scholarly journals Modelling postural discomfort perception using CHAID decision tree algorithm

2019 ◽  
Vol 63 (1) ◽  
pp. 1749-1750
Author(s):  
Soomin Hyun ◽  
Woojin Park
Keyword(s):  
Decision Tree ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Body Part ◽  
Upper Body ◽  
Good Prediction ◽  
Practical Applications ◽  
High Prediction Accuracy ◽  
Good Prediction Accuracy ◽  
High Prediction

Developing quantitative models that predict discomfort levels of working postures has been an important ergonomics research topic. Such modeling not only has practical applications, but also may serve as a useful research method to improve our understanding of the human postural discomfort perception process. While the existing models have focused on achieving high prediction accuracy, less attention has been given to model interpretability, which is vital for understanding a process through modeling. Research is needed to identify the model types or modeling methods that offer high interpretability as well as good prediction accuracy. The goal of this study was to evaluate the utility of the Chi-square Automatic Interaction Detector (CHAID) decision tree modeling method in developing postural discomfort prediction models. Ten individual-specific decision tree models were developed, which predicted overall upper-body discomfort from local body part discomfort ratings. The prediction models were found to achieve high prediction accuracy and interpretability. (150 words)

Stock Price’s Prediction with Decision Tree

2011 ◽  
Vol 48-49 ◽  
pp. 1116-1121
Author(s):  
Tun Li ◽  
Gong Shen Liu
Keyword(s):  
Decision Tree ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Price Change ◽  
Financial Data ◽  
Average Error ◽  
Careful Selection ◽  
High Prediction Accuracy ◽  
High Prediction ◽  
Error Distance

Establishment of one process and some ameliorations of decision tree’s algorithm in order to predict the second day’s price change. The experiment builds a J48 tree, which is comfortable with continuous attributes, based on 10 years historical stock prices. After careful selection and preprocessing of financial data, high prediction accuracy is obtained. An introduction of dynamic-constructed tree reduces tree’s cost, and increases prediction’s quality on accuracy as well as average error distance.

scholarly journals Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees

2018 ◽  
Vol 10 (8) ◽  
pp. 1260 ◽  
Author(s):  
Brigitte Colin ◽  
Michael Schmidt ◽  
Samuel Clifford ◽  
Alan Woodley ◽  
Kerrie Mengersen
Keyword(s):  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Landsat Imagery ◽  
Grid Cell ◽  
Spatial Aggregation ◽  
Boosted Regression Trees ◽  
Good Prediction ◽  
Boosted Regression Tree ◽  
Good Prediction Accuracy

Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four spatial aggregation schemes on Landsat imagery on prediction accuracy of green photosynthetic vegetation (PV) based on fractional cover (FCover). To reduce data volume we created an evenly spaced grid, overlaid that on the PV band and delineated the arithmetic mean of PV fractions contained within each grid cell. The aggregated fractions and the corresponding geographic grid cell coordinates were then used for boosted regression tree prediction models. Model goodness of fit was evaluated by the Root Mean Squared Error (RMSE). Two spatial resolutions (3000 m and 6000 m) offer good prediction accuracy whereas others show either too much unexplained variability model prediction results or the aggregation resolution smoothed out local PV in heterogeneous land. We further demonstrate the suitability of our aggregation scheme, offering an increased processing time without losing significant topographic information. These findings support the feasibility of using geographic coordinates in the prediction of PV and yield satisfying accuracy in our study area.

scholarly journals A Robust UWSN Handover Prediction System Using Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5777
Author(s):  
Esraa Eldesouky ◽  
Mahmoud Bekhit ◽  
Ahmed Fathalla ◽  
Ahmad Salah ◽  
Ahmed Ali
Keyword(s):  
Decision Tree ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Performance Metrics ◽  
Prediction Models ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Water Current ◽  
Gradient Boosting ◽  
Marine Data

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

scholarly journals Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6756
Author(s):  
DongHyun Ko ◽  
Seok-Hwan Choi ◽  
Sungyong Ahn ◽  
Yoon-Ho Choi
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Indoor Localization ◽  
Artificial Noise ◽  
Good Prediction ◽  
Localization Method ◽  
Good Prediction Accuracy ◽  
Attack Scenario ◽  
Natural Noise

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

scholarly journals Research on Rubber Spring Model of High-speed EMU Based on Non-hyperelastic Forces

Mechanika ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 12-21
Author(s):  
Chuanbo XU ◽  
Maoru CHI ◽  
Liangcheng DAI ◽  
Yiping JIANG ◽  
Yongfa CHEN ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Accuracy ◽  
High Speed ◽  
Dynamic Condition ◽  
Static Condition ◽  
Good Prediction ◽  
Calculation Formula ◽  
Good Prediction Accuracy ◽  
Calculation Results ◽  
Better Than

The research on the mechanical model of rubber spring is one of the hot spots in train dynamics. In order to accurately calculate the viscoelastic force of the rubber spring, especially the non-hyperelastic forces (NHEF) part, a NHEF model is proposed based on the elliptic approximation method. Furthermore, the calculation formula of periodic energy consumption is put forward. The NHEF model is verified by experiments, and the function λ isconstructed to verify the formula of periodic energy consumption. The calculation results showed that the NHEF model had high accuracy in predicting the dynamic and quasi-static NHEF of rubber spring, the prediction accuracy of shear condition was better than that of compression condition, and the accuracy of quasi-static condition was better than that of dynamic condition; the calculation formula of periodic energy consumption had a good prediction accuracy in all working conditions.

scholarly journals A Neural Network Model for the Compressive Strength of a Hybrid LM6 Aluminium Alloy Composite

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1652-1654
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Aluminium Alloy ◽  
Prediction Accuracy ◽  
Good Prediction ◽  
Central Composite Rotatable Design ◽  
Alloy Composite ◽  
Ann Model ◽  
Good Prediction Accuracy ◽  
Central Composite

Adding more than one reinforcement increases the flexibility in composites. The objective of the work is to develop a model to predict the compressive strength in an LM6 aluminium alloy reinforced with SiC and flyash particles. Central composite rotatable design had been employed to carry out the experiments with size and composition of the reinforcements as the parameters. ANN model developed has good prediction accuracy with error being less than 5%.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

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