Artificial neural network and optimization algorithm to improve soil resistance by means of aggregation size variation

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.

In this study, we proposed a new approach in estimating a minimum value of machining performance. In this approach, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated in order to search for a set of optimal cutting condition points that leads to the minimum value of machining performance. Three machining cutting conditions for end milling operation that were considered in this study are speed (v), feed (f) and radial rake angle (γ). The considered machining performance is surface roughness (R a). The minimum R a value at the optimal v, f and γ points was expected from this approach. Using the proposed approach, named integrated ANN–GA, this study has proven that R a can be estimated to be 0.139 µm, at the optimal cutting conditions of f = 167.029 m/min, v = 0.025 mm/tooth and γ = 14.769°. Consequently, the ANN–GA integration system has reduced the R a value at about 26.8%, 25.7%, 26.1% and 49.8%, compared to the experimental, regression, ANN and response surface method results, respectively. Compared to the conventional GA result, it was also found that integrated ANN–GA reduced the mean R a value and the number of iterations in searching for the optimal result at about 0.61% and 23.9%, respectively.

Eggshell concrete is a novel green material that aids the recycling of eggshell powder (ESP) waste while decreasing the environmental damage due to higher manufacture to develop sustainable energies. Nevertheless, current investigations on eggshell concrete are limited, and the results might vary according to admixture design variations. Despite the fact that the design of experiments is utilized to simplify and optimize the research of sustainable energies, the studies employing eggshell concrete are still uncommon. The powdered egg shells were employed as fine concrete aggregate as a tool of sustainable energies. The flexural and compressive strength of concrete with (5%, 10%, and 15%) and without egg shell are examined, and the findings are predicted by artificial neural network (ANN) and genetic algorithm (GA) as a hybridized model of ANN-GA. The contour plot research revealed that eggshell powder boosted the energy stability in an appropriate replacement proportion of 5% to 10%. Conversely, for mix designs with a larger water ratio, the partial substitution with eggshell powder is preferable. The findings demonstrate that with 5% ESP replacement, the strengths were greater than in control concrete, indicating that 5% ESP is an ideal content for maximal strength. Furthermore, in terms of transport qualities, the performance of ESP concretes was equivalent to control concrete up to 15% ESP substitution. The statistical regression indices as determination coefficient (R2) and root-mean-square error demonstrated that the ANN-GA model is an effective tool for formulating and predicting the flexural and compressive strength of eggshell concrete to develop sustainable energies.

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.

The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial voids ratio. Multi-layer perceptron training by the backpropagation algorithm was used in creating the network. It was found that both models can predict shear strength parameters for gypseous soils with good reliability. Sensitivity analysis of the first model indicated that dry unit weight and plasticity index have the most significant effect on the predicted cohesion. While in the second model, the results indicated that the gypsum content and plasticity index have the most significant effect on the predicted angle of internal friction.

The number of studies concerning the shear strength of resedimented alluvial soils is extremely limited compared to the studies conducted on fine-grained marine sediments, since alluvial soils are generally tested in remolded or reconstituted state especially in the studies investigating their liquefaction potential. In this study, estimation models were developed to predict cohesion (c) and internal friction angle (ϕ) parameters of a fine-grained alluvial soil using resedimented samples. A total of 60 undisturbed soil samples were obtained from Bafra district of Samsun province (Türkiye) by core drilling. A cone penetration test with pore water pressure measurement (CPTu) was also carried out alongside each borehole to determine the over-consolidation ratios of the samples. Physical-index property determinations and triaxial tests were conducted on the undisturbed samples. 20 sample sets were created with known physical, index, and strength characteristics. The samples are classified as CH, CL, MH, and ML according to the Unified Soil Classification System, with liquid and plastic limits ranging from 31.6–75% and 19.3 to 33.6% respectively. The c and ϕ values of the samples varied from 4.1 to 46.1 kPa and 26 to 35º respectively. The samples were then resedimented in the laboratory under conditions reflecting their original in-situ properties, and triaxial tests were repeated. The c and ϕ values of the resedimented samples ranged from 5.3 to 24.5 kPa and 28 to 32º respectively. The results indicate that the c values of the resedimented samples are generally lower than those of the undisturbed samples, whereas upper and lower bounds for ϕ values are similar. Multivariate regression analyses (MVR) were utilized to develop estimation models for predicting c and ϕ using strength and physical properties of 20 soil samples as independent variables. Three estimation models with R2 values varying between 0.723 and 0.797 were proposed for c and ϕ which are statistically significant for p ≤ 0.05. Using artificial neural networks (ANN), the estimation models developed by MVR were replicated to validate the models. ANN yielded very similar results to the MVR, where the R2 values for the correlations between c and ϕ values predicted by both methods varied from 0.852 to 0.955. The results indicate that c and ϕ values of undisturbed samples can be estimated with acceptable accuracy by determining basic physical and index properties of the disturbed samples and shear strength parameters of the resedimented samples. This approach, which enables the reuse of disturbed soil samples, can be used when undisturbed soil samples cannot be obtained from the field due to economic, logistical, or other reasons. Further research on the shear strength parameters of resedimented alluvial soils is needed to validate the estimation models developed in this study and enhance their applicability to a wider range of alluvial soils.

