{"id":"https://openalex.org/W4378976958","doi":"https://doi.org/10.1109/icai58407.2023.10136622","title":"Machine Learning Based Classification of crystal system using rendered images from X-ray diffraction (XRD) dataset","display_name":"Machine Learning Based Classification of crystal system using rendered images from X-ray diffraction (XRD) dataset","publication_year":2023,"publication_date":"2023-02-22","ids":{"openalex":"https://openalex.org/W4378976958","doi":"https://doi.org/10.1109/icai58407.2023.10136622"},"language":"en","primary_location":{"id":"doi:10.1109/icai58407.2023.10136622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icai58407.2023.10136622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","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/A5050758092","display_name":"Ali Hamza","orcid":"https://orcid.org/0000-0002-6619-6638"},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Ali Hamza","raw_affiliation_strings":["NUST,Islamabad,Pakistan","NUST, Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NUST,Islamabad,Pakistan","institution_ids":["https://openalex.org/I929597975"]},{"raw_affiliation_string":"NUST, Islamabad, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101962927","display_name":"Umar Hayat","orcid":"https://orcid.org/0000-0002-1677-0144"},"institutions":[{"id":"https://openalex.org/I59225215","display_name":"Bahria University","ror":"https://ror.org/02v8d7770","country_code":"PK","type":"education","lineage":["https://openalex.org/I59225215"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Umar Hayat","raw_affiliation_strings":["Bahria University,Islamabad,Pakistan","Bahria University, Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bahria University,Islamabad,Pakistan","institution_ids":["https://openalex.org/I59225215"]},{"raw_affiliation_string":"Bahria University, Islamabad, Pakistan","institution_ids":["https://openalex.org/I59225215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076874733","display_name":"Wahid Hussain","orcid":"https://orcid.org/0009-0008-9520-0553"},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Wahid Hussain","raw_affiliation_strings":["NUST,Islamabad,Pakistan","NUST, Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NUST,Islamabad,Pakistan","institution_ids":["https://openalex.org/I929597975"]},{"raw_affiliation_string":"NUST, Islamabad, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110984031","display_name":"Anam Mumtaz","orcid":null},"institutions":[{"id":"https://openalex.org/I59225215","display_name":"Bahria University","ror":"https://ror.org/02v8d7770","country_code":"PK","type":"education","lineage":["https://openalex.org/I59225215"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Anam Mumtaz","raw_affiliation_strings":["Bahria University,Islamabad,Pakistan","Bahria University, Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bahria University,Islamabad,Pakistan","institution_ids":["https://openalex.org/I59225215"]},{"raw_affiliation_string":"Bahria University, Islamabad, Pakistan","institution_ids":["https://openalex.org/I59225215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1784,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.39135697,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"64","last_page":"69"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12613","display_name":"X-ray Diffraction in Crystallography","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.974399983882904,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/triclinic-crystal-system","display_name":"Triclinic crystal system","score":0.8067358732223511},{"id":"https://openalex.org/keywords/monoclinic-crystal-system","display_name":"Monoclinic crystal system","score":0.7509121894836426},{"id":"https://openalex.org/keywords/orthorhombic-crystal-system","display_name":"Orthorhombic crystal system","score":0.7429112195968628},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6942145824432373},{"id":"https://openalex.org/keywords/diffraction","display_name":"Diffraction","score":0.6745192408561707},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6217246055603027},{"id":"https://openalex.org/keywords/crystal","display_name":"Crystal (programming language)","score":0.5920496582984924},{"id":"https://openalex.org/keywords/tetragonal-crystal-system","display_name":"Tetragonal crystal system","score":0.5836191773414612},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.5582185983657837},{"id":"https://openalex.org/keywords/x-ray-crystallography","display_name":"X-ray crystallography","score":0.5337648391723633},{"id":"https://openalex.org/keywords/crystallography","display_name":"Crystallography","score":0.4852590262889862},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45863640308380127},{"id":"https://openalex.org/keywords/crystal-structure","display_name":"Crystal structure","score":0.3994821608066559},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39900752902030945},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.23340418934822083},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.17008569836616516},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.10517606139183044}],"concepts":[{"id":"https://openalex.org/C42228675","wikidata":"https://www.wikidata.org/wiki/Q376927","display_name":"Triclinic