Neural computation as a tool for galaxy classification: methods and examples

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

This paper presents an algorithm for reducing speckle noise from optical coherence tomography (OCT) images using an artificial neural network (ANN) algorithm. The noise is modeled using Rayleigh distribution with a noise parameter, sigma, estimated by the ANN. The input to the ANN is a set of intensity and wavelet features computed from the image to be processed, and the output is an estimated sigma value. This is then used along with a numerical method to solve the inverse Rayleigh function to reduce the noise in the image. The algorithm is tested successfully on OCT images of Drosophila larvae. It is demonstrated that the signal-to-noise ratio and the contrast-to-noise ratio of the processed images are increased by the application of the ANN algorithm in comparison with the respective values of the original images.

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

The computational cost of training artificial neural network (ANN) algorithms limits the use of large systems capable of processing complex problems. Implementing ANNs on a parallel or distributed platform to improve performance is therefore desirable. This work illustrates a method to predict and evaluate the performance of distributed ANN algorithms by analyzing the performance of the comparatively simple mathematical operations, which are used to construct the ANN. The ANN algorithms are divided into simple components: matrix and vector multiplication, matrix processed through a function, competition in a matrix. These basic operational parts are examined individually and it is demonstrated that the computation processes of distributed neural networks can be derived from the composition of these basic operations. Three popular network architectures are examined: multi-layer perceptrons with back-propagation learning, self-organizing map, and radial basis functions network.

Ship stiffened panels, typically flat plates reinforced with various types of stiffeners, form a substantial part of a ship’s structure and are susceptible to resonance, especially in areas such as the after peak structure, engine room, and accommodation compartments. These vibrations are primarily excited by main engine and propeller forces. Conventional methods such as finite element analysis (FEA) and plate theory are widely used to estimate vibration frequencies, but they are time-consuming and computationally intensive when applied to numerous stiffened panels. This study proposes a machine learning approach using an artificial neural network (ANN) algorithm to efficiently predict the vibration frequencies of ship stiffened panels. A crude oil tanker is chosen as the case study, and FEA is conducted to generate the vibration frequency and mass data for panels across critical regions. The input layer features for the ANN include panel area, thickness, number and area of stiffeners, fluid density, number of fluid contact sides, and overall structural stiffness. The ANN model predicts two outputs: the fundamental vibration frequency and the mass of the panel structure. To evaluate the model performance, hyperparameters such as the number of hidden neurons are optimized. The results indicate that the ANN achieves accurate predictions while significantly reducing the time and resources required compared with conventional methods. This approach offers a promising tool for accelerating the local vibration analysis process in ship structural design.

This study analyzed consumer intention to repurchase a smartphone, using an analysis of previous research and suggesting this as a research model. To this end, factors influencing “intention to repurchase” the smartphone were derived as social influence, consumer satisfaction, emotional loyalty, and habit. In addition, statistical analysis was conducted to investigate consumer repurchase intentions and the causal relationships. It is also possible to investigate whether consumer habits are linked to repurchases by analyzing their psychological inclinations; therefore, in this study, 390 people who repurchased a smartphone over the past two years were surveyed, and data were collected. This study analyzed the causal relationships among the factors using SPSS 24.0. Moreover, the causal relationship analysis was enhanced using the artificial neural network (ANN) algorithm. With multiple regression analysis and the ANN algorithm, consumer satisfaction (0.71), emotional loyalty (0.108), and social influence (0.062) were determined to affect the intention to repurchase. This means that the ANN algorithm can be used over multiple regression analysis and improve the results of the analysis. In addition, this study also provided practitioners with a way to improve their understanding of consumer behavior intention to repurchase smartphones.

Machine learning algorithms are used to analyze medical data sets effectively in present. Today machine learning gives us several necessary tools for intelligent data analysis and research. Especially in very recent years, the digital world has provided relatively inexpensive and available means to collect and store the data. The main aim is to implement supervised machine learning concept by using datasets regarding blood cells collected from blood cells detecting and counting sensors, of a human as the input which is trained by artificial neural network algorithm and apply decision tree classification learning algorithm to perform classification which results in recognizing and also possibly predict the disease based on the nature of blood cells and classify accordingly. Artificial Neural network algorithm seems to avoid pruning problem and has higher efficiency and accuracy in training the datasets. Also the using of Decision tree is because they are easy to interpret, understand and also possess non-linear characteristics between values. This holds well the performance of the tree constructed which gives better outputs. Beside all the application developed using machine learning in day today life, the use of such learning algorithm in such medical application will enhance and benefit the medical field.

