Results Of Artificial Neural Network Model Research Articles

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

Effects of CO2 concentration and time on algal biomass film, NO3–N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II

The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3–N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3–N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3–N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3–N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3–N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3–N content.

Prediction of Electrical Resistance with Conductive Sewing Patterns by Combining Artificial Neural Networks and Multiple Linear Regressions.

This study aims to estimate the impact of sewing thread patterns on changes in the resistance of conductive yarns coated with silver paste. Firstly, the structure of the conductive yarns was examined, and various variations in the length and angle of individual sewing stitches were observed and analyzed through experiments. The results revealed that as the length of an individual stitch decreased, the width of the conductive yarn increased. Additionally, variations in the stitch angle resulted in different resistance values in the conductive yarn. These findings provide essential information for optimizing sewing patterns and designing components. Secondly, the comparison between models using multiple linear regression analysis and sewing neural networks was included to show optimized resistance prediction. The multiple linear regression analysis indicated that the stitch length and angle were significant variables affecting the resistance of the conductive thread. The artificial neural network model results can be valuable for optimizing sewing patterns and controlling resistance in various applications that utilize conductive thread. In addition, understanding the resistance variation in conductive thread according to sewing patterns and using optimized models to enhance component performance provides opportunities for innovation and progress. This research is necessary for the textile industry and materials engineering fields and holds high potential for practical applications in industrial settings.

Machine learning-based modelling using ANN for performance prediction of a solar air heater design with jet impingement

The prediction of heat transfer performance for solar air heater (SAH) designs is important to assess its potential without relying on extensive experimental studies. This work aimed to predict the heat transfer characteristics of a SAH design with jet impingement using an extensive artificial neural network (ANN) study and covering a complete year-long ambient and various operating parameters (commensurate and non-commensurate). Total 84 ANN models were developed using different network types, transfer functions, and training functions. They were tested on 174 representative datasets collected throughout a year. The best ANN model was found to be the feed forward backpropagation model with tangent-sigmoid transfer function and LM training algorithm, which produced accurate predictions for thermal efficiency, exergy efficiency, and thermo-hydraulic efficiency, with sample errors within ±5 %. The numerical values of R2, MAE, RMSE, and COV from the ANN model results were respectively 0.9972, 0.9053, 1.1884, and 2.0618 for thermal efficiency, 0.9947, 0.2631, 0.3363, and 1.7157 for exergy efficiency, and 0.996, 0.0139, 0.0193, and 0.8076 for thermo-hydraulic efficiency. The study concluded that ANN models can accurately predict the heat transfer characteristics of an SAH design, requiring less time and less computational resources compared to conventional experimental or computational methods.

Reinforcement mechanism of nano-SiO2 and polypropylene fibers on concrete abrasion resistance subject to dynamic sandy water

Abrasion resistance of concrete is essential for the safety assessment of hydraulic structures subject to sandy water scouring. In this study, ten types of concrete specimens were designed to investigate the effect of nano-SiO2 (NS) and polypropylene fibers (PF) on the abrasion resistance and mechanical properties of concrete. In view of the discreteness of the results, grey theory, Support vector machine (SVR) and artificial neural network (ANN) model were selected to explore the relationship between abrasion resistance and factors involving NS, PF, compressive strength, splitting tensile strength and curing age. The results revealed that the appropriate addition of NS and PF promote the hydration process and remarkably enhanced the performance of concrete. And the grey relation analysis indicated that the optimal content of NS and PF were 3 % and 0.9 kg/m3, respectively. Compared with GM (1, N) and SVR, ANN model benefited from more comprehensive consideration of the effects of various factors to predict abrasion resistance more reliably. The results of grey relation analysis and ANN model showed that there was a close correlation between the mechanical properties, admixtures and the abrasion resistance.

