{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GW5YXWRRPD5XDZSVE5G5LJFLBE","short_pith_number":"pith:GW5YXWRR","schema_version":"1.0","canonical_sha256":"35bb8bda3178fb71e655274dd5a4ab0911caa31e4878aff86b15f22be9b494ec","source":{"kind":"arxiv","id":"2607.18515","version":1},"attestation_state":"computed","paper":{"title":"Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Nazanin Mahjourian, Saleh Valizadeh Sotubadi, Vinh Nguyen","submitted_at":"2026-07-20T21:15:12Z","abstract_excerpt":"This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets. The methodology is centered around a reinforcement learning-inspired policy updating mechanism, where multiple machine learning models are trained on both full feature sets and feature subsets selected through Shapley-based Explainable AI (SHAP XAI) across 217 datasets. At each episode, the framework assesses the predictive accuracy and F1-scores "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.18515","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-20T21:15:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2e54349e6b790a1e455e4a367e6d25bf44f75f2602701f5832d991d3dda358bc","abstract_canon_sha256":"86043f6cbd42ad934f8ac82d69000692082ea8bcaf8ee5fa3fb9824d75b62e48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:52.394708Z","signature_b64":"F9BH4GHGSNOo+pl5ZAXdyDhMHiGINRPOKIVTpJH4fW2mkY8Eq0KeEVKiySd1e4eI6bqy3ssAdBGZIBuvf6uWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35bb8bda3178fb71e655274dd5a4ab0911caa31e4878aff86b15f22be9b494ec","last_reissued_at":"2026-07-22T00:22:52.393929Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:52.393929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Nazanin Mahjourian, Saleh Valizadeh Sotubadi, Vinh Nguyen","submitted_at":"2026-07-20T21:15:12Z","abstract_excerpt":"This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets. The methodology is centered around a reinforcement learning-inspired policy updating mechanism, where multiple machine learning models are trained on both full feature sets and feature subsets selected through Shapley-based Explainable AI (SHAP XAI) across 217 datasets. At each episode, the framework assesses the predictive accuracy and F1-scores "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18515","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.18515/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.18515","created_at":"2026-07-22T00:22:52.394363+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.18515v1","created_at":"2026-07-22T00:22:52.394363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18515","created_at":"2026-07-22T00:22:52.394363+00:00"},{"alias_kind":"pith_short_12","alias_value":"GW5YXWRRPD5X","created_at":"2026-07-22T00:22:52.394363+00:00"},{"alias_kind":"pith_short_16","alias_value":"GW5YXWRRPD5XDZSV","created_at":"2026-07-22T00:22:52.394363+00:00"},{"alias_kind":"pith_short_8","alias_value":"GW5YXWRR","created_at":"2026-07-22T00:22:52.394363+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE","json":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE.json","graph_json":"https://pith.science/api/pith-number/GW5YXWRRPD5XDZSVE5G5LJFLBE/graph.json","events_json":"https://pith.science/api/pith-number/GW5YXWRRPD5XDZSVE5G5LJFLBE/events.json","paper":"https://pith.science/paper/GW5YXWRR"},"agent_actions":{"view_html":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE","download_json":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE.json","view_paper":"https://pith.science/paper/GW5YXWRR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.18515&json=true","fetch_graph":"https://pith.science/api/pith-number/GW5YXWRRPD5XDZSVE5G5LJFLBE/graph.json","fetch_events":"https://pith.science/api/pith-number/GW5YXWRRPD5XDZSVE5G5LJFLBE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE/action/storage_attestation","attest_author":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE/action/author_attestation","sign_citation":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE/action/citation_signature","submit_replication":"https://pith.science/pith/GW5YXWRRPD5XDZSVE5G5LJFLBE/action/replication_record"}},"created_at":"2026-07-22T00:22:52.394363+00:00","updated_at":"2026-07-22T00:22:52.394363+00:00"}