Multi-layer Perceptron based Comparative Analysis between CNTFET and Quantum Wire FET for Optimum Design Performance

In the oil industry, the drag-reducing agent has been used to reduce turbulent friction of fluids. The main effort of this study is to examine the feasibility of four novel machine learning models, namely multilayer perceptron, M5Rules, decision table (DT), and trees M5P to estimate the percentage of drag reduction. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the crude oil pipeline system. The parameter percentage of drag reduction was taken as the essential output. In contrast, the input parameters selected the flow rate of oil, polymer concentration, kind of polymer, temperature, as well as pipe diameter and roughness. The predicted results obtained by the tools mentioned above were evaluated according to several known statistical indices, namely coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of color intensity rating and total ranking method. The training and testing results of the DT learning method for the R2, MAE, RMSE, RAE, and RRSE were (0.9616, 3.9008, 5.8698, 24.5259%, and 27.4406%) and (0.8964, 6.937, 10.318, 43.3841%, and 45.6581%), respectively. The obtained results, in analyzing the training and testing datasets, proved that DT is the best predictive network to predict the percentage of drag reduction.

Flexural and split tensile strength of concrete with basalt fiber: An experimental and computational analysis

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjustedR2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

Modelling and prediction of binder content using latest intelligent machine learning algorithms in carbon fiber reinforced asphalt concrete

Electricity demand forecasting is one of the most important components in the power system analysis. Furthermore, it is difficult and complicated process to forecast energy consumption. This study deals with modeling of the electrical energy consumption in Bosnia and Herzegovina in order to forecast future consumption of electrical loads based on temperature variables using machine learning methods. We used three different machine learning methods for analyzing short term forecasting. The methods were trained using historical load data, collected from JP Elektroprivreda electrical power utility in BiH, and also considering weather data which is known to have a big impact on the use of electric power. Comparing the results it was seen that prediction for 500 hours is pretty good in range from 92,92% for reactive power till 98.84% for active power. Four different parameters were analyzed mean absolute error, root mean squared error, relative absolute error and root relative square error. The best results for apparent power were gotten with linear regression and are presented as for mean absolute error 9.84, root mean squared error 13.62, relative absolute error 14.06%, root relative squared error 14.39%. It is also seen from the results that, the short term power consumption can be predicted which is important for maintaining of the voltage at the consumer side.

Electricity demand forecasting is one of the most important components in the power system analysis. Furthermore, it is difficult and complicated process to forecast energy consumption. This study deals with modeling of the electrical energy consumption in Bosnia and Herzegovina in order to forecast future consumption of electrical loads based on temperature variables using machine learning methods. We used three different machine learning methods for analyzing short term forecasting. The methods were trained using historical load data, collected from JP Elektroprivreda electrical power utility in BiH, and also considering weather data which is known to have a big impact on the use of electric power. Comparing the results it was seen that prediction for 500 hours is pretty good in range from 92,92% for reactive power till 98.84% for active power. Four different parameters were analyzed mean absolute error, root mean squared error, relative absolute error and root relative square error. The best results for apparent power were gotten with linear regression and are presented as for mean absolute error 9.84, root mean squared error 13.62, relative absolute error 14.06%, root relative squared error 14.39%. It is also seen from the results that, the short term power consumption can be predicted which is important for maintaining of the voltage at the consumer side.

The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.

Concrete stability is required in the design of building materials. To improve the fracture resistance of concrete, a variety of techniques and additives have been effectively employed. In this study, the capability of multiple machine learning approaches, random forest, bagging random forest, stochastic random forest, and M5P tree-based was assessed in order to discover the optimum algorithm for predicting the flexural strength of concrete mix using glass fiber. A data collection of 102 observations was analyzed for this investigation: 70% of the data was randomly chosen, while the remaining 30% data was chosen to test the models. Five statistical criteria are used to determine the efficiency of the model, i.e., the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE). The results show that the stochastic random forest approach outperforms other techniques with lower errors. According to statistical analysis, stochastic random forest predicts superior results, with a higher CC (0.991), MAE (0.4059), RMSE (0.503), RAE (15.31%), and RRSE (14.37%) in a testing stage. As per sensitivity analysis, curing days is a critical variable among other parameters in determining the flexural strength of the concrete mix.

Using the method of Stochastic Gradient Boosting, ten SMO‐SVR are constructed into a strong prediction model (SGBS model) that is efficient in predicting the breakdown field strength. Adopting the method of in situ polymerization, thirty‐two samples of nanocomposite films with different percentage compositions, components, and thicknesses are prepared. Then, the breakdown field strength is tested by using voltage test equipment. From the test results, the correlation coefficient (CC), the mean absolute error (MAE), the root mean squared error (RMSE), the relative absolute error (RAE), and the root relative squared error (RRSE) are 0.9664, 14.2598, 19.684, 22.26%, and 25.01% with SGBS model. The result indicates that the predicted values fit well with the measured ones. Comparisons between models such as linear regression, BP, GRNN, SVR, and SMO‐SVR have also been made under the same conditions. They show that CC of the SGBS model is higher than those of other models. Nevertheless, the MAE, RMSE, RAE, and RRSE of the SGBS model are lower than those of other models. This demonstrates that the SGBS model is better than other models in predicting the breakdown field strength of polyimide nanocomposite films.

Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.