Heating energy demand estimation of the EU building stock: Combining building physics and artificial neural networks

The aim of this study is to present a novel data-driven approach developed for space heating energy demand calculation of the whole EU building stock. To develop a computationally efficient bottom-up model that takes into account building physics parameters and details of the building stock make-up, an artificial neural network (ANN) is trained on a dataset of precise building-physics models. For this purpose, 2025 building energy simulations were performed in this study, ensuring representativeness for the entire EU, in terms of building stock properties (3 building geometries and 27 combinations of building envelope properties) and climatic conditions (25 weather files). The developed model is the first comprehensive hybrid (ANN and building physics) approach based on a condensed selection of input features, yet covering a wide range of climatic and building stock values, hence suitable for consideration of the spatial, temporal and multinational scale of the EU building stock. A unique building stock model was developed for this study at the EU regional level. The performance of the ANNs was validated by assessing the RMSE and R2 against the training (85%), testing (15%) and total dataset (100%, i.e. 2025 data points), to ensure adequate robustness, accuracy and generalisation capacity. As to the application of the ANN model on the building stock of 27 EU countries, a good accordance between ANN model results and measured consumption over the nine years from 2011 to 2019, was obtained, with an average overestimation by 9.3% (standard deviation (SD) = 5%), which confirmed the adequacy of the adopted methodology. Additional consideration of the occupant behaviour resulted in average overestimating the actual measurements by only 2.4% (SD = 4.7%). In addition to the EU and country level, the added value of the implemented bottom-up approach is its ability to assess the heating energy demand at regional level. Overall, the presented approach can be considered accurate and reliable to be used in future research on the long-term impact of building renovation with respect to energy savings and reductions of greenhouse gas emissions.

Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt

The assessment and prediction of water quality are important aspects of water resource management. Therefore, the groundwater (GW) quality of the Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as the drinking water quality index (DWQI) and health index (HI), supported with multivariate analysis, artificial neural network (ANN) models, and geographic information system (GIS) techniques. For this, physical and chemical parameters were measured for 140 GW wells, which indicated Ca–Mg–SO4, mixed Ca–Mg–Cl–SO4, Na–Cl, Ca–Mg–HCO3, and mixed Na–Ca–HCO3 water facies under the influence of silicate weathering, rock–water interactions, and ion exchange processes. The GW in El Kharga Oasis had high levels of heavy metals, particularly iron (Fe) and manganese (Mn), with average concentrations above the limits recommended by the World Health Organization (WHO) for drinking water. The DWQI categorized most of the samples as not suitable for drinking (poor to very poor class), while some samples fell in the good water class. The results of the HI indicated a potential health risk due to the ingestion of water, with the risk being higher for children in only one location. However, for both children and adults, there was a low risk of dermal and ingestion exposure to the water in all locations. The contaminants could be from natural sources, such as minerals leaching from rocks and soil, or from human activities. Based on the results of ANN modeling, ANN-SC-13 was the most accurate prediction model, since it demonstrated the strongest correlation between the best characteristics and the DWQI. For example, this model’s thirteen characteristics were extremely important for predicting DWQI. The R2 value for the training, cross-validation (CV), and test data was 0.99. The ANN-SC-2 model was the best in measuring HI ingestion in adults. The R2 value for the training, CV, and test data was 1.00 for all models. The ANN-SC-2 model was the most accurate at detecting HI dermal in adults (R2 = 0.99, 0.99, and 0.99 for the training, CV, and test data sets, respectively). Finally, the integration of physicochemical parameters, water quality indices (WQIs), and ANN models can help us to understand the quality of GW and its controlling factors, and to implement the necessary measures that prevent outbreaks of various water-borne diseases that are detrimental to human health.

