{"id":"https://openalex.org/W4409868887","doi":"https://doi.org/10.1145/3718751.3718922","title":"Research on Risk Assessment Technology for Military Physical Training Based on Deep Learning","display_name":"Research on Risk Assessment Technology for Military Physical Training Based on Deep Learning","publication_year":2024,"publication_date":"2024-11-15","ids":{"openalex":"https://openalex.org/W4409868887","doi":"https://doi.org/10.1145/3718751.3718922"},"language":"en","primary_location":{"id":"doi:10.1145/3718751.3718922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3718751.3718922","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","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":null,"display_name":"Hao Li","orcid":"https://orcid.org/0000-0003-3536-5715"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hao Li","raw_affiliation_strings":["Beijing Jinghang Computation and Communication Research Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3536-5715","affiliations":[{"raw_affiliation_string":"Beijing Jinghang Computation and Communication Research Institute, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Chi Zhang","orcid":"https://orcid.org/0009-0007-0011-5157"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chi Zhang","raw_affiliation_strings":["Beijing Jinghang Computation and Communication Research Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-0011-5157","affiliations":[{"raw_affiliation_string":"Beijing Jinghang Computation and Communication Research Institute, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3311,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70538242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1042","last_page":"1046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7789999842643738,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7789999842643738,"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/T13176","display_name":"Winter Sports Injuries and Performance","score":0.7379000186920166,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/training","display_name":"Training (meteorology)","score":0.692537784576416},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5730196237564087},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.569330096244812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5040039420127869}],"concepts":[{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.692537784576416},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5730196237564087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.569330096244812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5040039420127869},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3718751.3718922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3718751.3718922","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2000620183","https://openalex.org/W3083417718","https://openalex.org/W3098308927","https://openalex.org/W4317738358","https://openalex.org/W4379616147"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"To":[0],"solve":[1],"the":[2,28,54,59,63,102,121],"problem":[3],"of":[4],"timely":[5],"and":[6,42,75,89,106],"accurate":[7],"risk":[8,17,124],"assessment":[9,18],"in":[10,99],"military":[11,35],"physical":[12,36],"training,":[13,37],"a":[14,113],"deep":[15],"learning-based":[16],"method":[19,23],"is":[20],"developed.":[21],"This":[22],"specifically":[24],"takes":[25],"into":[26,112],"account":[27],"biological":[29],"signals":[30,47,52,61,81],"generated":[31],"by":[32],"participants":[33],"during":[34],"which":[38],"include":[39],"both":[40],"electrical":[41,46],"non-electrical":[43,60],"signals.":[44],"The":[45,78],"are":[48,82,110],"surface":[49],"electromyography":[50],"(sEMG)":[51],"from":[53],"muscles":[55],"being":[56],"trained,":[57],"while":[58],"encompass":[62],"participants'":[64],"current":[65],"heart":[66],"rate,":[67,74],"blood":[68,76],"oxygen":[69],"levels,":[70],"body":[71],"temperature,":[72],"respiratory":[73],"pressure.":[77],"processed":[79,103],"sEMG":[80],"analyzed":[83],"using":[84],"Convolutional":[85],"Neural":[86,115],"Networks":[87],"(CNN)":[88],"Long":[90],"Short-Term":[91],"Memory":[92],"(LSTM)":[93],"networks":[94],"to":[95],"assess":[96],"muscle":[97,107],"fatigue":[98,108],"participants.":[100],"Subsequently,":[101],"physiological":[104],"parameters":[105],"metrics":[109],"fed":[111],"Fuzzy":[114],"Network":[116],"(FNN)":[117],"for":[118],"analysis,":[119],"yielding":[120],"final":[122],"training":[123],"assessment.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