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.

Bearing capacity is one of the key properties required in the design of shallow and deep foundations. In conventional engineering practice, simple approaches are widely used for determining the bearing carrying capacity of foundations based on the shear strength parameters of saturated soils. However, foundations are typically placed in part or fully in the soil zone above the natural ground water table, which is in a state of unsaturated condition. The shear strength of unsaturated soils is significantly influenced by the matric suction. The bearing capacity of foundations cannot be reliably determined by extending conventional soil mechanics principles for soils that are in a state of unsaturated condition. This Companion Paper I, introduces how the shear strength can be used as a tool in the interpretation and prediction of the bearing capacity of foundations in unsaturated soils. In addition, both theoretical and experimental studies related to the bearing capacity of unsaturated soils are succinctly summarized. Numerical techniques that can be used for predicting the stress versus settlement behavior used for the design of shallow and deep foundations are summarized in Companion Paper II. The succinctly summarized information in the companion papers are valuable for geotechnical engineers for understanding and implementing the mechanics of unsaturated soils in the design of shallow and deep foundations.

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.

This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a classic control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem "cart-pole”. In this paper we have tried to train the Artificial Neural Network (ANN) with using Genetic Algorithms (program is written in MATLAB) which is compared with the output obtained using the Artificial Neural Network Toolbox provided in MATLAB. In proposed approach we have used both ANNs and Genetic Algorithm to get more optimal solution. Here we applied the approach for the Fuzzy logic technique to design a Fuzzy Logic Controllers (FLC) using ANNs and Genetic Algorithm (GA). The Fuzzy rules which are needed to control the problem will be framed with the combination of Artificial Neural Networks and Genetic Algorithm. It has been found that such a searching technique converges intelligently and much faster than conventional learning means. Performance of the presented neural network training using the genetic algorihtms is much better and providing more accurate results.

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.

In this paper, an integrated artificial neural network (ANN) and multi-objective genetic algorithm (GA) are developed to optimize the resistance spot welding (RSW) of AZ61 magnesium alloy. Since the stability and strength of a welded joint are strongly dependent on the size of the nugget and the residual stresses created during the welding process, the main purpose of the optimization is to achieve the maximum size of the nugget and minimum tensile residual stress in the weld zone. It is identified that the electrical current, welding time, and electrode force are the main welding parameters affecting the weld quality. The experiments are carried out based on the full factorial design of experiments (DOE). In order to measure the residual stresses, an X-ray diffraction technique is used. Moreover, two separate ANNs are developed to predict the nugget size and the maximum tensile residual stress based on the welding parameters. The ANN is integrated with a multi-objective GA to find the optimum welding parameters. The findings show that the integrated optimization method presented in this study is effective and feasible for optimizing the RSW joints and process.

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.

Using artificial neural network and genetics algorithm to estimate the resilient modulus for stabilized subgrade and propose new empirical formula