Weight Prediction of Landlly Pigs from Morphometric Traits in Different Age Classes Using ANN and Non-Linear Regression Models

The present study was undertaken to identify the best estimator(s) of body weight based on various linear morphometric measures in Landlly pigs using artificial neural network (ANN) and non-linear regression models at three life stages (4th, 6th and 8th week). Twenty-four different linear morphometric measurements were taken on 279 piglets individually at all the stages and their correlations with body weight were elucidated. The traits with high correlation (≥0.8) with body weight were selected at different stages. The selected traits were categorized into 31 different combinations (single, two, three, four and five) and subjected to ANN modelling for determining the best combination of body weight predictors at each stage. The model with highest R2 and lowest MSE was selected as best fit for a particular trait. Results revealed that the combination of heart girth (HG), body length (BL) and paunch girth (PG) was most efficient for predicting body weight of piglets at the 4th week (R2 = 0.8697, MSE = 0.4419). The combination of neck circumference (NCR), height at back (HB), BL and HG effectively predicted body weight at 6 (R2 = 0.8528, MSE = 0.8719) and 8 (R2 = 0.9139, MSE = 1.2713) weeks. The two-trait combination of BL and HG exhibited notably high correlation with body weight at all stages and hence was used to develop a separate ANN model which resulted into better body weight prediction ability (R2 = 0.9131, MSE = 1.004) as compared to age-dependent models. The results of ANN models were comparable to non-linear regression models at all the stages.

Prediction model for the compressive strength of green concrete using cement kiln dust and fly ash

Integrating artificial intelligence and green concrete in the construction industry is a challenge that can help to move towards sustainable construction. Therefore, this research aims to predict the compressive strength of green concrete that includes a ratio of cement kiln dust (CKD) and fly ash (FA), then recommend the optimum sustainable mixture design. The artificial neural network (ANN) and multiple linear regression techniques are used to build prediction models and statistics using MATLAB and IBM SPSS software. The input parameters are based on 156 data points of concrete components and compressive strengths that are collected from the literature. The developed models have been trained, validated, and tested for each technique. TOPSIS method is used to assign the optimum mixture design according to three sustainable criteria: compressive strength, carbon dioxide (CO2) emission, and cost. The results of ANN models showed a better prediction of the compressive strength with regression (R) equal to 0.928 and 0.986. The optimum mixture includes CKD 10–20% and FA 0–30%. Predicting the compressive strength of green concrete is a non-destructive approach that has sustainable returns including preservation of natural resources, reduction of greenhouse gas emissions, cost, time, and waste to landfill as well as saving energy.

Prediction of hydraulic conductivity of porous granular media by establishment of random forest algorithm

The present study investigates the possibility of using random forest (RF) algorithm for prediction of hydraulic conductivity of coarse-sized granular material. Meanwhile, the conventional soft computing methods named artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) are considered to assess the robustness of the established approach. The characterized properties related to the particle size distribution as well as the air void content are utilized to predict the hydraulic conductivity coefficient. In addition, owing to the direct effect of the applied hydraulic head on the measured values of water flow velocity, the hydraulic gradient is also characterized as an influential parameter. The statistical comparison between the prediction capability of the RF and the results of ANN and ANFIS models corroborates the robustness of the developed procedure based on the machine learning algorithm to indirectly predict the hydraulic conductivity of the provided porous media. The results of parametric analysis mainly demonstrate that the air void content among aggregates is by far the most influential factor affecting the permeability of granular media made of coarse-sized aggregate.

Application of artificial neural network for prediction of fluoride removal efficiency using neutralized activated red mud from aqueous medium in a continuous fixed bed column.

The present research work approaches the removal of fluoride from aqueous medium using neutralized activated red mud (NARM) in a continuous fixed bed column. Artificial neural network (ANN) technique was applied effectively for optimization of the model for the practicability of the removal process. The consequences of various experimental variables, like bed length, adsorbate concentration, experimental time, and adsorbate solution flow rate are studied to know the breakthrough point and saturation times. The highest removal potentiality of NARM was considered to be 3.815mgg-1 of F- in the bed height of 15cm, starting concentration 1ppm, susceptible time 120min, adsorbate solution flow rate 0.5mLmin-1, and constant room temperature, respectively. Bohart-Adams and Thomas models were considered to describe the fixed bed column effect to the bed height and adsorbate concentrations. The experimental data were applied to a back propagation (BP) learning algorithm programme with a four-seven-one architecture model. The artificial neural network model was considered to be functioning correctly as absolute relative percentage error throughout the learning period. Differentiation between the predicted outcomes from ANN model and actual results from experimental analysis affords a high degree of correlation (R2 = 0.998) stipulating that the model was able to predict the adsorption efficiency. Experimented adsorbent materials were characterized using different instrumental analysis that is scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD).

Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing.

This study aimed to predict some soil water contents and soil erodibility indices with a multilayer perceptron (MLP) artificial neural network (ANN) using remote sensing data (Landsat 8 OLI TIRS) and topographic variables from a digital elevation model (DEM) in a semi-arid ecosystem. In models, the input variables were derived from remote sensing imaging and DEM. The output variables were field capacity, wilting point, aggregate stability index, structural stability index, dispersion ratio, and clay flocculation index. This study was realized in the watersheds of the Koruluk dam, the Kızlarkalesi, and the Telme ponds built for agricultural irrigation in Gümüşhane-Şiran. The soil samples were obtained from two depths (0-10cm and 10-20cm) from 59 soilprofiles. Besides field capacity, wilting point, and aggregate stability analysis, undispersed/dispersed sand, silt, clay contents, and organic matter analysis were performed due to their strong effect on soil moisture, soil water content, and erodibility indices. The correlation analysis results showed significant relationships between soil characteristics and soil water contents/soil erodibility indices. The remote sensing variables were derived from three Landsat images of 2015 (June, July, and September). The performance results of MLP ANN models predicted for soil water contents and erodibility indices ranged from 0.75 to 0.90 for R2, 0.046-4.115 for root mean square error (RMSE), 4.46-6.54 for normalized root mean square error (NRMSE), and 0.042-0.186 for mean absolute error (MAE). Topography was a more significant group of variables that affected soil water contents and soil erodibility indices and the feature importance of topography in the prediction was over 55%. The results showed that the use of topographic variables together with remote sensing variables in MLP ANN modeling increased the performance of the models.

Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study.

BackgroundMachine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models.MethodsThe subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset (n = 1,033) was used for model development, and a testing dataset (n = 443) was used for internal validation. Another 167 patients with stroke recruited from October, to December, 2019, were enrolled in the dataset for external validation. A feature importance analysis was also performed to identify the significance of the selected input variables.ResultsFor predicting 30-day readmission after stroke, the ANN model had significantly (P < 0.001) higher performance indices compared to the other models. According to the ANN model results, the best predictor of 30-day readmission was PAC followed by nasogastric tube insertion and stroke type (P < 0.05). Using a machine learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients.ConclusionUsing a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.

Application of response surface methodology and artificial neural network for the preparation of Fe-loaded biochar for enhanced Cr(VI) adsorption and its physicochemical properties and Cr(VI) adsorption characteristics.

In this study, we optimized and explored the effect of the conditions for synthesizing Fe-loaded food waste biochar (Fe@FWB) for Cr(VI) removal using the response surface methodology (RSM) and artificial neural network (ANN). The pyrolysis time, temperature, and Fe concentration were selected as the independent variables, and the Cr(VI) adsorption capacity of Fe@FWB was maximized. RSM analysis showed that the p-values of pyrolysis temperature and Fe concentration were less than 0.05, indicating that those variables were statically significant, while pyrolysis time was less significant due to its high p-value (0.2830). However, the ANN model results showed that the effect of pyrolysis time was more significant on Cr(VI) adsorption capacity than Fe concentration. The optimal conditions, determined by the RSM analysis with a lower sum of squared error than ANN analysis, were used to synthesize the optimized Fe@FWB (Fe@FWB-OPT) for Cr(VI) removal. From the equilibrium model fitting, the Langmuir model showed a better fit than the Freundlich model, while the Redlich-Peterson isotherm model overlapped. The Cr(VI) sorption capacity of Fe@FWB-OPT calculated from the Langmuir model was 377.71mg/g, high enough to be competitive to other adsorbents. The kinetic Cr(VI) adsorption was well described by the pseudo-second-order and Elovich models. The XPS results showed that Cr adsorbed on the surface of Fe-FWB-OPT was present not only as Cr(VI) but also as Cr(III) by the reduction of Cr(VI). The results of Cr(VI) adsorption by varying the pH indicate that electrostatic attraction is a key adsorption mechanism.

