Support Vector Machine Regression Techniques Research Articles

Improved 3D laser point cloud reconstruction for autonomous mobile robot applications by using SVM-R technique

PurposeIn autonomous mobile robots, high-level accuracy and precision in 3D perception are required for object detection, shape estimation and obstacle distance measurement. However, the existing methods suffer from limitations like inaccurate point clouds, noise in sensor data and synchronization problems between 2D LiDAR and servomotor. These factors can lead to the wrong perception and also introduce noise during sensor registration. Thus, the purpose of this study is to address these limitations and enhance the perception in autonomous mobile robots.Design/methodology/approachA new sensor mounting structure is developed for 3D mapping by using a 2D LiDAR and servomotor. The proposed method uses a support vector machine regression (SVM-R) technique to optimize the waypoints of the servomotor for the point cloud reconstruction process and to obtain a highly accurate and detailed representation of the environment.FindingsThe study includes an analysis of the SVM-R model, including Linear, radial basis function (RBF) and Polynomial kernel. Results show that the Linear kernel performs better with the lowest 3.67 mean absolute error (MAE), 26.24 mean squared error (MSE) and 5.12 root mean squared error (RMSE) values than the RBF and Polynomial kernels. The 2D to 3D point cloud reconstruction shows that the proposed method with a new sensor mounting structure performs better in perception accuracy and achieves an error of 0.45% in measuring the height of the target objects whereas in previous techniques the error was very large.Originality/valueThe study shows the effectiveness of SVM-R in the 3D point cloud reconstruction process and exhibits remarkable performance for object height measurement. Further, the proposed technique is applicable for future advanced visual applications and has a superior performance over the other conventional methods.

Comparative Analysis of Machine Learning Algorithms for Used Car Price Prediction

After 2021, over 90 million passenger automobiles were produced, marking a significant increase in auto production. This growth has led to a flourishing used car market, which has become a highly lucrative sector. One of the most critical and fascinating areas of research within this market is automobile price prediction. Accurate price prediction models can greatly benefit buyers, sellers, and businesses in the used car industry. This paper presents a detailed comparative analysis of two supervised machine learning models: K-Nearest Neighbour and Support Vector Machine regression techniques, to predict used car prices. We utilized a comprehensive dataset of used cars sourced from the Kaggle website for training and testing our models. The K Nearest Neighbour algorithm is known for its simplicity and effectiveness in regression tasks. On the other hand, the Support Vector Machine regression technique uses a different approach, finding the optimal hyperplane that best fits the data. Both methods have their strengths and weaknesses, which we explored in this study. Our results indicated that both KNN and SVM models performed well in predicting used car prices, but with slight variations in accuracy. Consequently, the suggested models fit as the optimum models and have an accuracy of about 83 percent for KNN and 80 percent for SVM. The results indicate that the KNN model slightly outperforms the SVM model in predicting used car prices.

Mid-Infrared Reflectance Spectroscopy for Estimation of Soil Properties of Alfisols from Eastern India

Mid-infrared (MIR) spectroscopy is emerging as one of the most promising technologies, as it is a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. This study was conducted to evaluate the potential of mid-infrared spectroscopy for the rapid and nondestructive measurement of some important soil properties of Alfisols. A total of 336 georeferenced soil samples fromthe 0–15 cm soil layer of Alfisols that were collected from the eastern Indian states of Odisha and Jharkhand were used. The partial least-squares regression (PLSR), random forest, and support vector machine regression techniques were compared for the calibration of the spectral data with the wet chemistry soil data. The PLSR-based predictive models performed better than the other two regression techniques for all the soil properties, except for the electrical conductivity (EC). Good predictions with independent validation datasets were obtained for the clay and sand percentages and for the soil organic carbon (SOC) content, while satisfactory predictions were achieved for the silt percentage and the pH value. However, the performance of the predictive models was poor in the case of the EC and the extractable nutrients, such as the available phosphorus and potassium contents of the soil. Specific regions of the MIR spectra that contributed to the prediction of the soil SOC, the pH, and the clay and sand percentages were identified. The study demonstrates the potential of the MIR spectroscopic technique in the simultaneous estimation of the SOC content, the sand, clay, and silt percentages, and the pH of Alfisols from eastern India.

