{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DJS5IWF7E4UELGLATUJTECVDLH","short_pith_number":"pith:DJS5IWF7","schema_version":"1.0","canonical_sha256":"1a65d458bf27284599609d13320aa359eecd899fa996176d4a4414a7085a79e1","source":{"kind":"arxiv","id":"1610.04028","version":2},"attestation_state":"computed","paper":{"title":"A fuzzy expert system for earthquake prediction, case study: the Zagros range","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Arash Andalib, Farid Atry, Mehdi Zare","submitted_at":"2016-10-13T11:18:02Z","abstract_excerpt":"A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system (FIS) and to infer the results. In this paper, we have used a Sugeno type fuzzy inference system to build the FES. At the next step, the adaptive network-based fuzzy inference system (ANFIS) is used to "},"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":"1610.04028","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-10-13T11:18:02Z","cross_cats_sorted":[],"title_canon_sha256":"04add4328f854256cebcd5c38e408caf3e238962e5463efc88c75f1be977d681","abstract_canon_sha256":"8e3f76a2b0e1fe226dd89e46d1ae3c389548604adc63dac8bbe122621e5fe769"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:15.865130Z","signature_b64":"ULFdVm+Zc23XnBrPGZj4Bjeq9ERCFB6mpgCmZIKVKLQG5ro4b83eojMAry3v+R8sApALQW56ltXKUiqZXlN6DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a65d458bf27284599609d13320aa359eecd899fa996176d4a4414a7085a79e1","last_reissued_at":"2026-05-18T00:44:15.864500Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:15.864500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A fuzzy expert system for earthquake prediction, case study: the Zagros range","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Arash Andalib, Farid Atry, Mehdi Zare","submitted_at":"2016-10-13T11:18:02Z","abstract_excerpt":"A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system (FIS) and to infer the results. In this paper, we have used a Sugeno type fuzzy inference system to build the FES. At the next step, the adaptive network-based fuzzy inference system (ANFIS) is used to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.04028","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":""},"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":"1610.04028","created_at":"2026-05-18T00:44:15.864613+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.04028v2","created_at":"2026-05-18T00:44:15.864613+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.04028","created_at":"2026-05-18T00:44:15.864613+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJS5IWF7E4UE","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJS5IWF7E4UELGLA","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJS5IWF7","created_at":"2026-05-18T12:30:12.583610+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/DJS5IWF7E4UELGLATUJTECVDLH","json":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH.json","graph_json":"https://pith.science/api/pith-number/DJS5IWF7E4UELGLATUJTECVDLH/graph.json","events_json":"https://pith.science/api/pith-number/DJS5IWF7E4UELGLATUJTECVDLH/events.json","paper":"https://pith.science/paper/DJS5IWF7"},"agent_actions":{"view_html":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH","download_json":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH.json","view_paper":"https://pith.science/paper/DJS5IWF7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.04028&json=true","fetch_graph":"https://pith.science/api/pith-number/DJS5IWF7E4UELGLATUJTECVDLH/graph.json","fetch_events":"https://pith.science/api/pith-number/DJS5IWF7E4UELGLATUJTECVDLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH/action/storage_attestation","attest_author":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH/action/author_attestation","sign_citation":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH/action/citation_signature","submit_replication":"https://pith.science/pith/DJS5IWF7E4UELGLATUJTECVDLH/action/replication_record"}},"created_at":"2026-05-18T00:44:15.864613+00:00","updated_at":"2026-05-18T00:44:15.864613+00:00"}