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system can enable informed decisions regarding lifetime extensions. Unfortunately direct measurement of fatigue loads typically revolves around the use of strain gauges, and the installation of strain gauges on all turbines of a given farm is generally not considered economically feasible. However, when we consider that great numbers of data, such as supervisory control and data acquisition (SCADA) and accelerometer data (of cheaper installation than strain gauges), are already being captured, these data might be used to circumvent the lack of direct measurements. It is then highly relevant to know what is the minimal sensor instrumentation required for a proper fatigue assessment. In order to determine this minimal instrumentation, a data-driven methodology is developed for real-world jacket-foundation offshore wind turbines (OWTs). In the current study the availability of high-frequency SCADA (1 Hz) and acceleration data (&gt;1 Hz) as well as regular 10 min SCADA is taken as the starting point. Along these measured values, the current work also investigates the inclusion of an estimate of the quasi-static thrust load using the 1 s SCADA using an artificial neural network (ANN). After data collection all data are transformed to features on a 10 min interval (feature generation). When considering all possible variations a total of 430 features was obtained. To reduce the dimensionality of the problem this work performs a comparative analysis of feature selection algorithms. The features selected by each method are compared and related to the sensors to decide on the most cost-effective instrumentation of the OWT. The variables chosen by the best-performing feature selection algorithm then serve as the input for a second ANN, which estimates the tower fore–aft (FA) bending moment damage equivalent loads (DELs), a valuable metric closely related to fatigue. This approach can then be understood as a two-tier model: the first tier concerns itself with engineering and processing 10 min features, which will serve as an input for the second tier that estimates the DELs. It is this two-tier methodology that is used to assess the performance of eight realistic instrumentation setups (ranging from 10 min SCADA to 1 s SCADA, thrust load and dedicated tower SHM accelerometers). Amongst other findings, it was seen that accelerations are essential for the model's generalization. The best-performing instrumentation setup is looked at in greater depth, with validation results of the tower FA DEL ANN model showing an accuracy of around 1 % (MAE) for the training turbine and below 3 % for other turbines, with a slight underprediction of fatigue rates. Finally, the ANN DEL estimation model – based on two intermediate instrumentation setups (combinations of 1 s SCADA, thrust load, low quality accelerations) – is employed in a farm-wide setting, and the probable causes for outlier behaviour are investigated.

Predicting Slump Values of Concrete Made by Pozzolans and Manufactured Sand using ANN

Large amounts of natural fine aggregate (NFA) and cement are used in building, which has major environmental consequences. This view of industrial waste can be used in part as an alternative to cement and part of the sand produced by the crusher as fine aggregate, similar to slag sand (GGBFS), fly ash, metacaolin, and silica fume. Many times, there are issues with the fresh characteristics of concrete when using alternative materials. The ANN tool is used in this paper to develop a Matlab software model that collapses concrete made with pozzolanic material and partially replaces natural fine aggregate (NFA) with manufactured sand (MS). Predict. The slump test was carried out in reference with I.S11991959, and the findings were used to create the artificial neural network (ANN) model. To mimic the formation, a total of 131 outcome values are employed, with 20% being used for model testing and 80% being used for model training. 25 enter the material properties to determine the concrete slump achieved by partially substituting pozzolan for cement and artificial sand (MS) for natural fine aggregate (NFA). According to studies, the workability of concrete is critically harmed as the amount of artificial sand replacing natural sand grows. The ANN model's results are extremely accurate, and they can forecast the slump of concrete prepared by partly substituting natural fine aggregate (NFA) and artificial sand (MS) with pozzolan.