Today, the amount of biological data types obtained are increasing every day. Among these data types are micro arrays that play an important role in cancer diagnosis. The data analysis that are carried out through traditional approaches have proven unsuccessful in delivering efficient results on data types where data complexity is high and where sampling is low. For this reason, using a hybrid algorithm by merging the effective features of two distinct algorithms will yield effective results. In this study, a classification process was performed firstly by dimension reduction on micro array data that were obtained from the tissues from patients with a tumor in their central nervous system and then by using an artificial neural network algorithm that was optimized through Fire Fly Algorithm (FF), a hybrid approach. The data obtained were compared to K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) classification algorithms, which are frequently used in the literature. Also, the results were compared to the findings that were obtained from artificial neural networks, which are reinforced by Genetic Algorithm (GA), another hybrid approach. Then the results were shared. The performance results obtained show that hybrid approaches present a highly precise and more efficient classification process but they show a slower performance than basic classification algorithms.

Residual thermal stress analysis in cylindrical steel bars using finite element method and artificial neural networks

Inversion for acoustic impedance of a wall by using artificial neural network

Maintenance and repair of ship systems and prediction of fault probability have become an important issue for dynamic ship systems recently. Increasing the usability of systems by detecting the fault analysis is one of the current work areas. In recent years, the rapid development of information technologies and the machine learning approaches developing accordingly have made it possible to integrate machine learning techniques into ship systems. The use of machine learning in the studies has enabled this method to be tested in the areas of maintenance, repair, and fault analysis. To be able to predict the fault that may occur in the ship machinery systems and prevent the fault accordingly, can increase the lifespan of ship machinery's. In this study, the data obtained from an LM-2500 type ship engine were analyzed by regression and Artificial Neural Networks (ANN) algorithms to predict the fault of ship machinery. The results were compared for linear regression, decision tree regression, k nearest neighbours’ regression, random forest regression, bayesian ridge regression, extra tree regression, linear SVR regression and ANN algorithms. As a result of the analysis, it was revealed that the ANN method gave better results in machine fault prediction compared to the regression methods.

The searching for patterns and model construction in Data-mining is the important contribution. Previous works report that the component search (CS) algorithm is successful and effective for datamining. Conventional research in pattern search and modeling in data-mining only consider data in a static state. Studies data-mining in dynamic environments are scarce. The artificial neural network (ANN) algorithm can solve dynamic condition problems. This study integrates the CS and ANN algorithm to create the novel (CS-ANN) algorithm that solves pattern-recognition problems and can simply construct manufacturing inspection models for dynamic environments. Simply experimental results indicate that the CS algorithm is computationally efficient for solving pattern-recognition problems and that the ANN algorithm enables simple and efficient modeling of dynamic systems. The CSANN algorithm is effective for pattern-recognition and modeling of dynamic systems.

This paper compares known architectures and training algorithms of multi-layered artificial neural networks (ANNs) and their ability to solve one specific type of chess problem (the Hamiltonian Cycles known as the Knight's Tours). The networks used here are trained to solve tours starting from the center of a five-by- five chessboard. The ANN architectures focused on are the function approximation feedforward architecture; the classification feedforward architecture; and two customized variations of the function approximation feedforward architecture. The training algorithms compared include Levenberg-Marquardt; variable learning (adaptive) gradient descent; resilient backpropagation; the Fletcher-Reeves conjugate gradient; and the Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton algorithm. These five training algorithms are used for each of the four architectures. The real world application of this research is limited only by the functions given and the parameters used to define the environment. This can be expanded to include a variety of chess pieces (functions) in a variety of environments (any m-by-n sized board or even multidimensional problem solving). The practical limitation on the application is the software necessary to run extremely large neural networks.

Spam detection is one of the major problems which considered by many researchers by different developed strategies. Artificial Neural Network (ANN) is one of many others being proposed. However designing an ANN is a difficult task as it requires setting of ANN structure and tuning of some complex parameters. In this study, ANN was hybridized with Genetic algorithm (GA) in order to optimize the performance of ANN for spam detection. GA was used to determine some ANN parameters and suggest optimum weights to efficiently enhance the ANN learning. Experimental results show that the hybrid ANN and GA has superior performance when compared to conventional ANN.

As an innovative approach in this study, artificial neural network and cuckoo algorithm have been applied to estimate and optimize the main cutting forces of various Si brass alloys during turning operation due to economic reasons. Accordingly, the chemical composition (Cu, Zn and Si contents) and process parameters (cutting speed, feed rate and depth of cut) are simultaneously implemented as input variables and the main cutting force is adjusted as an output variable. Moreover, the genetic algorithm is used to determine the optimum condition of the input parameters to obtain the lowest amounts of the main cutting force. Coupling of the hybrid cuckoo algorithm with artificial neural network has resulted in decreasing the mean absolute percentage error of the optimum structure (6-10-7-1) from 9.025 to 1.59E–6%. The validation of the proposed model has been done by performing the new set of experimental tests. The measured and predicted main cutting forces are in good agreement. The Si brass alloys including Zn equivalent about 44.97 wt% has the lowest main cutting force due to the formation of the Widmanstäetten morphologies in the microstructure. The outcome of this study may be useful for machining industry of the free-cutting Si brasses.