{"id":"https://openalex.org/W4404031251","doi":"https://doi.org/10.1109/icccnt61001.2024.10724833","title":"Leveraging CNN-RF Models for Enhanced Serve Shot Strategies in Table Tennis","display_name":"Leveraging CNN-RF Models for Enhanced Serve Shot Strategies in Table Tennis","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4404031251","doi":"https://doi.org/10.1109/icccnt61001.2024.10724833"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt61001.2024.10724833","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10724833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5047312965","display_name":"Vikrant Sharma","orcid":"https://orcid.org/0000-0003-3178-8657"},"institutions":[{"id":"https://openalex.org/I60054993","display_name":"Graphic Era University","ror":"https://ror.org/03wqgqd89","country_code":"IN","type":"education","lineage":["https://openalex.org/I60054993"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vikrant Sharma","raw_affiliation_strings":["Graphic Era Hill University,Computer Science and Engineering,Dehradun,India,248002"],"affiliations":[{"raw_affiliation_string":"Graphic Era Hill University,Computer Science and Engineering,Dehradun,India,248002","institution_ids":["https://openalex.org/I60054993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061558029","display_name":"Shiva Mehta","orcid":"https://orcid.org/0009-0002-5537-7027"},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shiva Mehta","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology, Chitkara University,Punjab,India"],"affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology, Chitkara University,Punjab,India","institution_ids":["https://openalex.org/I74319210"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5047312965"],"corresponding_institution_ids":["https://openalex.org/I60054993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26908586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10157","display_name":"Sports Performance and Training","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10157","display_name":"Sports Performance and Training","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12677","display_name":"Sports Dynamics and Biomechanics","score":0.9733999967575073,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11246","display_name":"Sports injuries and prevention","score":0.9136999845504761,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.710059642791748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6689176559448242},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.6687636375427246},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.4335022270679474},{"id":"https://openalex.org/keywords/single-shot","display_name":"Single shot","score":0.4216955602169037},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4098407030105591},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.33188122510910034},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1947633922100067},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16652658581733704},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.05729633569717407}],"concepts":[{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.710059642791748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6689176559448242},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.6687636375427246},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.4335022270679474},{"id":"https://openalex.org/C3019835501","wikidata":"https://www.wikidata.org/wiki/Q1310130","display_name":"Single shot","level":2,"score":0.4216955602169037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4098407030105591},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.33188122510910034},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1947633922100067},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16652658581733704},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.05729633569717407},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt61001.2024.10724833","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10724833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/W3096769599","https://openalex.org/W3134727238","https://openalex.org/W3139160332","https://openalex.org/W3174134442","https://openalex.org/W3204161309","https://openalex.org/W3209176766","https://openalex.org/W4285815705","https://openalex.org/W4292296511","https://openalex.org/W4305082654","https://openalex.org/W4320039102","https://openalex.org/W4385452282","https://openalex.org/W4386427013","https://openalex.org/W4386920900","https://openalex.org/W4386921193","https://openalex.org/W4387642931","https://openalex.org/W4388937090","https://openalex.org/W4388937959","https://openalex.org/W4399557791"],"related_works":["https://openalex.org/W3142396426","https://openalex.org/W2471333042","https://openalex.org/W2497720472","https://openalex.org/W4292659306","https://openalex.org/W2955491601","https://openalex.org/W4396643691","https://openalex.org/W146529714","https://openalex.org/W4402383816","https://openalex.org/W2316500695","https://openalex.org/W1999226266"],"abstract_inverted_index":{"The":[0,51,67,148,186,210,234],"definition":[1],"of":[2,22,140,166,174,193,203,247,255],"which":[3,154],"serve":[4,28,107,129],"strokes":[5],"to":[6,38,76,99,243],"use":[7,33],"depends":[8],"on":[9],"the":[10,14,19,23,54,74,79,102,106,137,144,152,171,182,197,200,214,239,244,248],"technique":[11],"practiced":[12],"and":[13,40,49,64,158,179,196,231,236],"specific":[15],"tactical":[16],"conditions":[17],"during":[18,164],"game":[20],"because":[21],"strategic":[24],"complexity":[25],"inherent":[26],"in":[27,78,134,206,229,251],"play.":[29],"This":[30],"research":[31],"will":[32],"a":[34,97,118,162,191],"precise":[35],"CNN-RF":[36,215],"model":[37,52,103,187,216,250],"identify":[39],"classify":[41],"different":[42,253],"server":[43,85],"shot":[44,108],"motions":[45,109],"like":[46],"backhands,":[47],"forehands,":[48],"volleys.":[50],"categorizes":[53],"shots":[55,86],"into":[56],"five":[57],"classes:":[58],"topspin,":[59],"backspin,":[60],"sidespin,":[61],"fade":[62],"spin,":[63],"knives":[65,68],"spin.":[66],"pin":[69],"does":[70],"not":[71],"effectively":[72],"cause":[73],"ball":[75],"deviate":[77],"air.":[80],"Reviewing":[81],"that":[82,213,257],"dataset,":[83],"including":[84],"played":[87],"by":[88,117,122],"pro":[89],"players,":[90],"eventually":[91],"revealed":[92],"an":[93],"87.83%":[94],"accuracy":[95,114,178,230],"rate,":[96],"testament":[98],"how":[100],"well":[101],"can":[104],"follow":[105],"very":[110],"accurately.":[111],"A":[112],"remarkable":[113],"was":[115,132,188],"demonstrated":[116],"model,":[119],"as":[120],"evidenced":[121],"its":[123,204],"92.81%":[124],"maximum":[125],"score":[126],"for":[127,143],"individual":[128],"motions.":[130],"It":[131],"superior":[133],"memory,":[135],"with":[136,226],"correct":[138],"replay":[139],"around":[141],"96.23%":[142],"most":[145],"identifiable":[146],"serves.":[147],"key":[149],"indicator":[150],"is":[151,159],"F1-Score,":[153],"measures":[155],"precision-recall":[156],"balance":[157],"maintained":[160],"at":[161],"high-level":[163],"diagnostics":[165],"all":[167],"classes.":[168,262],"In":[169],"macro,":[170],"average":[172],"F1-Score":[173],"87.63":[175],"indicates":[176],"equitable":[177],"impartiality":[180],"regarding":[181],"precision-binary":[183],"class":[184],"trade-off.":[185],"assessed":[189],"using":[190],"dataset":[192],"2670":[194],"images,":[195],"results":[198],"indicated":[199],"strong":[201],"possibilities":[202],"adoption":[205],"real-life":[207],"sports":[208],"forecasting.":[209],"investigation":[211],"demonstrates":[212],"performs":[217],"better":[218],"than":[219],"other":[220],"single":[221],"CNN":[222],"or":[223],"RF":[224],"models,":[225],"observable":[227],"improvements":[228],"recall":[232],"rates.":[233],"weighted":[235],"macro-averages":[237],"ended":[238],"last":[240],"argument":[241],"related":[242],"potential":[245],"effectiveness":[246],"proposed":[249],"handling":[252],"sets":[254],"classes":[256],"are":[258],"biased":[259],"toward":[260],"some":[261]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