Assessment and Evaluation of Diabetic Foot using Biothesiometry and Artificial Neural Networks

Introduction: Diabetes is a common disorder that is prevalent in the general population. As it advances, it causes a multitude of consequences, some of which are fatal. Diabetic Neuropathy (DN) is one such illness that causes nerve damage, and it is quickly recognised and diagnosed using a technique known as biothesiometry. Aim: To create an assessment tool based on an Artificial Neural Network (ANN) that evaluates diabetic foot based on Vibration Perception Threshold (VPT) values. Materials and Methods: This experimental and predictive study was done using VPT values for 696 controlled and diabetic groups selected by purposive sampling. The VPT was measured by a biothesiometre. A metal probe was placed under the foot of the person and the voltage was increased gradually from zero and the transition from no vibration to the point of vibration is marked as VPT. Average of three measurements were taken to calculate the VPT value of the given patient. This involves the VPT value which helps in the assessment of severity of the condition. The recorded data was fed as an input to the ANN model which predicted the average VPT value of the left and right foot. Furthermore, the ANN model was assessed my means of statistical measures and parameters. Results: The results of the study confirmed the correlation between the values of VPT acquired at different points of the foot and the coefficient for the left and right foot was found to be 0.99549 and 1.0000 respectively. Furthermore, the efficiency of the proposed ANN model was assessed using statistical measures like Mean Square Error (MSE), Mean Absolute Error (MAE), Square Sum Error (SSE) and Coefficient of determination (R2 ). The predicted values were very close to the experimental VPT results, and the correlation coefficient R are 0.99549 and 0.99975 for the left and right foots respectively, which shows the best settlement. Conclusion: The study concluded that VPT acquired from the foot of diabetic patients is useful in categorising the level of severity of DN. Furthermore, the results of ANN model proved that there exists a strong correlation between the average VPT values of left and right foot and those that are acquired from different points of the foot such as Great Toe, First metatarsal, Third metatarsal, Fifth metatarsal, In step and heel which concluded the study to be effective in the assessment and diagnosis of DN.

Classification of hazelnut varieties by using artificial neural network and discriminant analysis

Aim of study: This study was conducted to classify hazelnut (Corylus avellana L.) varieties by using artificial neural network and discriminant analysis. Area of study: Samsun Province, Turkey. Material and methods: The physical, mechanical and optical properties of 11 hazelnut varieties were determined for three major axes. The parameters of physical, mechanical and optical properties were included as independent variables, while hazelnut varieties were included as dependent variables. Models were created for each of the three axes to classify hazelnut varieties. Main results: Classification success rates with Artificial Neural Networks (ANN) and Discriminant Analysis (DA) were found as 89.1% and 92.7% for X axis, as 92.7% and 92.7% for Y axis and as 86.8% and 88.7% for Z axis, respectively. The classification results of ANN and DA models were found to be very close to each other. Both models can be used in the classification of hazelnut varieties. Research highlights: The results obtained for the identification and classification of hazelnut varieties show the feasibility and effectiveness of the proposed models.

Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis

In this paper, artificial neural networks (ANNs) combined with sensitivity analysis was applied to predict greenhouse tomato yield (Lycopersicon esculentum Mill.) and bring out the most influencing inputs on tomato production. The models were created using data randomly collected by a face-to-face survey from 25 greenhouse tomato farms in Biskra Province, Algeria. Results showed that total energy inputs were 94 748.2 MJ.ha−1 while energy ratio was 1.055 indicating a relatively low energy efficiency. Different ANN models were tested by varying the number of neurons in the hidden layer from 1 to 40. Based on statistics criteria, the best structure found is 12–34–1. This ANN model was used to estimate tomato yield using the energy inputs. Then, the results of ANN model were compared with those from the multiple linear regression (MLR) technique. The results illustrated that ANN provided more accurate predictions than the MLR technique. Sensitivity analysis revealed that insecticides, farmyard manure (FYM), potassium (K2O), nitrogen (N), electricity and fungicides are the most significant inputs in the greenhouse tomato production. In conclusion, ANNs are simple and effective tools for predicting tomato yield and extracting knowledge via sensitivity analysis.