Time ordering in the evolution of information processing and modulation systems

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.

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.

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.

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.

We propose a complex learning algorithm for sigmoid Artificial Neural Networks (ANN). We introduce the concept of the working area of a neuron for sigmoid ANNs in the form of a band in the attribute space, its width and location associated with the center line of the band to a fixed point. We define of the centers and widths of the working areas of neurons by analogy to the radial ANNs. On this basis, an algorithm for selecting the initial approximation of network parameters, ensuring uniform coverage of the data area with neuron working areas was developed. Network learning is carried out using a non-smooth regularizer designed to smooth and remove non-informative neurons. The results of the computational experiment illustrate the efficiency of the proposed integrated approach.

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.

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.

Floods are natural disasters that often occur in the DKI Jakarta area. DKI Jakarta government needs to anticipate the impact of the flood disaster by estimating the number of people affected by the flood. The number of people affected by floods that are uncertain every month can be predicted for the future. There are many ways that can be predict the number of people affected by floods, one of them is artificial neural network method. One of learning algorithms in artificial neural networks is backpropagation algorithm. This research applies an artificial neural network method with backpropagation algorithm to predict the number of people affected by floods in DKI Jakarta. In this research, training process was carried out 100 times on each network architecture (12-10-1, 12-12-1, 12-14-1) with several parameters such as epoch, momentum constant, and learning rate. The best results in the training process are carried out to testing process to test the network. In the testing process, the best results are 12-10-1 architecture with an accuracy rate 98.704%. Based on these results, it can be said that this network can predict well and can be implemented for forecasting the number of people affected by floods in DKI Jakarta.

On-line learning algorithms for artificial neural networks (ANNs) are expected to adapt network parameters in order to face new control situations. A new on-line learning algorithm, based on sliding mode control (SMC) is presented. The results show that ANN inherits some of the advantages of SMC: high speed of learning and robustness.

A highly efficient implementation of a backpropagation learning algorithm using matrix ISA