Rice crop yield prediction in India using support vector machines

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions.

This study aimed to forecast the daily reference evapotranspiration (ETo) using a gene-expression programming (GEP) algorithm with limited public weather forecast information over Gaoyou station, located in Jiangsu province, China. To calibrate and validate the gene-expression code, important meteorological data and weather forecast information were collected from the local meteorological station and public weather media, respectively. The GEP algebraic formulation was successfully constructed based only on daily minimum and maximum air temperature using the true FAO56 Penman-Monteith (PM) set as reference values. The performance of the models was then assessed using the correlation coefficient (R), root mean squared error (RMSE), root relative squared error (RRSE) and mean absolute error (MAE). The study demonstrated that GEP is able to calibrate ETo (all errors ≤0.990 mm/day, R = 0.832–0.866) and forecast the daily ETo with good accuracy (RMSE = 1.207 mm/day, MAE = 0.902 mm/day, RRSE = 0.629 mm/day, R = 0.777). The model accuracies slightly decreased over a 7-day forecast lead-time. These results suggest that the GEP algorithm can be considered as a deployable tool for ETo forecast to anticipate decision on short-term irrigation schedule in the study zone.

Analyzing the hidden information from the images are helpful to identify the various causes. In general processing of images includes Pre-processing, segmentation, Feature extraction and Classification. Significance of classifier is essential since results are always based on the classifier. The ultimate aim is to explain how WEKA tool is used for rheumatoid arthritis and investigate the performance of the various classifiers for huge data. In our method we are distinctively give attention to the classification methods like ADTree, Best First Decision tree(BF), Decision stump, J48Pruned tree, J48 Graft Pruned tree, Least Absolute Deviation regression trees (LAD), Logistic Model Tree(LMT), Naïve-Bayes (NB), Random tree, Random forest tree, CART Decision tree. The features like Area, perimeter, circularity, integrated density, Median, Skewness, Raw integrated density, and Roundness and solidity are obtained from the Lymphocytes images and formed the data set. Different classifier is applied for RA facet for Validation. RA facet contains 108 rows and 10 columns. Using classifier to find out the various values like Relative Absolute Error, and Kappa Statistic, Mean Absolute Error, Root Mean Squared Error and Root Relative Squared Error. From those values compare with all the methods ADT classifier is suggested for use in huge data.

Reservoir permeability is a crucial parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Permeability can be conventionally estimated from traditional approaches such as core analysis and well-test data, which are time-consuming and expensive. Many scientists tried to estimate permeability from nuclear magnetic resonance (NMR) logs utilizing complex mathematical equations that may achieve imprecise approximation of the permeability values. Gradient boosting generates additive regression models by successively fitting a straightforward base learner to present pseudo-residuals using least squares at every iteration. The execution speed and accuracy of gradient boosting can be considerably enhanced by applying randomization process. Besides, the randomization process improves robustness against over fitting of the base learner. So, the novelty of the current study is the development of a Stochastic Gradient Boosting (SGB) regression model to predict the permeability of petroleum reservoirs based on well logs. Besides benchmark machine learning techniques are used to predict reservoir permeability from well logs. The correlation coefficient (R), relative absolute error (RAE), mean-absolute error (MAE), root mean-squared error (RMSE), and root relative squared error (RRSE) are utilized to check the overall fitting between measured permeability versus predicted ones. It is found that Stochastic Gradient Boosting model achieved overall better performance than the other models.

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE).

In this article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing data from 1990 to 2000. Comparisons were based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Square Error (RRSE) as well as Simulation Time. The best results were obtained for the Multilayer Perceptron.