crystal system","level":3,"score":0.8067358732223511},{"id":"https://openalex.org/C61276311","wikidata":"https://www.wikidata.org/wiki/Q624543","display_name":"Monoclinic crystal system","level":3,"score":0.7509121894836426},{"id":"https://openalex.org/C37243968","wikidata":"https://www.wikidata.org/wiki/Q648961","display_name":"Orthorhombic crystal system","level":3,"score":0.7429112195968628},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6942145824432373},{"id":"https://openalex.org/C207114421","wikidata":"https://www.wikidata.org/wiki/Q133900","display_name":"Diffraction","level":2,"score":0.6745192408561707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6217246055603027},{"id":"https://openalex.org/C2781285689","wikidata":"https://www.wikidata.org/wiki/Q21921428","display_name":"Crystal (programming language)","level":2,"score":0.5920496582984924},{"id":"https://openalex.org/C170751736","wikidata":"https://www.wikidata.org/wiki/Q503601","display_name":"Tetragonal crystal system","level":3,"score":0.5836191773414612},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.5582185983657837},{"id":"https://openalex.org/C50515024","wikidata":"https://www.wikidata.org/wiki/Q826582","display_name":"X-ray crystallography","level":3,"score":0.5337648391723633},{"id":"https://openalex.org/C8010536","wikidata":"https://www.wikidata.org/wiki/Q160398","display_name":"Crystallography","level":1,"score":0.4852590262889862},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45863640308380127},{"id":"https://openalex.org/C115624301","wikidata":"https://www.wikidata.org/wiki/Q895901","display_name":"Crystal structure","level":2,"score":0.3994821608066559},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39900752902030945},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.23340418934822083},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.17008569836616516},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.10517606139183044},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icai58407.2023.10136622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icai58407.2023.10136622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W144023353","https://openalex.org/W2080562691","https://openalex.org/W2129906471","https://openalex.org/W2136132422","https://openalex.org/W2154493413","https://openalex.org/W2408488656","https://openalex.org/W2521926936","https://openalex.org/W2606436201","https://openalex.org/W2625386340","https://openalex.org/W2767577269","https://openalex.org/W2789876780","https://openalex.org/W2883780447","https://openalex.org/W2897562460","https://openalex.org/W2940802411","https://openalex.org/W2945155737","https://openalex.org/W2999309192","https://openalex.org/W3111832747","https://openalex.org/W3182799677","https://openalex.org/W3184555191","https://openalex.org/W4230735991","https://openalex.org/W4244693581","https://openalex.org/W6605951998","https://openalex.org/W6682576084","https://openalex.org/W6753767121","https://openalex.org/W6762759938"],"related_works":["https://openalex.org/W2283894052","https://openalex.org/W2009826639","https://openalex.org/W2070844528","https://openalex.org/W2952938871","https://openalex.org/W1550031554","https://openalex.org/W2005241827","https://openalex.org/W2952169498","https://openalex.org/W2002785438","https://openalex.org/W2852219841","https://openalex.org/W2260173655"],"abstract_inverted_index":{"X-ray":[0,70,99,113,137],"diffraction(XRD)":[1,71,100,114,138],"is":[2,19],"an":[3,20,149],"essential":[4],"characterization":[5],"technique":[6],"to":[7,79,85,119,147,169],"study":[8,111],"the":[9,12,27,57,69,74,87,97,107,121,124,130,136,141,164,172],"properties":[10],"of":[11,109,123,151,175],"materials.":[13,176],"Finding":[14],"a":[15,81],"material's":[16,88],"crystal":[17,41,59,89,173],"system":[18],"important":[21],"step":[22],"in":[23,73],"its":[24],"analysis.":[25],"So,":[26],"process":[28],"should":[29],"be":[30],"fast":[31],"as":[32,34,133],"well":[33],"accurate.":[35],"In":[36,106],"total":[37],"there":[38],"are":[39],"seven":[40],"systems:":[42],"triclinic,":[43],"monoclinic,":[44],"orthorhombic,":[45],"tetragonal,":[46],"trigonal,":[47],"hexagonal,":[48],"and":[49],"cubic.":[50],"Previous":[51],"studies":[52,68],"have":[53],"worked":[54],"on":[55,96],"finding":[56],"material":[58],"structure":[60],"by":[61],"introducing":[62],"machine":[63,82,92,125,142,165],"learning":[64,83,93,126,143,166],"approaches.":[65],"In,":[66],"recent":[67],"dataset":[72,101,140],"tabular":[75,98,139],"form":[76],"was":[77,145],"used":[78,118],"train":[80],"model":[84,144],"classify":[86,171],"system.":[90],"The":[91,153],"models":[94],"trained":[95],"didn't":[102],"maximize":[103,120],"their":[104],"performance.":[105],"scope":[108],"this":[110],"rendered":[112,131,159],"images":[115,132,160],"had":[116,156,162],"been":[117],"performance":[122],"models.":[127],"By":[128],"using":[129],"input":[134],"from":[135],"able":[146],"achieve":[148],"accuracy":[150],"98%-99%.":[152],"final":[154],"findings":[155],"shown":[157],"that":[158],"datasets":[161],"improved":[163],"model's":[167],"ability":[168],"correctly":[170],"systems":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
