Identification of a Moss Growth System using an Artificial Neural Network Model

Purpose – This paper aims to predict the needle penetration force (NPF) in denim fabrics using the artificial neural network (ANN) and multiple linear regression (MLR) models based on the effects of various sewing parameters. Design/methodology/approach – In order to design the ANN and MLR models, four parameters including fabric weight, number of fabric layers, weave pattern, and sewing needle size are taken into account as the input parameters and NPF as the output parameter. According to these parameters, 140 samples of data were resulted. Each sample was tested five times. From these 140 data (input-output data pairs), 112 were used for training the ANN and MLR models and 28 samples were used to test the performance of ANN and MLR. Also, the NPF was measured on the Instron tensile tester to simulate sewing process. Findings – The results indicated that the NPF in denim fabrics can be well predicted in terms of sewing parameters by using ANN and MLR models, in which the ANN model exhibits greater performance than MLR (RANN=0.989> RMLR=0.901). Research limitations/implications – The NPF measurement method is limited at low speed. Originality/value – Using the ANN model for forecasting NPF in denim fabrics can help the garment manufactures to produce high-quality denim products and improve the sewing process through reducing seam damage. The NPF could be also measured in the cycle loading conditions similar to sewing machine process by using a special designed tools mounted on the Instron tensile tester.

In this study, ground vibrations caused by blasting applications in a quarry were recorded and these values were evaluated and estimated by using an artificial neural network (ANN) model. Of the 28 vibration data measured, 20 were used for ANN training, 4 for validation and the remaining 4 for testing. In the model, peak particle velocity (PPV) was used as the output parameter, and the maximum explosive amount per delay and scaled distance were used as input parameters. In addition, MAPE, RMSE and R2 performance criteria were calculated from the realized, predicted by ANN and PPV values obtained from the field equation. The maximum amount of explosives used per delay and the sensitivity analysis of the scaled distance on the highest particle velocity were also determined. As a result, when the vibration data calculated from the field equation and estimated from the ANN model were compared with the realized vibration data, it was seen that the values obtained by the ANN model had a higher correlation.

Estimating olive (Olea europaea L.) leaf area is an important aspect of monitoring plant health and evaluating growth processes in agriculture. Accurate estimation of leaf area allows for a better understanding of processes such as water and nutrient utilization, photosynthesis efficiency, respiration, and yield potential. This study aims to determine the most accurate, easy, and reliable leaf area estimation model using the geometric properties (length and width) of olive leaves. Additionally, the predictive performances of multiple linear regression (MLR) and artificial neural network (ANN) were compared. A total of 1320 leaf samples collected from 22 olive cultivars were used in the study. Leaf length and width were taken as input parameters, and both MLR and ANN models were developed for each cultivar. Both multiple linear regression (MLR) and artificial neural network (ANN) models demonstrated high predictive accuracy for olive leaf area estimation across 22 cultivars. The MLR models explained up to 96% of the variation in leaf area using leaf length (LL) and leaf width (LW), with low root mean square errors, indicating strong reliability. When cultivar identity was modeled as a categorical factor through dummy encoding, the model captured significant cultivar-specific effects without altering the overall predictive performance. The ANN models achieved slightly higher accuracy, with determination coefficients exceeding 0.99 and minimal prediction errors, confirming their superior ability to model nonlinear relationships. Across both approaches, leaf width contributed more strongly to leaf area than leaf length. Cultivar-specific differences were statistically significant for only a few genotypes, while most cultivars exhibited comparable patterns after adjustment for multiple testing. In conclusion, both MLR and ANN models demonstrated high accuracy in predicting olive leaf area, with ANN models showing slightly superior performance. However, MLR models also yielded highly reliable results, indicating that both approaches are viable for practical applications in olive cultivation. These predictive models can be effectively used for rapid, non-destructive phenotyping, growth monitoring, and precision management in olive breeding and production systems.