This paper presents development of an artificial intelligence (AI)‐based model, genetic expression programming (GEP) to predict long‐term streamflow using large‐scale climate drivers as predictors. GEP is chosen over artificial neural networks (ANNs) model, as ANN is a black‐box model, whereas GEP is able to explain the developed forecast models with mathematical expressions. As a case study, 12 streamflow measuring stations were selected from four different regions of New South Wales (NSW) in eastern Australia. A number of climate indices, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO) and ENSO Modoki index (EMI), were selected as candidate predictors based on the findings of some preliminary studies. Higher predictabilities of the GEP‐based models are evident from the Pearson correlation (r) values ranging between 0.57 and 0.97, which are mostly about twice the values achieved by multiple linear regression (MLR) models in the preliminary study. Performances of the developed models were assessed using standard statistical measures such as root relative squared error (RRSE), relative absolute error (RAE), root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and Pearson correlation (r) values. The developed models are able to predict spring streamflow up to 5 months in advance with significantly high correlation values.

Energy-saving design is becoming a trending topic and top-priority over the past decades due to high energy costs, limited available resources and growing urban development. Buildings are alluded to as the major contributors of energy consumption and environmental emissions across the globe. This calls for the development of precise forecasting models of energy consumption and carbon emissions. Hence, this research paper harnesses the implementation of several contemporary metaheuristics to accurately project heating and cooling energy (HEN and CEN) in residential buildings. In this respect, black widow optimization, dandelion optimization, dingo optimization, sparrow search, and red-tailed hawk optimization are among the studied metaheuristics in this research study. The prediction accuracies of the developed models are assessed stepping on the measures of i) relative absolute error (RAE), ii) mean absolute error (MAE), iii) mean absolute percentage error (MAPE), iv) root mean squared error (RMSE) and v) Nash-Sutcliffe efficiency (NSE). It is shown that the developed red-tailed hawk optimization-based model succeeded in accomplishing the most precise results of HEN and CEN. In this context, it predicted HEN with RAE (0.201), MAE (1.838), MAPE (7.626%), RMSE (2.826), and NSE (0.921). Besides, it anticipated CEN with RAE (0.234), MAE (2.009), MAPE (7.519%), RMSE (3.246), and NSE (0.883). It can be argued that this research study could benefit architects and designers in creating more energy-efficient buildings at an early stage.

Geopolymer concrete is a new invention of the concrete industry. It could be the future of all construction fields due to its performance against severe conditions, and strength. It is a perfect alternative to conventional concrete. It is more sustainable, ecological, durable, and economic than conventional concrete. In the present era, machine learning techniques are also the future of all research and development industries. These techniques predict the results based on their previous data. In the construction industry, the find the results or value are very difficult, time consumable, and laborious. These techniques make them very easier to predict the strength of mix design without making samples and destructive tests. The aim of this study is to predict the compressive strength of flyash-based geopolymer concrete by using deep learning and random forest algorithm and comparing them with different errors and coefficient correlation. After the simulation of data, it is proved that the random forest algorithm is the most suitable technique for the prediction of compressive strength. After the developing a model, the various errors were found for accuracy. The mean absolute error, root mean square error, relative absolute error, and root relative squared error are 1.63%, 2.68%, 30.28%, and 37.47%, respectively for the deep learning predicted compressive strength. The errors provide the proof of model accuracy to predict the compressive strength on the basis of ingredients proportions.KeywordsGreen concreteGeopolymer concreteSustainableMachine learningRandom forest algorithm

An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML) is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS), root relative square error (RRSE), normalized mean absolute error (MAE), and correlation factor (R). Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5-Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91), the lowest RRSE errors (79.46% and 79.78%), the lowest average MAE errors (18.12% and 19.42%), and the highest average correlation factors (0.41 and 0.42). Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.

In the smart grid environment, the electricity consumers will act in response to electricity demand. And the construction of large power generation plants such as thermal, nuclear, atomic, and wind power stations and plants takes many years. In any field, demand has to be forecasted for smooth running of all works in the society. So, the government must determine the electricity needs well in advance. Many statistical and mathematical methods have been developed to forecast the energy demand in the market environment since restructuring of the electricity power industry. In this paper, predictive data mining models named support vector machine, multilayer perceptron, linear regression, and Gaussian processes are analyzed using real-time electricity data with data mining tool Weka for forecasting the electricity load demand. Many accuracy parameters such as mean absolute error, root-mean-squared error, root relative squared error, and relative absolute error were analyzed to find the best of those four models for forecasting the electricity load demand.KeywordsForecasting demandSupport vector machineMultilayer perceptronGaussian processesLinear regression