Several researchers have reported the results of adding a variety of fibers to asphalt concrete described as fiber-reinforced asphalt concrete (FRAC). This research paper finds the most suitable prediction model for Marshall Stability and the optimistic bitumen content useful in glass fiber-reinforced asphalt mix by performing Marshall Stability tests and further analyzing the data in consonance with published research. Four machine learning approaches were used to find the best prediction model i.e., Artificial Neural Network, Support Vector Machine, Gaussian Process, and Random Forest. Seven statistical metrics were used to evaluate the performance of the applied models i.e., Coefficient of correlation (CC), Mean absolute-error (MAE), Root mean squared error (RMSE), Relative absolute error (RAE), Root relative squared error (RRSE), Scattering index (SI), and Bias. Test results of the testing stage indicated that the Support Vector Machine (SVM_PUK) model performs the best in validation amongst all applied models with CC values as 0.8776 MAE as 1.2294, RMSE as 1.9653, RAE as 38.33%, RRSE as 55.22%, SI as 1.0648 and Bias as 0.5005. The Taylor diagram of the testing dataset also confirms that the model based on SVM outperforms the other models. Results of sensitivity analysis show that the bitumen content of about 5% has a significant effect on the Marshall Stability.

Rice crop production contributes to the food security of India, more than 40% to overall crop production. Its production is reliant on favorable climatic conditions. Variability from season to season is detrimental to the farmer's income and livelihoods. Improving the ability of farmers to predict crop productivity in under different climatic scenarios, can assist farmers and other stakeholders in making important decisions in terms of agronomy and crop choice. This study aimed to use neural networks to predict rice production yield and investigate the factors affecting the rice crop yield for various districts of Maharashtra state in India. Data were sourced from publicly available Indian Government's records for 27 districts of Maharashtra state, India. The parameters considered for the present study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. The dataset was processed using WEKA tool. A Multilayer Perceptron Neural Network was developed. Cross validation method was used to validate the data. The results showed the accuracy of 97.5% with a sensitivity of 96.3 and specificity of 98.1. Further, mean absolute error, root mean squared error, relative absolute error and root relative squared error were calculated for the present study. The study dataset was also executed using Knowledge Flow of the WEKA tool. The performance of the classifier is visually summarized using ROC curve.

Crude oil price forecasting is an important task in the field of energy research because crude oil is a world’s major commodity with a high volatility level. This study proposes the Adaptive Neuro-Fuzzy Inference System (ANFIS) with parameters optimized by Biogeography-Based Optimization (BBO) algorithm and Mutual Information (MI) technique for forecasting crude oil price. The MI is utilized to maximize relevance between inputs and output and minimize the redundancy of the selected inputs. The proposed approach combines the strengths of fuzzy logic, neural network and the heuristic algorithm to detect the trends and patterns in crude oil price data, and thus have been successfully applied to crude oil price forecasting. Other different forecasting methods, including artificial neural network (ANN) model, ANFIS model, and linear regression method are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (R). The performance indexes show that the ANFIS-BBO model achieves lower MAE, RMSE, RAE and RRSE, as well as higher R, indicating that the ANFIS-BBO model is a better method.

The main objective of this study is to examine the feasibility of several novel machine learning models and compare their network performance with the hybrid evolutionary based algorithm. In this regard, the best fit from the above machine learning-based solutions (i.e., known as M5Rules) were combined with the genetic algorithm (GA). These techniques were used to estimate the amount of heating load (HL) mitigation from an EEB (energy efficiency buildings) system. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the EEB system. The amount of HL was taken as the essential output of the EEB system, while the input parameters were relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. The predicted results for datasets from each of the above-mentioned models were evaluated according to several known statistical indices such as correlation coefficient (R2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of colour intensity rating and total ranking method. The M5Rules has been proposed as the best predictive network in this study and combined with the GA optimization algorithm. The results of the M5Rules–GA network indicated the R2, MAE, RMSE, RAE, and RRSE for the training and testing datasets were (0.9992, 0.0406, 0.0617, 6.2156, and 6.0189) and (0.9984, 0.0401, 0.0548, 6.4058, and 6.1785), respectively. Comparing to another non-hybrid proposed model with high accuracy (i.e., MLP Regressor with the R2 equal to 0.9876 and 0.9903 for the training and testing datasets, respectively), the results revealed that the M5Rules-GA network model could accomplish better performance.

The concept of data mining is to classify and analyze the given data and to examine it clearly understandable and discoverable for the learners and researchers. The different types of classifiers are there exist to classify a data accordingly for the best and accurate results. Taking a primary data, and then classifying it into different portions of parts, then to analyze and remove any ambiguities from it and finally make it possible for understanding. With this process, that data will become secondary from primary and will called information. So, the classifiers are doing the same strategy for the solution and accuracy of the data. In this paper, different data mining approaches have been used by applying different classifiers on the taken data set. The data-set consists of 500 candidates’ segregated data for the analysis and evaluation to perfectly classify and to show the accurate results by using the proposed Algorithms. The data mining approaches have been used in which HUGO (Highly Undetectable steGO) Algorithm, Naïve Bayes Classification, k-nearest neighbors and Logistic Regression are used with the extension of the other classification methods that are Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) as classifiers. These classifiers are given names for further analysis that are Classifier-1 and Classifier-2 respectively. Along with these, a tool is used named WEKA (Waikato Environment for Knowledge Analysis) for the analysis of the classifier-1 and 2. For performance evaluation and analysis the parameters are used for best classification that which classifier has given best performance and why. These parameters are RRSE (Root Relative Square Error), RAE (Relative Absolute Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). For the best and outstanding accuracy of the proposed work, these parameters have been tested under the simulation environment along with the incorrect, correct classifying and the %age has been witnessed and calculated. From simulation results based on RRSE, RAE, MAE and RMSE, it has been shown that classifier-1 has given outstanding performance among the others and has been placed in highest priority.