Constructing processing map of Ti40 alloy using artificial neural network

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

Genetic algorithm (GA) - Artificial neural network (ANN) modeling for the emission rates of toxic volatile organic compounds (VOCs) emitted from landfill working surface

Frequently drilling activity causes formation damage. The damage can be estimated from the drill stem test analysis. Formation damage is indicated by a skin factor (S) with higher than 0 or flow efficiency parameter (FE) lower than 100%. Improvement of formation damage can cause an increase in flow rate. Artificial neural network (ANN) model is used to estimate the increase flowrate of productive zones on “X” Field with FE 20% to 100%. The ANN model used reservoir pressure, temperature, permeability, formation thickness, specific gravity of oil, gas to oil ratio, oil viscosity, and flow efficiency as input parameters. While the flow rate is as the output parameter. Based on predictions with the ANN model obtained the increase in flow rate varies between 1.1% to 78.0%. Besides, it is found that flowrate of the layers on the field is influenced by the product of kh, where kh is equivalent with the flowrate.

Mathematical and neural network prediction model of three-phase immiscible recovery process in porous media

An Artificial Neural Network (ANN) model was created in this research to estimate and predict the amount of avocado production in Mexico. In the development of the ANN model, the years that are time variable were used as the input parameter, and the avocado production amount (tons) was used as the output parameter. The research data includes avocado production in Mexico for 1961-2020 period. Mean Squared Error (MSE) and Mean Absolut Error (MAE) statistics were calculated using hyperbolic tangent activation function to determine the appropriate model. ANN model is a network architecture with 12 hidden layers, 12 process elements (12-12-1) and Levenberg-Marquardt back propagation algorithm. The amount of avocado production was estimated between 2021 and 2030 with the ANN. As a result of the prediction, it is expected that the amount of avocado production for the period 2021-2030 will be between 2,410,741-2,502,302 tons.

The efficiency of the compressor's thermodynamic, mechanical, and electric motors can be increased by minimizing their losses. Based on the design characteristics of the compressor, the current study proposes an artificial neural network (ANN) model to forecast the mass flow rate, cooling capacity, compression power, and discharge line loss of the hermetic reciprocating compressor as outputs. This study uses experimental and numerical results obtained by the fluid–structure interaction (FSI) method, and the whole findings are used as inputs for the machine learning method. Using 36 numerical and 3 experimental data sets, a multilayer network is created with input parameters defining mass flow rate, cooling capacity, compression power, and discharge line energy loss. The input parameters for the network model are the compressor's design parameters, which include the compressor speed, discharge valve thickness, and discharge valve length. When comparing the experimental and FSI methods for compressor speeds of 1300, 2100, and 3000 rpm, the FSI study's mass flow rate and cooling capacity parameters converged at 11.9%, 9.1%, and 9.3%, respectively, to their actual values. The convergence rate to experimental compression power data was −0.3%, 2.6%, and 11%. The numerical-experimental deviation for discharge line energy losses is 14.9%, −3.6%, and 7.8%, respectively, for 1300, 2100, and 3000 rpm. With the Levenberg–Marquardt (LM) ANN model, which is used in this study, the average squared error value is calculated as 1.75E-02 and the R2 value is calculated as 0.99976. ANN model can predict outputs with average deviation rates of less than 0.88%. It is seen that results obtained with the ANN method provide high convergence on the experimental and FSI results, while the results obtained with the regression model deviate from the compressor exhaust line results by more than ±20%, and the ANN method is more successful than regression.

