{"id":"https://openalex.org/W7019621055","doi":null,"title":"Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data","display_name":"Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W7019621055"},"language":"en","primary_location":{"id":"pmh:oai:eprints.uthm.edu.my:1064","doi":null,"is_oa":true,"landing_page_url":"http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf","pdf_url":"http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf","source":{"id":"https://openalex.org/S4306400770","display_name":"UTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I930072361","host_organization_name":"Tun Hussein Onn University of Malaysia","host_organization_lineage":["https://openalex.org/I930072361"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049365883","display_name":"Mohd Hafizul Afifi Abdullah","orcid":"https://orcid.org/0000-0002-1427-2571","observed_orcids":["https://orcid.org/0000-0002-1427-2571"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Abdullah, Mohd Hafizul Afifi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5049365883"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.29589998722076416,"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":0.29589998722076416,"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":0.1648000031709671,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.1234000027179718,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spike-timing-dependent-plasticity","display_name":"spike-timing-dependent plasticity","score":0.7689999938011169},{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"reservoir computing","score":0.6629999876022339},{"id":"https://openalex.org/keywords/classification-accuracy","display_name":"classification accuracy","score":0.5419999957084656},{"id":"https://openalex.org/keywords/parameter-optimization","display_name":"parameter optimization","score":0.609000027179718},{"id":"https://openalex.org/keywords/rainfall-prediction","display_name":"precipitation forecasting","score":0.796999990940094},{"id":"https://openalex.org/keywords/stock-index-forecasting","display_name":"stock index forecasting","score":0.7599999904632568},{"id":"https://openalex.org/keywords/gold-price-forecasting","display_name":"gold price forecasting","score":0.7760000228881836},{"id":"https://openalex.org/keywords/event-prediction","display_name":"event prediction","score":0.8579999804496765}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7260000109672546},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7041000127792358},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5871999859809875},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4742000102996826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4652000069618225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4415999948978424},{"id":"https://openalex.org/C2780724565","wikidata":"https://www.wikidata.org/wiki/Q5227256","display_name":"Data classification","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.3668000102043152},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C2780049643","wikidata":"https://www.wikidata.org/wiki/Q5511139","display_name":"Fuzzy rule","level":4,"score":0.2872999906539917},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C132778050","wikidata":"https://www.wikidata.org/wiki/Q2065430","display_name":"Weighted voting","level":4,"score":0.26089999079704285},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:eprints.uthm.edu.my:1064","doi":null,"is_oa":true,"landing_page_url":"http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf","pdf_url":"http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf","source":{"id":"https://openalex.org/S4306400770","display_name":"UTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I930072361","host_organization_name":"Tun Hussein Onn University of Malaysia","host_organization_lineage":["https://openalex.org/I930072361"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"pmh:oai:eprints.uthm.edu.my:1064","doi":null,"is_oa":true,"landing_page_url":"http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf","pdf_url":"http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf","source":{"id":"https://openalex.org/S4306400770","display_name":"UTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I930072361","host_organization_name":"Tun Hussein Onn University of Malaysia","host_organization_lineage":["https://openalex.org/I930072361"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310112","display_name":"Universiti Tun Hussein Onn Malaysia","ror":"https://ror.org/01c5wha71"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7019621055.pdf","grobid_xml":"https://content.openalex.org/works/W7019621055.