I’m preparing a full-length research article that applies neural-network models, coded in Python, to detect fabric defects in an industrial setting. The goal is not only to craft a solid, data-driven manuscript but to shepherd it all the way to acceptance in a Q2-ranked Scopus journal. Scope of work The study must cover end-to-end experimentation: sourcing or curating an open or proprietary fabric-image dataset, implementing and tuning the model, analysing results against relevant baselines, and articulating the implications for real-world textile quality control. Once the research is solid, the manuscript needs structuring to meet the target journal’s author guidelines, followed by submission, peer-review revisions, and final proof corrections until the paper receives an online-first DOI. Key expectations • Strong, reproducible Python code (TensorFlow/PyTorch preferred) and a concise repository or supplemental ZIP. • Clear methodology, statistically sound evaluation, and visuals (confusion matrices, heatmaps, sample detections). • Proper referencing in the journal’s citation style and compliance with originality checks (≤10 % similarity). • Active liaison with the editorial office and prompt turnaround on reviewer comments. • Final deliverables: accepted manuscript PDF, proofs, DOI link, source code, and any auxiliary data that can be publicly shared. Acceptance criteria The engagement is complete once the article is live on the journal’s site with an assigned DOI and indexing confirmation in Scopus. If you have recent experience publishing in engineering or computer-vision venues indexed by Scopus and can manage both the technical research and the publishing workflow, let’s move forward.