scholarly journals Characterization and statistical modelling of thermal resistance of cotton/polyester blended double layer interlock knitted fabrics

2017 ◽  
Vol 21 (6 Part A) ◽  
pp. 2393-2403 ◽  
Author(s):  
Ali Afzal ◽  
Sheraz Ahmad ◽  
Abher Rasheed ◽  
Muhammad Mohsin ◽  
Faheem Ahmad ◽  
...  
Keyword(s):  
Thermal Resistance ◽  
Double Layer ◽  
Input Parameter ◽  
Statistical Modelling ◽  
Absolute Error ◽  
Knitted Fabrics ◽  
Data Set ◽  
Unseen Data ◽  
Fabric Layer ◽  
Interlock Knitted Fabrics

The aim of this study was to analyse and model the effect of knitting parameters on the thermal resistance of cotton/polyester double layer interlock knitted fabrics. Fabric samples of areal densities ranging from 310-495 g/m2 were knitted using yarns of three different cotton/polyester blends, each of two different linear densities by systematically varying knitting loop lengths for achieving different cover factors. It was found that by changing the polyester content in the inner and outer fabric layer from 40 to 65% in the double layer knitted fabric has statistically significant effect on the fabric thermal resistance. Fabric thermal resistance increased with increase in relative specific heat of outer fabric layer, yarn linear density, loop length, and fabric thickness while decrease in fabric areal density. It was concluded that response surface regression modelling could be successfully used for the prediction of thermal resistance of double layer interlock knitted fabrics. The model was validated by unseen data set and it was found that the actual and predicted values were in good agreement with each other with less than 10% absolute error. Sensitivity analysis was also performed to find out the relative contribution of each input parameter on the air permeability of the double layer interlock knitted fabrics.

scholarly journals Influence of Fabric Parameters on Thermal Comfort Performance of Double Layer Knitted Interlock Fabrics

2017 ◽  
Vol 17 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Ali Afzal ◽  
Sheraz Ahmad ◽  
Abher Rasheed ◽  
Faheem Ahmad ◽  
Fatima Iftikhar ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Thermal Resistance ◽  
Thermal Comfort ◽  
Specific Heat ◽  
Double Layer ◽  
Thermal Insulation ◽  
Loop Length ◽  
Knitted Fabrics ◽  
Thermal Transmittance ◽  
Thermal Absorptivity

Abstract The aim of this study was to analyse the effects of various fabric parameters on the thermal resistance, thermal conductivity, thermal transmittance, thermal absorptivity and thermal insulation of polyester/cotton double layer knitted interlock fabrics. It was found that by increasing fibre content with higher specific heat increases the thermal insulation while decreases the thermal transmittance and absorptivity of the fabric. It was concluded that double layer knitted fabrics developed with higher specific heat fibres, coarser yarn linear densities, higher knitting loop length and fabric thickness could be adequately used for winter clothing purposes.

scholarly journals Predicting the infiltration characteristics for semi-arid regions using regression trees

2021 ◽  
Author(s):  
Parveen Sihag ◽  
Munish Kumar ◽  
Saad Sh. Sammen
Keyword(s):  
Input Parameter ◽  
Regression Tree ◽  
Soil Surface ◽  
Infiltration Rate ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Infiltration Process ◽  
Data Set ◽  
Cumulative Infiltration ◽  
Tree Methods

Abstract The study of infiltration process is considered as essential and necessary for all hydrology studies. Therefore, accurate predictions of infiltration characteristics are required to understand the behavior of subsurface flow of water through the soil surface. The aim of the current study is to simulate and improve the prediction accuracy of infiltration rate and cumulative infiltration of soil using regression tree methods. Experimental data recorded with a double ring infiltrometer for 17 different sites are used in this study. Three regression tree methods: Random tree, Random forest (RF) and M5 tree are employed to modelling the infiltration characteristics using the basic soil characteristics. The performance of the modelling approaches is compared in predicting the infiltration rate as well as cumulative infiltration, obtained results suggest that performance of RF model is better than other applied models with coefficient of determination (R2) = 0.97 & 0.97, root mean square error (RMSE) = 8.10 & 6.96 and mean absolute error (MAE) = 5.74 & 4.44 for infiltration rate and cumulative infiltration respectively. RF model is used to represent the infiltration characteristics of the study area. Moreover, parametric sensitivity is adopted to study the significance of each input parameter in estimating the infiltration process. Results suggest that time (t) is the most influencing parameter in predicting the infiltration process using this data set.

Investigation and modeling of air permeability of Cotton/Polyester blended double layer interlock knitted fabrics

2014 ◽  
Vol 15 (7) ◽  
pp. 1539-1547 ◽  
Author(s):  
Ali Afzal ◽  
Tanveer Hussain ◽  
Mumtaz Hassan Malik ◽  
Abher Rasheed ◽  
Sheraz Ahmad ◽  
...  
Keyword(s):  
Double Layer ◽  
Air Permeability ◽  
Knitted Fabrics ◽  
Interlock Knitted Fabrics

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

scholarly journals Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Bang Ly ◽  
Thuy-Anh Nguyen ◽  
Binh Thai Pham
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Soil Properties ◽  
Clay Content ◽  
Absolute Error ◽  
Experimental Methods ◽  
Liquid Limit ◽  
Support Vector ◽  
Data Set ◽  
Soil Cohesion

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

scholarly journals Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

2018 ◽  
pp. 57-61
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas
Keyword(s):  
Image Processing ◽  
Fuzzy System ◽  
Absolute Error ◽  
Data Set ◽  
Wheat Grains ◽  
Image Processing Techniques ◽  
Neuro Fuzzy ◽  
Length Width ◽  
Processing Techniques

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

scholarly journals Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 489
Author(s):  
Fadi Almohammed ◽  
Parveen Sihag ◽  
Saad Sh. Sammen ◽  
Krzysztof Adam Ostrowski ◽  
Karan Singh ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Performance Evaluation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Curing Time ◽  
Data Set ◽  
Soft Computing Techniques ◽  
Bacterial Concrete

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

One- year mortality in patients with advanced hepatocellular carcinoma on immunotherapy: Prediction using machine learning models (Preprint)

2021 ◽  
Author(s):  
Thomas Ka-Luen Lui ◽  
Ka Shing, Michael Cheung ◽  
Wai Keung Leung
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
False Negative ◽  
False Negative Rate ◽  
Absolute Error ◽  
Advanced Hepatocellular Carcinoma ◽  
Data Set ◽  
One Year ◽  
Related Mortality

BACKGROUND Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. OBJECTIVE This study aimed to evaluate the role of machine learning (ML) models in predicting the one-year cancer-related mortality in advanced HCC patients treated with immunotherapy METHODS 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) in 2014 - 2019 in Hong Kong were included. The whole data set were randomly divided into training (n=316) and validation (n=79) set. The data set, including 45 clinical variables, was used to construct six different ML models in predicting the risk of one-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and the mean absolute error (MAE) using calibration analysis. RESULTS The overall one-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.93 (95%CI: 0.86-0.98), which was better than logistic regression (0.82, p=0.01) and XGBoost (0.86, p=0.04). RF also had the lowest false positive (6.7%) and false negative rate (2.8%). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models. CONCLUSIONS ML models could predict one-year cancer-related mortality of HCC patients treated with immunotherapy, which may help to select patients who would most benefit from this new treatment option.

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