{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7Z3ZQBSFO2GS7HEOGW5DHJZORT","short_pith_number":"pith:7Z3ZQBSF","schema_version":"1.0","canonical_sha256":"fe77980645768d2f9c8e35ba33a72e8cda64130df53929617769ccea1b362e5f","source":{"kind":"arxiv","id":"2303.11949","version":2},"attestation_state":"computed","paper":{"title":"A fuzzy adaptive evolutionary-based feature selection and machine learning framework for single and multi-objective body fat prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Farshid Keivanian, Raymond Chiong, Zongwen Fan","submitted_at":"2023-03-20T07:38:57Z","abstract_excerpt":"Predicting body fat can provide medical practitioners and users with essential information for preventing and diagnosing heart diseases. Hybrid machine learning models offer better performance than simple regression analysis methods by selecting relevant body measurements and capturing complex nonlinear relationships among selected features in modelling body fat prediction problems. There are, however, some disadvantages to them. Current machine learning. Modelling body fat prediction as a combinatorial single- and multi-objective optimisation problem often gets stuck in local optima. When mul"},"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":"2303.11949","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2023-03-20T07:38:57Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1a55b443a1a5bf828031ecfbdb1a9a8f74e567536e896fd8739a249f192cadc8","abstract_canon_sha256":"72c38d8fe64203b3309c9c9791856de21e546636983151a74ce649591d50ebe8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:03:40.278961Z","signature_b64":"t943mBa+cjCtLZIK12VCCawv/4OlcNfEm4PMlUA7MWbYDs9dYSAG08QdjsdaDxWVS0coiP13cE1CMYvkltR2Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe77980645768d2f9c8e35ba33a72e8cda64130df53929617769ccea1b362e5f","last_reissued_at":"2026-06-08T01:03:40.277397Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:03:40.277397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A fuzzy adaptive evolutionary-based feature selection and machine learning framework for single and multi-objective body fat prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Farshid Keivanian, Raymond Chiong, Zongwen Fan","submitted_at":"2023-03-20T07:38:57Z","abstract_excerpt":"Predicting body fat can provide medical practitioners and users with essential information for preventing and diagnosing heart diseases. Hybrid machine learning models offer better performance than simple regression analysis methods by selecting relevant body measurements and capturing complex nonlinear relationships among selected features in modelling body fat prediction problems. There are, however, some disadvantages to them. Current machine learning. Modelling body fat prediction as a combinatorial single- and multi-objective optimisation problem often gets stuck in local optima. When mul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.11949","kind":"arxiv","version":2},"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/2303.11949/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":"2303.11949","created_at":"2026-06-08T01:03:40.277539+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.11949v2","created_at":"2026-06-08T01:03:40.277539+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.11949","created_at":"2026-06-08T01:03:40.277539+00:00"},{"alias_kind":"pith_short_12","alias_value":"7Z3ZQBSFO2GS","created_at":"2026-06-08T01:03:40.277539+00:00"},{"alias_kind":"pith_short_16","alias_value":"7Z3ZQBSFO2GS7HEO","created_at":"2026-06-08T01:03:40.277539+00:00"},{"alias_kind":"pith_short_8","alias_value":"7Z3ZQBSF","created_at":"2026-06-08T01:03:40.277539+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/7Z3ZQBSFO2GS7HEOGW5DHJZORT","json":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT.json","graph_json":"https://pith.science/api/pith-number/7Z3ZQBSFO2GS7HEOGW5DHJZORT/graph.json","events_json":"https://pith.science/api/pith-number/7Z3ZQBSFO2GS7HEOGW5DHJZORT/events.json","paper":"https://pith.science/paper/7Z3ZQBSF"},"agent_actions":{"view_html":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT","download_json":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT.json","view_paper":"https://pith.science/paper/7Z3ZQBSF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.11949&json=true","fetch_graph":"https://pith.science/api/pith-number/7Z3ZQBSFO2GS7HEOGW5DHJZORT/graph.json","fetch_events":"https://pith.science/api/pith-number/7Z3ZQBSFO2GS7HEOGW5DHJZORT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT/action/storage_attestation","attest_author":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT/action/author_attestation","sign_citation":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT/action/citation_signature","submit_replication":"https://pith.science/pith/7Z3ZQBSFO2GS7HEOGW5DHJZORT/action/replication_record"}},"created_at":"2026-06-08T01:03:40.277539+00:00","updated_at":"2026-06-08T01:03:40.277539+00:00"}