Fuzzy Knowledge Based Expert System for Prediction of Color Strength of Cotton Knitted Fabrics

2016 ◽  
Vol 11 (3) ◽  
pp. 155892501601100
Author(s):  
Ismail Hossain ◽  
Altab Hossain ◽  
Imtiaz Ahmed Choudhury ◽  
Abdullah Al Mamun
Keyword(s):  
Expert System ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Parameters Optimization ◽  
Model Parameters ◽  
Color Strength ◽  
Knitted Fabrics ◽  
Knowledge Based ◽  
Study Laboratory ◽  
Fuzzy Knowledge

The present study is intended to develop an intelligent model for the prediction of color strength of cotton knitted fabrics using fuzzy knowledge based expert system (FKBES). The factors chosen for developing the prediction model are dye concentration, dyeing time and process temperature. Besides, such factors are nonlinear and have mutual interactions among them; so it is not easy to create an exact correlation between the inputs variables and color strength using mathematical or statistical methods. In contrast, artificial neural network and neural-fuzzy models require massive amounts of experimental data for model parameters optimization which are challenging to collect from the dyeing industries. In this context, fuzzy knowledge based expert system is the most efficient modeling tool which performs exceptionally well in a non-linear complex domain with lowest amount of trial data like human experts. In this study, laboratory scale experiments were conducted for three types of cotton knitted fabrics to verify the developed fuzzy model. It was found that actual and predicted values of color strength of the knitted fabrics were in good agreement with each other with less than 5% absolute error.

scholarly journals Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ismail Hossain ◽  
Imtiaz Ahmed Choudhury ◽  
Azuddin Bin Mamat ◽  
Abdus Shahid ◽  
Ayub Nabi Khan ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mechanical Properties ◽  
Expert System ◽  
Prediction Model ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Fuzzy Expert System ◽  
Knitted Fabrics ◽  
Bursting Strength

The main objective of this research is to predict the mechanical properties of viscose/lycra plain knitted fabrics by using fuzzy expert system. In this study, a fuzzy prediction model has been built based on knitting stitch length, yarn count, and yarn tenacity as input variables and fabric mechanical properties specially bursting strength as an output variable. The factors affecting the bursting strength of viscose knitted fabrics are very nonlinear. Hence, it is very challenging for scientists and engineers to create an exact model efficiently by mathematical or statistical model. Alternatively, developing a prediction model via ANN and ANFIS techniques is also difficult and time consuming process due to a large volume of trial data. In this context, fuzzy expert system (FES) is the promising modeling tool in a quality modeling as FES can map effectively in nonlinear domain with minimum experimental data. The model derived in the present study has been validated by experimental data. The mean absolute error and coefficient of determination between the actual bursting strength and that predicted by the fuzzy model were found to be 2.60% and 0.961, respectively. The results showed that the developed fuzzy model can be applied effectively for the prediction of fabric mechanical properties.

scholarly journals Fuzzy Knowledge Based System for Suitability of Soils in Airfield Applications

2021 ◽  
Vol 7 (1) ◽  
pp. 140-152
Author(s):  
A. Sujatha ◽  
L. Govindaraju ◽  
N. Shivakumar ◽  
V. Devaraj
Keyword(s):  
Laboratory Test ◽  
Soft Computing ◽  
Fuzzy Model ◽  
Soil Classification ◽  
Knowledge Based System ◽  
Model Soil ◽  
Soil Suitability ◽  
Knowledge Based ◽  
Wide Range ◽  
Fuzzy Knowledge

Proper design of roads and airfield pavements requires an in-depth soil properties evaluation to determine suitability of soil. Soft computing is used to model soil classification system's dynamic behaviour and its properties. Soft computing is based on methods of machine learning, fuzzy logic and artificial neural networks, expert systems, genetic algorithms. Fuzzy system is a strong method for mimicking human thought and solves question of confusion. This paper proposes a new decision-making approach for soil suitability in airfield applications without a need to perform any manual works like use of tables or chart. A fuzzy knowledge - based approach is built to rate soil suitability in qualitative terms for airfield application. The proposed model describes a new technique by defining fuzzy descriptors using triangular functions considering the index properties of soils as input parameters and fuzzy rules are generated using fuzzy operators to classify soil and rate its suitability for airfield applications. The data obtained from the results of the laboratory test are validated with the results of the fuzzy knowledge-based system indicating the applicability of the Fuzzy model created. The approach developed in this work is more skilled to other prevailing optimization models. Due to its system’s flexibility, it can be suitably customized and applied to laboratory test data available, thus delivering a wide range for any geotechnical engineer. Doi: 10.28991/cej-2021-03091643 Full Text: PDF

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

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