{"id":"https://openalex.org/W2775265511","doi":"https://doi.org/10.1109/icacci.2017.8125987","title":"Classification of cognitive state using clustering based maximum margin feature selection framework","display_name":"Classification of cognitive state using clustering based maximum margin feature selection framework","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2775265511","doi":"https://doi.org/10.1109/icacci.2017.8125987","mag":"2775265511"},"language":"en","primary_location":{"id":"doi:10.1109/icacci.2017.8125987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2017.8125987","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"},"type":"article","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/A5021135216","display_name":"J. Ramakrishna","orcid":"https://orcid.org/0000-0003-0673-055X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"J. Siva Ramakrishna","raw_affiliation_strings":["Institute of Aeronautical Engineering, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"Institute of Aeronautical Engineering, Hyderabad, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081995395","display_name":"Hariharan Ramasangu","orcid":"https://orcid.org/0000-0002-7590-1749"},"institutions":[{"id":"https://openalex.org/I302410947","display_name":"M S Ramaiah University of Applied Sciences","ror":"https://ror.org/02anh8x74","country_code":"IN","type":"education","lineage":["https://openalex.org/I302410947"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hariharan Ramasangu","raw_affiliation_strings":["M. S. Ramaiah University of Applied Sciences, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"M. S. Ramaiah University of Applied Sciences, Bangalore, India","institution_ids":["https://openalex.org/I302410947"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021135216"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2563,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.56097614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1092","last_page":"1096"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7340092658996582},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7284272909164429},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7276673316955566},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.62879478931427},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6281808614730835},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.619637131690979},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.46813544631004333},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.466146856546402},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45710572600364685},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.43111705780029297},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4107052683830261},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3321559429168701}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7340092658996582},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7284272909164429},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276673316955566},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.62879478931427},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6281808614730835},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.619637131690979},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.46813544631004333},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.466146856546402},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45710572600364685},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.43111705780029297},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4107052683830261},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3321559429168701}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icacci.2017.8125987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2017.8125987","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6499999761581421,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W597555358","https://openalex.org/W1583223031","https://openalex.org/W1645271453","https://openalex.org/W1747314365","https://openalex.org/W2005321445","https://openalex.org/W2016461038","https://openalex.org/W2025306773","https://openalex.org/W2051674524","https://openalex.org/W2084493543","https://openalex.org/W2094177372","https://openalex.org/W2106773151","https://openalex.org/W2113537613","https://openalex.org/W2118813278","https://openalex.org/W2123927491","https://openalex.org/W2129158941","https://openalex.org/W2134303651","https://openalex.org/W2139280638","https://openalex.org/W2158068051","https://openalex.org/W2208192247","https://openalex.org/W2546486894","https://openalex.org/W2583247469","https://openalex.org/W2950559108","https://openalex.org/W6617849257","https://openalex.org/W6637780520","https://openalex.org/W6683368909"],"related_works":["https://openalex.org/W3027020613","https://openalex.org/W2016533837","https://openalex.org/W3167885074","https://openalex.org/W2892386716","https://openalex.org/W1998563493","https://openalex.org/W4306164210","https://openalex.org/W4313316311","https://openalex.org/W4362608745","https://openalex.org/W2383143032","https://openalex.org/W3125011624"],"abstract_inverted_index":{"Over":[0],"the":[1,5,12,18,40,74,114,148],"past":[2],"few":[3,153],"years,":[4],"dimensionality":[6],"of":[7,14,20,27,42,49,54,71,76,79,90,113,155],"functional":[8],"MRI":[9],"(fMRI)":[10],"effects":[11],"analysis":[13],"brain":[15],"data.":[16,149,162],"In":[17],"field":[19],"machine":[21],"learning":[22,34,55],"and":[23,126],"statistical":[24],"analysis,":[25],"classification":[26,89],"objects":[28],"plays":[29],"a":[30,47,98,132],"significant":[31],"role.":[32],"Machine":[33],"classifiers":[35],"are":[36,129,141,166],"used":[37],"to":[38,61,143,170],"discover":[39],"class":[41],"new":[43],"data":[44,50,59,68,122],"points":[45],"from":[46,147,159],"set":[48],"points.":[51],"The":[52,111,150,163],"application":[53],"techniques":[56],"on":[57,119],"fMRI":[58,67,121,161],"alleviates":[60],"cognitive":[62,91],"state":[63],"classification.":[64],"High":[65],"dimensional":[66],"has":[69],"thousands":[70],"features":[72,146,158,169],"in":[73,108],"form":[75],"voxels.":[77],"Choice":[78],"appropriate":[80,157],"voxels":[81,165],"as":[82,168],"attributes":[83],"is":[84,106,117],"an":[85],"ambitious":[86],"activity":[87],"for":[88],"state.":[92],"A":[93],"novel":[94],"feature":[95,127],"selection":[96,128],"framework,":[97],"clustering":[99,125],"based":[100],"maximum":[101],"margin":[102],"through":[103],"sparse":[104],"constraints":[105],"proposed":[107,115],"this":[109],"paper.":[110],"performance":[112],"method":[116,151],"examined":[118],"benchmark":[120],"set.":[123],"K-means":[124],"merged":[130],"into":[131],"consistent":[133],"framework.":[134],"L":[135],"<inf":[136],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[137],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">21</inf>":[138],"norm":[139],"regularization":[140],"executed":[142],"obtain":[144],"relevant":[145],"provides":[152],"number":[154],"most":[156],"StarPlus":[160],"obtained":[164],"furnished":[167],"Naive":[171],"Bayes":[172],"classifier.":[173]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