In the current article, an experimental investigation has been implemented of flow and heat transfer characteristics in a parabolic trough solar collector (PTSC) using both nano-fluids and artificial neural networks modeling. Water was used as a standard working fluid in order to compare with two different types of nano-fluid namely, nano-CuO /H2O and nano-TiO2/ H2O, both with a volume concentration of 0.02. The performance of the PTSC system was eval-uated using three main indicators: outlet water temperature, useful energy and thermal efficiency under the influence of mass flowrate ranging from 30 to 80 Lt/hr. In parallel, an artificial neural network (ANN) has been proposed to predict the thermal efficiency of PTSC depending on the experimental re-sults. An Artificial Neural Network (ANN) model consists of four inputs, one output parameter and two hidden layers, two neural network models (4-2-2-1) and (4-9-9-1) were built. The experimental results show that CuO/ H2O and TiO2/H2O have higher thermal performance than water. Overall, it was veri-fied that the maximum increase in thermal efficiency of TiO2/H2O and CuO/H2O compared to water was 7.12% and 19.2%, respectively. On the oth-er hand, the results of the model 4-9-9-1 of ANN provide a higher reliability and accuracy for predicting the Thermal efficiency than the model 4-2-2-1. The results revealed that the agreement in the thermal efficiency between the ANN analysis and the experimental results about of 91% and RMSE 3.951 for 4-9-9-1 and 86% and RMSE 5.278 for 4-2-21.

Predicting heat release properties of flammable fiber-polymer laminates using artificial neural networks

Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

We present an Artificial Neural Network (ANN) model and a new correlation for predicting natural gas density under nanoconfined conditions, accounting for pressure, temperature, fluid composition, pore size, and fluid-solid interaction coefficients. We investigate a five-component, single-phase dry shale-gas system over a broad composition space, generating 7,500 data points using the multicomponent simplified local density (MSLD) method. Gas compositions together with key input parameters – pressure (P), temperature (T), pore size (d), solid-solid interaction energy (ɛss), and solid-solid molecular diameter (σss) are sampled via Latin Hypercube Sampling (LHS) to obtain a space-filling design that covers the full range of each input with far fewer runs than a full factorial design. These data points are divided into 70% for the training set, 15% for the validation set, and 15% for the testing set. The ANN model is then optimized by adjusting key parameters such as the number of hidden layers, the number of neurons per layer, and the choice of training algorithms, among others. The performance of the ANN model is evaluated by comparing its predictions with those of MSLD. An empirical correlation is further developed from the MSLD-generated dataset to provide a simple, explicit, and fast predictive expression suitable for direct implementation in reservoir simulators. The results demonstrate that the ANN model can effectively replace the computationally intensive MSLD calculations, offering a significantly faster alternative without compromising accuracy. The ANN model achieved over 99% prediction accuracy. The fine-tuned ANN model and empirical correlation enable highly accurate predictions while delivering substantial computational savings, making it well-suited for compositional reservoir simulations of hydrocarbon flow in nanopores. To demonstrate the practical applicability of the proposed approach, a compositional reservoir simulation case of CO2 injection into a shale gas system is performed using MSLD, the ANN model, and the developed empirical correlation, enabling a direct comparison of their predictive capability and computational efficiency.

In this research work, we developed a mathematical model of a digital currency market, involving daily closing price as a function of time. We proposed the Artificial Neural Network (ANN) model. We observed that our ANN model was able to predict the daily closing price of Bitcoin and also make six weeks forecast to a reasonable degree of accuracy. We equally observe that the time dependent ANN model can actually give digital currency traders and investors a clue on when to trade off their digital assets with minimum risk. We therefore, recommend that ANN model should be incorporated into digital currency trading platforms as a signal tool to enable digital currency traders take more informed and less risky trading decisions. From our findings, we would advise traders who wish to employ ANN model to consider a smaller time frame say a few weeks’ time interval for their predictions. We observed also that ANN models have limitations when it comes to manual computation or implementation in Microsoft Excel, especially when dealing with very large input values. This is because of the saturation characteristic of our ANN inner layer activation function (viz; tanh function) which can lead to identical output values for different input values, making it difficult to replicate the ANN model's behavior. Furthermore, ANN models often involve complex interactions between multiple neurons, layers, and activation functions, which can be challenging to replicate manually.