{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:AFC43KYQHEWI5OYL4NUFNHOH7N","short_pith_number":"pith:AFC43KYQ","canonical_record":{"source":{"id":"1403.5488","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.NE","submitted_at":"2014-03-21T15:11:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c6b63db930ff5afdf9554beeaf6d5fd53a752c45c5ac3014f57ab9bffeb52c5a","abstract_canon_sha256":"225f7df06498f5a4eaa953daf4e3c948758e4a6913cfa69e7ecc1bd345e1ba6f"},"schema_version":"1.0"},"canonical_sha256":"0145cdab10392c8ebb0be368569dc7fb6040275e341f5d9185af0960d27fbbfc","source":{"kind":"arxiv","id":"1403.5488","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.5488","created_at":"2026-05-18T02:55:56Z"},{"alias_kind":"arxiv_version","alias_value":"1403.5488v1","created_at":"2026-05-18T02:55:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.5488","created_at":"2026-05-18T02:55:56Z"},{"alias_kind":"pith_short_12","alias_value":"AFC43KYQHEWI","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_16","alias_value":"AFC43KYQHEWI5OYL","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_8","alias_value":"AFC43KYQ","created_at":"2026-05-18T12:28:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:AFC43KYQHEWI5OYL4NUFNHOH7N","target":"record","payload":{"canonical_record":{"source":{"id":"1403.5488","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.NE","submitted_at":"2014-03-21T15:11:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c6b63db930ff5afdf9554beeaf6d5fd53a752c45c5ac3014f57ab9bffeb52c5a","abstract_canon_sha256":"225f7df06498f5a4eaa953daf4e3c948758e4a6913cfa69e7ecc1bd345e1ba6f"},"schema_version":"1.0"},"canonical_sha256":"0145cdab10392c8ebb0be368569dc7fb6040275e341f5d9185af0960d27fbbfc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:55:56.243245Z","signature_b64":"N+uhBm1hi2P2YrRpzQ+gTcI0LrFc1sakPT3w05TdNCmksjfkqcjW0A8r6e4nb6cl4JYzA3VRQMN7hOEuxU7/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0145cdab10392c8ebb0be368569dc7fb6040275e341f5d9185af0960d27fbbfc","last_reissued_at":"2026-05-18T02:55:56.242584Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:55:56.242584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1403.5488","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:55:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IvEJ8+jMHUKirJV6EBg5wYk0Cace9A6lirtWkaJaTyy527CUaBGMSkHfzR2TMl5MAdKtaA0jR95sG7gyCX6YCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T10:12:50.700636Z"},"content_sha256":"ab9c869e63455d8e1a3293b3a540f3cb397f89a9169a5b92cd9b3633622cd83d","schema_version":"1.0","event_id":"sha256:ab9c869e63455d8e1a3293b3a540f3cb397f89a9169a5b92cd9b3633622cd83d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:AFC43KYQHEWI5OYL4NUFNHOH7N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Missing Data Prediction and Classification: The Use of Auto-Associative Neural Networks and Optimization Algorithms","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Bhekisipho Twala, Collins Leke, T. Marwala","submitted_at":"2014-03-21T15:11:52Z","abstract_excerpt":"This paper presents methods which are aimed at finding approximations to missing data in a dataset by using optimization algorithms to optimize the network parameters after which prediction and classification tasks can be performed. The optimization methods that are considered are genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), random forest (RF) and negative selection (NS) and these methods are individually used in combination with auto-associative neural networks (AANN) for missing data estimation and the results obtained are compared. The methods suggest"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.5488","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:55:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AoAl6cUBmTJYxkbIfBn6m5Ld4DB52sXqjYj64lHBBICrlnRv+XupMAMIELDWXpw6+ajRIe7KsGdKJ2jsIe1xCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T10:12:50.701002Z"},"content_sha256":"b09e1c2a16cab3de3ec0bb1d3ddc25d92cf773122c8bd99e327c8542b991b669","schema_version":"1.0","event_id":"sha256:b09e1c2a16cab3de3ec0bb1d3ddc25d92cf773122c8bd99e327c8542b991b669"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AFC43KYQHEWI5OYL4NUFNHOH7N/bundle.json","state_url":"https://pith.science