scholarly journals Prediction of BP Reactivity to Talking Using Hybrid Soft Computing Approaches

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Gurmanik Kaur ◽  
Ajat Shatru Arora ◽  
Vijender Kumar Jain
Keyword(s):  
Prediction Models ◽  
Statistical Tests ◽  
Least Square ◽  
Predictor Variables ◽  
Percentage Error ◽  
Support Vector ◽  
Inference System ◽  
Biological Variables ◽  
Increased Risk ◽  
Svm Model

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

scholarly journals Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Gurmanik Kaur ◽  
Ajat Shatru Arora ◽  
Vijender Kumar Jain
Keyword(s):  
Prediction Models ◽  
Fuzzy Inference ◽  
Research Work ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Inference System ◽  
Forward Stepwise ◽  
Artificial Neural Network Ann

Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination (R2) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R2 = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.

Workload Prediction on Google Cluster Trace

2014 ◽  
Vol 6 (3) ◽  
pp. 34-52 ◽  
Author(s):  
Md. Rasheduzzaman ◽  
Md. Amirul Islam ◽  
Rashedur M. Rahman
Keyword(s):  
Resource Utilization ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Experimental Result ◽  
Percentage Error ◽  
Support Vector ◽  
Workload Prediction ◽  
Inference System ◽  
Squared Error ◽  
Efficient Resource

Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear Autoregressive Network with Exogenous inputs (NARX), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Public trace data (workload trace version II) which is made available by Google were used to verify the accuracy, stability and adaptability of different models. Finally, this paper compares these prediction models to find out the model which ensures better prediction. Performance of forecasting techniques is measured by some popular statistical metric, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Sum of Squared Error (SSE), Normalized Mean Squared Error (NMSE). The experimental result indicates that NARX model outperforms other models, e.g., ANFIS, ARIMA, and SVR.

scholarly journals Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Support Vector Machine ◽  
Soft Computing ◽  
Prediction Models ◽  
Base Isolation ◽  
Least Square ◽  
Support Vector ◽  
Isolation System ◽  
Inference System ◽  
Comparison Results ◽  
Soft Computing Techniques

Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB) isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.

scholarly journals Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Amevi Acakpovi ◽  
Alfred Tettey Ternor ◽  
Nana Yaw Asabere ◽  
Patrick Adjei ◽  
Abdul-Shakud Iddrisu
Keyword(s):  
Urban Areas ◽  
Fuzzy Inference ◽  
Predictive Accuracy ◽  
Resource Planning ◽  
Electricity Demand ◽  
Least Square ◽  
Quality Data ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

Hybrid wavelet-support vector machine approach for modelling rainfall–runoff process

2016 ◽  
Vol 73 (8) ◽  
pp. 1937-1953 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fuzzy Inference ◽  
The Other ◽  
Support Vector ◽  
Rainfall Runoff ◽  
Model Concept ◽  
Inference System ◽  
Svm Model ◽  
Artificial Intelligence Models

Because of the importance of water resources management, the need for accurate modeling of the rainfall–runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall–runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall–runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

Influence of Modeling Methods for Housing Price Forecasting

2013 ◽  
Vol 798-799 ◽  
pp. 885-888
Author(s):  
Xiao Li Yang ◽  
Qiong He
Keyword(s):  
Housing Price ◽  
Correlation Coefficients ◽  
Partial Least Square ◽  
Least Square ◽  
Percentage Error ◽  
Support Vector ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Modeling Methods ◽  
Variables Selection

In this work, we estimate Yunnan housing price from 1999 to 2009. Firstly, we analyze the correlation coefficients between housing price and characteristic variables, identify the characteristic variables. Then, we build the forecasting model using four techniques, support vector regression (SVR), radial basis function neural network (RBFNN), partial least square (PLS) and multiple regression analysis (MRA), based on whole variables and characteristic variables. The results show that PLS technique is the best one for housing price forecasting. Its mean absolute percentage error (MAPE) is only 2.45%. SVR and RBFNN are better techniques to obtain a satisfactory forecasting result with almost 5% MAPE. Furthermore, the performance of MRA and SVR can be obviously improved through variables selection.

Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

2013 ◽  
Vol 27 (10) ◽  
pp. 3803-3823 ◽  
Author(s):  
Afiq Hipni ◽  
Ahmed El-shafie ◽  
Ali Najah ◽  
Othman Abdul Karim ◽  
Aini Hussain ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Water Levels ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Svm Model

scholarly journals Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from j group of the ATC classification in humans

2012 ◽  
Vol 62 (3) ◽  
pp. 305-323 ◽  
Author(s):  
Bruno Louis ◽  
Vijay K. Agrawal
Keyword(s):  
Statistical Tests ◽  
External Validation ◽  
Volume Of Distribution ◽  
Support Vector ◽  
Quantitative Structure ◽  
Beta Lactam ◽  
Svm Model ◽  
Correlation Based Feature Selection ◽  
Artificial Neural Network Ann ◽  
D Values

In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs.

scholarly journals Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

2020 ◽  
Author(s):  
Mahdi Ghadiri ◽  
Azam Marjani ◽  
Samira Mohammadinia ◽  
Manouchehr Shokri
Keyword(s):  
Relative Humidity ◽  
Statistical Learning ◽  
Air Conditioning ◽  
Fuzzy Inference ◽  
Least Square ◽  
Support Vector ◽  
Cooling Towers ◽  
Learning Approaches ◽  
Inference System ◽  
Data Points

The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

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