Extracting Knowledge from Big Data for Sustainability: A Comparison of Machine Learning Techniques

Abstract

At present, due to the unavailability of natural resources, society should take the maximum advantage of data, information, and knowledge to achieve sustainability goals. In today’s world condition, the existence of humans is not possible without the essential proliferation of plants. In the photosynthesis procedure, plants use solar energy to convert into chemical energy. This process is responsible for all life on earth, and the main controlling factor for proper plant growth is soil since it holds water, air, and all essential nutrients of plant nourishment. Though, due to overexposure, soil gets despoiled, so fertilizer is an essential component to hold the soil quality. In that regard, soil analysis is a suitable method to determine soil quality. Soil analysis examines the soil in laboratories and generates reports of unorganized and insignificant data. In this study, different big data analysis machine learning methods are used to extracting knowledge from data to find out fertilizer recommendation classes on behalf of present soil nutrition composition. For this experiment, soil analysis reports are collected from the Tata soil and water testing center. In this paper, Mahoot library is used for analysis of stochastic gradient descent (SGD), artificial neural network (ANN) performance on Hadoop environment. For better performance evaluation, we also used single machine experiments for random forest (RF), K-nearest neighbors K-NN, regression tree (RT), support vector machine (SVM) using polynomial function, SVM using radial basis function (RBF) methods. Detailed experimental analysis was carried out using overall accuracy, AUC–ROC (receiver operating characteristics (ROC), and area under the ROC curve (AUC)) curve, mean absolute prediction error (MAE), root mean square error (RMSE), and coefficient of determination (R2) validation measurements on soil reports dataset. The results provide a comparison of solution classes and conclude that the SGD outperforms other approaches. Finally, the proposed results support to select the solution or recommend a class which suggests suitable fertilizer to crops for maximum production.

Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems

“…Furthermore, we also chose a quantitative method to compare competing time series analysis approaches by implementing various methods in combination of different cost function, while comparable papers often select promising configurations beforehand without further examination. This rigorous (in-depth) proceeding, however, results in a trade-off so that only a limited number of different algorithms (or rather pathways) could be implemented in the scope of this scientific work, which is admittedly smaller than comparable studies, such as [19,20] produced.…”

Spaceborne GNSS-R Retrieving on Global Soil Moisture Approached by Support Vector Machine Learning

“…They concluded that LS-SVM based on multi-satellite fusion results is a more accurate estimation on retrieving soil moisture than single satellite means single station. Garg, R., et al (2019) extracted big data for the sustainability of soil nutrition composition comparatively analyzed with machine learning techniques as support vector machine (SVM) using the polynomial function, radial basis function (RBF) methods and others.…”

“…It has significantly improved the prediction of soil moisture with short, medium and huge changes in climate, and hazardous crisis such as in disaster mitigated land. Raghu Garg et al (2019) process using with several machines techniques figured out to extract knowledge from big data learning methods for sustainability on plant-related studied. In this research, attempts have using SVM machine learning methods to retrieve global soil moisture, taking the TDS-1 DDM input and the SMOS SMC as reference.…”

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

“…The preprocessing step includes feature elimination, missing data imputation, normalization, and data division. In the importance measurement step, to measure the importance of each feature, the relevancy between each feature and the failure is analyzed using the random forest algorithm [23][24][25][26]. Then, the feature selection and model building steps are conducted iteratively.…”