{"id":"https://openalex.org/W1986016693","doi":"https://doi.org/10.1145/2668260.2668307","title":"Semi-Synthetic Data for Enhanced SMS Spam Detection","display_name":"Semi-Synthetic Data for Enhanced SMS Spam Detection","publication_year":2014,"publication_date":"2014-09-15","ids":{"openalex":"https://openalex.org/W1986016693","doi":"https://doi.org/10.1145/2668260.2668307","mag":"1986016693"},"language":"en","primary_location":{"id":"doi:10.1145/2668260.2668307","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2668260.2668307","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039986753","display_name":"Ala\u2019 Abdulmajid Eshmawi","orcid":"https://orcid.org/0000-0002-4610-2972"},"institutions":[{"id":"https://openalex.org/I178169726","display_name":"Southern Methodist University","ror":"https://ror.org/042tdr378","country_code":"US","type":"education","lineage":["https://openalex.org/I178169726"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ala' Eshmawi","raw_affiliation_strings":["HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275","HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275","institution_ids":["https://openalex.org/I178169726"]},{"raw_affiliation_string":"HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275#TAB#","institution_ids":["https://openalex.org/I178169726"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112639076","display_name":"Suku Nair","orcid":"https://orcid.org/0000-0002-7966-2343"},"institutions":[{"id":"https://openalex.org/I178169726","display_name":"Southern Methodist University","ror":"https://ror.org/042tdr378","country_code":"US","type":"education","lineage":["https://openalex.org/I178169726"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suku Nair","raw_affiliation_strings":["HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275","HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275","institution_ids":["https://openalex.org/I178169726"]},{"raw_affiliation_string":"HACNet Labs, Bobby Lyle School of Engineering, Southern Methodist University, Dallas, Texas 75275#TAB#","institution_ids":["https://openalex.org/I178169726"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I178169726"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.08364997,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"206","last_page":"212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":1.0,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9995999932289124,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/oversampling","display_name":"Oversampling","score":0.8646042943000793},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7305084466934204},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5376225113868713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5154112577438354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44541457295417786},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3852000832557678},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38337773084640503},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.05102851986885071}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.8646042943000793},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7305084466934204},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5376225113868713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5154112577438354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44541457295417786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3852000832557678},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38337773084640503},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.05102851986885071},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2668260.2668307","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2668260.2668307","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W9223698","https://openalex.org/W173006792","https://openalex.org/W1504694836","https://openalex.org/W1670263352","https://openalex.org/W1851422093","https://openalex.org/W1984457329","https://openalex.org/W2003798735","https://openalex.org/W2036166268","https://openalex.org/W2037556864","https://openalex.org/W2037790833","https://openalex.org/W2062614982","https://openalex.org/W2112076978","https://openalex.org/W2135332490","https://openalex.org/W2147934023","https://openalex.org/W2148143831","https://openalex.org/W2294370754","https://openalex.org/W2911964244","https://openalex.org/W2912934387"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W2781247653","https://openalex.org/W4206637278","https://openalex.org/W4386005305","https://openalex.org/W3082051559","https://openalex.org/W2953675148","https://openalex.org/W127528661","https://openalex.org/W123443654","https://openalex.org/W3055496383","https://openalex.org/W2903718012"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"study":[4,20],"the":[5,14,28,36,39,44],"effect":[6],"of":[7,16,27,38],"using":[8],"Synthetic":[9],"Minority":[10],"Oversampling":[11],"TEchnique":[12],"on":[13,31,43],"detection":[15,25],"SMS":[17],"spam.":[18],"The":[19],"shows":[21],"an":[22],"improved":[23],"spam":[24],"performance":[26,37],"classifiers":[29,41],"trained":[30,42],"semi-synthetic":[32],"datasets":[33],"compared":[34],"to":[35],"same":[40],"original":[45],"dataset.":[46]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
