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Academic conferences play a crucial role in fostering scholarly exchanges and discussions among core members, thereby shaping the core performance of these events. Understanding the contributions of scholars in academic conferences is essential for advancing the goals and development of such events. This paper aims to discuss the aforementioned idea.

This paper proposes a comprehensive measurement method for measuring scholars' academic activeness in academic conferences. Based on the essence of academic activeness, a measurement model is constructed, comprising the breadth of academic activeness using the RFM model and the depth of academic activeness using the ATM model. Additionally, effective categories of scholars are derived from the model, facilitating the identification of valuable scholars in academic conferences.

Empirical evidence from ASIS&T conference records over the past 20 years demonstrates that the proposed measurement indicators have better coverage compared to traditional evaluation indicators, thus complementing existing scholar evaluation indictators. Furthermore, the academic activeness measurement model presented in this paper exhibits good efficacy in identifying important value and active scholars.

This paper pays attention to the activeness of scholars in academic conferences. It broadens the evaluation of academic conferences and scholars and supplements the evaluation indicators of academic influence.

The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.

Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.

The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.

There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.