Cost Prediction of a Turbofan Engine Using the Robust Partial Least Squares Method

In recent years, the cost of engines has become increasingly important to engine manufacturers, who are consistently faced with major problems on how to reduce cost to a minimum. Cost has become a decisive factor for aircraft design. To control the continual rapid increased cost, engine cost prediction is indispensable early in the design phase. But the cost data of an aircraft engine is small; we introduce the Robust Partial Least Squares Method in solving this problem, and reducing or removing the effect of outlying data points, which is different from the Classical PLS. We use the MATLAB software doing several simulations; results and analysis of a real turbofan engine data set show the effectiveness and robustness of the Robust PLS method. The Robust PLS method can effectively be used to estimate Turbofan Engine cost with reasonable accuracy.

Extracting multi-objective multigraph features for the shortest path cost prediction:Statistics-based or learning-based?

Efficient airport airside ground movement(AAGM)is key to successful operations of urban air mobility.Recent studies have introduced the use of multi-objective multigraphs(MOMGs)as the conceptual prototype to formulate AAGM.Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs,however,previous work chiefly focused on single-objective simple graphs(SOSGs),treated cost enquires as search problems,and failed to keep a low level of computational time and storage complexity.This paper concentrates on the conceptual prototype MOMG,and investigates its node feature extraction,which lays the foundation for efficient prediction of shortest path costs.Two extraction methods are implemented and compared:a statistics-based method that summarises 22 node physical patterns from graph theory principles,and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space.The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction,while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs.Three regression models are applied to predict the shortest path costs to demonstrate the performance of each.Our experiments on randomly generated benchmark MOMGs show that(i)the statistics-based method underperforms on characterising small distance values due to severe overestimation;(ii)A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns;and(iii)the learning-based method consistently outperforms the statistics-based method,while maintaining a competitive level of computational complexity.

Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0

Purpose-In order to improve the accuracy of project cost prediction,considering the limitations of existing models,the construction cost prediction model based on SVM(Standard Support Vector Machine)and LSSVM(Least Squares Support Vector Machine)is put forward.Design/methodology/approach-In the competitive growth and industries 4.0,the prediction in the cost plays a key role.Findings-At the same time,the original data is dimensionality reduced.The processed data are imported into the SVM and LSSVM models for training and prediction respectively,and the prediction results are compared and analyzed and a more reasonable prediction model is selected.Originality/value-The prediction result is further optimized by parameter optimization.The relative error of the prediction model is within 7%,and the prediction accuracy is high and the result is stable.

A Novel System Model based on Cost-prediction for Spectrum Leasing in Cognitive Radio

In current researches on spectrum leasing, Common model and Property-right model are two main approaches to dynamic spectrum sharing. However, Common model does not consider the obligation of Primary System (PS) and is unfair to Secondary System (SS), while the cooperation based on Property-rights model has problems on its feasibility. This paper proposes a novel system model, in which a Cost-Prediction scheme for Spectrum Leasing (CPSL scheme) is designed to forecast the cost that PS would pay for leasing spectrum. Cost Function is introduced as a criterion to evaluate the potential cost of spectrum leasing for PS. The simulation results show that compared with Common model based scheme, CPSL scheme substantially improves the QoS of the delay-sensitive traffic in SS at the cost of a small degradation of PS performance.

An Analysis and Prediction of Health Insurance Costs Using Machine Learning-Based Regressor Techniques

One of the most significant annual expenses that a person has is their health insurance coverage. Health insurance accounts for one-third of GDP, and everyone needs medical treatment to varying degrees. Changes in medicine, pharmaceutical trends, and political factors are only a few of the many factors that cause annual fluctuations in healthcare costs. This paper describes how a system may analyse a person’s medical history to display their insurance plans and make predictions about their health insurance premiums. The performance of four ML models—XGBoost, Lasso, KNN, and Ridge—is evaluated using R2-score and RMSE. The analysis of medical health insurance cost prediction using Lasso regression, Ridge regression, and K-Nearest Neighbours (KNN), and XGBoost (XGB) highlights notable differences in performance. KNN has the lowest R2-score of 55.21 and an RMSE of 4431.1, indicating limited predictive ability. Ridge Regression improves on this by an R2-score of 78.38 but has a higher RMSE of 4652.06. Lasso Regression slightly edges out Ridge with an R2-score of 79.78, yet it suffers from an advanced RMSE of 5671.6. In contrast, XGBoost excels with the highest R2-score of 86.81 and the lowermost RMSE of 4450.4, demonstrating superior predictive accuracy and making it the most effective model for this task. The best method for accurately predicting health insurance premiums was XGBoost Regression. The findings beneficial for policymakers, insurers, and healthcare providers as they can use this information to allocate resources more efficiently and enhance cost-effectiveness in the healthcare industry.