Using Artificial Neural Network for System Education Eye Disease Recognition Web-Based

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.

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.

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.

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.

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.

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.

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.

Multitemporal Landsat TM imagery were classified to extract land cover information using principal component analysis (PCA) and backpropagation (BP) algorithm of artificial neural network. Data used are two Landsat TM data of in Jan. 1, 1991 (Data I) and May 9, 1994 (Data II). Twelve bands data were compressed to 4 bands data by the first and second PCA. Approximately 95 percent of the total variance of each Landsat TM data was included resulting from the first and second component analysis. Analyzed data through the PCA were classified by the BP training algorithm of artificial neural network. As a result of classification, it is concluded that this approach will become an attractive and effective method in extracting land cover or land use information using multitemporal Landsat TM data.

Aim:This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses.Settings and Design:This was an in vitro study.Materials and Methods:A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the L*, a*, and b* values were recorded. The relationship between the L*, a*, and b* values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. L*, a*, and b* values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated.Statistical Analysis Used:Data were analyzed with the Student t-test (α=0.05).Results:The mean RMSE values and MAE values for the ANN algorithm (0.029 ± 0.0152 and 0.045 ± 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 ± 0.0005 and 0.002 ± 0.0008, respectively) (P < 0.001).Conclusions:Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.

We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets from the ESO-LV catalogue, for which both machine parameters and human classification are available. The main methods we explore are: (i) Principal Component Analysis (PCA) which tells how independent and informative the input parameters are. (ii) Encoder Neural Network which allows us to find both linear (PCA-like) and non-linear combinations of the input, illustrating an example of unsupervised ANN. (iii) Supervised ANN (using the Backpropagation or Quasi-Newton algorithms) based on a training set for which the human classification is known. Here the output for previously unclassified galaxies can be interpreted as either a continuous (analog) output (e.g. $T$-type) or a Bayesian {\it a posteriori} probability for each class. Although the ESO-LV parameters are sub-optimal, the success of the ANN in reproducing the human classification is 2 $T$-type units, similar to the degree of agreement between two human experts who classify the same galaxy images on plate material. We also examine the aspects of ANN configurations, reproducibility, scaling of input parameters and redshift information.

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.

Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for prediction, and it could take advantage of the computer’s high-speed computing capabilities and find the optimal solution quickly. In this paper, four culture conditions: agar concentration, light time, culture temperature, and humidity were selected. And a three-layer neural network was used to predict the differentiation rate of melon under these four conditions. Ten-fold cross validation revealed that the optimal back propagation neural network was established with traingdx as the training function and the final architecture of 4-3-1 (four neurons in the input layer, three neurons in the hidden layer and one neuron in the output layer), which yielded a high coefficient of correlation (R2, 0.9637) between the actual and predicted outputs, and a root-mean-square error (RMSE) of 0.0108, suggesting that the artificial neural network worked well. According to the optimal culture conditions generated by genetic algorithm, tissue culture experiments had been carried out. The results showed that the actual differentiation rate of melon reached 90.53%, and only 1.59% lower than the predicted value of genetic algorithm. It was better than the optimization by response surface methodology, which the predicted induced differentiation rate is 86.04%, the actual value is 83.62%, and was 2.89% lower than the predicted value. It can be inferred that the combination of artificial neural network and genetic algorithm can optimize the plant tissue culture conditions well and with high prediction accuracy, and this method will have a good application prospect in other biological experiments.