I need a full-length research paper prepared and shepherded through publication in a Scopus-indexed Q2 journal. The topic is neural-network-based detection of visual defects in woven or knitted textiles, implemented in Python. I am flexible about the framework—TensorFlow, PyTorch, Keras or a comparable library is fine—so long as the final code is reproducible and well-commented. The paper must include a solid literature review, a clearly explained network architecture, an experimental section using a representative dataset of fabric images, and a results discussion that meets the methodological rigour typical of Q2 outlets. I will supply any proprietary images I have; if additional public datasets are needed, please curate them. Deliverables • Draft manuscript formatted to the target journal’s template • Complete Python code with instructions to run experiments • Figures, tables and supplementary material ready for submission • Response letter handling reviewers’ comments until final acceptance • Proof of online publication in a Scopus Q2 journal Acceptance criteria 1. Manuscript passes initial editorial screening without major scope comments. 2. Reviewers’ technical concerns are addressed within two revision cycles. 3. Final version appears online with a valid Scopus entry. Please outline your proposed journal, anticipated timeline and any dataset requirements in your bid.