/pith/AFC43KYQHEWI5OYL4NUFNHOH7N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AFC43KYQHEWI5OYL4NUFNHOH7N/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T10:12:50Z","links":{"resolver":"https://pith.science/pith/AFC43KYQHEWI5OYL4NUFNHOH7N","bundle":"https://pith.science/pith/AFC43KYQHEWI5OYL4NUFNHOH7N/bundle.json","state":"https://pith.science/pith/AFC43KYQHEWI5OYL4NUFNHOH7N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AFC43KYQHEWI5OYL4NUFNHOH7N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:AFC43KYQHEWI5OYL4NUFNHOH7N","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"225f7df06498f5a4eaa953daf4e3c948758e4a6913cfa69e7ecc1bd345e1ba6f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.NE","submitted_at":"2014-03-21T15:11:52Z","title_canon_sha256":"c6b63db930ff5afdf9554beeaf6d5fd53a752c45c5ac3014f57ab9bffeb52c5a"},"schema_version":"1.0","source":{"id":"1403.5488","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.5488","created_at":"2026-05-18T02:55:56Z"},{"alias_kind":"arxiv_version","alias_value":"1403.5488v1","created_at":"2026-05-18T02:55:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.5488","created_at":"2026-05-18T02:55:56Z"},{"alias_kind":"pith_short_12","alias_value":"AFC43KYQHEWI","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_16","alias_value":"AFC43KYQHEWI5OYL","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_8","alias_value":"AFC43KYQ","created_at":"2026-05-18T12:28:19Z"}],"graph_snapshots":[{"event_id":"sha256:b09e1c2a16cab3de3ec0bb1d3ddc25d92cf773122c8bd99e327c8542b991b669","target":"graph","created_at":"2026-05-18T02:55:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"This paper presents methods which are aimed at finding approximations to missing data in a dataset by using optimization algorithms to optimize the network parameters after which prediction and classification tasks can be performed. The optimization methods that are considered are genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), random forest (RF) and negative selection (NS) and these methods are individually used in combination with auto-associative neural networks (AANN) for missing data estimation and the results obtained are compared. The methods suggest","authors_text":"Bhekisipho Twala, Collins Leke, T. Marwala","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.NE","submitted_at":"2014-03-21T15:11:52Z","title":"Missing Data Prediction and Classification: The Use of Auto-Associative Neural Networks and Optimization Algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.5488","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ab9c869e63455d8e1a3293b3a540f3cb397f89a9169a5b92cd9b3633622cd83d","target":"record","created_at":"2026-05-18T02:55:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"225f7df06498f5a4eaa953daf4e3c948758e4a6913cfa69e7ecc1bd345e1ba6f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.NE","submitted_at":"2014-03-21T15:11:52Z","title_canon_sha256":"c6b63db930ff5afdf9554beeaf6d5fd53a752c45c5ac3014f57ab9bffeb52c5a"},"schema_version":"1.0","source":{"id":"1403.5488","kind":"arxiv","version":1}},"canonical_sha256":"0145cdab10392c8ebb0be368569dc7fb6040275e341f5d9185af0960d27fbbfc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0145cdab10392c8ebb0be368569dc7fb6040275e341f5d9185af0960d27fbbfc","first_computed_at":"2026-05-18T02:55:56.242584Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:55:56.242584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N+uhBm1hi2P2YrRpzQ+gTcI0LrFc1sakPT3w05TdNCmksjfkqcjW0A8r6e4nb6cl4JYzA3VRQMN7hOEuxU7/Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:55:56.243245Z","signed_message":"canonical_sha256_bytes"},"source_id":"1403.5488","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab9c869e63455d8e1a3293b3a540f3cb397f89a9169a5b92cd9b3633622cd83d","sha256:b09e1c2a16cab3de3ec0bb1d3ddc25d92cf773122c8bd99e327c8542b991b669"],"state_sha256":"30c61e43f952390bda4f58ad2f4cff03bf42a58b97e721ec9c3989ff25caa1fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GqnVvmWiYLEBbK+5S78zWUNnUr78kRXSiMoLAVWfnpp4dnxIycIpD9ULjF/y77Fyg/VC01OKENjWBNIcQCz3Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T10:12:50.702994Z","bundle_sha256":"28c120e155d9b196743b5b187bf7607ab47a462b89fab86bc28f43b0fe577039"}}