Deep learning-based alpha particles spectroscopy with solid-state nuclear track detector CR-39

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.

Solid-state nuclear track detectors (CR-39 type) are frequently used for the detection of ions accelerated by laser-plasma interaction because they are sensitive to each single particle. To the present day, CR-39 detectors are the main diagnostics in experiments focused on laser-driven proton-boron (p11B) fusion reactions to detect alpha particles, which are the main products of such a nuclear reaction, and to reconstruct their energy distribution. However, the acceleration of multispecies ions in the laser-generated plasma makes this spectroscopic method complex and often does not allow to unambiguously discriminate the alpha particles generated from p11B fusion events from the laser-driven ions. In this experimental work, performed at the PALS laser facility (600 J, 300 ps, laser intensity 1016 W/cm2), CR-39 detectors were used as main detectors for the angular distribution of the produced alpha particles during a p11B fusion dedicated experimental campaign. Additionally, a CR-39 detector was set inside a Thomson Parabola (TP) spectrometer with the aim to calibrate the CR-39 response for low energetic laser-driven ions originating from the plasma in the given experimental conditions. The detected ion energies were ranging from hundreds of keV to a few MeV, and the ion track diameters were measured for etching times up to 9 hours. The goal of the test was the evaluation of the detectors’ ability to discriminate the alpha particles from the aforementioned ions. Within this study, the calibration curves for protons and silicon low energy ions are accomplished, the overlapping of the proton tracks and alpha particles is verified, and a methodology to avoid this problem is realized.

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.

Unmanned Aerial Vehicles (UAVs) have become an integral part of several real-world applications. Their combination with other evolving paradigms such as image recognition using machine learning or deep learning algorithms has contributed to the suitability for use in smart agriculture and weed detection applications. In this paper, the performances of the Artificial Neural Network (ANN) and AlexNet algorithms for weed detection using UAV-based images have been studied. An image dataset containing 15336 segments with the following breakdown: 3249 of soil, 7376 of soybean, 3520 grass and 1191 of broadleaf weeds has been used. The partitioning for train and test sets has been done in the ratio of 70:30. Simulation results show that the conventional ANN algorithm provide an accuracy of 48.09% while the AlexNet algorithms gives an accuracy 99.8% on the test dataset.

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.

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.

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.

A fast training algorithm for artificial neural networks using a feedforward multilayer perceptron architecture is presented. The application of this algorithm to the problem of multiuser detection in the synchronous DS CDMA channel is investigated. The performance of this multiuser detector is shown to be very close to the single user bound. The training algorithm is compared with the conventional gradient descent-backpropagation algorithm. It is demonstrated that the new training algorithm converges significantly faster than the backpropagation algorithm, while keeping the performance constant. Finally, a pre-training algorithm is proposed in order to further reduce the length of the required training sequence.

An improved genetic algorithm is proposed in which a diffusing operator is designed. Gaussian mutation method is applied in diffusing operator and its task is mainly to perform local search. Connection weights of an artificial neural network are trained on standard XOR problem by using the proposed genetic algorithm. The results show that the proposed genetic algorithm can perform both global search and local search efficiently, therefore, it can be used to train artificial neural networks alone rather than incorporate other local search algorithms, such as BP to improve local search of training algorithm, so the proposed genetic algorithm is significant to simplify training algorithm of artificial neural networks and improve training efficiency

Experimental and theoretical evaluation of thermophysical properties for moist air within solar still by using different algorithms of artificial neural network

This paper presents the suitability of different artificial neural network (ANN) algorithms in estimating the voltage instability of power systems. The ANN models based on different training algorithm are designed and a comparative study is carried out to accurately predict the voltage collapse phenomenon. In the present study, L-index is used as the voltage collapse proximity indicator. This approach is tested on a sample 5-bus system taken from the literature. It is found that the results obtained are quite promising in predicting the voltage collapse phenomenon.

Artificial neural networks modelling: Gasification behaviour of palm fibre biochar