Minimization of Surface Deflection in Rectangular Embossing Using Automatic Training of Artificial Neural Network and Genetic Algorithm

The purpose of the research, the results of which are presented in this article, is to determine the possibility and evaluate the effectiveness of using a trained neural network in the diagnosis of ringworm. The article provides an analysis of the methods used for diagnosing dermatomycosis in veterinary practice. One of the actively developing areas at present is the use of artificial neural networks in the diagnosis of animal diseases. The authors have developed a method for diagnosing dermatophytosis using a trained neural network. To identify hair damaged by dermatophyte spores in cats, a trained artificial neural network YOLO v5 was used, based on the YOLO architecture (high-precision artificial neural network), which provides high accuracy and speed of object detection in images. Diagnostics was carried out in three stages. The first stage: the diagnosis of hair in cats damaged by dermatophyte spores was carried out using a trained artificial neural network. The second stage: microscopy by a veterinary specialist of the veterinary center. The third stage: comparison of the received data from the trained artificial neural network and veterinary specialists. Three comparative experiments were carried out on 20 depersonalized samples with different ratios from healthy and sick animals. As a result of testing the trichoscopy method using artificial neural networks for diagnosing spore-damaged hair dermatitis in cats, it was found that a trained artificial neural network of 60 studied samples diagnosed dermatophyte spore damage in 20 samples, a veterinarian - in 17. All positive results were confirmed by a mycological laboratory study. and identification of the pathogen. It has been established that the use of a trained artificial neural network increases the diagnostic efficiency by 15% and reduces the time to perform diagnostic microscopy by 60.3%. The application of the proposed method allows to reduce the time of microscopic examination, improve the accuracy of interpretation of the results, automate methods for identifying causative agents of ringworm in small animals and take timely measures to treat the animal.

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.

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.

Approaches presented today in the scientific literature suggest that there are no methodological solutions based on the training of artificial neural networks to predict the direction of industrial development, taking into account a set of factors—innovation, environmental friendliness, modernization and production growth. The aim of the study is to develop a predictive model of performance management of innovative industrial systems by building neural networks. The research methods were correlation analysis, training of neural networks (species—regression), extrapolation, and exponential smoothing. As a result of the research, the estimation efficiency technique of an innovative industrial system in a complex considering the criteria of technical modernization, development, innovative activity, and ecologization is developed; the prognostic neural network models allow to optimize the contribution of signs to the formation of target (set) values of indicators of efficiency for macro and micro-industrial systems that will allow to level a growth trajectory of industrial systems; the priority directions of their development are offered. The following conclusions: the efficiency of industrial systems is determined by the volume of sales of goods, innovative products and waste recycling, which allows to save resources; the results of forecasting depend significantly on the DataSet formulated. Although multilayer neural networks independently select important features, it is advisable to conduct a correlation analysis beforehand, which will provide a higher probability of building a high-quality predictive model. The novelty of the research lies in the development and testing of a unique methodology to assess the effectiveness of industrial systems: it is based on a multidimensional system approach (takes into account factors of innovation, environmental friendliness, modernization and production growth); it combines a number of methodological tools (correlation, ranking and weighting); it expands the method of effectiveness assessment in terms of the composition of variables (previously presented approaches are limited to the aspects considered).

Background. There are a large number of neural networks that have their advantages and disadvantages, for example, simple, fast and easy to use single-stranded perceptrons are suitable for linear and linearized regression tasks, and more complicated neural networks are expendable in training and prediction time. Therefore, the problem arises for the development of fast and efficient algorithms for training artificial neural networks. An additional factor for researching new methods for training neural networks is finding the smallest training and prediction errors.Objective. The aim of the paper is to search and analyze the properties of the most effective method of training artificial neural networks using a combined approximation of the response surface. Another step is to perform computational experiments on proposed artificial neural networks and compare the results of experiments with known and developed methods.Methods. Analysis of known methods of combined approximation of the response surface was used. New algorithms for training neural networks, based on clustering of data using k-means method were developed. The algorithm with the smallest errors of artificial neural network learning and data prediction is chosen.Results. The results of research of different methods of training of artificial neural networks are given. Peculiarities of the methods of combined approximation of the response surface are analyzed. It is shown that the two methods of combined approximation of the response surface for training of artificial neural networks and prediction confirm the effectiveness of the proposed approach. Combined approximation algorithm is selected, which provides the lowest learning and forecasting errors.Conclusions. It was investigated that developed methods of combined approximation of the response surface allow training neural networks and predicting data with less error than when using autoregressive model with moving average, multilayer perceptron or artificial neural networks of models of geometric transformations without additional data processing.

