The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and support vector machines

2019 ◽  
Vol 90 (13-14) ◽  
pp. 1558-1580
Author(s):  
Enver Can Doran ◽  
Cenk Sahin
Keyword(s):  
Textile Industry ◽  
Prediction Models ◽  
Fiber Quality ◽  
Principal Component ◽  
Quality Characteristics ◽  
Percentage Error ◽  
Support Vector ◽  
Production Plant ◽  
Artificial Neural ◽  
Core Yarn

Core yarn is a type of yarn that has a filament fiber in the center with a different fiber wrapped around it. This type of yarn is of growing importance in the textile industry. It is important to predict the quality characteristics of a core yarn before production to prevent the faulty production of fabrics. Therefore, the development of predictive models is a necessity in the textile industry. In this study, artificial neural network (ANN) and support vector machine (SVM) models are proposed to predict the quality characteristics of cotton/elastane core yarn, using fiber quality and spinning parameters. Principal component analysis and analysis of variance techniques are also used to reduce input dimensions, since high dimensional data may reduce a model’s potential for success in prediction. The prediction models are trained and tested using the data obtained from a textile production plant. The results of all the models are compared with each other on test data. Mean absolute percentage error (MAPE), mean absolute error (MAE) and correlation coefficient (R) are used to assess the prediction power of the models. Although on most of the tests SVM models fared slightly better than ANN models, both models provide accurate predictions for most of the yarn quality characteristics. The results show that the best models have over 90% success rate in MAPE and R. In particular, the Coefficient of Variance of mass (CVm) along the yarn, hairiness and Reisskilometer quality characteristics of the cotton/elastane core yarn are predicted with 91%, 93% and 95% accuracy, respectively.

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.

scholarly journals A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaohui Weng ◽  
Xiangyu Luan ◽  
Cheng Kong ◽  
Zhiyong Chang ◽  
Yinwu Li ◽  
...  
Keyword(s):  
Computer Vision ◽  
Electronic Nose ◽  
Prediction Models ◽  
Meat Products ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pattern Recognition Methods ◽  
Fusion Data ◽  
Objective Detection

The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

scholarly journals Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive

2020 ◽  
Vol 66 (No. 1) ◽  
pp. 1-7
Author(s):  
Mahdi Rashvand ◽  
Mahmoud Soltani Firouz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Electronic Device ◽  
Support Vector ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1–30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384–12–1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model.

scholarly journals Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties

2019 ◽  
Vol 8 (12) ◽  
pp. 562 ◽  
Author(s):  
Chrisgone Adede ◽  
Robert Oboko ◽  
Peter W. Wagacha ◽  
Clement Atzberger
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Weighted Averaging ◽  
Support Vector ◽  
Heterogeneous Model ◽  
Drought Prediction ◽  
Artificial Neural ◽  
Model Ensembles

For improved drought planning and response, there is an increasing need for highly predictive and stable drought prediction models. This paper presents the performance of both homogeneous and heterogeneous model ensembles in the satellite-based prediction of drought severity using artificial neural networks (ANN) and support vector regression (SVR). For each of the homogeneous and heterogeneous model ensembles, the study investigates the performance of three model ensembling approaches: (1) non-weighted linear averaging, (2) ranked weighted averaging, and (3) model stacking using artificial neural networks. Using the approach of “over-produce then select”, the study used 17 years of satellite data on 16 selected variables for predictive drought monitoring to build 244 individual ANN and SVR models from which 111 models were automatically selected for the building of the model ensembles. Model stacking is shown to realize models that are superior in performance in the prediction of future drought conditions as compared to the linear averaging and weighted averaging approaches. The best performance from the heterogeneous stacked model ensembles recorded an R2 of 0.94 in the prediction of future (1 month ahead) vegetation conditions on unseen test data (2016–2017) as compared to an R2 of 0.83 and R2 of 0.78 for ANN and SVR, respectively, in the traditional approach of selection of the best (champion) model. We conclude that despite the computational resource intensiveness of the model ensembling approach, the returns in terms of model performance for drought prediction are worth the investment, especially in the context of the continued exponential increase in computational power and the potential benefits of improved forecasting for vulnerable populations.