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1486852018","https://openalex.org/W1488961907","https://openalex.org/W1782305448","https://openalex.org/W1878915617","https://openalex.org/W1983245114","https://openalex.org/W2000129051","https://openalex.org/W2004277475","https://openalex.org/W2019238250","https://openalex.org/W2019598563","https://openalex.org/W2031783473","https://openalex.org/W2040870580","https://openalex.org/W2040895929","https://openalex.org/W2066636483","https://openalex.org/W2078384184","https://openalex.org/W2091644467","https://openalex.org/W2096189890","https://openalex.org/W2106256207","https://openalex.org/W2143962661","https://openalex.org/W2144848291","https://openalex.org/W2165236819","https://openalex.org/W2170679550","https://openalex.org/W2299899589","https://openalex.org/W2371946849","https://openalex.org/W2465999789","https://openalex.org/W2479657526","https://openalex.org/W2604988916","https://openalex.org/W2767856675","https://openalex.org/W2991599496"],"related_works":["https://openalex.org/W1983245114","https://openalex.org/W4414921476","https://openalex.org/W2031478132","https://openalex.org/W2767856675","https://openalex.org/W2103184508","https://openalex.org/W1998286421","https://openalex.org/W4375934812","https://openalex.org/W2002016196","https://openalex.org/W7097039969","https://openalex.org/W2965363187"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"technologies":[2],"have":[3,11],"been":[4,73,168,200,205],"growing":[5],"rapidly":[6],"in":[7,250],"recent":[8],"years.":[9],"Researchers":[10],"come":[12],"up":[13],"with":[14,265],"several":[15],"data":[16,30,43,48,131,149,172,183,240],"processing":[17,44],"architectures,":[18],"enabling":[19],"machines":[20],"to":[21,31,111,114,125,181,258],"consume,":[22],"interpret,":[23],"and":[24,66,219,222,229,263],"produce":[25],"understandable":[26],"output":[27,64],"from":[28],"real-world":[29],"improve":[32],"the":[33,82,91,97,99,106,121,127,138,144,159,164,186,192,209,226,235,237,259],"quality":[34],"of":[35,51,81,96,163,252],"our":[36],"lives.":[37],"The":[38,170,202],"NeuCube":[39,165,264],"architecture":[40,45,83,166],"is":[41,109],"a":[42,55,59,148,247],"for":[46,105,129,157,196,225],"spatio/spectro-temporal":[47],"which":[49,152],"consists":[50],"four":[52],"main":[53],"modules:":[54],"spike":[56],"encoding":[57],"module,":[58,65],"recurrent":[60],"SNN":[61],"reservoir,":[62],"an":[63,67,175],"optimization":[68],"module.":[69],"Despite":[70],"it":[71,117,134],"has":[72,167,198,204,245],"utilised":[74],"on":[75,85,120],"many":[76],"various":[77],"applications,":[78],"most":[79],"improvement":[80],"focuses":[84],"user":[86],"experience":[87],"rather":[88],"than":[89],"improving":[90,158],"result":[92],"accuracy.":[93],"Upon":[94],"exploration":[95],"architecture,":[98],"weighted":[100],"k-nearest":[101],"neighbours":[102],"algorithm":[103,156],"used":[104],"classification":[107,145,161,187,254],"module":[108],"found":[110],"be":[112],"prone":[113],"misclassification":[115],"as":[116,256],"relies":[118],"solely":[119],"majority":[122],"voting":[123],"rule":[124],"determine":[126],"class":[128],"new":[130,182],"vector.":[132],"Additionally,":[133],"does":[135],"not":[136],"consider":[137],"class-specific":[139,177],"fuzzy":[140,178],"weight":[141,179],"information":[142,180],"during":[143,185],"process.":[146,188],"Therefore,":[147],"modelling":[150,173,241],"mechanism":[151],"implements":[153],"PIfwkNN":[154,243],"classifier":[155,244],"overall":[160,253],"accuracy":[162,255],"proposed.":[169],"proposed":[171],"applies":[174],"additional":[176],"vectors":[184],"In":[189],"this":[190],"research,":[191],"optimal":[193],"parameters":[194],"set":[195],"experiments":[197],"also":[199],"identified.":[201],"approach":[203],"validated":[206],"by":[207],"using":[208,242],"Kuala":[210],"Krai":[211],"Rainfall":[212],"Dataset,":[213],"Dow":[214],"Jones":[215],"Index":[216],"Data":[217],"Set,":[218],"Gold":[220],"Price":[221],"Performance":[223],"Dataset":[224],"3-days":[227],"earlier":[228,231],"1-day":[230],"event":[232],"prediction.":[233],"From":[234],"experiments,":[236],"improved":[238],"personalised":[239],"shown":[246],"significant":[248],"increase":[249],"terms":[251],"compared":[257],"conventional":[260],"MLP,":[261],"fkNN,":[262],"wkNN":[266],"classifier.":[267]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}