Land use/land cover classification using machine learning models

An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers

Comparison of Different Machine Learning and Self-Learning Methods for Predicting Obesity on Generalized and Gender-Segregated Data

Obesity is a global health concern with long-term implications. Our research applies numerous Machine Learning models consisting of  Random Forest model, XGBT(Extreme Gradient Boosting) model, Decision Tree model, k-Nearest Neighbors technique, Support Vector Machine model, Linear Regression model, Naïve Bayes classifier  and a neural network named Multilayer Perceptron on an obesity dataset so that we can predict obesity and reduce it. The models are evaluated on recall, accuracy, F1-score, and precision. The findings reveal the performance of the algorithms on generalised and gender-segregated data providing insights concerning feature selection and early obesity identification. This research aims to demonstrate the comparative study of obesity prediction for gender-neutral and gender-specific datasets

A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems

The integrity failure in gas lift wells had been proven to be more severe than other artificial lift wells across the industry. Accurate risk assessment is an essential requirement for predicting well integrity failures. In this study, a machine learning model was established for automated and precise prediction of integrity failures in gas lift wells. The collected data contained 9,000 data arrays with 23 features. Data arrays were structured and fed into 11 different machine learning algorithms to build an automated systematic tool for calculating the imposed risk of any well. The study models included both single and ensemble supervised learning algorithms (e.g., random forest, support vector machine, decision tree, and scalable boosting techniques). Comparative analysis of the deployed models was performed to determine the best predictive model. Further, novel evaluation metrics for the confusion matrix of each model were introduced. The results showed that extreme gradient boosting and categorical boosting outperformed all the applied algorithms. They can predict well integrity failures with an accuracy of 100% using traditional or proposed metrics. Physical equations were also developed on the basis of feature importance extracted from the random forest algorithm. The developed model will help optimize company resources and dedicate personnel efforts to high-risk wells. As a result, progressive improvements in health, safety, and environment and business performance can be achieved.Cited as: Salem, A. M., Yakoot, M. S., Mahmoud, O. A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems. Advances in Geo-Energy Research, 2022, 6(2): 123-142. https://doi.org/10.46690/ager.2022.02.0

Robust algorithm for arrhythmia classification in ECG using extreme learning machine

<p>Abstract</p> <p>Background</p> <p>Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.</p> <p>Methods</p> <p>In this paper we propose a novel arrhythmia classification algorithm which has a fast learning speed and high accuracy, and uses Morphology Filtering, Principal Component Analysis and Extreme Learning Machine (ELM). The proposed algorithm can classify six beat types: normal beat, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature beat, and paced beat.</p> <p>Results</p> <p>The experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average specificity, and 98.72% in terms of average accuracy. These accuracy levels are higher than or comparable with those of existing methods. We make a comparative study of algorithm using an ELM, back propagation neural network (BPNN), radial basis function network (RBFN), or support vector machine (SVM). Concerning the aspect of learning time, the proposed algorithm using ELM is about 290, 70, and 3 times faster than an algorithm using a BPNN, RBFN, and SVM, respectively.</p> <p>Conclusion</p> <p>The proposed algorithm shows effective accuracy performance with a short learning time. In addition we ascertained the robustness of the proposed algorithm by evaluating the entire MIT-BIH arrhythmia database.</p

Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval

A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials’ shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 1006 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete’s γ-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R2score were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE