{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:64BWVOMYSFQW2QWRZBYCDDBLFI","short_pith_number":"pith:64BWVOMY","canonical_record":{"source":{"id":"2111.05976","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-10T22:41:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c96be9fbe65fcae246957bac03e548487913523441bfa999c953072f04c0dd0d","abstract_canon_sha256":"dfb57988a6064b505c021a8485299b507420ae48976fa6dccc65769602920afb"},"schema_version":"1.0"},"canonical_sha256":"f7036ab99891616d42d1c870218c2b2a32fcce8376598a42616580ac3d78d681","source":{"kind":"arxiv","id":"2111.05976","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.05976","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"arxiv_version","alias_value":"2111.05976v1","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.05976","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"pith_short_12","alias_value":"64BWVOMYSFQW","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"pith_short_16","alias_value":"64BWVOMYSFQW2QWR","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"pith_short_8","alias_value":"64BWVOMY","created_at":"2026-07-05T03:30:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:64BWVOMYSFQW2QWRZBYCDDBLFI","target":"record","payload":{"canonical_record":{"source":{"id":"2111.05976","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-10T22:41:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c96be9fbe65fcae246957bac03e548487913523441bfa999c953072f04c0dd0d","abstract_canon_sha256":"dfb57988a6064b505c021a8485299b507420ae48976fa6dccc65769602920afb"},"schema_version":"1.0"},"canonical_sha256":"f7036ab99891616d42d1c870218c2b2a32fcce8376598a42616580ac3d78d681","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:30:56.776310Z","signature_b64":"71t8hkAt5nKI0Q03eFoBBbWSQxFTGu2F9uSMmuTaVAf2Kj9Wb2FChiUur7IqyQH81x3f1KW41D/KMZ0X9Y3lAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7036ab99891616d42d1c870218c2b2a32fcce8376598a42616580ac3d78d681","last_reissued_at":"2026-07-05T03:30:56.775864Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:30:56.775864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.05976","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":"2026-07-05T03:30:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AZ4HuYGBq8AmdF+C/c9GhL67FEMDomSybeYzYjCmPMNBCHURFSDPM/i+MYDUDDGNMNuz6xxjjsgK0TNuZC3CDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T09:35:21.286964Z"},"content_sha256":"a9088dbe823a0ab90c4db8222553baa74ab6e186e389c4ce376fee4f6b1973c0","schema_version":"1.0","event_id":"sha256:a9088dbe823a0ab90c4db8222553baa74ab6e186e389c4ce376fee4f6b1973c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:64BWVOMYSFQW2QWRZBYCDDBLFI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Classification of the Chess Endgame problem using Logistic Regression, Decision Trees, and Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Mahmoud S. Fayed","submitted_at":"2021-11-10T22:41:17Z","abstract_excerpt":"In this study we worked on the classification of the Chess Endgame problem using different algorithms like logistic regression, decision trees and neural networks. Our experiments indicates that the Neural Networks provides the best accuracy (85%) then the decision trees (79%). We did these experiments using Microsoft Azure Machine Learning as a case-study on using Visual Programming in classification. Our experiments demonstrates that this tool is powerful and save a lot of time, also it could be improved with more features that increase the usability and reduce the learning curve. We also de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.05976","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/2111.05976/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:30:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gESplKslLIgqZYEF9AUNw7C3WpOOGCGcgf/ovFGxFXOoeGfeADyTKDzET8EIgAL15U1cZqaaaU4nXsqUyfj+BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T09:35:21.287351Z"},"content_sha256":"aae475d79de19f702231152ad6628f1c7c5872138ed584f59b4a6b1acf5ec4ae","schema_version":"1.0","event_id":"sha256:aae475d79de19f702231152ad6628f1c7c5872138ed584f59b4a6b1acf5ec4ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/64BWVOMYSFQW2QWRZBYCDDBLFI/bundle.json","state_url":"https://pith.science/pith/64BWVOMYSFQW2QWRZBYCDDBLFI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/64BWVOMYSFQW2QWRZBYCDDBLFI/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":"2026-07-19T09:35:21Z","links":{"resolver":"https://pith.science/pith/64BWVOMYSFQW2QWRZBYCDDBLFI","bundle":"https://pith.science/pith/64BWVOMYSFQW2QWRZBYCDDBLFI/bundle.json","state":"https://pith.science/pith/64BWVOMYSFQW2QWRZBYCDDBLFI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/64BWVOMYSFQW2QWRZBYCDDBLFI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:64BWVOMYSFQW2QWRZBYCDDBLFI","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":"dfb57988a6064b505c021a8485299b507420ae48976fa6dccc65769602920afb","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-10T22:41:17Z","title_canon_sha256":"c96be9fbe65fcae246957bac03e548487913523441bfa999c953072f04c0dd0d"},"schema_version":"1.0","source":{"id":"2111.05976","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.05976","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"arxiv_version","alias_value":"2111.05976v1","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.05976","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"pith_short_12","alias_value":"64BWVOMYSFQW","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"pith_short_16","alias_value":"64BWVOMYSFQW2QWR","created_at":"2026-07-05T03:30:56Z"},{"alias_kind":"pith_short_8","alias_value":"64BWVOMY","created_at":"2026-07-05T03:30:56Z"}],"graph_snapshots":[{"event_id":"sha256:aae475d79de19f702231152ad6628f1c7c5872138ed584f59b4a6b1acf5ec4ae","target":"graph","created_at":"2026-07-05T03:30:56Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2111.05976/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study we worked on the classification of the Chess Endgame problem using different algorithms like logistic regression, decision trees and neural networks. Our experiments indicates that the Neural Networks provides the best accuracy (85%) then the decision trees (79%). We did these experiments using Microsoft Azure Machine Learning as a case-study on using Visual Programming in classification. Our experiments demonstrates that this tool is powerful and save a lot of time, also it could be improved with more features that increase the usability and reduce the learning curve. We also de","authors_text":"Mahmoud S. Fayed","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-10T22:41:17Z","title":"Classification of the Chess Endgame problem using Logistic Regression, Decision Trees, and Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.05976","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:a9088dbe823a0ab90c4db8222553baa74ab6e186e389c4ce376fee4f6b1973c0","target":"record","created_at":"2026-07-05T03:30:56Z","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":"dfb57988a6064b505c021a8485299b507420ae48976fa6dccc65769602920afb","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-11-10T22:41:17Z","title_canon_sha256":"c96be9fbe65fcae246957bac03e548487913523441bfa999c953072f04c0dd0d"},"schema_version":"1.0","source":{"id":"2111.05976","kind":"arxiv","version":1}},"canonical_sha256":"f7036ab99891616d42d1c870218c2b2a32fcce8376598a42616580ac3d78d681","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7036ab99891616d42d1c870218c2b2a32fcce8376598a42616580ac3d78d681","first_computed_at":"2026-07-05T03:30:56.775864Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:30:56.775864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"71t8hkAt5nKI0Q03eFoBBbWSQxFTGu2F9uSMmuTaVAf2Kj9Wb2FChiUur7IqyQH81x3f1KW41D/KMZ0X9Y3lAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:30:56.776310Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.05976","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a9088dbe823a0ab90c4db8222553baa74ab6e186e389c4ce376fee4f6b1973c0","sha256:aae475d79de19f702231152ad6628f1c7c5872138ed584f59b4a6b1acf5ec4ae"],"state_sha256":"061e64a38b025829d45399791092409be437ea929f352235f360489e0013d3ee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bJMUvTq3YhJmkG83M4eHf7flrwVcQiqtaufzdpl3o6IaUXbIcgA6aKheREiKbFZNm29UqBpwvzlZ069bNBWuDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-19T09:35:21.289440Z","bundle_sha256":"9504271cd3b0386c875468ec329ef5ba67da3a5b5e48bea6e1c84dd49310d12f"}}