{"id":"https://openalex.org/W7204635273","doi":"https://doi.org/10.5281/zenodo.22160863","title":"Artificial Intelligence and Neural Networks Using IBM SPSS From Data to Intelligent Prediction","display_name":"Artificial Intelligence and Neural Networks Using IBM SPSS From Data to Intelligent Prediction","publication_year":2026,"publication_date":"2026-08-29","ids":{"openalex":"https://openalex.org/W7204635273","doi":"https://doi.org/10.5281/zenodo.22160863"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.22160863","doi":"https://doi.org/10.5281/zenodo.22160863","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22160863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Book"},"volume":null,"type":"book","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.22160863","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055321528","display_name":"Yousef Mohammad Iriqat","orcid":"https://orcid.org/0000-0002-0707-6089","observed_orcids":["https://orcid.org/0000-0002-0707-6089"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Iriqat, Yousef Mohammad","raw_affiliation_strings":["Academic Book Producer"],"raw_orcid":"https://orcid.org/0000-0002-0707-6089","affiliations":[{"raw_affiliation_string":"Academic Book Producer","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5055321528"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62711023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.38600000739097595,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.38600000739097595,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.3718000054359436,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13904","display_name":"Artificial Intelligence Applications","score":0.14470000565052032,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"artificial intelligence","score":0.8360000252723694},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.996999979019165},{"id":"https://openalex.org/keywords/ibm-spss-statistics","display_name":"IBM SPSS Statistics","score":0.8379999995231628},{"id":"https://openalex.org/keywords/predictive-modeling","display_name":"predictive modeling","score":0.9020000100135803},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.9459999799728394},{"id":"https://openalex.org/keywords/radial-basis-function-neural-network","display_name":"radial basis function neural network","score":0.9950000047683716},{"id":"https://openalex.org/keywords/customer-churn","display_name":"customer churn","score":0.7760000228881836},{"id":"https://openalex.org/keywords/model-evaluation","display_name":"model evaluation","score":0.8320000171661377},{"id":"https://openalex.org/keywords/threshold-probability","display_name":"threshold probability","score":0.9750000238418579},{"id":"https://openalex.org/keywords/predictor-importance","display_name":"predictor importance","score":0.949999988079071},{"id":"https://openalex.org/keywords/what-if-analysis","display_name":"what-if analysis","score":0.9990000128746033},{"id":"https://openalex.org/keywords/managerial-decision-making","display_name":"management decision-making","score":0.8450000286102295},{"id":"https://openalex.org/keywords/data-preparation","display_name":"data preparation","score":0.7260000109672546},{"id":"https://openalex.org/keywords/model-deployment","display_name":"model deployment","score":0.9980000257492065}],"concepts":[{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.8652999997138977},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7330999970436096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7315999865531921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6758000254631042},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5685999989509583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5145000219345093},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4535999894142151},{"id":"https://openalex.org/C56397880","wikidata":"https://www.wikidata.org/wiki/Q6044094","display_name":"Intelligent decision support system","level":2,"score":0.4153999984264374},{"id":"https://openalex.org/C77741850","wikidata":"https://www.wikidata.org/wiki/Q751046","display_name":"IBM PC compatible","level":3,"score":0.41359999775886536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3716999888420105},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.34290000796318054},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C44464901","wikidata":"https://www.wikidata.org/wiki/Q6770893","display_name":"Marketing and artificial intelligence","level":3,"score":0.32339999079704285},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C30112582","wikidata":"https://www.wikidata.org/wiki/Q712503","display_name":"Artificial Intelligence System","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.22160863","doi":"https://doi.org/10.5281/zenodo.22160863","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22160863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Book"},{"id":"doi:10.5281/zenodo.22160864","doi":"https://doi.org/10.5281/zenodo.22160864","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22160864","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Book"}],"best_oa_location":{"id":"doi:10.5281/zenodo.22160863","doi":"https://doi.org/10.5281/zenodo.22160863","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22160863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Book"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W6885084522","https://openalex.org/W6941499146","https://openalex.org/W2012595096","https://openalex.org/W3128343846","https://openalex.org/W6903294174","https://openalex.org/W4287574002","https://openalex.org/W218592683","https://openalex.org/W4242812027","https://openalex.org/W4412628390","https://openalex.org/W140191271"],"abstract_inverted_index":{"Artificial":[0],"Intelligence":[1],"and":[2,15,29,49,64,76,84],"Neural":[3],"Networks":[4],"Using":[5],"IBM":[6,24],"SPSS":[7,25],"provides":[8],"a":[9,30],"practical":[10],"introduction":[11],"to":[12,42,79],"developing,":[13],"evaluating,":[14],"applying":[16],"intelligent":[17,83],"predictive":[18],"models.":[19],"Through":[20],"clear":[21],"explanations,":[22],"step-by-step":[23],"procedures,":[26],"realistic":[27],"examples,":[28],"complete":[31],"customer-churn":[32],"case":[33],"study,":[34],"the":[35],"book":[36],"guides":[37],"readers":[38],"from":[39],"data":[40,81],"preparation":[41],"model":[43,54],"deployment.":[44],"It":[45],"covers":[46],"Multilayer":[47],"Perceptron":[48],"Radial":[50],"Basis":[51],"Function":[52],"networks,":[53],"evaluation,":[55],"probability":[56],"thresholds,":[57],"predictor":[58],"importance,":[59],"what-if":[60],"analysis,":[61],"responsible":[62,85],"implementation,":[63],"managerial":[65],"decision-making.":[66],"An":[67],"accessible":[68],"hands-on":[69],"guide":[70],"for":[71],"students,":[72],"researchers,":[73],"analysts,":[74],"consultants,":[75],"professionals":[77],"seeking":[78],"transform":[80],"into":[82],"predictions.":[86]},"counts_by_year":[],"updated_date":"2026-10-07T09:51:55.459714","created_date":"2026-08-30T00:00:00"}