https://ojs.wiserpub.com/index.php/RRCS <p><em>Research Reports on Computer Science</em> (<em>RRCS</em>) mainly reports on innovative research results that cover novel theories, technologies and engineering applications in the fields of computer science and engineering. The journal considers contributions in the form of original research papers, short communications and review articles on cross-cutting theories, technologies and real-world applications in line with the journal scope.</p> <p>Topics of interest include, but are not limited to: evolutionary computing, data analytics, computer architecture, software engineering, database technology, big data processing, quantum computers, artificial intelligence, artificial neural networks, virtual reality, machine learning and automated reasoning, Internet of Things and cloud computing, intelligent human-machine interface, soft computing, etc.</p> Universal Wiser Publisher en-US Research Reports on Computer Science 2811-0005 https://ojs.wiserpub.com/index.php/RRCS/article/view/4156 <p>Ensuring quality standards is a crucial stage within the textile sector. Automated classification of the fabric defects is a vital step during the fabric manufacturing process in order to prevent any faulted fabric from being supplied to the market. The defects on the surface of the fabric were manually identified by the individuals but this poses problems in terms of human-error and is also time-consuming. Efforts have been made to achieve better precision in defect detection through image processing studies, leading to the development of automated systems. In this study, some high-performing deep learning models are applied including ResNet and VGG-16 and illustrated how these algorithms can be used in the domain of textile manufacturing for fabric defect detection. A combination of images are used ranging from patterned and textured to plain for better defects recognition on any given fabric. The algorithm VGG-16 has displayed 73.91% accuracy while the ResNet algorithm has shown 67.59% accuracy.</p> Syeda Rabia Arshad Muhammad Khuram Shahzad Copyright (c) 2024 Syeda Rabia Arshad, et al. https://creativecommons.org/licenses/by/4.0/ 2024-03-20 2024-03-20 1–11 1–11 10.37256/rrcs.3120244156