texture features, but also accurately predict the fourth-order texture coefficients.

Hybrid swarm intelligent redundancy relevance (RR) with convolution trained compositional pattern neural network expert system for diagnosis of diabetes

According to the World Health Organization (WHO) report, 438 million peoples are affected by diabetics in the upcoming year of 2030. Due to the seriousness of the diabetics’ disease, it has to be predicted in the earlier stage but the minimum symptoms of diabetic failure to predict in earlier stage. So, the automatic and earlier diabetic prediction system needs to be created for eliminating the serious factors in medical field. There are several earlier diabetic prediction system is created with the help of hybridized machine learning techniques but they are difficult to process the huge dimension of data as well as consume more time for predicting diabetic related features. For overcoming the above issues, in this paper introduces the swarm intelligent redundancy relevance (RR) along with convolution trained compositional pattern neural network for predicting the diabetic disease. Initially, the diabetic data has been collected from Pima Indian Diabetic dataset, the dimensionality of data is reduced by swarm intelligence RR techniques, the selected features are trained by layers of convolution networks that helps to speed up the diabetic prediction process. Finally, diabetic classification process is done by compositional pattern neural network and the efficiency is evaluated using MATLAB based experimental results.

Technical objects diagnostics systems organising

While operating, repair and reconstruction works, maintaining particular level of technical objects' reliability, are carried out. Diagnostic systems are expected to be involved in these activities. As there are a great variety of technical objects available nowadays, diagnostic systems are based on wide variety of principles. Among them neural network technology and expert system technology are the most multi-purpose. A number of particular problems, expected to be solved in developing of diagnostic systems based on technologies mentioned above, is considered in these article; namely: rationing of diagnostic indicators, creating of general purpose functional framework for evaluating particular parameters, developing of neural functional network diagnostic system, developing of two fragments of conceptual diagnostic model, describing diagnostic context and diagnostic parameters in expert system database.

Fault Location Estimation on Transmission Lines using Neuro-Fuzzy System

Accurate fault location on transmission line is important in ensuring consistent and reliable operation of the power deliver to long distance destination. Conventional methods for locating fault on transmission lines based on travelling wave and impendence-based methods usually suffer from large error due to the complexity of fault modeling on different type of faults. In this paper, an intelligent system for detection of fault location on transmission line using a hybrid model that integrates artificial neural network (ANN) and fuzzy expert system called Adaptive Network-Based Fuzzy Inference System (ANFIS) is proposed. First, a three phase transmission lines is modeled and various types of faults are generated using MATLAB/Simulink. Then, the faulted current signal is segmented from the faulted transmission. Next, feature extraction is performed to obtained information from the faulted current signal. In this study, the extracted features are mean, standard deviation, energy, peak-to-peak and amplitude value. Feature selection is then applied to select important features that correlate with the fault location. For single-phase-to-ground fault, peak-to-peak value and energy is used. Meanwhile, for the line-to-line and double-phase-to-ground faults, only peak-to-peak value is used. Finally, ANFIS network is trained to locate the fault occurrence. Simulation results against two regression models; Linear Regression and Gaussian Process Regression indicated that the ANFIS network is superior in locating the fault. The network achieved the lowest mean squared error (MSE) (0.0012 to 0.0022).

Problems Solving of Cell Subscribers based on Expert Systems Neural Networks

With the growing demand for telecommunications services, the number of calls to telecommunications companies related to issues of using services, setting up and maintaining equipment, as well as resolving possible problems arising in the process of using services is also growing. From the point of view of system analysis, the problem is the mismatch between the existing and the required (target) state of the system for a given state of the environment at the moment in time. Based on this definition, we consider the problem of the subscriber of the cellular network a mismatch between the existing and the required state of the cellular network in this state of the environment at the moment in time. The state of the cellular network is characterized by the functioning of all devices, the proposed range of services. A short time for analyzing problem situations and making decisions, a large amount of information characterizing the current situation, the difficulty of solving poorly formalized and poorly structured tasks in the absence of complete and reliable information about the state of the cellular communication network and the functioning of its elements lead to a mismatch of human capabilities to effectively solve these problems. In this regard, the development and implementation of a precedent based neural network expert system for solving the problems of subscribers of a cellular communication network is an urgent scientific and technical task.

Signals Overlapping Detection and its Retrieval using Artificial Neural Network for Digital Gamma Ray Spectroscopy

In this paper, algorithms of power spectral density (PSD) approaches applied to the detection of pileup problem associated with gamma ray signal analysis. These techniques namely are: periodogram, Welch, Multiwindow, Yule-walker, Burg, covariance, and multiple signal classification (MUSIC) algorithm. Transform domain based methods are also studied, such as Fast Fourier Transform (FFT), Walsh-Hadamard, Hilbert, and Hankel transform. In both cases, the resultant extracted features are fed to train an artificial neural network (ANN) expert system using the Error BackPropagation Training (EBPT) algorithm. A comparison between studied algorithms is performed in terms of percentage recognition rate of original signal and execution time using the matlab environment. Studies showed that MUSIC and Welch in PSD methods have the highest recognition rate. Also, Hankel and walshhadamard in transform domain based methods have been given the highest recognition rate, under the application of different types of noise to the original signal, and FFT algorithm has the least execution time in all methods.1. Introduction

A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete

The compressive and tensile strength of high-performance concrete (HPC) is a highly nonlinear function of its constituents. The significance of expert frameworks for predicting the compressive and tensile strength of HPC is greatly distinguished in material technology. This study aims to develop an expert system based on the artificial neural network (ANN) model in association with a modified firefly algorithm (MFA). The ANN model is constructed from experimental data while MFA is used to optimize a set of initial weights and biases of ANN to improve the accuracy of this artificial intelligence technique. The accuracy of the proposed expert system is validated by comparing obtained results with those from the literature. The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties. The MFA-ANN is also much faster at solving problems. Therefore, the proposed approach can provide an efficient and accurate tool to predict and design HPC.

Neural network expert system for X-ray analysis of welded joints

The use of intelligent technologies for the automated analysis of product quality is one of the main trends in modern machine building. At the same time, rapid development in various spheres of human activity is experienced by methods associated with the use of artificial neural networks, as the basis for building automated intelligent diagnostic systems. Technologies of machine vision allow one to effectively detect the presence of certain regularities in the analyzed designation, including defects of welded joints according to radiography data.