Logarithm-approximate floating-point multiplier is applicable to power-efficient neural network training

This paper investigates the effectiveness of the Genetic Algorithm (GA) and Simulated Annealing algorithm (SA) training artificial neural network weights and biases for rainfall forecasting, namely GAS–ANN. Firstly, a hybrid GA and SA method is used to train the begining connection weights and thresholds of ANN. Secondly, the back propagation algorithm is used to search around the global optimum. Finally, a numerical example of monthly rainfall data in a catchment located in a subtropical monsoon climate in Linzhou of China, is used to elucidate the forecasting performance of the proposed GASA–ANN model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the autoregressive integrated moving average (ARIMA), back–propagation neural network (BP–NN) and pure Genetic Algorithm training Artificial Neural Network model (GA–ANN). Therefore, the GASA–ANN model is a promising alternative for rainfall forecasting.KeywordsRainfall ForecastingArtificial Neural NetworkGenetic AlgorithmSimulated Annealing

<abstract><p>The training of artificial neural networks (ANNs) with rectified linear unit (ReLU) activation via gradient descent (GD) type optimization schemes is nowadays a common industrially relevant procedure. GD type optimization schemes can be regarded as temporal discretization methods for the gradient flow (GF) differential equations associated to the considered optimization problem and, in view of this, it seems to be a natural direction of research to <italic>first aim to develop a mathematical convergence theory for time-continuous GF differential equations</italic> and, thereafter, to aim to extend such a time-continuous convergence theory to implementable time-discrete GD type optimization methods. In this article we establish two basic results for GF differential equations in the training of fully-connected feedforward ANNs with one hidden layer and ReLU activation. In the first main result of this article we establish in the training of such ANNs under the assumption that the probability distribution of the input data of the considered supervised learning problem is absolutely continuous with a bounded density function that every GF differential equation admits for every initial value a solution which is also unique among a suitable class of solutions. In the second main result of this article we prove in the training of such ANNs under the assumption that the target function and the density function of the probability distribution of the input data are piecewise polynomial that every non-divergent GF trajectory converges with an appropriate rate of convergence to a critical point and that the risk of the non-divergent GF trajectory converges with rate 1 to the risk of the critical point. We establish this result by proving that the considered risk function is <italic>semialgebraic</italic> and, consequently, satisfies the <italic>Kurdyka-Łojasiewicz inequality</italic>, which allows us to show convergence of every non-divergent GF trajectory.</p></abstract>

Extracting rules from trained neural network using GA for managing E-business

In this work a modification was made on the algorithm of Artificial Neural Networks (NN) Training of the Multilayer Perceptron type (MLP) based on multi-objective optimization (MOBJ), to increase its computational efficiency. Usually, the number of efficient solutions to be generated is a parameter that must be provided by the user. In this work, this number is automatically determined by an algorithm, through the usage of golden section, being generally less when specified, showing a sensible reduction in the processing time and keeping the high generalization capability of the obtained solution from the original method.

PurposeThe paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models.Design/methodology/approachThe study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics.FindingsThe study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes.Research limitations/implicationsA robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values.Originality/valueThis work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

Training of Artificial Neural Networks in a Distributed Environment is considered and applied to a typical example in High Energy Physics interactive analysis. Promising results showing a reduction of the wait time from 5 hours to 5 minutes obtained in a local cluster with 64 nodes are described. Preliminary tests in a wide area network studying the impact of latency time are described; and the future work for integration in a GRID framework, that will be carried in the CrossGrid European Project, is outlined.KeywordsHiggs BosonHigh Energy PhysicsLocal ClusterMaster NodeStandard Model Higgs BosonThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This chapter aimed to evaluate heuristic approach performances for artificial neural networks (ANN) training. For this purpose, software that can perform ANN training application was developed using four different algorithms. First of all, training system was developed via back propagation (BP) algorithm, which is the most commonly used method for ANN training in the literature. Then, in order to compare the performance of this method with the heuristic methods, software that performs ANN training with genetic algorithm (GA), particle swarm optimization (PSO), and artificial immunity (AI) methods were designed. These designed software programs were tested on the breast cancer dataset taken from UCI (University of California, Irvine) database. When the test results were evaluated, it was seen that the most important difference between heuristic algorithms and BP algorithm occurred during the training period. When the training-test durations and performance rates were examined, the optimal algorithm for ANN training was determined as GA.

The paper describes an evolutionary approach to artificial neural network (NN) training, which is used to determine the state of oil-production equipment. A new artificial NN weight coefficient coding method using multi- chromosomes is proposed. The genetic operators of crossingover and mutation applied to multi-chromosomes are examined. A genetic algorithm structure of artificial NN training based on the developed genetic operators is proposed. A comparison of the proposed approach to NN training with existing ones has been carried out.

Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river