scholarly journals Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mingzhong Li ◽  
Guodong Zhang ◽  
Jianquan Xue ◽  
Yanchao Li ◽  
Shukai Tang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Cylindrical Tube ◽  
Rectangular Prism ◽  
Support Vector ◽  
Hydrodynamic Drag ◽  
Artificial Neural

Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results.

scholarly journals Prediction of Compressive Strengths with Partial Fine Aggregate of Plastic Using Artificial Neural Network and Revisions

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Cornelius Ngunjiri Ngandu
Keyword(s):  
Compressive Strength ◽  
Prediction Models ◽  
Plastic Waste ◽  
Partial Replacement ◽  
Percentage Error ◽  
Fine Aggregate ◽  
Ann Model ◽  
Waste Plastic ◽  
Green Concrete ◽  
Artificial Neural

In recent past years, plastic waste has been a environmental menace. Utilization of plastic waste as fine aggregate substitution could reduce the demand and negative impacts of sand mining while addressing waste plastic challenges.This study aims at evaluating compressive strengths prediction models for concrete with plastic—mainly recycled plastic—as partial replacement or addition of fine aggregates, by use of artificial neural networks (ANNs), developed in OCTAVE 5.2.0 and datasets from reviews. 44 datasets from 8 different sources were used, that included four input variables namely:- water: binder ratio; control compressive strength (MPa); % plastic replacement or additive by weight and plastic type; and the output variable was the compressive strength of concrete with partial plastic aggregates.Various models were run and the selected model, with 14 nodes in hidden layer and 320,000 iterations, indicated overall root mean square error (RMSE) , absolute factor of variance (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 1.786 MPa, 0.997, 1.329 MPa and 4.44 %. Both experimental and predicted values showed a generally increasing % reduction of compressive strengths with increasing % plastic fine aggregate.The model showed reasonably low errors, reasonable accuracy and good generalization. ANN model could be used extensively in modeling of green concrete, with partial waste plastic fine aggregate. The study recommend ANNs models application as possible alternative for green concrete trial mix design. Sustainable techniques such as low-cost superplasticizers from recycled material and cost-effective technologies to adequately sizing and shaping plastic for fine aggregate application should be encouraged, so as to enhance strength of concrete with partial plastic aggregates.

scholarly journals Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak

2021 ◽  
Vol 7 ◽  
pp. e746
Author(s):  
Muhammad Naeem ◽  
Jian Yu ◽  
Muhammad Aamir ◽  
Sajjad Ahmad Khan ◽  
Olayinka Adeleye ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Statistical Measures ◽  
The Impact

Background Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results Statistical measures—Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)—are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

scholarly journals Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM

2019 ◽  
Vol 16 (12) ◽  
pp. 2133 ◽  
Author(s):  
Fei Qian ◽  
Li Chen ◽  
Jun Li ◽  
Chao Ding ◽  
Xianfu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Gas Diffusion ◽  
Support Vector ◽  
Artificial Neural ◽  
Toxic Gas ◽  
Lstm Network

Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models.

scholarly journals Detection of human emotions using features based on discrete wavelet transforms of EEG signals

2018 ◽  
Vol 7 (1.9) ◽  
pp. 119 ◽  
Author(s):  
The Jaswini S ◽  
K M Ravikumar
Keyword(s):  
Wavelet Transforms ◽  
Affective Computing ◽  
Prediction Models ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Neural Network Models ◽  
Classification Rate ◽  
Artificial Neural

Affective computing is an emerging area of research in human computer interaction where researchers have developed automated assessment of human emotion states using physiological signals to establish affective human compute interactions. In this paper wepresent an efficient algorithm for emotion recognition using EEG signals for the data acquired by audio- video stimuli. The desired frequency bands are extracted using discrete wavelet transforms. The Statistical features, Hjorth parameters, differential entropy and wavelet features are extracted. Artificial neural networks, Support Vector Machine (SVM) and K- nearest neighbor are used on the extracted feature set to develop prediction models and to classify intofour emotion states likeclam, happy, fear and sad .These Artificial neural network models are evaluated on the acquired dataset and emotions are classified into four different states with over all accuracy of 86.36%.The classification rate of calm, happy, fear and sad states are 90.9%, 63.63%, 90.90 and 100 % respectively.

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