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Quality assessment and fault detection are important topics in textile research. Human assessment in this field, however, is subjective and slow. Presents an automatic assessment using two fundamentally different kinds of neural networks: the Kohonen Map (an unsupervised system) and the backpropagation network (supervised system). Evaluates two case studies using these techniques: assessment of carpet wear and the assessment of set marks. Both show good results when applied to the aforementioned problems. Makes a comparison between the two techniques and shows that the unsupervised system also gives an evaluation of the objectivity of the human experts.

This paper seeks to assess the extent to which learning and Web 2.0 technologies are utilised to support learning and teaching in Africa's higher learning institutions, with a specific focus on Tanzania's public universities.

A combination of content analysis and semi‐structured interviews was used to collect data. Semi‐structured interviews were conducted with ICT personnel from six of the eight public universities in Tanzania in 2011.

The study found that the adoption of e‐learning and Web 2.0 technologies is still in its infancy in Tanzania's public universities. However, there was much enthusiasm amongst respondents for developing the potential of e‐learning and Web 2.0 tools in their universities.

The study seeks to promote academic inquiry about the need for innovative Web 2.0 technologies in learning and teaching and the adoption of these emerging technologies in Africa's higher learning institutions.

The study provides empirical findings on the use of e‐learning and Web 2.0 for higher education, specifically in the Tanzanian context. The study provides a basis for further research on the use of Web 2.0 technologies in higher education.

The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear.

In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management.

In result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise.

The regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models.

In this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided.

Robust parameter design is conventionally analyzed by means of statistical techniques. However, the statistical‐based approach is inefficient when optimizing a dynamic system in regards to quantitative control factors and missing observations. The aim of this paper is to propose an alternative approach based on data mining tools to model and optimize dynamic robust design with missing data.

A three‐phase approach based on data mining techniques is proposed. First, a back‐propagation network is trained to construct the response model of a dynamic system. Second, three formulas of performance measures are developed to evaluate the predicted responses of the response model. Finally, a genetic algorithm is then performed to obtain the optimal parameter combination via the response model.

The proposed approach is capable of dealing with both qualitative and quantitative control factors for dynamic systems as well as static systems. In addition, the proposed approach can efficiently treat parameter experiments with missing data. The proposed approach is demonstrated with a numerical example. Results show that this three‐phase data mining approach outperforms the conventional statistic‐based approaches.

This work provides a relatively effective approach to optimize the three types of dynamic robust parameter design. Performing the approach, practitioners do not require much background in statistics but instead rely on their knowledge of engineering.

The purpose of this paper is to optimize the frictional input parameters related to the yarn and woven fabric samples. Indeed, using metaheuristic techniques for optimization, it helps to attempt the best quality appearance of garment, by analysing their effects and relationships with the bagging behaviour of tested fabrics before and after bagging test. Using metaheuristic techniques allows us to select widely the minimal residual bagging properties and the optimized inputs to adjust them for this goal.

The metaheuristic methods were applied and discussed. Hence, the genetic algorithms (GA) and ant colony optimization (ACO) technique results are compared to select the best residual bagging behaviour and their correspondent parameters. The statistical analysis steps were implemented using Taguchi experimental design thanks to Minitab 14 software. The modelling methodology analysed in this paper deals with the linear regression method application and analysis to prepare to the optimization steps.

The regression results are essential for evaluate the effectiveness of the relationships founded between inputs and outputs parameters and for their optimizations in the design of interest.

This study is interesting for denim consumers and industrial applications during long and repetitive uses. Undoubtedly, the denim garments remained the largely used and consumed, hence, this particularity proves the necessity to study it in order to optimize the bagging phenomenon which occurs as function of number of uses. Although it is fashionable to have bagging, the denim fabric remains, in contrast with the worsted ones, the most popular fabric to produce garments. Moreover, regarding this characteristic, the large uses and the acceptable value of denim fabrics, their aesthetic appearance behaviour due to bagging phenomenon can be analysed and optimized accurately because compared to worsted fabrics, they have a high value and the repetitive tests to investigate widely bagged zones can fall the industrial. The paper has practical implications in the clothing appearance and other textile industry, especially in the weaving process when friction forms (yarn-to-yarn, yarn-to-metal frictions) and stresses are drastic. This can help to understand why residual bagging behaviour remained after garment uses due to the internal stress and excessive extensions.

Until now, there is no work dealing with the optimization of bagging behaviour using metaheuristic techniques. Indeed, all investigations are focused on the evaluation and theoretical modelling based on the multi linear regression analysis. It is notable that the metaheuristic techniques such as ACO and GA are used to optimize some difficult problems but not yet in the textile field excepting some studies using the GA. Besides, there is no sufficiently information to evaluate, predict and optimize the effect of the yarn-to-yarn friction as well as metal-to-yarn one on the residual bagging behaviour. Several and different denim fabrics within their different characteristics are investigated to widen the experimental analysis and thus to generalize the results in the experimental design of interest.

This paper aims to examine and analyze the effects of government policy, quality of human resources and professional institutions on workforce competitiveness using welding technology variable as a mediating variable.

This study used quantitative research by using partial least square – structural equation modeling (PLS-SEM) to analyze the collected data.

Based on the results of the analysis, it was noted that there was a significant influence between government policy, quality of human resources and professional institutions on welding technology. The coefficients are characterized by a positive direct relationship, which means that the higher the quality of government policy and human resources professionals variables, the higher the value of the institute of welding technology.

This study recommends that government should create policies that have benefits to competitiveness of Indonesian workforce. Implications from this study support government to use the model to determine and initiate policies in the field of welding as well as establish clear and standardized operating standards and recruitment process (government apparatus) that can accommodate the competitiveness of welding workers in Indonesia.

The originality of this paper is that the participatory approach was adopted in this study using PLS-SEM. In addition, this study was one of the first studies to carry out research at the BNSP office, BLK-Bandung-Jakarta, Makassar, B4T and dismiss the Ministry of Manpower and the Ministry of Industry in Jakarta, Indonesia, where there was no research in this location. Previous studies conducted research in various case studies.