This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

Climate variability poses a significant threat to global agricultural productivity and food security, particularly in regions that are highly dependent on climate-sensitive farming systems. Accurate and timely prediction of crop yields is therefore essential for informed decision-making and policy formulation. This study presents an AI-driven predictive modeling framework for estimating crop yields under varying climatic conditions using historical agricultural and climate data. A publicly available global crop yield dataset incorporating rainfall, temperature, and pesticide usage was utilized to develop and evaluate predictive models. The traditional statistical regression was compared to the machine learning approaches, namely Random Forest and Gradient Boosting regressors, to determine the predictive performance and strength of the models. The standard regression metrics, which included root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²), were used for model evaluation. The findings of the study showed that the models based on machine learning methods completely outclass the traditional linear regression in grasping the nonlinear relations between climate factors and crop yield. The analysis of feature importance also points out specific regional and temporal variations in rainfall and temperature as the two leading contributors to changes in crop yield fluctuations, thus implying the vulnerability of agricultural productivity to the effect of climate change. The results highlight the ability of models predicting crop yield using AI technology to be one of the main supports for climate-sensitive agricultural planning and global food security improvements. This work offers one more example of AI being used in the agricultural sector and the policy area, also as it is an input to making climate change and its effects on crop production data-driven matters, thus increasing the whole AI 'sustainable agriculture' and 'evidence-based policy area.

Topicality. Successful economic development of agriculture is one of the components of Ukraine's sustainable development. But this development very often leads to changes in natural and climatic conditions and to increased risks for doing business. Agricultural land is a natural asset and it should be taken into account that the change oflandscapes from natural vegetation to any other use can lead to loss, degradation, and fragmentation of the environment, all of which can have devastating consequences for biodiversity as a source of ecosystem services. Therefore, it is important to find ways to rationally manage the land.Aim and tasks. The purpose of our study was to substantiate the relevance of taxes for the management and rational use of land as a natural asset. As a tool, we consider a tax on crop rotation, which leads to a decrease in soil fertility and the release of carbon from dying plant residues. Crop rotation planning will help reprioritize crop selection, increase fertility and reduce carbon emissions.Research results. The main component of the sustainable development of the agricultural sector is soil fertility. At the same time, land use and agriculture are the main natural agents for reducing air pollution. Today, agriculture accounts for about 20% of greenhouse gas emissions, and humus losses range from 19% to 22% in different natural areas. Considering agricultural land as a natural asset, it should be taken into account that their irrational use leads to a change in natural and climatic conditions and an increase in risks for doing business. We have proposed to introduce carbon crop rotations as a tool for soil quality management. In our opinion, this should contribute to a change in priorities in the choice of agricultural crops, and as a result, an increase in fertility and an increase in carbon sequestration. To stimulate agricultural enterprises, we consider it expedient to introduce a tax on crop rotation. To substantiate this idea, we have created a model for the dependence of CO2 absorption/emission on the yield of various crops and a decision-making model for planning the costs of an agricultural enterprise.Conclusion. We have conducted a study of the relationship between the yield of various crops and the absorption/release of carbon dioxide. We put forward a hypothesis: there is a certain relationship between the yield of different crops and the balance of absorption/release of CO2. We have studied this connection. The model of dependence of CO2 uptake/emission on the yield of different crops shows that the ability to release carbon by plant residues of different agricultural crops has both a direct and inverse relationship with the yield of various crops. We have developed a decision-making model for planning the costs of an enterprise, subject to planning for environmental risks or paying a tax on carbon crop rotations. Thus, the problems of land use as a natural asset in agriculture can be solved by: the introduction of carbon crop rotations, which should help change priorities in the choice of agricultural crops, and the introduction of a balanced and evidence-based approach to the development and implementation of environmental taxes.