Machine learning based forecast for the prediction of inpatient bed demand

BackgroundOvercrowding is a serious problem that impacts the ability to provide optimal level of care in a timely manner. High patient volume is known to increase the boarding time at the emergency department (ED), as well as at post-anesthesia care unit (PACU). Furthermore, the same high volume increases inpatient bed transfer times, which causes delays in elective surgeries, increases the probability of near misses, patient safety incidents, and adverse events.ObjectiveThe purpose of this study is to develop a Machine Learning (ML) based strategy to predict weekly forecasts of the inpatient bed demand in order to assist the resource planning for the ED and PACU, resulting in a more efficient utilization.MethodsThe data utilized included all adult inpatient encounters at Geisinger Medical Center (GMC) for the last 5 years. The variables considered were class of inpatient encounter, observation, or surgical overnight recovery (SORU) at the time of their discharge. The ML based strategy is built using the K-means clustering method and the Support Vector Machine Regression technique (K-SVR).ResultsThe performance obtained by the K-SVR strategy in the retrospective cohort amounts to a mean absolute percentage error (MAPE) that ranges between 0.49 and 4.10% based on the test period. Additionally, results present a reduced variability, which translates into more stable forecasting results.ConclusionsThe results from this study demonstrate the capacity of ML techniques to forecast inpatient bed demand, particularly using K-SVR. It is expected that the implementation of this model in the workflow of bed capacity management will create efficiencies, which will translate in a more reliable, inexpensive and timely care for patients.

Prediction of Five Softwood Paper Properties from its Density using Support Vector Machine Regression Techniques

Predicting paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kernel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris) and cypress species (Cupressus lusitanica, C. sempervirens, and C. arizonica) beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination) with two variables: the numerical variable density and the categorical variable species.

Machine learning approaches to predict coagulant dosage in water treatment plants

Two machine learning methods, support vector machine and K-Nearest Neighbours (KNN) were investigated in this paper to predict the coagulant dosage in water treatment plants (WTPs). Two types of support vector machine regression techniques, e-SVR and v-SVR, using two different kernel functions (radial basis function (RBF) and polynomial function), and KNN were investigated in order to predict coagulant dosage in a large, a medium, and two small-sized WTPs. The results show that these two types of support vector machine regression techniques have good predictive capabilities for the large and medium WTPs as compared to small water systems. The performances of e-SVR with RBF kernel function were compared with that obtained from the KNN algorithm (as baseline) for four WTPs. The comparison shows that the KNN has similar performances as e-SVR for the large and medium- sized WTPs and performs better for two small-sized WTPs. The results show that different machine learning methods have competing predictive abilities.

Quality Assessment of Protein Models

Quality assessment (QA) is to judge the quality of a protein model without knowing its native structure. It plays an important role in the process of protein structure prediction by guiding us to select an appropriate combination of templates out of many possibilities [1]. We formulate the QA problem as mapping and regression. Protein models are mapped into R n space by extracting n features from each model, and the space is divided into subspaces according to the feature values in order to represent models’ closeness (between 0 and 1) to the native structure. For the feature values, we measure the degree of agreement between the predicted property from the sequence analysis and the calculated one from the 3D model. Properties that we use include the secondary structure, solvent accessibility, hydrophobicity, and energy components from MODELLER [2], DFIRE [3], and TASSER [4]. One advantage of using the consensustype feature is that models with similar quality are prone to be clustered together in R n space, and thus they tend to be well classified. Assigning closeness values to regions of R n space is carried out using decoy structures generated during the CASP7 with known native structures [1] by applying Support Vector Machine regression techniques (SVM). SVM that we used has the character of non-linearity which can be applied to the linearly non-separable cases. In other words, for given features, SVM is more efficient than the linear method such as linear programming. The proposed method was used to select final models during the CASP8 prediction. We will discuss both the usefulness and the limitation of the method for better protein modeling.

A priori prediction of adsorption isotherm parameters and chromatographic behavior in ion-exchange systems.

The a priori prediction of protein adsorption behavior has been a long-standing goal in several fields. In the present work, property-modeling techniques have been used for the prediction of protein adsorption thermodynamics in ion-exchange systems directly from crystal structure. Quantitative structure-property relationship models of protein isotherm parameters and Gibbs free energy changes in ion-exchange systems were generated by using a support vector machine regression technique. The predictive ability of the models was demonstrated for two test-set proteins not included in the model training set. Molecular descriptors selected during model generation were examined to gain insights into the important physicochemical factors influencing stoichiometry, equilibrium, steric effects, and binding affinity in protein ion-exchange systems. The a priori prediction of protein isotherm parameters can have direct implications for various ion-exchange processes. As proof of concept, a multiscale modeling approach was used for predicting the chromatographic separation of a test set of proteins using the isotherm parameters obtained from the quantitative structure-property relationship models. The simulated column separation showed good agreement with the experimental data. The ability to predict chromatographic behavior of proteins directly from their crystal structures may have significant implications for a range of biotechnology processes.